Sylvia atricapilla - Přírodovědecká fakulta - Univerzita Palackého v ...

Reprodukční strategie ptáků 

Habilitační práce 

Vladimír Remeš 

Katedra zoologie a ornitologická laboratoř 

Univerzita Palackého v Olomouci 

Přírodovědecká fakulta

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© Vladimír Remeš, 2011

Poděkování 

Jsem velmi rád, že se mohu naplno zabývat tím, co mne baví nejvíc – poznáváním přírody. 

Všem kdo mně v tom jakkoliv pomohli jsem velmi vděčný. 

Nejvýraznější finanční podpora mé práce pocházela od Ministerstva školství ČR, Grantové 

agentury ČR, Fulbrightovy komise a australského Ministerstva školství. 

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OBSAH 

1. Úvod ...........................................................................................................5 

2. Výběr hnízdního prostředí a hnízdní predace ..............................................6 

3. Evoluce a diverzita životních znaků .............................................................9 

4. Mateřské efekty a nutriční ekologie ..........................................................13 

5. Ornamenty a rodičovské investice.............................................................16 

6. Celkový závěr ............................................................................................19 

7. Literatura ..................................................................................................23 

8. Seznam zařazených prací...........................................................................30 

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1. Úvod 

Reprodukčními strategiemi rozumíme behaviorální, fyziologické a morfologické 

charakteristiky jedinců daného druhu, které jim umožňují úspěšně se rozmnožit a předat 

geny do další generace. Jsou to právě soubory těchto znaků, které svým perfektním 

"zapadnutím" do životního prostředí druhu vytvářejí dokonalou iluzi designu a designéra 

(Dawkins 2008). Sem, na místo předpokládaného designéra, přírodní teologové jako William 

Paley nebo John Ray (souhrn v Birkhead 2008) postulovali boha. Od vydání přelomového díla 

On the Origin of Species (Darwin 1859) však při studiu vzhledu a funkce živých bytostí 

převážil přístup evoluční biologie. Cesta evoluční biologie od stadia pouhé hypotézy k 

současnému statutu standardní vědecké teorie schopné vysvětlit celou řadu jevů v přírodě 

nebyla přímočará (Bowler 1989, Farber 2000). Dnes je však její postavení stejně pevné jako 

postavení jakékoliv jiné velké přírodovědné teorie (Dawkins 2009). 

Aplikace kritického evolučního myšlení v ekologii a etologii na sebe nechala čekat déle 

než v genetice, systematice a paleontologii, které se staly součástí takzvané moderní syntézy 

(Mayr 1982). Ekologie si prošla stádii ovlivněnými různými sociologickými teoriemi 

souvisejícími zejména se skupinovým výběrem (např. Mitman 1992) a vlastně až Williams 

(1966) etabloval důsledné evoluční myšlení zaostřené na jedince ve výzkumu reprodukčních 

a životních strategií živočichů a rostlin. Dawkins (1976) potom tento přístup zpřesnil a 

popularizoval, ač se jeho plné prosazení neobešlo bez dosti napjatých "vědeckých válek" 

(Segerstråle 2000). Etologie byla založena z velké části na revoltě proti neevolučnímu myšlení 

ve výzkumu chování živočichů zaměřenému hlavně na mechanismy a procesy učení 

(Burkhardt 2005). Klasičtí etologové jako Oskar Heinroth a Konrad Lorenz otočili pozornost k 

evolučně fixovaným vzorcům chování, instinktům. Tento přístup byl však z velké části 

fylogenetický, tzn. zaměřený na popis a fungování druhově specifických vzorců chování. 

Teprve další ze zakladatelů klasické etologie Niko Tinbergen zavedl naplno do výzkumu 

chování živočichů tzv. ultimativní přístup tázající se na adaptivní význam určitého chování v 

přirozených životních podmínkách daného druhu (Kruuk 2003). 

Tato větev ekologicko‐etologického výzkumu se ukázala nesmírně životaschopnou, 

zajímavou a plodnou, zejména poté, co ji N. Tinbergen implantoval v anglosaském světě 

svým profesorským angažmá na univerzitě v anglickém Oxfordu. Zde se adaptacionistický 

výzkum reprodukčních strategií živočichů, zejména ptáků, dále rozvinul pod vedením Davida 

Lacka. Jeho hluboce evoluční přístup v etologii a ekologii ptáků je patrný už od jeho prvních 

prací (Lack 1943, 1947), ač svého vrcholu dosahuje zejména v pracích pozdějších (např. Lack 

1968). Tato tradice pak byla rozvinuta v novém oboru behaviorální ekologie, která se 

etabluje od 70. a 80. let 20. století hlavně ve Velké Británii a USA. 

Je to právě tato tradice výzkumu adaptivní hodnoty behaviorálních, fyziologických a 

morfologických charakteristik živočichů v jejich přirozeném životním prostředí z níž vycházejí 

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studie zařazené do této habilitační práce. V těchto studiích se zaměřuji na analýzu adaptace 

v období rozmnožování u ptáků, ať již metodami srovnávacími, experimentálními nebo 

observačními. V následujícím textu bych chtěl stručně popsat výsledky svého výzkumu 

prováděného společně s celou řadou studentů a kolegů a vyzvednout originální aspekty 

jednotlivých prací a jejich přínos oboru. Na závěr bych se zamyslel nad některými 

obecnějšími metodologickými aspekty výzkumu adaptivní hodnoty chování v přírodních 

populacích živočichů. 

2. Výběr hnízdního prostředí a hnízdní predace 

Kontext a úvod 

Výběr hnízdního prostředí je jedním z nejdůležitějších rozhodnutí, která musí živočich udělat 

proto, aby se rozmnožil a úspěšnost rozmnožení na něm často kriticky závisí (Newton 1998). 

Obecný typ prostředí, ve kterém daný druh hnízdí, je daný evolucí habitatových preferencí 

(Jaenike & Holt 1991), ale každému rozmnožovacímu pokusu předchází většinou vždy znovu 

hierarchický rozhodovací proces: ve které oblasti, ve kterém lese, u které paseky, ve kterém 

keři (Cody 1985). Z evolučního hlediska je velmi zajímavé, nakolik je tento proces adaptivní. 

Rozhodují se ptáci při volbě hnízdního prostředí vždy optimálně a vede tedy jejich volba k 

nejvyšší možné produkci potomstva za daných podmínek (Martin 1998)? 

Nejdetailnější prostorovou úrovní, na níž proces výběru hnízdního prostředí probíhá, je 

volba samotného hnízdního místa (Martin 1993). Kvalitní hnízdní místo by mělo vykazovat 

nejen vhodné mikroklima pro inkubaci vajec (D'Alba et al. 2009) a vývin mláďat (Lloyd and 

Martin 2004), ale mělo by být také bezpečné proti hnízdním predátorům (Fontaine et al. 

2007); tyto charakteristiky spolu samozřejmě mohou souviset (Robertson 2009). Výběr 

hnízdního místa tedy můžeme považovat z velké části za antipredační strategii (Caro 2005, 

Lima 2009), alespoň u druhů, u nichž je predace hlavním zdrojem mortality hnízd. Sem 

ovšem patří velká většina ptačích druhů (Ricklefs 1969a). Vedle charakteristik hnízdního 

místa (Weidinger 2004) má na pravděpodobnost nalezení a zničení hnízda predátorem vliv 

také chování rodičů. Na to ukazuje fakt, že pravděpodobnost predace hnízd bývá vyšší 

během péče o mláďata než během inkubace (negativní vliv rodičovského chování na 

pravděpodobnost přežití hnízda; Martin et al. 2000, Muchai & du Plessis 2005), a dále 

víceméně univerzální existence agresivního chování rodičů vůči hnízdním predátorům 

(pozitivní vliv; přehled v Montgomerie & Weatherhead 1988). Pak ovšem vyvstává důležitá 

otázka, jaká je relativní důležitost charakteristik hnízdního místa (např. výška, ukrytí) a 

rodičovského chování. 

Výsledky a poznatky 

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Výběr hnízdního prostředí a hnízdní úspěch v různých typech lesa jsem studoval u pěnice 

černohlavé (Sylvia atricapilla) v lesích kolem rybníků u Hodonína. Predaci hnízd a identitu 

hnízdních predátorů jsem studoval u pěnice černohlavé v lese Království u obce Grygov. 

Pěnici černohlavou jsem studoval ve dvou typech lesa: v lužním lese a v monokultuře 

akátu (Robinia pseudoacacia) s uniformním podrostem černého bezu (Sambucus nigra; 

příspěvek A‐2). Zjistil jsem, že reprodukční charakteristiky těchto dvou populací se neliší ve 

velikosti vajec ani snůšek nebo v počtu mláďat vyváděných z úspěšného hnízda – zdá se tedy, 

že ani jedna populace netrpěla nedostatkem potravy pro výchovu mláďat. Populace v 

akátině však vykazovala vyšší denzitu hnízdících párů (20 párů na 10 ha) než populace v 

lužním lese (12 párů), ovšem při stejné velikosti zpěvného teritoria, a zároveň vykazovala 

mnohem vyšší míru predace hnízd. Z toho by se zdálo, že pěnice preferují k hnízdění horší 

prostředí. To bylo potvrzeno zjištěním, že samci přilétající na jaře ze zimoviště se usídlují 

dříve v akátině než v lužním lese, což je vzhledem k vyšší predaci v tomto typu lesa zřejmě 

neadaptivní chování (příspěvek A‐2). Takovýto neadaptivní výběr horšího prostředí k 

hnízdění však nemusí být v přírodě ojedinělý a jeho výskyt může souviset s vytvářením 

neobvyklých prostředí lidskou činností. Ptáci nemají s takovým prostředím evoluční 

zkušenost a mohou v krajině narušené lidskou činností volit podprůměrné prostředí k 

hnízdění, což může mít negativní důsledky pro jejich populační dynamiku (příspěvek A‐1). 

Tato studie však byla provedena jen na dvou plochách na geograficky velmi omezeném 

území a pokud chceme hodnotit přežívání hnízd a hnízdní produktivitu nějakého druhu a 

jejich potenciální význam pro přetrvání druhu v krajině, musíme vycházet z dat pocházejících 

z velkých území (příspěvek A‐3). 

V další studii jsem se zaměřil na detailní experimentální analýzu vztahu mezi 

pravděpodobností predace hnízda a jednou z charakteristik hnízdního místa, jeho ukrytí ve 

vegetaci. Po nalezení hnízda jsem u zhruba poloviny hnízd (experimentální) odstranil část 

vegetace v jejich bezprostředním okolí, takže došlo k poklesu ukrytí z 87 % na 34 %; u 

kontrolních hnízd jsem ponechal vegetaci nedotčenu (příspěvek A‐5). Když bylo hnízdo 

prázdné (mláďata vylétla nebo byl obsah predován), vložil jsem do něj umělá vejce vyrobená 

z plastelíny. Ta jsem ponechal v hnízdě po dobu 14 dnů a sledoval jsem jejich osud. V případě 

poškození potenciálním predátorem jsem byl schopen na základě vtisků do plastelíny 

identifikovat, zda se jednalo o ptáka nebo drobného savce (v tomto případě hlodavce). Zjistil 

jsem, že u přirozených hnízd jsou experimentální a kontrolní hnízda ničena predátory se 

stejnou pravděpodobností. Naopak u umělých snůšek byla pravděpodobnost predace vyšší u 

experimentálních hnízd než u hnízd kontrolních. Tato zjištění byla konzistentní s vysvětlením, 

že u přirozených hnízd rodiče špatné ukrytí hnízda kompenzují jeho obranou (příspěvek A‐5). 

Když jsem analyzoval zvlášť jen umělé snůšky zjistil jsem, že dobré ukrytí snižovalo 

pravděpodobnost predace v případě ptačích predátorů, kteří se řídí při vyhledávání kořisti 

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zrakem. Naopak u savčích predátorů ukrytí hnízda vliv nemělo. Zároveň se ukázalo, že ptáci 

ničí hlavně vysoko postavená hnízda, zatímco savci hnízda nízko postavená (příspěvek A‐4). 

Závěr 

Pěnice černohlavé si po příletu ze zimoviště k hnízdění na jižní Moravě vybíraly přednostně 

akátovou monokulturu, ve které dosahovaly vyšších populačních hustot, ale trpěly vysokou 

hnízdní predací ve srovnání se sousedním, méně preferovaným lužním lesem. Je otázkou 

proč. Jednou z možných odpovědí je dřívější olisťování keřového patra v akátině, které tak 

může být v časném jaře pro pěnice atraktivnější než dosud holý podrost v lužním lese. Toto 

chování odpovídá definici takzvané ekologické pasti: živočichové si aktivně vybírají prostředí, 

ve kterém dosahují nižšího hnízdního úspěchu, případně dokonce fitness (tj. biologické 

zdatnosti, Schaepfer et al. 2002). Z hlediska evoluční teorie je vznik a přetrvávání takového 

neadaptivního chování záhadou, bylo však nalezeno u více druhů ptáků (Battin 2004, 

Robertson & Hutto 2006). 

Vysvětlením může být zpoždění v adaptaci živočichů na krajinu pozměněnou člověkem. 

Podněty, které dříve signalizovaly kvalitní prostředí (např. husté listí) tento svůj informační 

obsah mohly ztratit. Jedinci, kteří se těmito podněty stále řídí, pak dělají chybná rozhodnutí s 

negativními dopady na jejich reprodukční úspěch (Gilroy & Sutherland 2007). Tomu by 

napovídal fakt, že chybná volba hnízdního prostředí nebo místa byla často identifikována v 

prostředích charakterizovaných narušením původních ekologických vztahů člověkem 

(Misenhelter & Rotenberry 2002, Rodewald et al. 2010). Přestože mikroevoluce může v 

současných populacích živočichů pracovat překvapivě rychle (Stockwell et al. 2003), některé 

změny prostředí jsou zřejmě příliš rychlé na to, aby na ně už vznikly adaptivní behaviorální 

reakce (např. vyhýbat se špatnému prostředí). Mechanismus ekologické pasti má 

samozřejmě mnohem širší použití a implikace, zejména v ochraně přírody. 

Výhled 

Výběr hnízdního místa u ptáků byl a je atraktivním problémem v behaviorální ekologii. 

Většina studií je však observačních, což s sebou nese velké riziko nalezení falešných vztahů. 

Experimentálních studií je málo (např. Peak 2003, Chalfoun & Martin 2009), ale přinášejí 

zásadní vhled do fungování vztahu mezi predátorem a kořistí. Mnoho studií například 

nenalezlo vztah mezi ukrytím hnízda a pravděpodobností jeho zničení predátory (přehled v 

Martin 1992). Bez experimentu však nevíme, zda tento vztah skutečně neexistuje, nebo zda 

existuje vztah mezi ukrytím a pravděpodobností nalezení hnízda predátorem ale rodiče toto 

vyšší riziko u málo ukrytých hnízd kompenzují nenápadným chováním nebo obranou hnízda 

(Eggers et al. 2008). Navíc pokud observační studie nenalezne vztah mezi ukrytím a 

pravděpodobností predace, je chybou vyvodit z toho závěr, že v té konkrétní populaci není 

selekce na vyšší ukrytí hnízda. Naopak, pokud rodiče musí špatné ukrytí hnízda kompenzovat 

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a tato kompenzace je nákladná (např. riziko zranění nebo smrti při obraně hnízda), pak jistě 

existuje selekce na vyšší ukrytí hnízda. Právě tyto skryté vztahy odhalí jen experimentální 

přístup. Ten navíc generuje další zajímavé otázky: pokud je nevýhodné mít odkryté hnízdo, 

proč některé páry volí takto nevýhodné hnízdní místo? Je to dáno nějakým omezením 

(nedostatek dostatečně krytých míst) nebo je to výsledek kompromisu (vyšší ukrytí může 

vést k vyššímu riziku predace dospělce na hnízdě; Wiebe & Martin 1998)? 

Zřejmým nedostatkem dosavadních studií výběru hnízdního místa a jeho adaptivní 

hodnoty je neznalost identity hnízdních predátorů. Ač s aplikací moderní a levnější 

monitorovací techniky data o identitě hnízdních predátorů narůstají, jedná se dosud pouze o 

observační studie (přehled v Weidinger 2008). V nejbližší době bude možné spojit sílu 

moderních monitorovacích studií se sílou experimentálního přístupu. Bylo například 

ukázáno, že pravděpodobnost hnízdní predace se u různých druhů predátorů (hadi, dravci a 

vlhovci hnědohlaví) liší ve vztahu k průběhu sezony a vzdálenosti k ekotonu (Benson et al. 

2010). Spojení takového přístupu kde známe jménem všechny účastníky ekologické hry a 

experimentálního přístupu umožní zodpovědět fascinující otázky: liší se hnízdní místa ve své 

zranitelnost vůči různým druhům predátorů? Existují při výběru hnízdního místa nějaké 

kompromisy – jsou například dobře ukrytá hnízdní místa dobrou obranou proti ptačím 

predátorům, ale ne proti savčím? Jsou rodiče schopni kompenzovat špatné ukrytí hnízda ve 

vztahu k predátorům hledajícím potravu ve dne, ale ne ve vztahu k nočním druhům? 

Podobných otázek existuje celá řada, ale zásadním bude právě bezprecedentně detailní 

vhled do behaviorálních mechanismů vztahu mezi predátory a jejich kořistí. 

3. Evoluce a diverzita životních znaků 


Mezi tzv. životní znaky počítáme charakteristiky s těsným vztahem k fitness, jako jsou 

plodnost, přežívání nebo růst (Roff 2002). Tyto znaky se výrazně liší mezi jednotlivými druhy 

živočichů, a často jsou charakteristické pro dané čeledi a řády (Bennett & Owens 2002). 

Například pro trubkonosé (Procellariiformes) je typická snůška o jednom vejci snášená 

jednou za rok nebo více let, zatímco zástupci řádu hrabavých (Galliformes) typicky snášejí 5‐ 

10 vajec i několikrát do roka (Lack 1968, Jetz et al. 2008, Perrins 2009). Jedním z hlavních cílů 

evoluční biologie je tuto velkou mezidruhovou rozmanitost v životních znacích vysvětlit a 

nalézt faktory prostředí, které jsou za ni zodpovědné (Harvey & Pagel 1991). 

Jedním ze zásadních životních znaků je ontogenetický vývin jednotlivce: jeho 

načasování, tvar a rychlost jak v embryonální, tak v postnatální fázi (O'Connor 1984, Starck & 

Ricklefs 1998). Variabilita v rychlosti ontogeneze existuje na dvou základních úrovních: 

vnitrodruhová plasticita a mezidruhové rozdíly. Vnitrodruhová plasticita může být dána 

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například intenzitou zahřívání vajec během inkubace (Deeming 2002) nebo množstvím 

potravy dodávané mláďatům rodiči (Schew & Ricklefs 1998). Mezidruhové rozdíly v rychlosti 

růstu jsou samozřejmě z hlavní části geneticky fixované, ale mohou k nim přispívat i 

negenetické mechanismy. Druhově specifická délka inkubační periody je například částečně 

závislá na množství steroidních hormonů ve žloutku (Schwabl et al. 2007) nebo na intenzitě 

zahřívání vajec samicí nebo oběma rodiči (Martin et al. 2007, Martin & Schwabl 2008). Z 

hlediska evolučního výzkumu je však nejzajímavější otázka, nakolik jsou mezidruhové rozdíly 

v rychlosti růstu evolučně určovány faktory vnějšího prostředí (např. potrava, predátoři, 

paraziti), tedy do jaké míry jsou tyto selekční faktory prostředí určující pro evoluci růstových 

strategií na mezidruhové úrovni. 


Evoluci rychlosti růstu jsme studovali u pěvců severní Ameriky, řádu s typicky krmivými 

mláďaty závislými během pobytu v hnízdě plně na rodičovské péči (zahřívání, krmení, obrana 

atd.). Rychlost růstu jsme kvantifikovali pomocí růstové křivky sigmoidního tvaru, kterou 

jsme proložili růstovými daty pomocí nelineárního modelu. Parametr K této křivky vyjadřuje 

rychlost nárůstu tělesné hmotnosti mláděte v hnízdě a je nezávislý na absolutní hmotnosti. 

Proto je parametr K vhodný pro mezidruhová srovnání, což je přístup, který jsme v naší práci 

použili. Na základě rozsáhlé literární rešerše jsme shromáždili údaje o rychlosti růstu mláďat 

u více než 100 druhů severoamerických pěvců. Pro každý druh jsme zároveň shromáždili 

informace o jeho velikosti těla, dalších životních znacích (velikost snůšky, přežívání dospělců, 

atd.) a ekologických charakteristikách jeho životního prostředí (míra predace hnízd, 

parazitace vlhovcem hnědohlavým Molothrus ater, atd.). Lineárními statistickými modely 

jsme pak studovali vztahy mezi jednotlivými proměnnými na mezidruhové úrovni, což 

znamená že ve všech analýzách vystupoval vždy druh jako jednotka analýzy. Protože 

fylogenetické vztahy mezi druhy mohou narušit statistický předpoklad nezávislosti datových 

bodů mezi sebou, ve všech analýzách jsme provedli korekci pro fylogenetické vztahy. 

Rychlost růstu (vyjádřená parametrem K) u mláďat severoamerických pěvců klesala s 

rostoucí hmotností těla dospělců, rostla se zeměpisnou šířkou a intenzitou ničení hnízd 

predátory a byla nižší u druhů živících se létajícím hmyzem. Délka pobytu mláďat v hnízdě 

rostla s hmotností těla dospělců, klesala s intenzitou predace hnízd a byla delší u druhů 

živících se létajícím hmyzem. Relativní hmotnost při opuštění hnízda (hmotnost mláďat při 

opuštění hnízda / hmotnost dospělců) klesala s rostoucí hmotností dospělců a intenzitou 

predace hnízd a byla vyšší u druhů živících se létajícím hmyzem (příspěvek B‐1). Rychlost 

ontogenetického vývinu se zvyšovala také s mortalitou dospělců. Se zvyšující se mortalitou 

dospělců se zkracovala délka inkubační periody a zvyšovala se rychlost růstu mláďat 

(příspěvek B‐4). Na základě těchto srovnávacích studií jsme sestrojili kvantitativní model pro 

předpověď načasování opouštění hnízda u pěvců vzhledem k mortalitě hnízd a poté mláďat 

10

po opuštění hnízda. Tento model předpovídal růstové strategie severoamerických pěvců 

poměrně přesně za předpokladu, že mortalita mláďat po opuštění hnízda poměrně rychle 

klesá na hodnotu kolem 30–40 % hodnoty dosahované v hnízdě (příspěvek B‐2). 

Evoluci růstových strategií severoamerických pěvců zřejmě také ovlivnilo soužití s 

obligátním hnízdním parazitem vlhovcem hnědohlavým (příspěvek B‐3). Mláďata tohoto 

parazita nevytlačují hostitelova vejce nebo mláďata z hnízda, jak je známo u evropské 

kukačky obecné (Cuculus canorus), ale vyrůstají s nimi společně. Přítomnost parazitického 

mláděte jiného druhu v hnízdě však snižuje průměrnou genetickou příbuznost hostitelových 

mláďat, což by mělo vést k zintenzivnění konkurence mezi sourozenci o zdroje. Protože 

úspěch v konkurenci o potravu přinášenou rodiči na hnízdo je dán z velké části tělesnou 

velikostí, měla by přítomnost mláděte parazita vést k evoluci rychlého růstu. Z toho pak 

plyne, že u silně parazitovaných hostitelů by mělo dojít k evoluci rychlejšího růstu mláďat než 

u hostitelů málo parazitovaných nebo druhů neparazitovaných vůbec. V naší studii jsme 

skutečně pozorovali kladný vztah mezi rychlostí růstu hostitelových mláďat a intenzitou 

parazitace vlhovcem. S intenzitou parazitace se dále zkracovala doba pobytu v hnízdě a 

mláďata opouštěla hnízdo v relativně nižší tělesné hmotnosti. U druhů, které neslouží jako 

hostitelé vlhovce, byl růst mláďat nejpomalejší, doba pobytu v hnízdě nejdelší a relativní 

hmotnost při opouštění hnízda nejvyšší (příspěvek B‐3). Všechny tyto vztahy navíc platily 

pouze u středně velkých a velkých druhů pěvců (asi nad 30 g hmotnosti v dospělosti)―zdá 

se, že u menších druhů je mortalita hostitelových mláďat způsobená přítomností 

parazitického mláděte tak vysoká, že prakticky žádné takové mládě se nedožije reprodukce a 

tak nemohlo u těchto malých druhů dojít k evoluční úpravě růstových strategií vzhledem k 

intenzitě parazitace. 

Je zajímavé, že rychlost růstu hostitele naopak ovlivňuje růst mláděte parazitického 

vlhovce. V tomto případě se ovšem jedná o plasticitu růstu v rámci jednoho druhu. Rychlost 

růstu a hmotnost při opouštění hnízda vlhovcem korelovaly kladně s rychlostí růstu 

hostitelových mláďat; parazitická mláďata zároveň lépe rostla u hostitelů o velké tělesné 

hmotnosti (příspěvek B‐5). 

Závěr 

Naše srovnávací analýzy růstových strategií severoamerických pěvců ukázaly, že rychlost 

růstu a načasování událostí v ontogenezi (např. opouštění hnízda mláďaty) jsou poměrně 

úzce vázány k specifickým faktorům životního prostředí druhu, kam patří intenzita ničení 

hnízd predátory, intenzita napadání hnízdním parazitem, nebo potrava. Náš kvantitativní 

model dále ukázal, že růstové strategie mohou být poměrně přesně modelovány pomocí 

jednoduchých environmentálních faktorů (mortalita hnízd, mortalita mláďat po opuštění 

hnízda), což opět ukazuje na jejich adaptivní vyladění vzhledem k druhově specifickým 

selekčním faktorům prostředí. 

11

Tradiční pohled na růst u obratlovců byl však jiný. Uvažovalo se, že rychlý růst je 

výhodný za všech podmínek, protože má jen pozitivní stránky, a proto je pro každý druh 

výhodné růst tak rychle jak to dovolí fyziologická a funkční omezení organismu (Ricklefs 

1969b, 1984). Přestože existovaly i názory jiné, které vnímaly růst jako více adaptovaný 

typickým životním podmínkám daného druhu (Lack 1968, Case 1978), pohled "maximálního 

růstu v rámci omezení" převažoval (Ricklefs et al. 1998). Později se však ukázalo, že rychlý 

růst s sebou může přinášet značné negativní efekty pro jedince jak hned, tak v pozdějším 

životě po mnoha měsících či letech (přehled v Metcalfe & Monaghan 2001). To samozřejmě 

mění rovnováhu v neprospěch rychlého růstu za všech okolností a ukazuje to, že rychlý růst 

může být preferován přírodním výběrem jen za podmínek, kdy jej faktory prostředí opravdu 

zvýhodňují (např. při intenzivní predaci hnízd). Naše srovnávací studie takové jemné vyladění 

růstových strategií vzhledem k faktorům prostředí potvrdily na rozsáhlém datovém souboru 

a přispěly tak ke změně pohledu na evoluci růstu a vývinu u obratlovců. 

Výhled 

Zatím se nám podařilo poměrně přesvědčivě dokumentovat evoluci růstových strategií 

vzhledem k selekčním faktorům prostředí daných druhů u severoamerických pěvců. V 

budoucnu bude zajímavé sledovat dvě linie výzkumu. Zaprvé bude důležité zjistit, které 

proximativní mechanismy mohou být zodpovědné za diverzifikaci růstových strategií. To 

bude vyžadovat analýzy např. steroidních hormonů ve žloutku (Gil et al. 2007), inkubačního 

chování rodičů (Conway & Martin 2000, Martin 2002) nebo intenzity krmení rodiči 

(příspěvek B‐4) na rozsáhlých mezidruhových souborech. Zadruhé bude zajímavé zjistit, 

nakolik platí závislosti zjištěné u severoamerických pěvců na jiných kontinentech a u jiných 

řádů ptáků. My jsme shromáždili data o růstu evropských, australských a novozélandských 

pěvců a jejich předběžná analýza ukazuje, že mezidruhové závislosti jsou zde stejné jako u 

pěvců severoamerických (Remeš, V. et al., 25th International Ornithological Congress, 

Campos do Jordão, Brazílie; 22.‐28.8.2010). To by potvrzovalo univerzálnost našich zjištění a 

zvyšovalo jejich význam pro poznání obecných zákonitostí evoluce růstových strategií u 

obratlovců. 

Zajímavým problémem je také podíl geneticky fixovaných rozdílů a vnitrodruhové 

plasticity na evoluci růstu a výsledné velikosti těla. Oba tyto zdroje variability se mohou 

uplatňovat v rozdílech v růstu a velikosti mezi plemeny domácích zvířat, protože tady se 

pohybujeme na vnitrodruhové úrovni, ale mezi v podstatě reprodukčně izolovanými liniemi, 

plemeny. My jsme na takovéto úrovni variability analyzovali pohlavní dimorfismus ve 

velikosti těla u plemen kura domácího (příspěvek B‐6); dále existují analýzy pohlavního 

dimorfismu u koz, ovcí (Polák & Frynta 2009) a skotu (Polák & Frynta 2010). Zvláště plodným 

přístupem do budoucna se zdá kontrastování výsledků získaných analýzou domestikovaných 

skupin a jejich divoce žijících příbuzných. 

12

4. Mateřské efekty a nutriční ekologie 


Mateřskými efekty rozumíme negenetické mechanismy, jimiž samice ovlivňují fenotyp svých 

potomků (Mousseau & Fox 1998). U ptáků s jejich vysoce rozvinutou rodičovskou péčí (viz 

kap. č. 5) mohou hrát mateřské efekty při určování kvality potomstva samozřejmě velkou roli 

(Price 1998). Důležité je ovšem odlišit neadaptivní modifikaci fenotypu mláďat (je málo 

potravy a tak jsou podvyživené matky nuceny krmit mláďata málo) od adaptivních 

mateřských efektů (je málo potravy a tak matky krmí mláďata málo proto, aby došlo k 

vytvoření fenotypů odolných proti nedostatku potravy v dospělosti). Mateřské efekty jsou 

adaptivní jen tehdy, pokud zvyšují fitness matky nebo potomků (Marshall & Uller 2007) a lze 

je potom také chápat jako adaptivní transgenerační fenotypovou plasticitu. V kontextu 

našich studií lze mezi mateřské efekty zařadit např. velikost a složení vajec, kvalitu hnízdního 

místa nebo hnízda samotného. 

Vedle vlastní velikosti vajec jsou jedním z důležitých zdrojů mateřských efektů u ptáků 

biologicky účinné látky ukládané matkami do vejce, případně poskytované mláďatům v 

potravě. Mezi biologicky aktivní látky ukládané do vejce patří steroidní hormony (Groothuis 

et al. 2005), protilátky (Hasselquist & Nilsson 2009) nebo antioxidanty (Surai 2002) ve 

žloutku a antibakteriální enzymy, hlavně lysozym (Shawkey et al. 2008), v bílku. Z látek 

poskytovaných mláďatům v potravě byly studovány opět antioxidanty (např. O’Brien & 

Dawson 2008, DeAyala et al. 2006, Hall et al. 2010) nebo aminokyselina taurin, která se 

vyskytuje ve velkém množství v pavoucích a má zřejmě zásadní význam pro rozvoj 

kognitivních schopností mláďat (Arnold et al. 2007). Právě ekologie těchto nutrientů je v 

současnosti ve středu pozornosti mnoha ekologů a etologů a my jsme se jí v naších studiích 

také věnovali. 


Problematiku mateřských efektů u ptáků jsme studovali u volně žijících populací lejska 

bělokrkého (Ficedula albicollis) a sýkory koňadry (Parus major) na svazích Velkého Kosíře u 

obce Služín. Zabývali jsme se také metodickými aspekty výzkumu mateřských efektů a 

možnými artefakty, které vznikají využíváním budkových populací ptáků (což byl i případ 

našich studií). Konkrétně jsme se zabývali vlivem velikosti vejce na přežívání a růst mláďat u 

lejska bělokrkého, vlivem složení hnízda na množství ektoparazitů a vlivem složení vejce 

(obsah antioxidantů) na přežívání a kvalitu mláďat u sýkory koňadry. V současné době se 

zabýváme vlivem obsahu vitamínu E v potravě mláďat u sýkory koňadry na jejich přežívání a 

růst, ale výsledky této studie ještě nebyly publikovány. 

13

Při studiu vlivu velikosti vejce na přežívání a růst mláďat u lejska bělokrkého jsme 

použili tzv. vnitrosnůškový design studie (příspěvek C‐1). Ten spočívá ve zvážení vajec těsně 

po jejich nakladení a zjištění, ze kterého vejce se to které mládě vylíhlo (v našem případě 

pomocí líhnutí mláďat v inkubátoru). Poté je sledován růst a přežívání mláďat a tyto dva 

parametry jsou následně vztaženy k velikost vejce, ze kterého se konkrétní mládě vylíhlo. 

Tato velikost je navíc takzvaně vycentrována, tzn. je vypočítána odchylka v hmotnosti 

konkrétního vejce od průměrné hmotnosti vajec ve snůšce a růst a přežívání mláděte je 

vztažen právě k této odchylce. Tato metoda má jednu zásadní výhodu ve srovnání s více 

používanou metodou výměny mláďat mezi hnízdy (tzv. cross‐fostering; příspěvek C‐2). 

Kontroluje totiž pro potenciální korelaci mezi aditivními geny matky pro velikost a velikostí 

vejce, a to díky tomu, že všechna porovnávaná vejce mají stejnou matku a chromozomy při 

meióze segregují náhodně vzhledem k velikost vejce. Tato vnitrosnůšková metoda také z 

velké části kontroluje pro případnou korelaci mezi kvalitou prostředí a velikostí vejce 

(příspěvek C‐2). 

Růst mláďat lejska bělokrkého koreloval pozitivně s velikostí vejce, z něhož se mládě 

vylíhlo, ale jen asi v prvním týdnu života (příspěvek C‐1). Potom mláďata, která se vylíhla z 

relativně malých vajec náskok sourozenců dohnala a hmotnost a velikost mláďat při 

opouštění hnízda na velikost vejce nezávisely. Pravděpodobnost přežití i růst mláďat nejvíce 

závisely na pořadí vylíhnutí mláděte. Mláďata, která se vylíhla nejpozději na tom byla nejhůř. 

Na stejné lokalitě jsme experimentálně studovali vliv koncentrace karotenoidů, 

konkrétně luteinu, na přežívání a růst mláďat v hnízdě a na rodičovskou péči, tentokrát však 

u sýkory koňadry. Lutein je hlavním karotenoidem, který samice sýkory koňadry ukládají do 

žloutku (dále ukládají zeaxanthin, α‐karoten a β‐karoten; příspěvek C‐4). Těsně před 

kladením vajec a během něho jsme dokrmovali samice sýkor potravním doplňkem, který 

obsahoval lutein. Samice tento doplněk ochotně přijímaly a lutein, který takto dostaly, 

ukládaly do žloutku; experimentální samice měly asi 1,6× vyšší koncentraci luteinu ve žloutku 

ve srovnání s kontrolními samicemi (určeno přesnou HPLC metodou). Tento vyšší obsah 

luteinu však nevedl k lepšímu prospívání mláďat, která se z těchto vajec vylíhla. Ani jeden ze 

sledovaných parametrů (přežívání, hmotnost, velikost a imunokompetence mláďat) 

nevykazoval lepší hodnotu u experimentálních hnízd. Stejně tak nemělo toto dokrmování 

pozitivní vliv na parametry rodičovské péče, kromě zvýšené frekvence krmení samcem na 

dokrmovaných hnízdech (příspěvek C‐4). 

Další z důležitých charakteristik, kterými může samice ovlivnit kvalitu vyváděných 

mláďat je složení a velikost hnízda. Hnízdo u lejska bělokrkého i sýkory koňadry staví pouze 

samice a hnízda těchto dvou druhů se liší ve složení. Lejsek používá ke konstrukci hnízda 

hlavně suchou trávu a lýko, zatímco sýkora hlavně větvičky, mech a srst. Mimo jiných 

charakteristik může mít složení hnízda vliv na množství ektoparazitů v hnízdě, kteří sají krev 

na rostoucích mláďatech a tak negativně ovlivňují jejich růst a kondici. Složení hnízda může 

14

ovlivňovat množství ektoparazitů díky mikroklimatu, které vytváří. V hnízdech obou druhů 

jsme studovali larvy much rodu Protocalliphora (příspěvek C‐3). Během kladení (u lejska) 

nebo časné inkubace (u koňadry) jsme vyměnili hnízda buď mezi lejskem a koňadrou (hnízda 

experimentální) nebo mezi páry lejsků a koňader (hnízda kontrolní). Takto jsme dostali 

všechny čtyři možné kombinace druhu a hnízda a mohli jsme izolovat nezávislé efekty druhu 

mláďat a typu hnízda na množství larev parazitických much v hnízdě po vylétnutí mláďat. 

Zjistili jsme, že na množství parazitických much má vliv druh mláďat (je jich více u mláďat 

koňader, ať jsou v jakémkoliv hnízdě), zatímco typ hnízda ne (příspěvek C‐3). Je zřejmé, že 

výsledek tohoto experimentu, stejně jako dalších podobných studií, mohl být ovlivněn tím, 

že byl proveden v populacích hnízdících v budkách. Přirozené dutiny jsou většinou pod 

větším tlakem ektoparazitů než hnízdní budky, a proto by se u nich mohl repelentní vliv 

určitého složení hnízda projevit více než u hnízdních budek (příspěvek C‐5). V přirozených 

dutinách se však bohužel tento typ experimentů provádět nedá. 

Závěr 

Vnitrosnůšková variabilita ve velikost vajec neměla vliv na růst a přežívání mláďat lejska 

bělokrkého. Tento závěr byl pro nás překvapivý, protože výroba vajec je pro samice ptáků 

obecně nákladná (Nager 2006), i když přesný fyziologický mechanismus této nákladnosti 

neznáme (Williams 2005). Pokud by tedy samice kladly všechna vejce ve velikosti rovné 

nejmenšímu vejci ve snůšce, ušetřily by zdroje bez negativního vlivu na jejich mláďata. Tento 

závěr byl poopraven důkladnější studií na téže populaci, která zjistila, že velikost mláďat 

pozitivně koreluje s velikostí vejce, ovšem na mezisnůškové úrovni (Krist 2009). Stejně tak 

nová metaanalýza korelací mezi kvalitou mláďat a velikostí vajec u ptáků potvrdila, že 

velikost, přežívání a kondice mláďat obecně s velikostí vejce stoupá (Krist 2011). Tyto studie 

ukazují, že závěr vyvozený z naší starší studie by mohl být mylný a ukazují na důležitost 

replikace studií a jejich sumarizace pomocí kvantitativních metod literárních rešerší (viz kap. 

6). 

Absence pozitivního efektu vyšší koncentrace luteinu ve žloutku na růst a přežívání 

mláďat u sýkory koňadry byla také překvapivá proto, že karotenoidy mají obecně pozitivní 

fyziologické efekty, jako například stimulaci imunitního systému (Chew & Park 2004) nebo 

tlumení škodlivého působení volných radikálů (Krinsky 2001, ale viz Pérez‐Rodríguez 2009). 

Navíc tyto pozitivní fyziologické efekty byly demonstrovány u celé řady ptačích druhů 

(přehled v Surai 2002) a také novější experimenty potvrdily pozitivní efekt koncentrace 

karotenoidů ve žloutku na kvalitu, přežívání a růst mláďat u několika druhů pěvců (např. 

McGraw et al. 2005, Ewen et al. 2009, Tanvez et al. 2009), dokonce i u švýcarské populace 

našeho studijního druhu, sýkory koňadry (Berthouly et al. 2008). 

Výhled 

15

Tyto konfliktní výsledky ukazují, že kritickým parametrem, který musí studie mateřských 

efektů a nutriční ekologie kontrolovat, je dostupnost zdrojů a nutrientů v prostředí. Několik 

recentních studií demonstrovalo, že jednotlivé druhy hmyzu sloužící za potravu ptákům se 

liší v obsahu karotenoidů a jiných antioxidantů (Isaksson & Andersson 2007, Sillanpää et al. 

2008, Arnold et al. 2010). V prostředí, kde je dostatek kvalitní potravy s vysokým obsahem 

antioxidantů pak dokrmování antioxidanty nebude mít na kvalitu mláďat žádný vliv, protože 

antioxidanty nejsou v tomto prostředí limitujícím zdrojem (Eeva et al. 2009). Jinými slovy, 

všechny samice jich mají k dispozici dostatek a ukládají jich do žloutku takové množství, které 

zajistí optimální kondici a přežívání mláďat. Stejná situace se může týkat i obsahu 

antioxidantů v potravě mláďat. V naší další studii jsme dokrmovali rostoucí mláďata sýkory 

koňadry vitamínem E, ale tato suplementace měla na růst a přežívání mláďat malý vliv (J. 

Matrková a V. Remeš, nepubl. data). Důvodem může být opět poměrně vysoký obsah 

vitamínu E v housenkách, kterými sýkory svá mláďata krmí (Arnold et al. 2010) a vitamín E 

tedy u sýkor asi není limitujícím mikronutrientem. Budoucí studie nutriční ekologie by tedy 

jistě výrazně profitovaly z komplexního přístupu, kdy by kvantifikovaly cestu mikronutrientů 

z rostlin přes herbivory až k insektivorním ptákům, jak bylo provedeno už v klasické práci 

Partaliho et al. (1987). Adaptivnost mateřských efektů musí být tedy studována v 

realistickém ekologickém kontextu. 

5. Ornamenty a rodičovské investice 


Péče o potomstvo je široce rozšířená u nejrůznějších skupin živočichů, od hmyzu po 

obratlovce (Clutton‐Brock 1991). U ptáků dosahuje intenzita rodičovské péče a její důležitost 

pro potomstvo jednoho ze svých vrcholů mezi živočichy (Burley & Johnson 2002). Mláďata 

žádného ptačího druhu by nebyla schopna bez péče ať již vlastních nebo cizích (u hnízdních 

parazitů) rodičů přežít a dosáhnout dospělosti. Ač se v detailech evoluční záměry rodičů a 

potomků liší (Trivers 1974, Parker et al. 2002), jejich cílem je v podstatě totéž: aby mláďata 

vyrostla, dospěla a rozmnožila se. Všichni rodiče se však o mláďata nestarají se stejnou 

intenzitou. V intenzitě péče se liší jak jednotlivé druhy, tak páry v rámci druhu (Conway & 

Martin 2000, Webb et al. 2010). Protože víme, že intenzita rodičovské péče se pozitivně 

odráží na kvalitě a pravděpodobnosti přežití mláďat (např. Schwagmeyer & Mock 2008, 

Harrison et al. 2009), je jednou z klíčových otázek evolučního výzkumu to, které faktory 

předpovídají a ovlivňují míru péče u jednotlivých párů v rámci druhu, tedy tzv. rodičovské 

investice. 

Z obecného pohledu existují dva základní faktory, které mohou určovat míru investic do 

potomstva, a to kvalita jedince a kvalita hnízdního prostředí, teritoria. Kvalitní jedinci mohou 

16

investovat do potomstva více než jedinci nekvalitní, aniž by to snížilo jejich pravděpodobnost 

přežití nebo schopnost dalšího rozmnožení (Wilson & Nussey 2010). Jejich individuální 

kvalita může být signalizována tzv. ornamenty, které můžeme pro naše potřeby definovat 

jako vizuální struktury (výrazně zbarvené peří a pod.), které je náročné vyrobit a/nebo 

udržovat, a proto jsou jejich plné exprese schopni jen jedinci kvalitní. Náročnost výroby 

případně držení výrazného ornamentu může být dána celou řadou fyziologických a 

behaviorálních mechanismů (přehledy v Griffith et al. 2006, Hill & McGraw 2006a,b, Ducrest 

et al. 2008, McGraw 2008). Výrazné ornamenty pak mohou signalizovat jedince, který se 

bude dobře starat o potomstvo a kterého je tedy výhodné si vybrat za partnera (Andersson 

1994, Maynard‐Smith & Harper 2003). Investice do potomstva může být usnadněna dobrým 

hnízdním teritoriem, a proto je třeba při studiu faktorů majících vztah k rodičovské investici 

pro kvalitu prostředí kontrolovat, případně použít experimenty. 


Vztah mezi intenzitou rodičovské péče, ornamenty a kvalitou hnízdního teritoria jsme 

studovali u volně žijící populace sýkory koňadry v lužním lese Království u obce Grygov. Naše 

práce měly výhody simultánního studia ornamentů založených na karotenoidech (intenzita 

žluté náprsenky) i melaninech (plocha černého hrudního pruhu, pravidelnost bíle lícní 

skvrny), a to jak u samců tak u samic, kombinace observačního a experimentálního přístupu 

a velkého datového souboru nashromážděného během čtyř let terénního výzkumu. Naše 

studie jsou zároveň zaměřeny na méně probádané období kladení (sledování velikosti a 

složení vajec) a inkubace (zahřívání vajec samicí, krmení zahřívající samice samcem). 

Zjistili jsme, že intenzita exprese ornamentů u samice, ať se jedná o karotenoidové 

nebo melaninové ornamenty, nepředpovídá ani investici do vajec během kladení (velikost 

vejce, žloutku, množství steroidních hormonů; příspěvek D‐4), ani investici do zahřívání vajec 

(příspěvek D‐2), a ani úspěšnost jejich líhnutí (příspěvek D‐2). Tyto poznatky odpovídají 

našemu dalšímu zjištění, že intenzita karotenoidových ani melaninových ornamentů 

nekoreluje s rychlostí růstu ocasních per při pelichání, která je často považována za ukazatel 

kvality jedince (příspěvek D‐1). Zdá se tedy, že samičí ornamenty v naší populaci sýkory 

koňadry nejsou dobrým ukazatelem investice do potomstva ani v období kladení ani během 

inkubace. Protože výsledky observačních studií mohou být zkresleny nekontrolovanými 

proměnnými, zaměřili jsme se dále na experimentální studium žluté náprsenky založené na 

karotenoidech v blízké populaci sýkory koňadry na Velkém Kosíři u obce Služín. Samicím jsme 

během inkubace zvýšili energetickou náročnost letu tím, že jsme jim odstranili několik per z 

křídel a ocasu. Takto hendikepované samice nesnížily intenzitu zahřívání vajec, ale jejich 

tělesná hmotnost poklesla během inkubace (asi 12 dnů) více než u samic kontrolních. Z 

našeho pohledu zajímavější výsledek byl, že ani změna intenzity inkubace ani pokles tělesné 

hmotnosti nekorelovaly s intenzitou žluté náprsenky. Samice s výraznějším karotenoidovým 

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ornamentem tedy nebyly schopny se s letovým hendikepem vyrovnat lépe (příspěvek D‐3). 

Tato experimentální studie tedy nezávisle (tj. na jiné populaci koňadry) potvrdila, že samičí 

ornamenty nejsou u tohoto druhu ukazatelem investice do potomstva ani kvality jedince. 

Co se týká samčích ornamentů, tak ani žlutost náprsenky ani plocha černého hrudního 

pruhu samce nekorelovaly s intenzitou samčího inkubačního krmení (příspěvek D‐2). Stejně 

tak intenzita zahřívání vajec samicí nekorelovala ani s jedním z ornamentů samce (V. Remeš, 

nepubl. data). Naopak hmotnost žloutku korelovala pozitivně s plochou černého hrudního 

pruhu samce a koncentrace steroidních hormonů ve žloutku (testosteron a androstenedion) 

korelovala pozitivně s intenzitou žluté náprsenky samce (příspěvek D‐4). Tyto výsledky 

ukazují, že zatímco u intenzity zahřívání vajec zbarvení samce roli nemá, během kladení ji má 

a samice spárovaná se samcem s výraznějšími ornamenty klade vejce s více nutrienty a 

steroidními hormony. Interpretace těchto výsledků by mohla být komplikována nejistotou 

genetického otcovství, ale z důvodů uváděných v příspěvku D‐4 se zdá, že tomu tak není. 

Kvalita hnízdního teritoria korelovala s rodičovskými investicemi jak ve fázi kladení 

vajec, tak ve fázi inkubace. Koncentrace steroidních hormonů ve žloutku se zvyšovala s 

kvalitou teritoria, definovanou jako frekvence obsazenosti daného teritoria během 

posledních pěti let (příspěvek D‐4). Tato metoda předpokládá, že ptáci sami poznají nejlepší 

teritoria a proto je obsazují častěji než jiná (nehledě na identitu jednotlivců z roku na rok). 

Tato metoda však nedokáže identifikovat vlastní klíč ke kvalitě teritoria. Ve studii zaměřené 

na inkubační chování jsme proto jako kvalitu teritoria zvolili skutečnou nabídku potravy v 

konkrétním teritoriu. Ukázalo se, že množství potravy v teritoriu sice nepředpovídá investici 

samice do zahřívání vajec, ale předpovídá intenzitu krmení inkubující samice samcem 

(příspěvek D‐2). Pozitivní korelace mezi samčím inkubačním krmením a potravní nabídkou 

však byla patrná jen v letech s celkové nízkým množstvím potravy, což naznačuje, že kvalita 

teritoria se může projevit hlavně ve špatných letech kdy je rodičovská péče obecně 

náročnější. 

Závěr 

Samičí ornamenty nepředpovídaly v našich dvou populacích sýkory koňadry intenzitu 

rodičovské péče. Tento závěr je podporován našimi dalšími, dosud nepublikovanými, 

výsledky které ukazují, že ani koncentrace ani množství antioxidantů ve žloutku (vitamíny E a 

A, lutein, zeaxanthin, β‐karoten) nekorelují s ornamenty samice. Naopak, stejně jako v 

případě steroidních hormonů, korelují s ornamenty samce a s podmínkami prostředí (datum 

kladení a teplota při kladení; V. Remeš et al., nepubl. rukopis). 

Tyto závěry jsou ve zdánlivém rozporu s následujícími dvěmi skutečnostmi. a) Pomocí 

experimentu s výměnou mláďat mezi hnízdy jsme v populaci sýkory koňadry u Grygova 

zjistili, že plocha černého hrudního pruhu jak genetických tak pěstounských matek 

předpovídá kvalitu mláďat před vyvedením z budky. Jak je tedy možné, že samičí ornamenty 

18

nic nevypovídají o intenzitě rodičovské péče ale korelují s kvalitou mláďat? b) Jiné studie 

prováděné u sýkor (koňadra a příbuzná sýkora modřinka, Cyanistes caeruleus) v období péče 

o mláďata několikrát zjistily, že intenzita exprese samičích ornamentů založených na 

karotenoidech indikuje individuální kvalitu a předpovídá rodičovské schopnosti (Ferns & 

Hinsley 2008, Doutrelant et al. 2008). Jak tedy vysvětlit rozpor mezi těmito studiemi a našimi 

výsledky (viz výše)? 

Výhled 

Nabízejí se tři základní vysvětlení, která zároveň ukazují kam by se mohl výzkum samičích 

ornamentů v nejbližší budoucnosti ubírat. 1) Funkce ornamentů může být geograficky 

variabilní. Práce provedené na hýlovi mexickém, Carpodacus mexicanus (Hill 2002), 

lesňáčkovi žlutohrdlém, Geothlypis trichas (Dunn et al. 2010), lejskovi bělokrkém (Pärt & 

Qvarnström 1997, Török et al. 2003) nebo lejskovi černohlavém, Ficedula hypoleuca (Dale et 

al. 1999, Galván & Moreno 2009) ukazují, že mechanismy exprese a signalizační funkce 

ornamentů u samců se mohou mezi populacemi v rámci druhu výrazně lišit. Zatím neexistují 

studie geografické proměnlivosti ve funkci u jednotlivých nebo mnohočetných ornamentů u 

samic. 2) Signalizační funkce ornamentů se může lišit během hnízdního cyklu. Jiná může být 

během snášení, inkubace, péče o mláďata v hnízdě či po jeho opuštění. Žádná studie 

signalizační funkce ornamentů nebyla na jedné populaci během více fází hnízdního cyklu 

zatím provedena. 3) Zjistili jsme, že plocha černého hrudního pruhu samic předpovídá kvalitu 

mláďat, ale ne samičí investici do potomstva. Studovali jsme však pouze malou část všech 

komponent samičí rodičovské péče. Existuje mnoho dalších látek (např. protilátky, 

Hasselquist & Nilsson 2009, antibakteriální enzymy, Shawkey et al. 2008) a chování (např. 

zahřívání mláďat), kterými samice do potomstva investují. V budoucnu by se měly studie 

zaměřit buď na kvantifikaci co největší části repertoáru rodičovské péče, nebo na 

experimentální manipulaci konkrétních mechanismů. 

6. Celkový závěr 

Každý obor vědy by měl směřovat k co nejlépe designovaným a provedeným studiím, které 

by měly poskytovat co největší množství robustních poznatků. V sekcích "Závěr" a "Výhled" 

pod jednotlivými kapitolami mé habilitační práce jsem se pokusil identifikovat témata, na 

která by bylo vhodné zaměřit pozornost v dalším bádání. Zde bych chtěl nabídnout 

komplementární pohled a zamyslet se nad problémy, které je třeba v oblasti analýzy 

reprodukčních strategií v blízké budoucnosti překonat, a to jak na vnitrodruhové tak na 

mezidruhové úrovni. 

19

Problém 1: Místo fitness jsou používány neověřené zástupné znaky. Používání 

netestovaných zástupných znaků místo fitness (tj. vlastností, o kterých se pouze 

předpokládá, že pozitivně korelují s fitness, aniž by to bylo testováno) považuji osobně za 

jednu z největších překážek v rozvoji evoluční ekologie. Nejde jen o to, že korelace s fitness 

je pouze předpokládána, ale také o to, že jednotlivé komponenty fitness (plodnost, přežívání, 

stárnutí) mohou spolu navzájem korelovat negativně. Potom zvýšená výkonnost v jedné 

komponentě (např. plodnosti) může být vyvážena sníženou výkonností v jiné komponentě 

(např. přežívání), což při používání pouze jedné komponenty jako zástupu za fitness 

nezjistíme (viz např. Hunt et al. 2004a, Lailvaux et al. 2010) a jsme vedeni k mylným závěrům. 

Za současného stavu metodických přístupů je jednou z dosažitelných podob fitness 

celoživotní produkce potomstva. Díky pokroku molekulárních metod v určování paternity je 

dnes možno kvantifikovat produkci potomstva i u samců, kde byla dříve paternita nejistá 

(Griffith et al. 2002). Relativně nízké ceny molekulárních analýz už dnes umožňují nasazení 

této technologie velkoplošně na celou studijní populaci. Rutinní kvantifikace celoživotní 

produkce potomstva by vedla doslova k revoluci v behaviorální a evoluční ekologii. Umožnila 

by relativně přesně kvantifikovat náklady a zisky nejrůznějších chování a strategií. Kdybych 

měl tento obecný princip ilustrovat na tématech z této habilitační práce, umožnila by 

kvantifikovat náklady a zisky výrazné versus matné náprsenky u samců, intenzivního versus 

slabého zahřívání vajec samicí nebo potravy mláďat bohaté versus chudé na antioxidanty. 

Takové detailní analýzy by umožnily přesnou analýzu adaptivnosti současných charakteristik 

druhů. 

Velkým problémem je však skutečnost, že zavedení takového přístupu by vyžadovalo 

mnohem delší období studia populací (v řádech mnoha let). Paradoxně je tak pokrok 

evoluční ekologie v této oblasti brzděn tříletým až čtyřletým cyklem doktorských studií a 

grantů, tj. sociálními aspekty organizace vědy, a ne technickými či metodickými omezeními, 

jak tomu bylo v minulosti. Možná ale právě proto bude o to těžší tento limit překonat. 

Problém 2: U srovnávacích studií jsou často používány nerealistické modely evoluce a 

sporné fylogeneze. Publikace zásadní metodické práce (Felsenstein 1985) odstartovala v 

evoluční ekologii výrazné nasazení srovnávacích metod založených na fylogenezích. Ač už 

dříve existovalo povědomí o problému, jaký způsobuje fylogenetická příbuznost druhů pro 

správnou analýzu mezidruhových dat (přehled v Harvey & Pagel 1991), neexistovala jasná 

metodika, jak se s tímto problémem vypořádat. Felsenstein takovou metodiku navrhl a 

Purvis a Rambaut (1995) ji implementovali v programu CAIC. Tato metoda se stala na 

dobrých deset let standardním přístupem, aniž by značná část badatelů byť jen tušila, že 

může být někde chyba. 

Problémem je, že tato metoda předpokládá, že dochází k evoluci znaků podle 

náhodného modelu tzv. Brownova pohybu a dále předpokládá vysokou korelaci znaků s 

20

fylogenetickou strukturou. Přitom nikdo neví, jak často tyto dva zásadní předpoklady platí. 

Pagel (1997, 1999) vyvinul metodu, která umožňuje kvantifikovat míru korelace reziduí v 

modelu a flexibilně pro ni kontrolovat (Freckleton et al. 2002, Revell 2010). Mimo široké 

aplikace této metody by dalším pokrokem měla být rutinnější aplikace a testování 

alternativních modelů evoluce znaků, kam patří zejména model omezené evoluce znaků (tzv. 

Orstein‐Uhlenbeckův proces, Butler & King 2004, Freckleton & Pagel 2010). Důležité bude 

také současné modelování mezidruhové variability, vnitrodruhové variability (Ives et al. 

2007, Felsenstein 2008) a prostorové autokorelace znaků (Freckleton & Jetz 2009). 

Dalším problémem srovnávacích analýz, kterého si je málokdo mimo komunitu 

teoretiků a autorů analytických metod vědom, je obrovská nejistota v používaných 

fylogenetických hypotézách. Nejistoty jsou hned dvě, a sice výběr znaků, na jejichž základě 

jsou fylogeneze konstruovány a výběr finálního fylogenetického stromu. V současné době se 

prakticky veškerá fylogenetická aktivita přesunula do oblasti sekvencí DNA. Zatímco ještě 

před několika málo lety byla většina fylogenetických prací založena na jediném genu, dnes 

jsou práce s více geny běžné a výjimkou nejsou ani fylogenomické práce založené na analýze 

desítek až stovek genů (např. Meusemann et al. 2010, Timmermans et al. 2010). Pokud je 

fylogenetický signál shodný ve většině znaků, můžeme výsledné fylogenezi věřit. Problém je, 

že tento pokrok se týká pouze specializovaných fylogenetických prací. V oblasti srovnávacích 

analýz, kde fylogeneze není cílem studie ale jejím vstupním údajem, je kvalita fylogenezí 

řádově horší. Odhadem v 90 % případů se jedná o fylogenetickou informaci shromážděnou z 

více primárních fylogenetických prací založených na jednom nebo několika genech. Takových 

fylogenezí lze zpravidla vytvořit celou řadu, což však málokdo bere v potaz a svoji analýzu 

pro to koriguje. 

Druhým zdrojem nejistoty stran fylogenetické hypotézy je astronomický počet 

potenciálních fylogenetických stromů. Například pro 20 druhů, což je počet pro srovnávací 

analýzu málo dostačující, činí počet potenciálních stromů 8,2 × 10 21 , pro 50 druhů je to 2,7 × 

10 76 , tedy více než je počet elektronů ve viditelném vesmíru! Z toho plyne, že i při plném 

využití dnešních výkonných počítačů a software lze prohledat jen malou část parametrického 

prostoru fylogenezí. Větším problémem je však skutečnost, že v závěru se většinou vybere 

jen jeden "nejlepší" fylogenetický strom, který je jakýmsi kompromisem mnoha stejně 

"dobrých" stromů (pro příslušné metodiky viz např. Hillis et al. 1996). Zanedbání celého pole 

fylogenetických stromů, které jsou jen o "krok" horší (v případě analýzy kritériem úspornosti) 

nebo o něco málo "méně věrohodné" (v případě analýzy kritériem maximální věrohodnosti), 

zásadně snižuje důvěryhodnost dosažených výsledků. Naštěstí existuje metoda, která 

umožňuje tento problém překonat. Jedná se o Bayesovu metodu konstrukce fylogenetických 

stromů, kdy všechny stromy obdrží vlastní pravděpodobnost. Potom lze provést srovnávací 

analýzu na náhodně vybrané podmnožině těchto stromů a výsledky vážit pravděpodobností 

konkrétního fylogenetického stromu (Huelsenbeck et al. 2000). Tuto metodu považuji za 

21

velmi slibnou. Jejímu většímu využití však zatím brání to, že nebyla programově 

implementována v dostupné podobě pro běžného uživatele. 

Velkým problémem je také značná náročnost výše uvedených technik a jejich rychlý 

rozvoj, který je pro běžného uživatele dosti obtížné sledovat. Pokrok ve výzkumné praxi ve 

většině těchto oblastí snad přinese implementace moderních přístupů k analýze 

mezidruhových a mezipopulačních dat v univerzálních a flexibilních programovacích jazycích 

jako je jazyk R (Paradis 2006). 

Problém 3: Výsledky studií jsou prezentovány neúplně a chybí přístup k primárním 

datovým souborům. Tento problém se netýká designu konkrétních studií, ale celkové 

strategie oboru. Pro empirický pokrok v evoluční a behaviorální ekologii jsou nezbytné 

kvantitativní sumarizace dosavadního výzkumu pomocí tzv. metaanalýz (např. Arnqvist & 

Wooster 1995, Gates 2002). Jen ty umožní zjistit, co opravdu víme a poměřit toto poznání s 

teorií tak, aby obor postoupil dopředu. Tato metodika je založena na kvantitativním 

zpracování výsledků určitého souboru studií týkajících se daného problému (Pullin & Stewart 

2006). Metaanalýzy jsou velmi důležité proto, že na rozdíl od lékařského výzkumu nejsou 

studie v evoluční a behaviorální ekologii skoro nikdy skutečně replikovány (Palmer 2000, 

Kelly 2006). Jistě k tomu přispívá mnoho různých faktorů, mezi jinými tlak na originalitu, 

který znemožňuje financování replikací dřívějších, byť zásadních, studií. Dalším problémem je 

často unikátnost studijních systémů. Zatímco replikovat studii na myši nebo buněčné kultuře 

se obejde bez větších problémů, replikovat studii na karibských ještěrech rodu Anolis jinde 

než v Karibiku je prakticky nemožné. Z toho však plyne jiná povinnost pro autory dílčích 

studií, a sice prezentovat své výsledky tak, aby byly použitelné v pozdějších metaanalýzách 

(Nakagawa & Cuthill 2007). A to je jeden z nejdůležitějších požadavků na prezentaci dobře 

designované a provedené studie. Jen málo primárních prací bude citováno ještě za 20 let, ale 

každá, pokud je kvalitně provedená, může přispět k pokroku poznání jako jeden datový bod v 

budoucí metaanalýze. 

Problém neúplné prezentace výsledků jednotlivých studií by byl odstraněn také 

dostupností kompletních primárních dat, na nichž je studie založena. V molekulárních 

vědách je podobný přístup povinný a studie není publikována, dokud autor nezpřístupní 

sekvenční data ve veřejně přístupné databázi (např. GenBank). Podobný přístup se 

připravuje také pro ekologické a evoluční vědy (např. Rausher et al. 2010). Toto řešení by 

bylo nejlepší, protože by umožňovalo pozdější re‐analýzu dat modernějšími metodami, 

případně analýzu více datových souborů dohromady, což by přineslo robustnější a zásadnější 

výsledky. 

22

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8. Seznam zařazených prací 

Tato habilitační práce je založena na následujících primárních vědeckých příspěvcích: 

A. Výběr hnízdního prostředí a hnízdní predace 

[1] Remeš, V. 2000. How can maladaptive habitat choice generate source‐sink population 

dynamics? Oikos 91:579‐582. 

[2] Remeš, V. 2003. Effects of exotic habitat on nesting success, territory density, and 

settlement patterns in the Blackcap (Sylvia atricapilla). Conservation Biology 17:1127‐ 

1133. 

[3] Remeš, V. 2003. Breeding biology of the Blackcap (Sylvia atricapilla) in the Czech 

Republic: an analysis of nest record cards. Sylvia 39:25‐34. 

[4] Remeš, V. 2005. Birds and rodents destroy different nests: a study of Blackcap (Sylvia 

atricapilla) using the removal of nest concealment. Ibis 147:213‐216. 

[5] Remeš, V. 2005. Nest concealment and parental behaviour interact in affecting nest 

survival in the blackcap (Sylvia atricapilla): an experimental evaluation of the parental 

compensation hypothesis. Behavioral Ecology and Sociobiology 58:326‐332. 

B. Evoluce a diverzita životních znaků 

[1] Remeš, V. & T. E. Martin. 2002. Environmental influences on the evolution of growth 

and developmental rates in passerines. Evolution 56:2505‐2518. 

[2] Roff, D.A., V. Remeš & T.E. Martin. 2005. The evolution of fledging age in songbirds. 

Journal of Evolutionary Biology 18:1425‐1433. 

[3] Remeš, V. 2006. Growth strategies of passerine birds are related to brood parasitism by 

the Brown‐headed Cowbird (Molothrus ater). Evolution 60:1692‐1700. 

[4] Remeš, V. 2007. Avian growth and development rates and age‐specific mortality: the 

roles of nest predation and adult mortality. Journal of Evolutionary Biology 20:320‐325. 

[5] Remeš, V. 2010. Explaining postnatal growth plasticity in a generalist brood parasite. 

Naturwissenschaften 97:331‐335. 

[6] Remeš, V. & T. Székely. 2010. Domestic chickens defy Rensch’s rule: sexual size 

dimorphism in chicken breeds. Journal of Evolutionary Biology 23:2754‐2759. 

C. Mateřské efekty a nutriční ekologie 

[1] Krist, M., V. Remeš, L. Uvírová, P. Nádvorník & S. Bureš. 2004. Egg size and offspring 

performance in the collared flycatcher (Ficedula albicollis): a within‐clutch approach. 

Oecologia 140:52‐60. 

[2] Krist, M. & V. Remeš. 2004. Maternal effects and offspring performance: in search of 

the best method. Oikos 106:422‐426. 

30

[3] Remeš, V. & M. Krist. 2005. Nest design and the abundance of parasitic Protocalliphora 

blow flies in two hole‐nesting passerines. Écoscience 12:549‐553. 

[4] Remeš, V., M. Krist, V. Bertacche & R. Stradi. 2007. Maternal carotenoid 

supplementation does not affect breeding performance in the Great Tit (Parus major). 

Functional Ecology 21:776‐783. 

[5] Lambrechts, M. & 55 coauthors (incl. V. Remeš ). 2010. The design of artificial 

nestboxes for the study of secondary hole‐nesting birds: a review of methodological 

inconsistencies and potential biases. Acta Ornithologica 45:1‐26. 

D. Ornamenty a rodičovské investice 

[1] Matysioková, B. & V. Remeš. 2010. Assessing the usefulness of ptilochronology in the 

study of melanin‐ and carotenoid‐based ornaments in the Great Tit Parus major. Ibis 

152:397‐401. 

[2] Matysioková, B. & V. Remeš. 2010. Incubation feeding and nest attentiveness in a 

socially monogamous songbird: role of feather colouration, territory quality, and 

ambient environment. Ethology 116:596‐607. 

[3] Matysioková, B. & V. Remeš. 2011. Responses to increased costs of activity during 

incubation in a songbird with female‐only incubation: does feather colour signal coping 

ability? Journal of Ornithology, in press. 

[4] Remeš, V. 2011. Yolk androgens in great tit eggs are related to male attractiveness, 

breeding density, and territory quality. Behavioral Ecology and Sociobiology, in press. 

31

A. Výběr hnízdního prostředí a hnízdní predace

O N 

P O 

I I 

N 

I I 

P O 

O N 

Opinion is intended to facilitate communication between reader and author and reader and 

reader. Comments, viewpoints or suggestions arising from published papers are welcome. 

Discussion and debate about important issues in ecology, e.g. theory or terminology, may 

also be included. Contributions should be as precise as possible and references should be 

kept to a minimum. A summary is not required. 

How can maladapti�e habitat choice generate source-sink 

population dynamics? 

Vladimír Remesˇ, Dept of Zoology and Anthropology, Faculty of Science, Palacky´ Uni�., trˇ. S�obody 26, CZ-771 46 

Olomouc, Czech Republic (remes@prfnw.upol.cz). 

Several theoretical models have been proposed to describe population 

dynamics in a spatially heterogeneous environment. The 

source-sink model is among the most popular. Diffendorfer 

recently summarized its assumptions and predictions. Given the 

model reviewed, he argued that source-sink population dynamics 

arises if dispersal is somehow constrained. I offer an additional 

mechanism by suggesting that source-sink population dynamics 

can be generated by anthropogenic changes in landscapes that 

occur so quickly that organisms no longer make optimal habitat 

selection decisions. Individuals select the same habitats as their 

ancestors but these decisions no longer provide high fitness 

because of human-induced changes in habitat quality, such as 

increased rates of predation and/or parasitism. Provided that 

some of the habitats selected are turned by human-induced 

changes into sink habitats, source-sink population dynamics can 

emerge. 

Habitat heterogeneity in a spatial, landscape context is 

an important issue in contemporary ecology (Blondel 

and Lebreton 1996). Individual habitat patches can be 

of varying suitability for growth, survival and reproduction 

of individuals and these differences have consequences 

for their population dynamics. Many 

population models based on different assumptions and 

leading to different predictions have been proposed to 

account for the population dynamics in heterogeneous 

landscapes (Kareiva 1990). Among the most popular, 

the source-sink model (Holt 1985, Pulliam 1988) assumes 

habitat patches of highly varying quality. In 

sources natality exceeds mortality and they are net 

exporters of individuals whereas in sinks mortality exceeds 

natality and they are net importers of individuals. 

This model predicts a net flow of individuals from 

sources to sinks (or to pseudosinks sensu Watkinson 

and Sutherland 1995; reviewed in Dias 1996). 

OIKOS 91:3 (2000) 

It may be rewarding to consider population models 

in terms of an individual’s behavioural decisions 

(L�omnicki 1999). There is no benefit for an individual 

to select a habitat where mortality exceeds natality. 

This logic stems from the ideal free distribution model 

of Fretwell and Lucas (1970). Then, for source-sink 

population dynamics to arise, we should explain why 

an individual would arrive at a habitat where mortality 

exceeds natality, i.e., at a sink. Three possible mechanisms 

have been proposed, all of them involving some 

constraint on dispersal: 1) passive dispersal (e.g., wind 

or water dispersal of propagules forcing individuals to 

grow and reproduce in a poor habitat), 2) territoriality, 

or despotic distribution (in active dispersers dominant 

individuals can exclude subordinates from good habitats 

thereby forcing them to live in poor habitats; Dias 

1996, Diffendorfer 1998), and 3) temporal barrier (preventing 

successful colonisation of otherwise suitable 

habitat; Boughton 1999). 

For a better understanding of the subsequent reasoning, 

two facts should be stressed. First, habitat selection 

can be viewed as a two-stage process. On the proximate 

level, an individual selects, through an interaction of its 

preferences with environmental cues, a part of the 

environment that becomes its habitat. The appropriateness 

of these behavioural decisions involved in the 

selection process is evaluated by natural selection on 

the ultimate level (Fretwell and Lucas 1970). Phenotypes 

possessing the best habitat selection rules (judged 

in practice by some appropriate correlate of fitness) 

produce most offspring. A prerequisite for this mechanism 

to allow animals to adapt is a long-term correlation 

between cues used by individuals while selecting 

habitat and the real suitability of that habitat. Second, 

organisms are adapted to their past environments. Nat- 

579

ural selection is backward, not forward looking and the 

usefulness of an individual’s adaptation is dependent on 

the temporal ‘‘sameness’’ of its environment (Freeman 

and Herron 1998: 45). 

Anthropogenic changes in landscapes 

Habitat fragmentation generally results in a landscape 

consisting of remnant areas of native vegetation surrounded 

by a matrix of agricultural or other developed 

land. Typically, it is characterized by loss of the original 

habitat, reduction in habitat patch size, and increasing 

isolation of habitat patches (Andrén 1994). The 

effects of habitat fragmentation can lower the quality of 

fragmented habitats, potentially generating source-sink 

population dynamics (Donovan et al. 1995, Robinson 

et al. 1995). These effects include increased nest predation 

and parasitism rates a) on habitat edges and b) 

inside remnant patches of native habitat. 

The ecological trap hypothesis was originally developed 

by Gates and Gysel (1978) to account for maladaptive 

nest-site selection by open-nesting passerines 

on abrupt forest-field edges. The argument goes as 

follows: birds, especially small passerines, are expected 

to select highly concealed nest sites to avoid nest predation 

(Martin 1993). Modified dense vegetation at edges 

can exploit these habitat preferences and function as a 

(supernormal, see Manning and Dawkins 1998: 143) 

stimulus soliciting settling response and attracting birds 

to breed. This leads to overcrowding of nests in edge 

vegetation and can enhance, through density dependence, 

nest predation and parasitism rates (McCollin 

1998). Birds breeding in/near edge vegetation consequently 

suffer from poor breeding performance (reviewed 

in Paton 1994). Such a habitat can become a 

demographic (pseudo)sink. 

Besides creating abrupt vegetation edges, habitat 

fragmentation has a negative impact on forest-interior 

birds through increasing the intensity of predation pressure 

on their nests inside remnant patches of native 

habitat (e.g., Andrén 1992, Donovan et al. 1995). Increased 

nest predation can be explained by a landscapewide 

increase of generalist predators supported by a 

matrix of highly productive agricultural land (Andrén 

et al. 1985). The increase in generalist predators must 

outweigh the decrease in forest-interior predators to 

result in rising predation rates in fragmented forests. 

Where the matrix ecosystems are not capable of supporting 

generalist predators (Donovan et al. 1997), or 

when we are concerned with naturally scattered habitats 

(Tewksbury et al. 1998), the effect of an increased 

overall predation pressure is absent. Another mechanism 

which can combine with that outlined above is a 

mesopredator release, i.e., an increase in the abundance 

of small omnivores (= mesopredators) in the absence 

of human-exterminated top predators (large canids and 

felids). The mesopredators can induce high predation 

pressure on open-nesting passerines (Rogers and Caro 

1998, Crooks and Soulé 1999). 

Negative effects of nest predation can be further 

strengthened by increased intensity of brood parasitism 

in fragmented landscapes (Trine et al. 1998). Humancaused 

changes in landscapes resulted in cowbirds 

(brood parasites) expanding their ranges and using new 

host species. In particular, the fragmentation of forests 

and the spread of animal husbandry in North America 

over the past 150 years appear to have favoured cowbirds 

(Cruz et al. 1998). 

Habitat selection is a hierarchical process and migratory 

birds encounter multiple stimuli on their journey 

from the wintering grounds (Cody 1985). Deterioration 

of habitat quality through habitat fragmentation can 

certainly occur on several spatial scales. On these different 

spatial scales different processes occur while birds 

are approaching their breeding grounds. At larger spatial 

scales site tenacity and species-specific natal/breeding 

dispersal may be important while at finer spatial 

scales habitat selection controlled by specific stimuli is 

more important. With the help of such hierarchically 

occurring processes birds can be led to forest fragments 

not being aware of their poor quality caused by recent 

human disturbances (extermination of top predators, 

maintenance of highly productive surrounding matrix, 

see above) because there are no stimuli they can use to 

assess the poor quality. 

In summary, a potentially strong effect of habitat 

fragmentation is the deterioration of the link between 

the cues used by animals for making habitat selection 

decisions and the qualities of those chosen habitats. If 

these habitats are recently created sinks, then sourcesink 

dynamics can emerge. The most important thing, 

however, is that these changes come too quickly for 

animals to be able to cope with them (see Holt and 

Gomulkiewicz 1997, and references therein, for prerequisites 

for adaptive evolution in sink habitats, Thompson 

1998 for examples of rapid evolution). 

Conclusions 

Current models of source-sink population dynamics 

(summarized by Diffendorfer 1998) propose constraints 

on dispersal (territoriality, or despotic distribution in 

active dispersers) as the only mechanism causing 

source-sink dynamics because individuals are unable to 

achieve all possible dispersal patterns. I suggest that 

human-induced alterations of natural habitats can have 

the same effect: either through providing animals with 

novel cues which they misinterpret as signals of highquality 

habitats (abrupt edges), or through connecting 

cues used as indicators of good habitats with poor 

580 OIKOS 91:3 (2000)

fitness rewards (elevated predation and parasitism rates 

inside forest patches). 

The two approaches – the traditional approach (i.e., 

territoriality, or despotic distribution) and maladaptive 

habitat choice approach – should generate different 

predictions to be testable and separable. Whereas the 

despotic distribution predicts that animals should in the 

first instance select the best habitat and as these are 

filled poor ones, the main prediction of the maladaptive 

habitat choice hypothesis is that animals should first 

select a poor habitat (the other two predictions, namely 

that poor habitat is a true sink and that there is a net 

flow of individuals from sources to sinks, are common 

to both approaches and are readily testable with the use 

of standard methods). There are several ways to show 

that individuals prefer a poor habitat to a good one. 

Either directly – animals should settle, after arrival on 

the breeding grounds, at first in a poor habitat (i.e., we 

can use a temporal settling pattern), or indirectly – 

individuals in a poor habitat are to be bigger (Dias and 

Blondel 1996) or to have better developmental stability 

(Møller 1995). Since the indirect approach assumes that 

competitive abilities of individuals are identifiable from 

some of their phenotypic traits, the direct approach is 

superior. However, the direct approach is unfortunately 

applicable only for migratory animals. 

What is important to realize is that dispersal constraints 

and maladaptive habitat choice are completely 

different mechanisms. Previous models of source-sink 

dynamics assume that animals are able to correctly 

assess habitat quality but they cannot for some reason 

settle in a good one. Diffendorfer (1998) argues that in 

organisms with active dispersal, which can assess habitat 

quality and make decisions regarding whether to 

stay or leave, source-sink dynamics should not appear 

because it makes little evolutionary sense for an individual 

to remain in a sink habitat. He suggests that such 

individuals will be rarely found in poor habitats because 

they can assess poor quality and avoid it. On the 

contrary, I assume that animals make, using cues that 

worked well in the past, bad habitat selection decisions. 

This scenario is most plausible in habitats that have 

changed so rapidly that natural selection has not yet 

reshaped habitat choice. Consequently, we could find 

source-sink population dynamics even in active dispersers 

living in human-altered habitats (as might be 

the case in North American forest-interior passerines, 

Donovan et al. 1995, Robinson et al. 1995). 

On the other hand, purely ecological processes can be 

responsible. For example, population fluctuations 

(Dunn 1977, Hogstad 1995), range expansions (Rogers 

and Caro 1998), fires and storms all change the strength 

of biotic interactions; this can result in changes in 

quality of some habitats, with the potential consequences 

for population dynamics outlined above. I 

focused on habitat fragmentation because it is rapidly 

ongoing, the best studied and currently the most important 

factor influencing species’ habitats. 

Acknowledgements – I thank Paula C. Dias, Tomásˇ Grim, 

Milosˇ Krist, Václav Pavel, David Storch, Emil Tkadlec, Karel 

Weidinger and especially James E. Diffendorfer for helpful 

discussion or comments on the manuscript. 

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582 OIKOS 91:3 (2000)

Effects of Exotic Habitat on Nesting Success, Territory 

Density, and Settlement Patterns in the Blackcap 

( Sylvia atricapilla) 

VLADIMÍR REMESˇ 

Department of Zoology, Palack y´ University, T rˇ . Svobody 26, 771 46 Olomouc, Czech Republic, 

and U.S. Geological Survey Biological Resources Division, Montana Cooperative Wildlife Research Unit, 

University of Montana, Missoula, MT 59812, U.S.A. 

Abstract: Animals are expected to distribute themselves in a heterogeneous environment in such a way that 

they maximize their reproductive output. When the environment is profoundly changed by human pressure, 

however, cues used for habitat selection in the past may no longer provide reliable information about habitat 

quality. I monitored the nesting success of Blackcaps ( Sylvia atricapilla) 

in two types of forest in southern 

Moravia in the Czech Republic. I assessed their breeding density and territory size using a territory mapping 

method and the minimum convex polygon method. I determined spring arrival through direct observations 

and measured vegetation characteristics and pattern of spring leafing of shrubs in both forests. I show that 

Blackcaps preferentially settled in a plantation of introduced black locust ( Robinia pseudoacacia) upon their 

return from spring migration. In this plantation, they reached twice the density as that observed in a natural 

floodplain forest nearby. However, they had significantly lower nesting success (15.5%) than in the floodplain 

forest (59%). Returning migrant Blackcaps may be lured by early-leafing shrubs in the exotic plantation 

to settle earlier and at higher densities in the reproductively inferior habitat. My results show that (1) it 

is not possible to assess habitat quality based solely on breeding densities, (2) human-modified habitats can 

function as ecological traps by luring settling birds into unsuitable habitats, and (3) by replacing exotic 

plant species with native ones we can restore native communities and increase the breeding productivity of 

bird populations. 

Efectos de Hábitat Exótico sobre el Éxito de Anidación, Densidad Territorial y Patrones de Establecimiento de 

Sylvia atricapilla 

Resumen: Se espera que los animales se distribuyan en un medio heterogéneo de tal modo que maximicen 

su producción reproductiva. Sin embargo, cuando el ambiente es cambiado profundamente por la presión 

humana, los indicios para la selección de hábitat usados en el pasado pueden no proporcionar información 

confiable sobre la calidad del hábitat en la actualidad. Se hizo un monitoreo del éxito reproductivo de Sylvia 

atricapilla en dos tipos de bosque en el sur de Moravia, república Checa. Se evaluó su densidad reproductiva y 

el tamaño del territorio utilizando un método de mapeo de territorio y el método de mínimo polígono convexo. 

Se determinó la llegada en primavera por medio de observaciones directas y se midieron las características 

de la vegetación y el patrón de desarrollo foliar de arbustos en los dos bosques. Se muestra que Sylvia atricapilla 

preferentemente se establecieron en una plantación de Robinia pseudoacacia a su regreso de la 

migración primaveral. En esta plantación alcanzaron una densidad dos veces mayor que la observada en 

un bosque inundable cercano. Sin embargo, tuvieron un éxito de anidación (15.5%) significativamente 

menor que en el bosque inundable (59%). Las aves retornantes pueden ser atraídas por los arbustos con hojas 

jóvenes en la plantación exótica y por lo tanto se establecen más temprano y en mayores densidades en el 

hábitat reproductivamente inferior. Los resultados muestran que (1) no es posible evaluar la calidad del 

Address correspondence to Department of Zoology, Palacky´ University, T ˇr 

. Svobody 26, 771 46 Olomouc, Czech Republic, 

email remes@prfnw.upol.cz 

1127 

Conservation Biology, Pages 1127–1133 

Volume 17, No. 4, August 2003

1128 

Introduction 

Effects of Exotic Habitat on Blackcap Remesˇ 

hábitat basado en las densidades reproductivas únicamente, (2) los hábitats modificados por humanos 

pueden funcionar como trampas ecológicas al atraer aves hacia hábitats inadecuados y (3) reemplazando 

las especies de plantas exóticas con nativas podemos reestablecer comunidades nativas e incrementar la productividad 

reproductiva de poblaciones de aves. 

Animals are expected to maximize their reproductive 

output in a heterogeneous environment through spatial 

distribution (Pulliam 1996). Under the ideal free distribution 

(IFD) model of habitat selection, individuals are 

free to settle anywhere and distribute themselves in such 

a way that their reproductive output in all habitats is the 

same (Fretwell & Lucas 1970; Bernstein et al. 1991). 

There is ample evidence, however, that the breeding success 

and reproductive output of birds can differ across 

habitats on multiple spatial scales (e.g., Donovan et al. 

1995; Dias 1996; Hatchwell et al. 1996; Donovan et al. 

1997; Huhta et al. 1998; Purcell & Verner 1998; Tewksbury 

et al. 1998). One possible explanation for this is 

provided by the ideal despotic distribution (IDD) model 

of habitat selection (Fretwell & Lucas 1970; Bernstein et 

al. 1991). Under this model, dominant individuals preempt 

better habitats, thus forcing subordinates to settle 

in poorer habitats, with reproductive parameters differing 

between habitats accordingly (Andrén 1990; Huhta 

et al. 1998). 

Another explanation for habitat-specific reproductive 

success is based on human-induced habitat changes. Humans 

are exerting strong pressure on native habitats, 

changing them and their biotic interactions profoundly 

(Yahner 1988; Paton 1994; Murcia 1995; Trine 1998; Fagan 

et al. 1999). These changes include, among others, 

the introduction of exotic species, which can lead to 

changes in the appearance, phenology, and functioning 

of communities (Wilson & Belcher 1989; Penloup et al. 

1997; Shigesada & Kawasaki 1997; Courchamp et al. 

2000; Martin et al. 2000; Roemer et al. 2002). In a recently 

human-modified landscape, the cues used by animals 

successfully in the past for detecting highly rewarding 

breeding habitats and the actual current quality of 

those habitats can be decoupled (Rolstad 1991). This 

means animals no longer have complete and reliable 

knowledge of potential habitats as presumed by habitat 

selection models. This can happen if, while choosing 

their breeding habitat, they rely on those features of habitats 

that were changed by the introduction of exotic 

species (e.g., phenology or physiognomy) (Schmidt & 

Whelan 1999). Animals can be misled and can make 

wrong decisions about where to settle, with potentially 

detrimental consequences for reproduction (Pulliam 

1996; Reme sˇ 2000). 

Conservation Biology 

Volume 17, No. 4, August 2003 

Human-modified habitats that look suitable but provide 

poor reproductive rewards are called ecological 

traps (Gates & Gysel 1978), which can be generated by 

habitat fragmentation (Gates & Gysel 1978; Rolstad 

1991; Purcell & Verner 1998) and introduction of exotic 

plant species (Schmidt & Whelan 1999; Misenhelter & 

Rotenberry 2000). Exotic plant species have high “trap” 

potential because they can change habitat phenology 

and physiognomy profoundly. One such species is black 

locust ( Robinia pseudoacacia), 

a tree of North American 

origin that was introduced into other parts of the 

world (including Europe) as an ornamental and timber 

species. Black locust accumulates soil nitrogen, facilitating 

dominance by nitrogen-responsive understory species, 

which leads to lower species diversity of shrub and 

herb layers (Peloquin & Hiebert 1999) and changes the 

phenology of the understory vegetation. 

I hypothesize that habitat changes caused by black locust 

can have detrimental effects on breeding birds. 

Here, I test this hypothesis by comparing settlement patterns, 

territory density, and nesting success of the Blackcap 

( Sylvia atricapilla), 

a small, insectivorous migratory 

songbird, between two habitats, a black locust 

plantation and a native floodplain forest. If the exotic 

black locust plantation is inferior for breeding birds 

compared with the native floodplain forest, the specific 

predictions generated by the different habitat selection 

models are (1) IFD, in which both habitats are settled simultaneously 

with higher density in the floodplain forest 

and no difference in nesting success; (2) IDD, in 

which native-floodplain forest is settled preferentially 

and with resulting higher nesting success; and (3) the 

ecological trap hypothesis, in which the black locust 

plantation is settled preferentially, with higher density 

but lower nesting success. 

Methods 

Study Sites 

This study was conducted on two 15- to 20-ha study 

plots, separated by 1.3 km, within the large Doubrava 

2 

forest of approximately 90 km , north of Hodonín in 

southern Moravia, Czech Republic (lat. 48�52�N, long. 

17�05�E, 170 m above sea level). Farmland and a chain 

of ponds surrounded the forest. The forest cover of Dou

Remesˇ Effects of Exotic Habitat on Blackcap 

brava is disrupted by openings, clearcuts, and roads but 

is still relatively continuous. Forest management activities 

include selective logging of trees and systematic cutting 

of shrub understory. The first plot was located 

within a black locust plantation. The shrub layer consisted 

solely of common elder ( Sambucus nigra), 

and 

the herb layer was dominated by catch-weed bedstraw 

( Galium aparine) 

and nettle ( Urtica dioica). 

The second 

plot was located within a natural floodplain forest 

that was floristically much richer. It had at least nine 

tree species (mostly oaks [ Quercus spp], limes [ Tilia 

spp], poplars [ Populus spp], European ash [ Fraxinus 

excelsior], and black alder [Alnus glutinosa]) and 14 

shrub species (mainly saplings of limes; hawthorns [ Crataegus 

spp], red dogwood [ Cornus sanquinea], 

raspberry 

[ Rubus idaeus], 

common buckthorn [ Rhamnus 

cathartica], 

bird-cherry [ Padus racemosa], 

blackthorn 

[ Prunus spinosa], 

wayfaring tree [ Viburnum lantana] 

and rose [ Rosa sp]). 

Vegetation Structure and Leafing 

I characterized the vegetation by measuring shrub cover 

and height and tree cover, height, and density on both 

study plots. I counted the trees (tree density) in 50 randomly 

distributed circles (5-m radius) on both plots. I divided 

each circle into four quarters, and in each quarter 

I measured the height of shrubs and estimated shrub and 

canopy cover. I averaged the values from each quarter 

to obtain shrub height and shrub and tree cover for the 

circle. I chose seven trees at random on each plot and 

measured their height with a clinometer. In 2000–2001 I 

measured the leafing of understory vegetation, which is 

defined as growth of new leaves from buds after a winter 

period of vegetative inactivity. On both study plots, I 

chose 20 shrubs at random. I chose one branch of each 

of these shrubs randomly and measured all its new 

leaves with a caliper. The average length of these leaves 

provided one data point for subsequent analyses. Thus, 

for both study plots I collected 20 measurements of leaf 

lengths (averages of individual shrubs) approximately 

every third day (for exact dates see Fig. 1). 

Nesting Success, Territory Density, and Settlement Patterns 

In 1998–1999 I monitored the nesting success of Blackcaps. 

I searched for nests by systematically inspecting all 

shrub and herbaceous vegetation on the study plots and 

subsequently checked them every 1–7 days (median, 3 

days). Nest searches and nest checks were performed 

from late April to early July. For each nest, I recorded 

height, supporting plant species, clutch (or brood) size, 

and egg size (1999 only). With a caliper, I took egg 

length and two measurements of egg width in two perpendicular 

directions. I used these measurements to calculate 

egg volume according to the formula given by 

1129 

Figure 1. A schematic depiction of leafing of understory 

shrubs in the black locust plantation (two upper 

curves) and the floodplain forest (two lower curves) 

in 2000 and 2001 (cm, mean � SE, n � 20 in all 

cases). Arrows show the date of the first detection of a 

singing male on each plot. Trees were completely 

without leaves at that time in both habitats. 

Hoyt (1979). For computation of nesting success, I used 

the Mayfield method (Mayfield 1975). Nests that fledged 

at least one young were considered successful. Disappearance 

of nest contents was taken as evidence of predation. 

Number of fledglings was defined as number of 

young deemed to have fledged from the nest. 

In 1997–1998 I quantified breeding density of the 

Blackcap using the territory mapping method, which 

provides absolute densities of breeding pairs (Bibby et 

al. 1992). I surveyed plots weekly, in either the morning 

or evening. Each census lasted over 3 hours. Morning 

censuses started before sunrise, and evening censuses 

continued until sunset. I began mapping territories in 

late April and continued until early July. During a slow 

walk through the study plot, I recorded all Blackcaps 

noted both visually and aurally and recorded their location 

on a map of the study plot. Typically, registrations 

from subsequent censuses form clusters on the map, and 

each cluster depicts a territory of a single male (minimum 

number of registrations forming a territory was 

set at three). To determine the size of singing territories, 

I connected outermost registrations in each cluster 

by a straight line and computed the extent of the area 

using the minimum convex polygon method (White & 

Garrott 1990). To compare spacing of territories in the 

two habitats, I calculated the mean distance between 

neighboring territories on both plots. These distances 

were taken between visually estimated centers of the 

territories. 

In 2000–2001 I monitored the spring arrival of Blackcaps 

on breeding grounds by careful visual and audi- 



1130 

Effects of Exotic Habitat on Blackcap Remesˇ 

Table 1. Vegetation structure characteristics of the two study plots 

(mean � SE). 

Variable 

tory inspection of the study plots and their broader surroundings 

approximately each third day ( for exact 

dates see Fig. 1). On both study plots I established six 

observation points 100 m apart. From each observation 

point I recorded all birds for 30 minutes. I also recorded 

all birds I observed while moving between the 

observation points. 

Potential Predators 

To assess potential nest predators, I used snap-traps to 

catch small mammals on both study sites from late April 

to mid-July in 1997 (25 traps � 2 transects � 1 night exposure 

� 5 times per season � 250 trap-nights per study 

site). During censuses of Blackcaps and nest searches, I 

also recorded observations of potential avian predators. 

Of the species known to prey on Blackcap nests (Sell 

1998; Weidinger 2002), I trapped bank vole ( Clethrionomys 

glareolus) 

and wood/yellow-necked mouse ( Apodemus 

sylvaticus/A. flavicollis) 

and recorded Jay ( Garrulus 

glandarius ) and Great-spotted Woodpecker 

( Dendrocopos major) 

on both study sites. The presence 

of Eurasian badger ( Meles meles), 

Pine martin ( Martes 

martes), 

and stoat ( Mustela erminea) 

was probable but 

not directly proved. 



Plantation 

(n) 

Floodplain 

forest 

(n) 

Shrub cover (%) 32.8 � 0.60 (50) 31.6 � 0.58 (50) 

Shrub height (m)* 2.9 � 0.03 (50) 3.7 � 0.03 (50) 

Tree cover (%) 93.8 � 1.73 (50) 92.5 � 1.70 (50) 

Tree height (m) 27.8 � 1.32 (7) 24.5 � 1.22 (7) 

Tree density (no. in 

10-m-diameter circle) 2.1 � 0.03 (50) 2.0 � 0.03 (50) 

* Significantly different at p � 0.01. All other variables not significant 

(Mann-Whitney U tests). 

Statistical Analyses 

Breeding parameters of Blackcaps were similar in all 

years (all p � 0.1); thus, I pooled data across years, except 

for egg volume, which was measured in 1999 only. 

Vegetation height, density, and cover data were not normally 

distributed, and no transformation improved their 

distribution, so I tested the difference between the two 

plots by Mann-Whitney U tests. I analyzed the pattern of 

leaf growth with repeated-measures analysis of variance 

and the difference in arrival date of the first male by Wilcoxon 

signed-ranks test for paired data. All statistical 

tests were two-tailed, and the differences were considered 

significant at p � 0.05. All statistical analyses were 

performed with SPSS (SPSS 1996). 

Results 

Vegetation Structure and Leafing 

Despite floristic differences, the two plots had similar 

vegetation structure and differed only in the height of 

the shrub layer (Table 1). However, the difference in 

timing of leafing between the two plots was highly significant 

( F1,76 

� 961.16, p � 0.001, Fig. 1). There was 

also significant difference between years ( F1,76 

� 8.70, 

p � 0.004) due to an overall earlier growth of leaves in 

2000 than in 2001 in the black locust plantation. Finally, 

there was a significant interaction between leaf growth 

and plot ( F3,74 

� 3755.31, p � 0.001) and between leaf 

growth and year ( � 6007.64, p � 0.001; Fig. 1). 

F 

3,74 

Nesting Success, Territory Density, and Settlement Patterns 

Table 2. Parameters of Blackcap breeding populations on the plantation and floodplain forest study plots. 

a Variable 

Of all the breeding parameters I measured, only nesting 

success differed between the two populations: nesting 

success in the black locust plantation was much lower 

than in the natural floodplain forest (Table 2). 

Year Plantation Floodplain forest 

Density (pairs/10 ha) 1997–1998 20 12 

Singing territory size [ha, mean � SE ( n)] 

Distance to the nearest neighboring territory 

1997–1998 0.23 � 0.034 (40) 0.26 � 0.049 (24) 

[m, mean � SE (n)] b 

1997–1998 44.96 � 1.708 (40) 63.94 � 4.704 (24) 

Daily nest survival rate [mean � SE (n)] c 1998–1999 0.9253 � 0.018 (48) 0.9783 � 0.011 (25) 

Nesting success [%, mean (95% CI), per 24 days] 1998–1999 15.5 (8.2 � 28.9) 59.0 (40.7 � 85.5) 

Clutch size [mean � SE (n)] 1998–1999 4.69 � 0.155 (28) 4.81 � 0.090 (20) 

No. of fledglings [mean � SE (n)] 1998–1999 3.71 � 0.194 (14) 3.67 � 0.291 (18) 

Egg volume [cm3 , mean � SE (n)] 1999 2.17 � 0.046 (22) 2.17 � 0.045 (15) 

Nest height [m, mean � SE (n)] 1998–1999 0.56 � 0.065 (45) 0.54 � 0.100 (25) 

a Except for “distance to nearest neighboring territory” and “daily nest survival rate,” variables are not significant (t tests). 

b Test: t test, t � �4.41, p � 0.001. 

c Tested according to Johnson (1979): z � 3.71, p � 0.001. All other variables not significant (t tests).

Remesˇ Effects of Exotic Habitat on Blackcap 1131 

Blackcaps reached much higher density in the plantation 

than in the floodplain forest, but territory size did 

not differ between the two plots (Table 2). Given the almost 

two times higher density of the black locust population 

and the identical territory sizes, there must have 

been much more interstitial, unoccupied space in the 

floodplain forest. This can be seen when comparing 

mean distance to the nearest neighboring territory: it 

was almost 1.5 times greater in the floodplain forest 

than in the black locust plantation (Table 2). The higher 

density of Blackcaps in the plantation was not due to differences 

in the availability of nest sites because the two 

plots did not differ in the cover of shrubs (Table 1), 

which is the nesting substrate for Blackcaps. Instead, the 

higher density of Blackcaps in the black locust plantation 

appears to reflect a preference for the exotic habitat. 

This was supported by the 2000–2001 arrival data. 

Males settled and began to sing about 1 week earlier in 

the black locust plantation than in the floodplain forest 

(Fig. 1). Although the difference in the arrival date between 

the plots was not statistically significant (Wilcoxon 

signed ranks test: Z � �1.34, p � 0.18), this was 

caused by the extremely small power of the test (only 2 

years of data were available). Although statistically nonsignificant, 

the average difference of 8.5 days in arrival 

time is biologically highly significant. Moreover, at the 

time of detection of the first singing male in the floodplain 

forest, it was possible to see or hear four to five 

males simultaneously in the black locust plantation from 

one observation point. Thus, it seems that not only the 

arrival date of the first male but also the overall settling 

pattern differed between the two plots, the black locust 

plantation being at least partially filled before any male 

even appeared in the floodplain forest. The settling pattern 

could not be explained by any geographical effect 

because the study plots were 1.3 km apart, a trivial distance 

for a long-distance migrant. 

Discussion 

Arriving Blackcaps settled earlier in the black locust 

plantation at higher densities but suffered from lower 

nesting success. These findings are in accordance with 

predictions derived from the ecological trap hypothesis 

applied to this study system. 

One potential mechanism leading to earlier settling 

and higher density at the black locust plantation is the 

earlier leafing of shrubs in the plantation (Fig. 1). Moreover, 

because of monospecific (elder) species composition, 

all shrubs leafed simultaneously. Male Blackcaps arrive 

earlier in the spring than females and strive to 

defend territories with the biggest potential for dense 

vegetation later in the breeding season (Hoi-Leitner et al. 

1995), perhaps because territories with dense foliage 

around a nest have better prospects for nesting success 

(Martin 1992; Hoi-Leitner et al. 1995; Weidinger 2002). 

Thus, I suggest that early and simultaneously leafing elders 

lured arriving males into the black locust plantation. 

Although alternative explanations for the observed 

pattern could be invoked (e.g., that arriving Blackcaps 

cue on food instead on foliage), concealment serves to 

deter nest predation in Blackcaps (Hoi-Leitner et al. 

1995; Weidinger 2002) and predation is the main cause 

of nest failure in this species (Sell 1998; Weidinger 

2000, 2002; this study). Furthermore, Weidinger (2000) 

showed that Blackcaps breed earlier at the sites with earlier 

leafing of shrubs, and there is no evidence of food 

limitation in this species (V. R., personal observation). 

Because my study lacks true spatial replicates, different 

nesting success at the two study plots could be explained 

in several ways. One, there could simply be different 

densities of nest predators on the two study sites. 

Two, the high density of birds in the black locust plantation 

(Table 2) could lead to low nesting success caused 

by density-dependent nest predation. With the exception 

of one nestling found dead on the nest, all nest 

losses on both plots were attributable to predation. 

Moreover, Blackcap nests in the plantation were almost 

all ( 91.9% in 1998, 95.7% in 1999 ) placed in elder, 

whereas nests in the floodplain forest were more evenly 

distributed among various support plants (maximum 

proportions of 22.2% in Prunus spinosa, Tilia, and Crataegus 

in 1998 and 56.3% in Tilia in 1999). A highdensity 

concentration of nests in one shrub species 

could facilitate nest searching by predators (Martin 

1988, 1996; Schmidt & Whelan 1998). However, more 

studies are needed to ascertain the actual cause of the 

differences in rates of nest predation. 

Several researchers, based on ecological trap reasoning, 

suggest that birds exhibit maladaptive selection of 

breeding habitat (Gates & Gysel 1978; Purcell & Verner 

1998) and nest sites (Misenhelter & Rotenberry 2000). 

However, all these researchers based their conclusions 

on indirect, correlative evidence. For example, higher 

density in a certain habitat was taken as evidence of its 

higher attractiveness for breeding birds and for their active 

selection of it. Here, I show directly that birds arriving 

on the breeding grounds preferentially selected reproductively 

inferior breeding habitat. Furthermore, I 

suggest that cueing on dense understory vegetation may 

have been a rewarding strategy in natural habitats but 

that human alteration of the landscape has decoupled 

this cue from actual habitat suitability (Gates & Gysel 

1978; Reme sˇ 

2000). This finding shows that one of the 

most important assumptions of the habitat selection theory, 

namely that dispersing individuals should first select 

the most rewarding habitat (Fretwell & Lucas 1970; 

Bernstein et al. 1991), does not always hold true. Moreover, 

for all Blackcap life-history parameters, the black 

locust population falls under the self-sustaining threshold 

(V. R., unpublished data). This implies that human 



1132 Effects of Exotic Habitat on Blackcap Remesˇ 

alteration can create demographically inferior habitats 

that are nevertheless attractive to breeding animals 

(Reme sˇ 2000). 

It is known that introduced, non-native vegetation 

can alter the community composition and nesting success 

of birds ( Wilson & Belcher 1989; Schmidt & 

Whelan 1999). Introduced black locust replaces native 

habitats and destroys native communities ( Hruska 

1991). Because this study was conducted in a single location, 

however, with one plot within each habitat 

type (native and exotic), larger-scale and multispecies 

studies should be conducted to confirm that black locust 

plantations have detrimental consequences for regional 

breeding bird populations. Nevertheless, three 

conclusions are worth stressing: (1) it is not possible 

to assess habitat quality based solely on breeding densities 

(see also Van Horne 1983; Vickery et al. 1992); (2) 

human-modified habitats can function as ecological 

traps by luring settling birds into unsuitable habitats 

(Gates & Gysel 1978; Rolstad 1991), possibly leading to 

demographically non-self-sustaining populations (Remesˇ 

2000); and (3) by replacing exotic plant species with 

native ones, we might gain multiple benefits—not only 

the restoration of native communities but also an increase 

in the breeding productivity of bird populations 

(Schmidt & Whelan 1999). 

Acknowledgments 

I am obliged to K. Weidinger for his advice at all stages of 

this work. J. D. Lloyd, T. E. Martin, M. A. Villard, and two 

anonymous reviewers provided valuable comments that 

greatly improved the manuscript. My father, V. Reme sˇ , 

and my brother, R. Reme sˇ , helped in various stages of this 

project. I am very grateful for their assistance. 

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ÚVOD 

Ptáci jsou dÛleÏitou a populární modelovou 

skupinou ekologického v˘zkumu. 

SYLVIA 39 /2003 

Hnízdní biologie pûnice ãernohlavé (Sylvia atricapilla) 

v âeské republice: anal˘za hnízdních karet 

Breeding biology of the Blackcap (Sylvia atricapilla) 

in the Czech Republic: an analysis of nest record 

cards 

Vladimír Reme‰ 

Ornitologická laboratofi, Univerzita Palackého, tfi. Svobody 26, CZ-771 46 Olomouc; 

e-mail: remes@prfnw.upol.cz 

Projekty hnízdních karet mohou pfiinést cenné údaje o hnízdní biologii mnoha ptaãích druhÛ. 

V této práci jsem analyzoval hnízdní karty pûnice ãernohlavé (n = 232, z toho 180 aktivních 

hnízd) získané mezi lety 1945–1983 bûhem projektu hnízdních karet na území âeské republiky. 

PrÛmûrné stáfií nalezeného hnízda bylo 8,5 dne. Medián data zahájení snÛ‰ky byl 16. kvûtna. 

Denní míra pfieÏívání hnízd byla 0,9799 a neli‰ila se mezi fázemi kladení vajec, inkubace 

a péãe o mláìata. Hnízdní úspû‰nost byla 57,4 %, líhnivost 93,4 %, ãásteãné ztráty se vyskytly 

u 17,7 % hnízd. Hnízda byla nalezena na 40 rÛzn˘ch podkladov˘ch rostlinách. PrÛmûrná snÛ‰ka 

dosáhla 4,78 vejce a v˘‰ka hnízda 1,09 m. Velikost snÛ‰ky i v˘‰ka hnízda klesaly bûhem sezóny. 

Velikost snÛ‰ky se mezi roky nemûnila, zatímco v˘‰ka hnízda vzrÛstala. Rok nalezení 

hnízda, sezóna ani v˘‰ka hnízda nemûly vliv na pravdûpodobnost pfieÏití hnízda. Pomocí anal˘zy 

hnízdních karet je tedy moÏné zjistit mnoho údajÛ o hnízdní biologii ptákÛ, coÏ je potû‰itelné 

vzhledem k právû probíhajícímu novému projektu hnízdních karet na na‰em území. 

Nest record cards schemes can produce valuable data on breeding biology of many bird species. 

In this study, nest record cards of the Blackcap (n = 232; n = 180 for active nests) from a national 

nest record cards scheme run on the territory of the Czech Republic in 1945–1983, are 

analysed. Average age of the found nest was 8.5 days. Median nest initiation date was 16 May. 

Daily mortality rate of nests was 0.9799 and did not differ between egg-laying, incubation, and 

nestling phases. Nesting success was 57.4%, hatchability 93.4%, and partial losses occurred in 

17.7% of nests. The nests were found on 40 different supporting plant species. Clutch size averaged 

4.78 eggs and nest height 1.09 m. Both clutch size and nest height decreased within season. 

Clutch size did not differ among years, whereas nest height increased. Year, season, and 

nest height had no influence on nest survival. Nest record cards can produce valuable information 

on breeding biology of birds, which is of particular importance with regard to a new 

nest record cards scheme in the Czech Republic. 

Keywords: nest record cards, nesting success, Blackcap, Sylvia atricapilla, Czech Republic, 

breeding biology 

Detailní poznání Ïivotního cyklu ptákÛ 

a kvantifikace jeho jednotliv˘ch ãástí 

25

Remeš V. / Hnízdní biologie pěnice černohlavé 

jsou nejen cílem základního v˘zkumu, 

ale také nezbytn˘m pfiedpokladem fundovan˘ch 

rozhodování v ochranû pfiírody. 

Av‰ak získání kvalitních údajÛ 

o hnízdní biologii jednotliv˘ch druhÛ je 

ãasovû a finanãnû nároãné. Existují 

v podstatû dva zpÛsoby jak tyto údaje 

získat – jsou to buì intenzivní studie zamûfiené 

na vybran˘ modelov˘ druh, nebo 

extenzivní shromaÏìování údajÛ na 

hnízdních kartách vyplÀovan˘ch dobrovolníky 

a lidmi, ktefií pfiíleÏitostnû naleznou 

ptaãí hnízdo. Intenzivní v˘zkum se 

vminulosti osvûdãil napfiíklad pfii v˘zkumu 

biologie s˘kor v Holandsku a Anglii 

nebo lejskÛ ve ·védsku. Tyto a podobné 

v˘zkumy pomohly vyfie‰it mnoho zajímav˘ch 

otázek v ekologii. BohuÏel není 

moÏné z finanãních a ãasov˘ch dÛvodÛ 

takto intenzivnû zkoumat v‰echny ptaãí 

druhy, u kter˘ch by to bylo Ïádoucí. 

A právû u nich mohou b˘t velmi uÏiteãné 

hnízdní karty. 

Dlouholeté projekty hnízdních karet 

úspû‰nû bûÏí napfi. ve Velké Británii, Holandsku, 

Finsku nebo USA (napfi. Koskimies 

& Väisänen 1991, Weso∏owski & 

Czapulak 1993, Martin et al. 1997, Aebischer 

1999). Data shromáÏdûná díky tûmto 

projektÛm pomohla osvûtlit napfiíklad 

takové otázky, jako je v˘bûr hnízdního 

místa a jeho vliv na hnízdní úspû‰nost 

(Tuomenpuro 1991), prostorová variabilita 

v hnízdní úspû‰nosti (Paradis et al. 

2000) nebo anal˘za faktorÛ, které mohou 

zapfiíãiÀovat mizení nûkter˘ch druhÛ 

pûvcÛ ze zemûdûlské krajiny (Siriwardena 

et al. 2000). Také na území 

âeské republiky bûÏel léta úspû‰nû projekt 

prÛzkumu hnízdní biologie ptákÛ 

s pomocí hnízdních karet vyplÀovan˘ch 

amatérsk˘mi ornitology, fiízen˘ pracovníky 

dne‰ního Ústavu biologie obratlovcÛ 

AV âR. Na datech získan˘ch díky tomuto 

projektu jsou zaloÏeny v˘sledky uvedené 

v ãástech pojednávajících o hnízdní 

biologii jednotliv˘ch druhÛ v základním 

26 

díle o na‰ich ptácích, Faunû âSSR (Hudec 

1983). Bez údajÛ získan˘ch díky tomuto 

projektu by nemohlo b˘t toto dílo 

v jeho souãasné podobû v podstatû vydáno. 

Cílem pfiedkládané práce je reanalyzovat 

hnízdní karty pûnice ãernohlavé 

shromáÏdûné v letech 1945–1983 bûhem 

v˘‰e uvedeného projektu hnízdních karet. 

Aãkoliv ãásteãné v˘sledky anal˘zy 

tûchto karet jiÏ byly publikovány (Hudec 

1983), pfii v˘poãtu hnízdního úspûchu 

nebyla pouÏita moderní metodika, hlavnû 

v‰ak nebyla vyuÏita ve‰kerá informace 

v kartách obsaÏená. Hlavním cílem této 

práce je ukázat, jak˘ typ dat a jaké 

jejich mnoÏství je moÏné z hnízdních karet, 

byÈ shromáÏdûn˘ch v na‰ich skromn˘ch 

podmínkách, získat. Tato otázka je 

nyní aktuální i v souvislosti s právû bûÏícím 

nov˘m projektem v˘zkumu hnízdní 

biologie na‰ich ptákÛ s pomocí hnízdních 

karet (Kulí‰ková & ·álek 2002). 

METODIKA 

Hnízdní karty, které jsem analyzoval, 

byly vypracovány amatérsk˘mi ornitology 

na území âeské Republiky v letech 

1945–1983 a jsou uloÏeny v Ústavu biologie 

obratlovcÛ AVâR v Brnû. Z kaÏdé 

hnízdní karty jsem se snaÏil získat tyto 

základní údaje: datum snesení prvního 

vejce, velikost snÛ‰ky, líhnivost a v˘‰ku 

hnízda. Z data nalezení hnízda a data 

snesení prvního vejce jsem vypoãítal stáfií 

hnízda pfii nalezení. Dále jsem vypoãítal 

hnízdní úspû‰nost a vliv prÛbûhu sezóny 

a roku nalezení hnízda na velikost 

snÛ‰ky, v˘‰ku hnízda a hnízdní úspû‰nost. 

Hnízdní úspû‰nost jsem poãítal Mayfieldovou 

metodou, která umoÏÀuje vypoãítat 

hnízdní úspû‰nost pfiesnûji neÏ 

tradiãní metoda podílu úspû‰n˘ch hnízd 

ze v‰ech nalezen˘ch hnízd (viz Mayfield 

1975). Pfii pouÏití této metody spoãítáme, 

jak dlouho byla v‰echna hnízda ve vzor-

ku pozorována, a to v tzv. hnízdodnech 

(jeden hnízdoden je jedno hnízdo pozorované 

jeden den). Denní míru úmrtnosti 

hnízd získáme podílem hnízdních ztrát 

(poãet hnízd, která nepfieÏila do vyvedení 

mláìat) ke hnízdodnÛm. Denní míra 

pfieÏívání hnízd je pak: 1 – denní míra 

úmrtnosti hnízd. Hnízdní úspû‰nost se 

spoãítá jako denní míra pfieÏívání umocnûná 

na poãet dní, které trvá hnízdní cyklus 

druhu. Tato hnízdní úspû‰nost je 

vlastnû pravdûpodobnost, Ïe hnízdo pfie- 

Ïije cel˘ hnízdní cyklus a vyletí z nûho 

alespoÀ jedno mládû. 

Rozdíl v denní mífie pfieÏívání hnízd 

mezi fázemi kladení vajec, inkubace 

a péãe o mláìata jsem analyzoval programem 

CONTRAST (Sauer & Williams 

1989). Vliv roku nalezení hnízda, data 

zahnízdûní a v˘‰ky hnízda na hnízdní 

úspûch jsem analyzoval pomocí zobecnûného 

lineárního modelu s logit spojovací 

funkcí a binominální distribucí chybového 

ãlenu modelu. ProtoÏe poãet hnízd 

v jednotliv˘ch letech se v˘raznû li‰il (viz 

obr. 1), rozdûlil jsem tuto promûnnou na 

osm ãasov˘ch úsekÛ s pfiibliÏnû stejn˘m 

poãtem hnízd (viz obr. 8). Jednotkou 

anal˘zy je v tomto pfiípadû hnízdo a modelovanou 

promûnnou je poãet úspû‰n˘ch 

hnízdodní s binominálním dûlitelem 

rovn˘m celkovému poãtu hnízdodní 

(Aebischer 1999). Tato anal˘za byla poãítána 

procedurou GENMOD v programu 

SAS (SAS Institute Inc. 2000). 

Pfii anal˘ze hnízdních karet byla délka 

inkubaãní periody stanovena na 11 dní 

od data, kdy byla snÛ‰ka kompletní, doba 

pobytu mláìat v hnízdû byla stanovena 

na 9 dní; dále jsem pfiedpokládal, Ïe 

vejce jsou kladena jedno dennû. Denní 

míra pfieÏívání byla umocnûna na 24, coÏ 

je pfiedpokládaná délka hnízdního cyklu 

pûnice ãernohlavé pfii modální snÛ‰ce 

5 vajec a zaãátku inkubace od pfiedposledního 

vejce. Pfiedpokládal jsem, Ïe ke 

hnízdním ztrátám do‰lo uprostfied inter- 

SYLVIA 39 /2003 

valu mezi dvûma následujícími kontrolami 

hnízda. Kategorie spolehlivosti pro 

urãení velikosti snÛ‰ky byly stanoveny 

takto (v pofiadí klesající spolehlivosti): 

pozorovatel sledoval hnízdo bûhem kladení 

vajec a po dva po sobû následující 

dny zaznamenal stejn˘ poãet vajec (kategorie 

1); pozorovatel kontroloval hnízdo 

alespoÀ dvakrát bûhem inkubaãní periody 

se stejn˘m poãtem vajec (kategorie 2); 

pozorovatel kontroloval hnízdo jednou 

bûhem inkubaãní periody (kategorie 3). 

Ostatní hnízda nebyla pro urãení velikosti 

snÛ‰ky pouÏita. 

Data byla analyzována programem 

JMP (SAS Institute Inc. 1995). V‰echny statistické 

testy byly oboustranné a za v˘znamnou 

byla povaÏována hladina a = 0,05. 

âísla uvádûná v závorce za odhadem regresního 

koeficientu jsou stfiední chyba 

prÛmûru (SE). 

V¯SLEDKY 

Analyzované hnízdní karty (n = 232) obsahovaly 

rÛzné mnoÏství vyplnûn˘ch informací. 

K anal˘ze jsem pouÏil jen hnízda, 

která byla nalezena jako aktivní, to 

znamená buì s vejci nebo s mláìaty 

(n = 180). Ale protoÏe i karty tûchto aktivních 

hnízd se li‰ily mnoÏstvím údajÛ, 

poãet hnízd, která bylo moÏné pouÏít 

pro jednotlivé anal˘zy, se li‰il a je vÏdy 

uveden. 

Hnízdní karty pocházely z let 1945–1983 

a jejich poãty se li‰ily mezi roky, s patrn˘m 

vrcholem poãetnosti v ‰edesát˘ch 

letech (obr. 1). PrÛmûrné stáfií nalezeného 

hnízda bylo 8,5 dne (SE = 0,77, minimum 

= -10, maximum = 30, n = 141, viz 

obr. 2) a toto stáfií se nemûnilo s hnízdní 

sezónou (obr. 3). 

Anal˘zou hnízdních karet jsem získal 

základní data o hnízdní biologii pûnice 

ãernohlavé. Nejãasnûj‰í datum zahájení 

snÛ‰ky bylo 13. dubna (v roce 1981), 

nejpozdnûj‰í 9. ãervence (v roce 1971), 

27


28 

Obr. 1. Zastoupení 

hnízdních karet 

v jednotliv˘ch letech 

(n = 175). 

Fig. 1. Distribution 

of nest record cards 

among years (n = 

175). 

Obr. 2. Distribuce 

dat zahájení hnízdûní 

(sloupce) a dat 

nalezení hnízda 

(ãára s body) bûhem 

hnízdní sezóny (n 

= 141). Patrn˘ je posun 

dat nalezení 

hnízda vÛãi datÛm 

zahájení hnízdûní. 

Fig. 2. Distribution 

of nest initiation 

dates (empty bars) 

and dates when the 

nest was found 

(black line with dots) 

throughout the 

breeding season 

(n = 141). 

Obr. 3. Závislost stáfií nalezeného hnízda (dny) na hnízdní sezónû vyjádfiené datem zahájení 

hnízdûní (lineární regrese: R 2 < 0,01; F 1,139 = 0,06; p = 0,805; n = 141; stáfií = 10,06 (6,27) – 

0,011 (0,044) * sezóna; den 1 = 1. leden). 

Fig. 3. Linear regression of the age of the found nest (days) on the breeding season (R 2 < 

0.01, F 1,139 = 0.06, p = 0.805, n = 141; age = 10.06 (6.27) – 0.01 (0.04) * season, day 1 = 1 

January).

zatímco medián byl 16. kvûtna (n = 141) 

(obr. 2). Celková denní míra pfieÏívání 

hnízd byla 0,9799 ± 0,0033 (odhad ± 1 

SE); pro jednotlivé fáze hnízdního cyklu 

byla denní míra pfieÏívání: 0,9894 ± 

0,0074 ve fázi kladení vajec (n = 46), 

0,9847 ± 0,0044 ve fázi inkubace (n = 92) 

a 0,9864 ± 0,0043 ve fázi péãe o mláìata 

(n = 95). Denní míra pfieÏívání se mezi 

fázemi hnízdního cyklu neli‰ila (χ 2 = 0,34; 

d.f. = 2; p = 0,8448). Hnízdní úspû‰nost 

pûnice ãernohlavé byl 57,4 % (tab. 1), 

zatímco líhnivost, vyjádfiená jako procento 

vajec, která se vylíhla z tûch, která se 

vylíhnout mohla, byla odhadnuta na 93,4 

Tab. 1. Celkov˘ pfiehled hnízdní úspû‰nosti u pûnice ãernohlavé. 

SYLVIA 39 /2003 

% (data viz tab. 2). âásteãné ztráty se vyskytly 

v 17,7 % hnízd (n = 124). Velikost 

snÛ‰ky bylo moÏné urãit u 82 hnízd a její 

prÛmûr byl 4,78 vejce (tab. 3). PrÛmûrná 

v˘‰ka hnízda byla 1,09 m (SE = 0,042; 

n = 175) (obr. 4). Ze 161 aktivních hnízd, 

u nichÏ byla uvedena podkladová rostlina, 

bylo 40 hnízd na bezu ãerném (Sambucus 

nigra), 25 na smrku (Picea abies), 

12 na habru (Carpinus betulus), 7 v maliníku 

(Rubus idaeus), 6 ve stfiem‰e (Padus 

avium) a shodnû 5 v rÛÏi (Rosa sp.) 

a v kopfiivách (Urtica dioica); ãetnost 

ostatních podkladov˘ch rostlin (n = 33) 

byla ménû neÏ 5 hnízd. 

poãet hnízd / no. of nests 134 

poãet hnízdodní / no. of successful nest-days 1805,3 

poãet ztrát / no. of losses 37 

denní míra pfieÏívání ± SE / daily nest survival rate ±SE 0,9799 ± 0,0033 

hnízdní úspû‰nost (24 dní) / nesting success (24 days) 61,40% 

líhnivost 1 / hatchability 1 0,934 

celková hnízdní úspû‰nost (hnízdní úspû‰nost x líhnivost) 57,40% 

overall nesting success (nesting success x hatchability) 

1 Data pro v˘poãet líhnivosti viz tab. 2 / Data for hatchability computation in Table 2 

Tab. 2. Poãet hnízd s neoplozen˘mi vejci (n = 124). 

Table 2. Number of nests with addled eggs (n = 124). 

líhnivost / hatchability poãet hnízd poãet neopl. vajec poãet úspû‰. vajec 

no. of nests no. of addled eggs no. of successful eggs 

hnízda s niÏ‰í líhnivostí / lowered-hatch. nests 16 19 58 

hn. se 100% líhnivostí / 100%-hatch. nests 48 0 230 

líhnivost neznámá / hatch. not known 60 – – 

Tab. 3. Velikost snÛ‰ky pûnice ãernohlavé. 

Table 3. Clutch size characteristics for the Blackcap. 

spolehlivost velikost snÛ‰ky poãet snÛ‰ek prÛmûr SE medián 

reliability clutch size no. of clutches mean median 

3 4 5 6 

1 0 1 8 3 12 5,17 0,17 5 

2 0 10 16 2 28 4,71 0,11 5 

3 2 11 26 3 42 4,71 0,1 5 

celkem / in total 2 22 50 8 82 4,78 0,07 5 

29

Zahlavi 

Analyzoval jsem zmûnu velikosti 

snÛ‰ky a v˘‰ky hnízda na dvou ãasov˘ch 

‰kálách: bûhem hnízdní sezóny a mezi 

roky. Bûhem hnízdní sezóny klesala jak 

velikost snÛ‰ky (s patrn˘m vrcholem 

v první tfietinû sezóny, obr. 5) tak v˘‰ka 

hnízda (obr. 6). Velikost snÛ‰ky se mezi 

roky nemûnila (R2 = 0,01; F1,78 = 0,10; 

p = 0,754; n = 80; snÛ‰ka = 11,01 (19,78) 

– 0,003 (0,010) * rok; rozmezí let 1945 – 

1983), zatímco v˘‰ka hnízda vzrÛstala 

(obr. 7). Analyzoval jsem také vliv roku 

nalezení hnízda, sezóny a v˘‰ky hnízda 

na pravdûpodobnost pfieÏití hnízda. 

30 

Obr. 4. Distribuce 

v˘‰ky hnízda pûnice 

ãernohlavé (metry) 

s kumulativní kfiivkou 

v procentech 

(n = 175). 

Fig. 4. Frequency 

histogram of nest 

height (m), with cumulative 

percentage 

function (n = 175). 

Tab. 4. Zobecnûn˘ lineární model analyzující 

vliv roku (osm kategorií, viz obr. 8), sezóny 

a v˘‰ky na pravdûpodobnost pfieÏití hnízda. 

Hodnoty p a χ 2 statistiky jsou z testu pomûru 

vûrohodností (test typu III), n = 107. 

Table 4. Generalized linear model with logit 

link and binomial error term relating nest 

survival to year (eight categories, see Fig. 8), 

season, and height. P and χ 2 -values are from 

likelihood-ratio test (type III test), n = 107. 

faktor / effect d.f. χ 2 p 

rok / year 7 10,95 0,1409 

sezóna / season 1 1,81 0,1785 

v˘‰ka / height 1 1,55 0,2124 

Obr. 5. Závislost velikosti 

snÛ‰ky (poãet 

vajec) na hnízdní sezónû, 

vyjádfiené datem 

zahájení hnízdûní (polynomiální 

regrese 

druhého stupnû: R 2 = 

0,30; F 2,79 = 18,36; p < 

0,001 pro oba ãleny; n 

= 82; velikost snÛ‰ky = 

-10,12 (3,79) + 0,223 

(0,053) * sezóna – 

0,0008 (0,0002) * sezona 

2 ; den 1 = 1. leden). 

Fig. 5. Second-order 

polynomial regression 

of clutch size (no. of eggs) on the breeding season (R 2 = 0.30, F 2,79 = 18.36, p < 0.001 for both 

terms, n = 82; clutch size = -10.12 (3.79) + 0.223 (0.053) * season – 0.0008 (0.0002) * 

season 2 , day 1 = 1 January).

SYLVIA 39 /2003 

Obr. 6. Závislost v˘‰ky 

hnízda (metry) na sezónû, 

vyjádfiené datem zahájení 

hnízdûní (lineární 

regrese: R 2 = 0,02; F 1,139 

= 4,01; p < 0,05; n = 

141; v˘‰ka = 1,77 (0,35) 

– 0,005 (0,002) * sezóna, 

den 1 = 1. leden). 

Fig. 6. Linear regression 

of nest height (m) on the 

breeding season 

(R 2 = 0.02, F 1,139 = 4.01, 

p < 0.05, n = 141; height 

= 1.77 (0.35) – 0.005 

(0.002) * season, day 1 

= 1 January). 

Obr. 7. Závislost v˘‰ky 

hnízda (metry) na roku 

nálezu hnízda (lineární 

regrese: R 2 = 0,03; 

F 1,168 = 6,18; p = 0,014; 

n = 170; v˘‰ka = -30,04 

(12,53) + 0,016 (0,006) * 

rok; rozmezí let 

1945–1983). 

Fig. 7. Linear regression 

of nest height (m) on the 

year when the nest was 

found (R 2 = 0.03, 

F 1,168 = 6.18, p = 0.014, 

n = 170; height = -30.04 

(12.53) + 0.016 (0.006) 

* year, years 1945 – 

1983). 

Obr. 8. Hnízdní úspû‰nost 

(v %, ± 1 SE) pro 

osm ãasov˘ch úsekÛ vybran˘ch 

tak, aby poãet 

hnízd v nich byl pfiibliÏnû 

stejn˘ (viz ãísla ve 

sloupcích). 

Fig. 8. Nesting success 

(in %, ± 1 SE) for eight 

time periods chosen so 

as to have similar number 

of nests (numbers in 

columns). 

31


Aãkoliv se hnízdní úspû‰nost mezi lety 

na první pohled li‰ila (obr. 8), vliv roku 

na pravdûpodobnost pfieÏití hnízda nebyl 

statisticky v˘znamn˘; stejnû tak nebyl 

v˘znamn˘ vliv sezóny ani v˘‰ky hnízda 

(tab. 4). 

DISKUSE 

Základní parametry hnízdní biologie se 

neli‰ily od publikovan˘ch údajÛ pro pûnici 

ãernohlavou (Berthold et al. 1990, 

Weidinger 2000, 2001, 2002, Reme‰ 2003). 

V˘jimkou nebyla ani hnízdní úspû‰nost, 

která je pro pûnici ãernohlavou uvádûna 

mezi 40 % – 60 % (viz Berthold et al. 

1990). Tyto hodnoty v‰ak byly vypoãítány 

klasickou metodou, tj. podílem úspû‰n˘ch 

hnízd ze v‰ech hnízd nalezen˘ch, 

pfiiãemÏ víme, Ïe tato metoda nadhodnocuje 

hnízdní úspû‰nost (Mayfield 1975). 

Tomu by také odpovídal fakt, Ïe hodnota 

hnízdní úspû‰nosti vypoãítaná Mayfieldovou 

metodou pro pûnici ãernohlavou 

b˘vá kolem 30 % (Weidinger 2000, Reme‰ 

2003, V. Reme‰, nepublikovaná data). 

Jak tedy vysvûtlit, Ïe hnízdní úspû‰nost 

zji‰tûná v této studii je tak vysoká, 

tj. 57,4 %, aã je spoãítan˘ Mayfieldovou 

metodou? Jedním z moÏn˘ch vysvûtlení 

je rozdíln˘ zpÛsob hledání hnízd: zatímco 

u projektÛ hnízdních karet jsou hnízda 

nalézána spí‰e náhodnû a pfiíleÏitostnû, 

u intenzivních studií zamûfien˘ch na 

jeden druh jsou hnízda hledána systematicky 

a velmi intenzivnû (Weidinger 2000, 

Reme‰ 2003). Tak mohou b˘t u hnízdních 

karet, v porovnání s intenzivními studiemi, 

nalézána ãastûji hnízda s vy‰‰í pravdûpodobností 

úspûchu (tedy ta, která jsou 

déle aktivní) a následnû mÛÏe b˘t zji‰tûná 

hnízdní úspû‰nost nadhodnocena. Tomuto 

vysvûtlení by odpovídala hnízdní 

úspû‰nost 56,2 % vypoãítaná Mayfieldovou 

metodou pro 87 hnízd pûnice ãernohlavé 

nalezen˘ch mezi lety 1959–1981 ve 

v˘chodním Pobaltí (Pajevskij 1985). 

32 

Vzhledem k tomu, Ïe ne v‰echny druhy 

ptákÛ, u nichÏ potfiebujeme znát základní 

parametry hnízdní biologie, je moÏné 

studovat systematicky v intenzivních 

studiích, metoda v˘zkumu hnízdní biologie 

pomocí programÛ hnízdních karet 

pro nû zÛstává jedinou alternativou. Tato 

studie ukazuje, Ïe je moÏné touto metodou 

nashromáÏdit údaje o pomûrnû velkém 

poãtu hnízd a Ïe jejich dÛkladná anal˘za 

mÛÏe poskytnout zajímavé vhledy do biologie 

tûchto druhÛ. V˘hodou programÛ 

hnízdních karet je navíc to, Ïe díky nim 

získáme data, která není moÏné získat jinak. 

Sem patfií napfiíklad anal˘zy dlouhodob˘ch 

ãasov˘ch trendÛ (viz V¯SLEDKY, 

tab. 4, obr. 7, 8) nebo anal˘zy geografické 

variability napfi. v hnízdní úspû‰nosti (napfi. 

Aebischer 1999, Paradis et al. 2000). Samozfiejmû 

naopak nûkteré údaje je moÏné 

získat jen intenzivními studiemi, napfiíklad 

produktivitu populace vztaÏenou na samici, 

protoÏe v tomto pfiípadû potfiebujeme 

znát navíc, které hnízdo patfií které 

samici. Ideální situací by ov‰em bylo mít 

pro kaÏd˘ druh údaje z obou typÛ v˘zkumu, 

jejichÏ v˘sledky by se doplÀovaly. 

Závûrem je tfieba zopakovat, Ïe projekty 

hnízdních karet jsou cenné neboÈ 

nám umoÏÀují získat údaje o hnízdní biologii 

mnoha druhÛ zároveÀ a navíc i takov˘ch 

druhÛ, které nejsou z rÛzn˘ch 

dÛvodÛ vhodné pro intenzivní studie. 

Z tohoto hlediska se slibnû rozvíjí nov˘ 

projekt hnízdních karet koordinovan˘ 

âSO (Kulí‰ková & ·álek 2002). Je v‰ak 

tfieba upozornit na to, Ïe hnízdní karty 

musí b˘t vyplÀovány peãlivû. Bezcenné 

jsou záznamy o neaktivních hnízdech 

(v této studii 52 z 232 hnízd, tj. 22,4 %) 

apro v˘poãet hnízdní úspû‰nosti i hnízda 

nav‰tívená pouze jednou (zde 46 ze 

180 aktivních hnízd, tj. 25,6 %). 

PODùKOVÁNÍ 

Dûkuji M. Honzovi za umoÏnûní pfiístu-

pu k hnízdním kartám a K. Weidingerovi 

za cenné rady bûhem práce. Nejvût‰í dík 

v‰ak patfií desítkám amatérsk˘ch ornitologÛ, 

bez jejichÏ úsilí by nebylo moÏné 

tuto práci napsat, a která je takto stejnû 

jejich prací jako mojí. 

SUMMARY 

The aim of this study was to re-analyse 

nest record cards for the Blackcap (Sylvia 

atricapilla) collected as a part of the national 

nest record cards scheme in the 

Czech Republic. This re-analysis should 

show what kind of data can such analysis 

produce. This is timely also with respect 

to a new nest record cards scheme 

initiated recently in the Czech Republic 

(Kulí‰ková & ·álek 2002). 

Nest cards (n = 232) contained varying 

amount of information. Only the nest 

cards of active nests (n = 180) were used 

in the analysis. Nest cards (from the 

years 1945–1983) were unevenly distributed 

among years with a marked 

peak in the 1960s (Fig. 1). The average 

age of the found nest was 8.5 days (SE = 

0.77, min = -10, max = 30, n = 141, see 

Fig. 2), and the age did not change with 

the season (Fig. 3). 

The earliest nest initiation date was 

May 13 (in 1981), the latest July 9 

(1971), with median being 16 May (n = 

141) (Fig. 2). The daily mortality rate estimated 

for the whole nesting cycle was 

0.9799 ± 0.0033 (mean ± 1 SE); it was 

0.9894 ± 0.0074 in egg-laying phase (n = 

46), 0.9847 ± 0.0044 in incubation 

phase (n = 92), and 0.9864 ± 0.0043 in 

nestling phase (n = 95). There was no 

difference in daily survival rate between 

phases of the nesting cycle (χ 2 = 0.34, 

d.f. = 2, p = 0.8448; tested by program 

CONTRAST, see Sauer & Williams 1989). 

The overall nesting success (Mayfield 

method) was 57.4% (Table 1), whereas 

hatchability was 93.4% (data in Table 

2). Partial losses occurred in 17.7% of 

nests (n = 124). Clutch size averaged 

4.78 eggs (Table 3). Average nest height 

was 1.09 m (SE = 0.042, n = 175) (Fig. 

4). Out of 161 active nests where the supporting 

plant was indicated, 40 nests 

were on Sambucus nigra, 25 on Picea 

abies, 12 on Carpinus betulus, 7 on Rubus 

idaeus, 6 on Padus avium, 5 on Rosa sp. 

and 5 on Urtica dioica; there were 33 

other supporting plant species with frequency 

less than 5 nests. 

Potential change in clutch size and 

nest height during the breeding season 

and among years was analysed. Both 

clutch size (Fig. 5) and nest height (Fig. 

6) decreased during the breeding season. 

There was no change of clutch size 

among years, whereas nest height increased 

(Fig. 7). Potential influence of 

year, season, and nest height on nest survival 

was also analysed (after Aebischer 

1999, in PROC GENMOD of SAS, unit of 

analysis was nest, dependent variable 

was the number of successful nest-days 

with the total number of nest-days as binomial 

denominator). Although nesting 

success differed markedly among years 

(Fig. 8), there was no statistically significant 

influence of year on nest survival; 

similarly, there was no influence of either 

season or nest height (Table 4). 

LITERATURA 

SYLVIA 39 /2003 

Aebischer N. J. 1999: Multi-way comparisons 

and generalized linear models of nest success: 

extensions of the Mayfield method. 

Bird Study 46: 22-31. 

Berthold P., Querner U. & Schlenker R. 1990: 

Die Mönchsgrasmücke. Ziemsen, Wittenberg. 

Hudec K. (ed.) 1983: Fauna âSSR. Ptáci 

3/I. Academia, Praha. 

33


Koskimies P. & Väisänen R. A. 1991: Nest 

record scheme. In: Koskimies P. & Väisänen 

R. A. (eds): Monitoring Bird Populations. 

Zoological Museum, Helsinki: 75-86. 

Kulí‰ková P. & ·álek M. 2002: V˘sledky projektu 

hnízdních karet za období 1999–2001. 

Zprávy âSO 54: 50-51. 

Martin T. E., Paine C. R., Conway C. J., 

Hochachka W. M., Allen P. & Jenkins W. 

1997: BBIRD Field Protocol. Montana Cooperative 

Wildlife Research Unit, University 

of Montana, Missoula, Montana, USA 

(http: //pica. wru. umt. edu/BBIRD). 

Mayfield H. F. 1975: Suggestions for calculating 

nest success. Wilson Bull. 87: 456-465. 

Pajevskij V. A. 1985: Uspe‰nosÈ rozmnoÏenija 

ptic i metody jejo opredûlenija. Ornitologija 

20: 161-169. 

Paradis E., Baillie S. R., Sutherland W. J., Dudley 

C., Crick H. Q. P. & Gregory R. D. 2000: 

Large-scale spatial variation in the breeding 

performance of Song Thrushes Turdus 

philomelos and Blackbirds T. merula in 

Britain. J. Appl. Ecol. 37: 73-87. 

Reme‰ V. 2003: Effects of exotic habitat on 

nesting success, territory density and settlement 

patterns in the Blackcap, Sylvia atricapilla. 

Conserv. Biol. 17: 1127–1133. 

SAS Institute Inc. 1995: JMP Statistics and 

Graphics Guide, Version 3.2. SAS Institute 

Inc., Cary. 

SAS Institute Inc. 2000: SAS Online Doc., Version 

8. SAS Institute Inc., Cary. 

34 

Sauer J. R. & Williams B. K. 1989: Generalized 

procedures for testing hypotheses about 

survival or recovery rates. J. Wildlife Manage. 

53: 137-142. 

Siriwardena G. M., Baillie S. R., Crick H. Q. 

P. & Wilson J. D. 2000: The importance of 

variation in the breeding performance of 

seed-eating birds in determining their population 

trends on farmland. J. Appl. Ecol. 

37: 128-148. 

Tuomenpuro J. 1991: Effect of nest site on 

nest survival in the Dunnock Prunella 

modularis. Ornis Fenn. 68: 49-56. 

Weidinger K. 2000: The breeding performance 

of blackcap Sylvia atricapilla in two 

types of forest habitat. Ardea 88: 225-233. 

Weidinger K. 2001: Laying dates and clutch 

size of open-nesting passerines in the 

Czech Republic: a comparison of systematically 

and incidentally collected data. Bird 

Study 48: 38-47. 

Weidinger K. 2002: Interactive effects of concealment, 

parental behaviour and predators 

on the survival of open passerine 

nests. J. Anim. Ecol. 71: 424-437. 

Weso∏owski T. & Czapulak A. 1993: Kartoteka 

gniazd i l´gów. Universytet Wroc∏awski, 

Wroc∏aw. 

Do‰lo 10. února 2003, pfiijato 18. dubna 2003. 

Received February 10, 2003; accepted April 

18, 2003.

Ibis (2005), 147, 213–216 

Blackwell Oxford, IBI Ibis 0019-1019 British ? 146 ? 2004 Ornithologists' UKPublishing, 

Ltd. Union, 2004 

Short communication 

Birds, V. Short Reme[scaron] communication 

rodents and nest predation 

Birds and rodents destroy 

different nests: a study of 

Blackcap Sylvia atricapilla 

using the removal of nest 

concealment 

VLADIMÍR REMEÍ* 

Laboratory of Ornithology, Palacky University, T‰. 

Svobody 26, CZ-771 46 Olomouc, Czech Republic 

Nest predation is a major factor limiting the reproductive 

output of small passerines (Ricklefs 1969). Thus, selecting 

safe nest-sites is critically important for these birds. Nest 

concealment can inhibit transmission of visual, chemical 

or auditory cues to predators (Martin 1993). However, 

although there are numerous studies demonstrating the 

positive effects of concealment on nest survival (reviewed 

in Martin 1992), other studies were unable to find such an 

effect (e.g. Willson & Gende 2000). 

There are at least two reasons for the mixed results. First, 

we typically do not know the identity of nest predators. 

Different predators can select for different aspects of nest 

concealment, with the net result that there is no single best 

‘value’ of concealment, and nests differing in their concealment 

may be depredated at similar rates (Filliater et al. 1994). 

Moreover, densities of different predators can fluctuate 

both among years and among sites, with net selection varying 

temporally and geographically. Secondly, the majority 

of studies have simply used a correlational approach. However, 

as the effects of concealment may be confounded by 

other nest-site features or with parental quality, an experimental 

approach is needed to separate independent 

effects of concealment. I am aware of only two experimental 

studies of the effects of concealment on nest survival 

(Howlett & Stutchbury 1996, Stokes & Boersma 1998). 

In this study I investigated the effects of concealment, 

height and season on nest survival in the Blackcap Sylvia 

atricapilla. I used natural Blackcap nests baited with artificial 

plasticine clutches to identify predators. I manipulated 

concealment by removing part of the foliage obscuring nests 

and tested whether different types of nest predators (birds vs. 

rodents) selected for different degrees of nest concealment. 

METHODS 

The Blackcap is a small (c. 20 g), insectivorous, migratory 

passerine that breeds from late April to July in mature 

forests and wood-lots, building its thin-walled, open-cup 

*Email: remes@prfnw.upol.cz 

© 2004 British Ornithologists’ Union 

nests in shrub and herbaceous layers, approximately 1 m 

above the ground. For details of the general breeding 

biology from similar habitats see Weidinger (2000) and 

Remes (2003). 

I conducted this study during two breeding seasons 

(2000, 2001) in a deciduous forest near Grygov (49°31′N, 

17°19′E, 205 m asl) in the eastern Czech Republic. The 

forest was dominated by Oak Quercus spp., Ash Fraxinus 

excelsior, Lime Tilia spp., Bird Cherry Prunus padus and 

Elder Sambucus nigra. 

I worked on two study plots (total area 30 ha, 150 m 

apart). Nests were located primarily in Bird Cherry, Elder 

and Nettle Urtica dioica. I estimated the degree of concealment 

of each nest visually as a percentage of the nest bowl 

obscured by foliage (10% increment). I estimated horizontal 

and vertical concealment 1 m from a nest in the four cardinal 

directions and 1 m above a nest, and averaged these five 

estimates to obtain a single percentage for a nest. 

In every second nest found I removed some of the foliage 

concealment by cutting twigs and leaves and removing 

them from the vicinity of a nest. In the vicinity of control 

nests, I spent an equivalent amount of time to that needed 

for foliage removal (c. 3 min). I estimated concealment 

of every nest twice: (1) when it was found and (2) when 

baited with plasticine eggs (in experimental nests also after 

foliage removal). 

I baited Blackcap nests with four brown plasticine eggs 

(clutch size in this population averages 4.7 eggs) of similar 

colour and size to natural eggs, i.e. 20 × 15 mm. The time 

between fledging/failure of a nest and its baiting was 

7.3 ± 0.3 days (mean ± se). Each plasticine egg was mounted 

on a thin wire anchored to the nest cup to prevent its 

removal. Clutches were checked after 14 days, equivalent to 

the egg-laying plus incubation period of natural Blackcap 

clutches, and removed. A predation event was recorded if 

any of the four plasticine eggs was marked by a potential 

predator. Predators were identified by noting triangular bill 

marks (bird predation) or incisor marks (rodent predation). 

The probability of an artificial clutch surviving was analysed 

by logistic regression. To identify variables associated 

with the probability of nest depredation by different types 

of predators, I performed logistic regressions with three 

different response variables: (a) nest survived vs. depredated 

by any predator, (b) nest survived vs. depredated by 

a bird predator and (c) nest survived vs. depredated by a 

rodent predator. Interactions of continuous variables with 

year were always non-significant and thus removed. In the 

predator-specific analyses, I excluded clutches depredated 

by both kinds of predators. All the analyses were done in 

JMP (SAS Institute Inc. 1995). 

RESULTS 

In total, there were 108 nests, from which 60 were control 

and 48 experimental. Of these, in 2000 there were 60 

nests (33 control, 27 experimental), and in 2001 there 

were 48 nests (27 control, 21 experimental).

214 V. Remeß 

Nest concealment measured when the nest was found 

decreased with nest height (t = −2.1, P = 0.03) but did not 

change with nest initiation date, year or experimental 

treatment (all P values > 0.05, n = 108, ls means ± se: 

experimental nests 87 ± 2%, control nests 81 ± 2%). After 

concealment was reduced in approximately half of the 

nests, its relationship to nest height disappeared and nest 

treatment (experimental vs. control) was the sole significant 

predictor (t = 12.0, P < 0.01, n = 108, ls means ± se: 

experimental nests 29 ± 3%, control nests 76 ± 2%). Again, 

neither date nor year had any effect. 

The proportion of nests depredated by both types of 

predators together differed significantly between years 

(Fig. 1, Table 1). However, the proportion depredated by 

birds vs. rodents did not differ (Pearson = 0.1, P = 0.71, 

n = 66). Artificial clutches in natural Blackcap nests survived 

better with higher nest concealment, whereas nest 

height or date had no influence (Table 1, Fig. 2). The probability 

of nest depredation by birds increased strongly with 

decreasing concealment and increasing nest height, 

whereas the probability of rodent depredation increased 

only with decreasing nest height (Table 1, Fig. 2). 

DISCUSSION 

Different nest predators (birds vs. rodents) selected for different 

nest-site characteristics in terms of nest concealment 

and height. Diurnal, visually orientated birds depredated 

less concealed nests with greater probability (see also Dion 

et al. 2000, Weidinger 2002), whereas there was no effect 

of concealment on depredation by nocturnal rodents, 

which chiefly use olfaction to find food. The effect of nest 

height on the probability of nest depredation by birds vs. 

rodents may be caused by different spatial patterns of 

activity of these two predator types. Whereas birds (most 

probably Jay Garrulus glandarius and woodpeckers Picoides 

spp.) forage predominantly in canopies and shrubs, rodents 

(most probably mice Apodemus spp., and Bank Vole 

© 2004 British Ornithologists’ Union, Ibis, 147, 213–216 

Clethrionomys glareolus) forage mainly on the ground, 

although they can climb shrubs and trees. 

These results demonstrate the substantial potential, 

driven by the composition of nest predator fauna, to alter 

patterns of selection on nest-site characteristics, as previously 

suggested (Filliater et al. 1994, Dion et al. 2000). In 

line with this, Clark and Nudds (1991), reviewing 28 

studies on natural nest survival in relation to concealment, 

found that concealment was a significant predictor of nest 

survival in 16 of 20 studies in which the predominant nest 

predators were birds, but only two of eight studies in 

which mammals were the major predators (see also Martin 

& Joron 2003). 

To minimize bias introduced by using artificial nests 

(Major & Kendal 1996, Weidinger 2001) I used natural 

Blackcap nests. Plasticine clutches were of clutch size, egg 

Table 1. Logistic regression of the influence of nest concealment (measured when the experiment was initiated), nest height (log 10 - 

transformed), date of initiation of experiment (day 1 = 1 January) and year (2000 = 0, 2001 = 1) on the probability of artificial clutch 

depredation by (a) both predator types together, (b) bird predators and (c) rodent predators, all over a 14-day period. Modelled is the 

probability of clutch survival, so positive parameter estimates mean higher nest survival with increasing value of the factor. 

Factor 

Parameter ± se 

Both Birds Rodents 

Wald 

χ 2 

P Parameter ± se 

Wald 

χ 2 

P Parameter ± se 

Concealment 0.016 ± 0.007 4.8 0.03 0.031 ± 0.011 8.6 < 0.01 0.011 ± 0.009 1.5 0.22 

Height 0.084 ± 0.950 < 0.1 0.93 −3.595 ± 1.523 5.6 0.02 2.724 ± 1.309 4.3 0.04 

Date 0.012 ± 0.018 0.5 0.50 0.017 ± 0.022 0.6 0.44 −0.001 ± 0.022 < 0.1 0.95 

Year 0.617 ± 0.238 6.7 0.01 0.296 ± 0.329 0.8 0.37 0.738 ± 0.303 5.9 0.02 

χ 4 2 

χ 1 2 

Figure 1. The proportion of natural Blackcap nests baited with 

artificial clutches depredated by different types of predators, 

separated for 2000 (n = 60) and 2001 (n = 48). The proportion of 

depredated nests differed significantly between years: Pearson 

2 

χ1 = 6.3, P = 0.01, n = 108. 

Overall models: (a) R 2 = 0.09, = 12.5, P = 0.01, n = 108, intercept ± se = −3.732 ± 3.505; (b) R 2 = 0.26, = 22.8, P < 0.01, n = 64, 

intercept ± se = 2.027 ± 4.841; (c) R 2 2 

= 0.11, χ4 = 10.7, P = 0.03, n = 68, intercept ± se = −4.893 ± 4.277. 

χ 4 2 

Wald 

χ 2 

P

Birds, rodents and nest predation 215 

Figure 2. The probability of survival of natural Blackcap nests baited with artificial plasticine clutches in relation to nest concealment 

(%) and height (cm) over a 14-day period, separated for nest mortality caused by different types of predators, in 2000 (lower curves) 

and 2001 (upper curves). Projections are made from multiple logistic regressions for nests initiated on 30 May and 0.5 m high (above) 

or with 50% concealment (below), respectively. 

size, egg shape and egg coloration as similar to a natural situation 

as possible. Although plasticine eggs may bias the 

composition of predators depredating the nests, e.g. because 

of the smell of plasticine (Maier & DeGraaf 2001), my 

results are not based on comparing predation rates on natural 

vs. artificial nests but on comparing predation patterns 

by different types of predators (birds vs. rodents) on the 

same kinds of nests (natural nests with artificial clutches). 

In natural nests, parental activity adds an additional 

layer of complexity to the benefits and costs of nest-site 

selection with respect to nest concealment. Parental activity 

around natural nests can disclose nests to visually orientated 

predators (Martin et al. 2000). The influence of 

concealment on nest survival may be different in natural 

nests, because parents defend nests and concealment can 

influence the ability of a parent to detect an approaching 

predator (Koivula & Rönkä 1998). Parents may also 

compensate for poor nest concealment by more vigorous 

nest defence (McLean et al. 1986). Moreover, concealment 

may affect not only the probability of nest survival 

but also the survival probability of the incubating parent 

(Wiebe & Martin 1998). However, the selection patterns 

© 2004 British Ornithologists’ Union, Ibis, 147, 213–216

216 V. Remeß 

on nest concealment and height uncovered in this study 

with artificial clutches may be understood to show the 

basic vulnerability of nest-sites to predation that the parents 

must cope with and take into account when selecting 

a nest-site. 

To find out whether the different predation patterns 

revealed in this study represent the natural situation, it is 

necessary to identify predators at natural nests. Existing 

results from studies with video cameras are promising in 

this respect (e.g. Sanders & Maloney 2002). 

I thank K. Weidinger for valuable comments on the manuscript. 

I was supported by PalackY University (grant 

IG32103014) and Ministry of Education of the Czech 

Republic (MSM153100012). 

REFERENCES 

Clark, R.G. & Nudds, T.D. 1991. Habitat patch size and duck 

nesting success: the crucial experiments have not been 

performed. Wildl. Soc. Bull. 19: 534–543. 

Dion, N., Hobson, K.A. & Larivière, S. 2000. Interactive effects 

of vegetation and predators on the success of natural and 

simulated nests of grassland songbirds. Condor 102: 629– 

634. 

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on Northern Cardinal nests – does choice of nest site matter? 

Condor 96: 761–768. 

Howlett, J.S. & Stutchbury, B.J. 1996. Nest concealment and 

predation in Hooded Warblers: experimental removal of nest 

cover. Auk 113: 1–9. 

Koivula, K. & Rönkä, A. 1998. Habitat deterioration and 

efficiency of antipredator strategy in a meadow-breeding 

wader, Temminck’s Stint (Calidris temminckii ). Oecologia 

116: 348–355. 

Maier, T.J. & DeGraaf, R.M. 2001. Differences in depredation by 

small predators limit the use of plasticine and Zebra Finch 

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Major, R.E. & Kendal, C.E. 1996. The contribution of artificial 

nest experiments to understanding avian reproductive 

success: a review of methods and conclusions. Ibis 138: 

298–307. 

Martin, T.E. 1992. Breeding productivity considerations: what 

are the appropriate habitat features for management? In 

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of Neotropical Migratory Landbirds: 455–473. Washington, 

DC: Smithsonian Institution Press. 

Martin, T.E. 1993. Nest predation and nest sites – new perspective 

on old patterns. Bioscience 43: 523–532. 

Martin, J.L. & Joron, M. 2003. Nest predation in forest birds: 

influence of predator type and predator’s habitat quality. 

Oikos 102: 641–653. 

Martin, T.E., Scott, J. & Menge, C. 2000. Nest predation 

increases with parental activity: separating nest site and parental 

activity effects. Proc. R. Soc. Lond. B 267: 2287–2293. 

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behavior, nest exposure, and breeding success in the 

American Robin. Behaviour 96: 171–185. 

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territory density and settlement patterns in the Blackcap 

(Sylvia atricapilla). Conserv. Biol. 17: 1127–1133. 

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Sanders, M.D. & Maloney, R.F. 2002. Causes of mortality at 

nests of ground-nesting birds in the Upper Waitaki Basin, 

South Island, New Zealand: a 5-year video study. Biol. Conserv. 

106: 225–236. 

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and reproductive success in Magellanic Penguins (Spheniscus 

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Received 10 July 2003; revision accepted 10 June 2004; 

first published (online) 18 August 2004 

(doi: 10.1111/j.1474-919x.2004.00339).

Behav Ecol Sociobiol (2005) 58: 326–333 

DOI 10.1007/s00265-005-0910-1 

ORIGINAL ARTICLE 

Vladimír Remeˇs 

Nest concealment and parental behaviour interact in affecting 

nest survival in the blackcap (Sylvia atricapilla): an experimental 

evaluation of the parental compensation hypothesis 

Received: 23 August 2004 / Revised: 6 December 2004 / Accepted: 11 January 2005 / Published online: 11 March 2005 

C○ Springer-Verlag 2005 

Abstract Nest concealment varies strongly within 

populations of many species. Although some studies 

have revealed the beneficial effects of concealment in 

mitigating predation pressure on nests, other studies were 

unable to find similar effects. One potential reason for the 

mixed results is that parental behaviour may compensate 

for the effects of nest cover, and specifically designed 

experimental studies are needed to reveal this compensation. 

I studied the effects of concealment on the probability 

of nest survival in the blackcap (Sylvia atricapilla), by 

experimentally manipulating the degree of nest-foliage 

cover. There was a significant effect of the treatment 

depending on nest type and the phase of nesting. Whereas 

there was no effect of concealment on nest survival in 

natural nests, there was a positive effect in real nests baited 

with plasticine clutches (i.e. without parental activity). 

Parents probably behaviourally compensated for poor 

concealment in natural nests (nest guarding, defence). In 

line with this, there was no effect of concealment on nest 

survival during incubation, whereas there was probably a 

positive effect in the nestling phase. Parents spent more 

time on the nest during incubation (80%) than during 

the care of nestlings (40%) and, consequently, had more 

opportunities to compensate for poor cover. In general, we 

cannot use single measures of behaviours or states (nest 

concealment) as an indication of predation risk because of 

the capacity for compensation in other behaviours. 

Keywords Predation . Nest-site selection . Nest defence . 

Nest concealment 

Communicated by C. Brown 

V. Remeˇs (�) 

Laboratory of Ornithology, Palacký University, 

Tˇr. Svobody 26, 

77146 Olomouc, Czech Republic 

e-mail: remes@prfnw.upol.cz 

Tel.: +420-58-5634221 

Fax: +420-58-5225737 

Introduction 

Nest predation is the main source of nest mortality in small 

passerines (Ricklefs 1969). Consequently, bird parents are 

expected to select a safe nest site to avoid destruction of 

their nests by predators. A well-concealed site is an obvious 

option, because foliage cover reduces the transmission of 

auditory, visual, and olfactory cues from the nest to potential 

predators (Martin 1993). Thus, well-concealed nests 

should have higher survival prospects compared to poorly 

concealed ones. However, both beneficial and no effects of 

concealment have been regularly found in studies looking 

at the effects of nest cover on nest survival (e.g. Willson and 

Gende 2000; Jakober and Stauber 2002; review in Martin 

1992a). Reasons for the mixed results are at least threefold. 

First, a diverse community of predators may depredate nests 

with different cover at a similar rate, and different predators 

may select for different degree of nest concealment (Clark 

and Nudds 1991; Martin and Joron 2003; Remeˇs 2005). 

Second, almost all studies made to date examine natural 

variation in nest concealment that may be selectively neutral 

(there are only three experimental studies: Howlett and 

Stutchbury 1996; Stokes and Boersma 1998; Peak 2003). 

Third, parental behaviour at the nest may lead to complex 

relationships between nest concealment and survival 

(Weidinger 2002). 

Parent birds often defend their nests by attacking and 

distracting potential predators (Montgomerie and Weatherhead 

1988). However, other parental behaviours (e.g. feeding) 

attract predators to the nest and increase the probability 

of its depredation (Martin et al. 2000a, 2000b; Tewksbury 

et al. 2002). Both these sets of behaviours may interact 

with nest concealment (Weidinger 2002). Although parents 

may compensate for poor nest concealment and nest conspicuousness 

by nest defence, they may, conversely, reveal 

poorly concealed nests to visually orientated predators by 

their activity around the nest (McLean et al. 1986; Cresswell 

1997a; Martin et al. 2000b). Thus the relationships 

between parental behaviour, nest cover, and survival may 

be complex and differ among species (Murphy et al. 1997; 

Flaspohler et al. 2000; Weidinger 2002).

One fruitful approach to studing the effect of nest cover 

on the survival prospects of nests and its interaction with 

parental behaviour is to compare natural nests with parental 

activity and artificial clutches without parents (Cresswell 

1997a; Weidinger 2002). There are three likely outcomes 

with respect to nest concealment: (1) no effect of concealment 

in both artificial and natural nests: nest cover is a 

selectively neutral trait and parental behaviour does not interact 

with it. (2) A positive effect of concealment in both 

artificial and natural nests: nest cover has beneficial effects, 

again no interaction. (3) A positive effect of concealment 

in artificial nests but no effect in natural nests: nest concealment 

has favourable effects, but parents are able to 

compensate for poor cover (the parental compensation hypothesis). 

There have been major reservations about the approaches 

using artificial nests in avian ecology (Burke et al. 2004; 

Moore and Robinson 2004; Thompson and Burhans 2004). 

However, these reservations concern studies that assume 

that predation rates are the same on both natural and artificial 

nests, which is obviously not true (review in Moore and 

Robinson 2004). The experimental approach described 

above does not make similar assumptions. Different 

patterns of predation on natural versus artificial clutches 

are assumed and are, in fact, the object of this study. Since 

the absence of parents on artificial nests is the essence of 

the approach, the only possible methodological bias could 

be introduced by the type of artificial nests and clutches 

used, which was minimised in this study (see Discussion). 

In this work, I studied nest survival in relation to nest concealment 

in the blackcap (Sylvia atricapilla). I manipulated 

nest cover and followed the effects of this manipulation 

on the probability of nest depredation. To reveal whether 

parental behaviour compensates for poor nest cover, I compared 

the effects of concealment on nest survival between 

natural and artificial clutches. I also analysed the effects 

of concealment on nest survival separately for the incubation 

and nestling phases. These phases differ strongly in 

the fraction of time parents spend on the nest, which could 

have important consequences for the potential of parents to 

defend their nest and distract predators from its vicinity. 

Methods 

The blackcap is a small (ca. 20 g), insectivorous, migratory 

passerine building its thin-walled, open-cup nests in the 

shrub and herbaceous layers of mature forests, about 1 m 

high. It breeds from late April until July and its nesting success 

is about 30% (Weidinger 2000;Remeˇs 2003a, 2003b). 

I conducted this study during two seasons (2000, 2001) on 

two study plots (total area 30 ha, 150 m apart) in a deciduous 

forest near Grygov, eastern Czech Republic (49 ◦ 31 ′ N, 

17 ◦ 19 ′ E, 205 m a.s.l.). The forest was dominated by oak 

(Quercus spp.), ash (Fraxinus excelsior), lime (Tilia spp.), 

bird cherry (Prunus padus), and elder (Sambucus nigra). 

Blackcaps built their nests primarily in bird cherry, elder 

(height of both shrubs ca. 3–6 m) and nettle (Urtica dioica, 

height increasing with the season up to ca. 2 m). 

327 

I searched nests from late April to early July by careful 

inspection of shrubs and herbaceous vegetation. Shrubs 

formed a loose patchwork with open space, and their cover 

was 30–70% depending on location. In every nest found, I 

determined its height above the ground, clutch/brood size 

and its age. I also estimated the degree of concealment of 

each nest visually as a percentage of the nest bowl obscured 

by foliage (10% increment). I estimated horizontal 

and vertical concealment 1 m from a nest in the four cardinal 

directions and 1 m above a nest, and averaged these five estimates 

to obtain a single percentage for a nest. Nests were 

most often built near the edge of a supporting shrub/herb, 

and vegetation cover within 1 m was critical for nest visibility 

even from a greater distance. I subsequently monitored 

the nest at 3-day intervals until fledging or predation. The 

majority of nest losses in this study were caused by depredation 

(97%, losses not due to predation were excluded 

from the analyses). Typically, the whole contents of a nest 

disappeared, sometimes leaving remnants of egg shells. 

In approximately half the nests, I removed a part of foliage 

concealment by cutting twigs and leaves and removing 

them from the vicinity of a nest. For the experiment, 

I used every other nest found. In the vicinity of control 

nests, I spent an equivalent amount of time to that needed 

for the foliage removal at experimental nests (ca. 3 min). I 

estimated concealment of every nest when it was found and 

when baited with plasticine eggs. To judge the effectiveness 

of the foliage-removal treatment, I estimated concealment 

of experimental nests also immediately after the foliage 

removal. 

I baited blackcap nests with 4 brown plasticine eggs 

(clutch size in this population averages 4.7 eggs) of similar 

colour and size to natural eggs (20×15 mm). The 

time between fledging/failure of a nest and its baiting was 

7.2±3.5 days (mean±SD, n=109). Each plasticine egg was 

mounted on a thin wire anchored to the nest cup to prevent 

its removal. Clutches were monitored and removed after 

14 days exposure (in the blackcap, incubation lasts 11 days, 

the nestling phase 9 days). A predation event was recorded 

if any of the four plasticine eggs was marked by a potential 

predator. There was no relationship between the fate of a 

natural nest and the same nest later on when baited with a 

plasticine clutch (χ 2 =0.03, df=1, P=0.871, n=109). 

I quantified the time parents spent on the nest by counting 

nest visits when at least one parent was present on the 

nest (events) and total nest visits (trials). I did this for 

every nest separately for incubation and the nestling phase, 

and analysed it in a binomial model with the events/trials 

response variable (i.e. binomial proportion; the phase of 

the nest cycle was treated as a within-subject factor in 

GEE analysis in proc GENMOD of SAS). First visits were 

excluded. 

I evaluated nest survival using the Mayfield method 

(Mayfield 1975), as implemented in a multivariate 

framework by Aebischer (1999). I analysed the effects of 

the experimental removal of concealment (experimental vs 

control nests) on the probability of survival in natural nests 

compared to artificial nests (by looking at the interaction 

between these terms). For natural nests, I compared

328 

the effects of the removal between the incubation and 

the nestling phases (again by looking at the interaction 

term). In both these analyses, I controlled for nest height 

[log10-transformed, median=0.48 m (range 0.1–2.2 m)], 

season [date of the first egg in natural nests, 7 May 

(22 April to 24 June); date of the initiation of the 

experiment in artificial clutches, 2 June (4 May to 6 

July)], and year. Initially, all two-way interactions of these 

factors were included and those that were non-significant 

were eliminated in a stepwise manner. Since natural and 

artificial clutches were studied in the same nests, and 

similarly, incubation and the nestling phase of the breeding 

cycle took place within the same nest, these factors were 

treated as within-subject factors in repeated-measures 

analyses. Both focal interactions were significant. Thus 

I analysed the effects of the cover-removal treatment 

separately for natural and artificial nests, and in natural 

nests also separately for incubation and the nesting period. 

Sample sizes in individual analyses vary because of 

missing data in some cells; for example, when a nest was 

depredated during incubation, it was no longer available 

for the nestling-phase analysis. All tests were two-tailed 

with α=0.05, and were done in SAS (SAS Institute 2000). 

Results 

The total number of nests was 124, of which 55 were experimental 

and 69 control. The concealment of nests before the 

foliage removal was 87.0±14.8% (mean±SD) in experimental 

and 79.8±19.7% in control nests, and decreased 

to 33.5±12.3% in experimental nests immediately after 

the experimental removal. The concealment of nests when 

they were baited with artificial clutches was 29.9±15.9% 

in experimental and 76.5±20.9% in control nests. This 

shows that the experimental treatment was highly effective 

in decreasing foliage concealment in experimental nests. A 

slight decrease of concealment on both control and experimental 

nests with artificial clutches compared to the natural 

situation can be ascribed to the desiccation of vegetation. 

Table 1 Generalised linear 

models (binomial error 

distribution, logit link) of the 

probability of nest survival in 

relation to selected factors for: 

(1) all nests (n=124) and (2) 

only natural nests (n=115). χ 2 

statistic is a score statistic for 

type 3 GEE analysis in proc 

GENMOD of SAS. Focal 

interactions showing whether 

the effect of the treatment is 

differential with respect to the 

nest type and/or the phase of the 

nesting cycle are given in bold. 

Within-subject effects are given 

in italics; “na” denotes the 

factors/interactions not 

applicable in the particular 

analysis 

Fig. 1 Probability of nest survival (over 1 day) separated for different 

types of nests and phases of the nesting cycle. Least squares means 

and their 95% confidence intervals from the analyses reported in 

Results. Note that the y-axis begins at 0.7 

Daily survival rate of natural nests was 0.926±0.007 

(mean±SE, n=124), and in artificial clutches it was 

0.915±0.009 (n=109). When nest survival was analysed 

in all nests together, there was a significant interaction 

between the nest type (natural vs artificial) and the experimental 

treatment (control vs experimental). In natural nests, 

there was also a significant interaction between the phase of 

the nesting cycle (incubation vs nestling) and the treatment 

(Table 1). This shows that the effects of the experimental 

removal of nest cover differed between: (a) natural and artificial 

nests, and (b) incubation and the nestling phase of the 

breeding cycle in natural nests. Thus, I analysed the effects 

of the removal treatment on the probability of nest survival 

separately for natural and artificial nests, and also for incubation 

and the nestling phase in natural nests. The removal 

of concealment had a significant negative effect on nest survival 

in artificial clutches (χ 2 =6.27, P=0.012, n=109) and, 

though not significant, a negative effect during the nestling 

phase in natural nests (χ 2 =3.08, P=0.079, n=64). In contrast, 

there was no effect in natural nests across both nesting 

phases (χ 2

Parents spent more time on their nest during incubation 

compared to the nestling phase (χ 2 =21.85, P

330 

Alternative explanations and potential 

methodological bias 

A major alternative explanation for the results of this study 

is the potentially different composition of the predator 

fauna robbing natural and artificial clutches. This could 

be caused by: (a) a methodological bias or (b) parental 

behaviour, in which case the cause would be regarded as 

natural. 

Inspection of the bill and tooth marks on plasticine eggs 

revealed that major predators in this system are birds [jay 

(Garrulus glandarius) and woodpeckers (Picoides spp.)] 

and small mammals [most probably mice (Apodemus spp.) 

and bank vole (Clethrionomys glareolus), Remeˇs 2005]. 

However, artificial nests and eggs may bias the composition 

of the predator fauna compared to the natural situation 

(Rangen et al. 2000; Maier and DeGraaf 2001; Thompson 

and Burhans 2004). Since I used natural blackcap nests in 

their original position, the most probable bias introduced 

by the use of plasticine eggs is an overestimation of the 

depredation by olfactorily orientated rodents. They may 

be attracted by the odour of the plasticine (Rangen et al. 

2000; but see Cresswell 1997b) or may more easily penetrate 

plasticine eggs as compared to real eggs (Maier and 

DeGraaf 2001). However, this would make my test of the 

parental compensation hypothesis conservative. If olfactorily 

orientated rodents were overrepresented as predators in 

artificial clutches, there should be no relationship between 

nest cover and success (see Remeˇs 2005). This prediction 

goes against that made by the parental compensation hypothesis, 

which predicts that there should be a positive 

effect of nest concealment in artificial nests (see above). 

Alternatively, overrepresentation of rodents as predators 

of artificial clutches may be offset by underrepresentation 

of the olfactorily orientated pine marten (Martes martes), 

which appears to be an important predator of natural nests 

in similar habitats (K. Weidinger, personal communication). 

Moreover, I used natural blackcap nests, which are 

much better than the generally discredited wicker nests 

(e.g. Davison and Bollinger 2000), and results obtained by 

the comparison of artificial with natural clutches were validated 

on natural nests by comparing incubation with the 

period of care for the young. 

Artificial nests may be found by predators according to 

their visibility, which may result in the positive effect of nest 

concealment on nest survival. In contrast, predators may 

locate natural nests predominately by cuing on parental behaviour 

(e.g. feeding), which would lead to the absence of 

any concealment effect. The feasibility of this alternative, 

if at work at all, would depend on the details of the foraging 

behaviour of potential predators, which is generally not 

known. However, two lines of evidence suggest that this alternative 

was not responsible for the pattern detected in this 

study. First, indirect evidence indicates that predators of the 

blackcap search for nests actively or find them randomly 

instead of observing adults (Schaefer 2004). Second, validation 

on natural nests points in the same direction. There 

was no effect of concealment on nest survival during incubation 

with low parental activity, whereas there was a 

positive, though not significant, effect during care for the 

young, when parental activity is high. Parental activity here 

means the number of approaches to the nest, which could 

be used by predators to locate nests, and which in small 

songbirds is generally lower during incubation than during 

care for the young (Martin et al. 2000b). 

Although the alternative explanation outlined above cannot 

be automatically ruled out, the arguments and the evidence 

presented indicate that active behavioural compensation 

for poor concealment by parents was the more probable 

causal mechanism behind the findings of this study. 

Conclusions 

Given that here behavioural compensation for poor nest 

cover was the major mechanism and provided it is costly, 

it pays parents to choose well-covered nest sites. From 

this point of view, it would be wrong to conclude that 

there is no selection on nest concealment, and similarly 

that predation is a random process (as evidenced by artificial 

clutches). However, such conclusions were often 

made based on the finding that there is no effect of nest 

cover on the probability of nest depredation in natural nests 

(e.g. Filliater et al. 1994; Howlett and Stutchbury 1996; 

Willson and Gende 2000). Moreover, many studies showed 

that birds select nest sites differentially with respect to 

some features (vegetative cover, support plant species etc.) 

but that later on these features do not affect the probability 

of nest depredation. They often concluded that birds selected 

a particular nest-site feature for other reasons than 

reducing predation (Braden 1999; Bisson and Stutchbury 

2000). However, predation can still be the causal agent 

because the selected features may disengage parents from 

the need to invest in costly compensation. Conclusions regarding 

the randomness of nest predation, the absence of 

selection on concealment or the irrelevance of predation in 

driving nest-site selection may often have originated from 

basing conclusions on observed patterns without analysing 

the processes behind them (see also Schmidt and Whelan 

1999). 

To sum up, the effects of the experimental manipulation 

of nest concealment on the probability of nest survival 

in the blackcap separated for natural and artificial nests, 

and for incubation and nestling care, suggest that parents 

behaviourally compensated for poor nest cover. Species 

systematically differ according to their life-history in their 

likelihood to take risks in nest defence (Ghalambor and 

Martin 2001). This may partly explain why researchers in 

the past have obtained varied and conflicting results concerning 

the relationship between the degree of nest concealment 

and survival. My results also demonstrate that the 

absence of positive effects of nest cover on nest survival 

does not automatically mean a lack of natural selection on 

higher nest concealment. Generally, we cannot use single 

measures of behaviours or states (here nest concealment) 

as an indication of predation risk because of the capacity 

for compensation in other behaviours (see also Cotton 

et al. 2004).

Acknowledgements I thank K. Weidinger for fruitful discussions 

and help, and C.R. Brown, W. Cresswell and an anonymous reviewer 

for suggestions that improved the manuscript. This study was supported 

by Palacký University (grant IG32103014) and the Ministry 

of Education of the Czech Republic (MSM153100012). The experiment 

carried out during this research complies with the current laws 

of the Czech Republic 

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B. Evoluce a diverzita životních znaků

Evolution, 56(12), 2002, pp. 2505–2518 

ENVIRONMENTAL INFLUENCES ON THE EVOLUTION OF GROWTH AND 

DEVELOPMENTAL RATES IN PASSERINES 

VLADIMíR REMESˇ 1,2 AND THOMAS E. MARTIN 1,3 

1 U.S. Geological Survey Biological Resources Division, Montana Cooperative Wildlife Research Unit, Natural Science Building, 

University of Montana, Missoula, Montana 59812 

3 E-mail: tmartin@selway.umt.edu 

Abstract. The reasons why growth and developmental rates vary widely among species have remained unclear. 

Previous examinations of possible environmental influences on growth rates of birds yielded few correlations, leading 

to suggestions that young may be growing at maximum rates allowed within physiological constraints. However, 

estimations of growth rates can be confounded by variation in relative developmental stage at fledging. Here, we reestimate 

growth rates to control for developmental stage. We used these data to examine the potential covariation of 

growth and development with environmental variation across a sample of 115 North American passerines. Contrary 

to previous results, we found that growth rates of altricial nestlings were strongly positively correlated to daily nest 

predation rates, even after controlling for adult body mass and phylogeny. In addition, nestlings of species under 

stronger predation pressure remained in the nest for a shorter period, and they left the nest at lower body mass relative 

to adult body mass. Thus, nestlings both grew faster and left the nest at an earlier developmental stage in species 

with higher risk of predation. Growth patterns were also related to food, clutch size, and latitude. These results support 

a view that growth and developmental rates of altricial nestlings are strongly influenced by the environmental conditions 

experienced by species, and they generally lend support to an adaptive view of interspecific variation in growth and 

developmental rates. 

Key words. Adaptation, aerial foraging, allometry, comparative analysis, development, independent contrasts, nest 

predation. 

A major question in evolutionary biology is to explain why 

life-history traits vary among species (Roff 1992, 2002; 

Stearns 1992; Charnov 1993). Most attention has focused on 

traits such as fecundity, age at first reproduction, survival, 

and rate of aging. Growth and developmental rates have received 

less attention even though they are integral components 

of life-history strategies and vary widely among species. 

As a result, our understanding of why growth and development 

varies among species remains unclear. 

Life-history theory predicts that individuals should grow 

and develop at an infinite rate to achieve maximum output 

of offspring (Stearns 1992). However, this ‘‘Darwinian demon’’ 

ideal is not possible because of basic limits set by 

physiological constraints on growth rates (Ricklefs 1969b; 

West et al. 2001). Nevertheless, organisms might be expected 

to grow and develop at the fastest rate possible within these 

constraints (Ricklefs 1969b, 1979a; Ricklefs et al. 1998). 

Such views have been reinforced by an absence of correlations 

between growth rates and environmental variation in 

past interspecific examinations (see Ricklefs et al. 1998). On 

the other hand, benefits of maximum growth rates might be 

compromised by physiological or environmental costs. For 

example, fast growth can yield costs to reproductive output 

(Roff 1992; Stearns 1992), antiherbivore defense (Herms and 

Mattson 1992), starvation, predation, parasitism, disease susceptibility 

(Nylin and Gotthard 1998; Lankford et al. 2001), 

physical performance (Billerbeck et al. 2001), and adult body 

size (Nylin and Gotthard 1998) based on intraspecific studies 

of growth in plants, insects, fish, frogs, and lizards (summarized 

in Arendt 1997; Metcalfe and Monaghan 2001). Con- 

2 Present address: Department of Zoology, Faculty of Science, 

Palacky´ University, Trˇ. Svobody 26, CZ-771 46 Olomouc, Czech 

Republic; E-mail: remes@prfnw.upol.cz. 

� 2002 The Society for the Study of Evolution. All rights reserved. 

Received April 11, 2002. Accepted August 20, 2002. 

2505 

sequently, we might expect growth rates to be optimized 

among species that occupy different environmental conditions 

that vary in such costs and benefits. A critical question 

then centers on which selection pressures cause variation in 

growth rates among species. 

Birds are ideal for addressing this question for four reasons. 

First, data on growth rates are relatively abundant. Second, 

birds vary widely in growth rates and nestling periods (at 

least a 30-fold difference from seabirds to small passerines; 

Ricklefs 1979a, 1983). Third, a rich array of factors has been 

hypothesized to influence the evolution of growth and developmental 

rates in birds, with little resolution of the relative 

importance of these differing factors (see Table 1; summarized 

in Starck and Ricklefs 1998a). Finally, different theoretical 

models of avian growth and development differ substantially 

in their assumptions and predictions. For instance, 

some predicted a strong relationship between time-dependent 

juvenile mortality and growth rates (Case 1978), whereas 

others predicted a very weak one or even the absence of such 

a relationship (Ricklefs 1969b, 1984). In support of the latter, 

some comparative studies found no correlation between nest 

mortality rates and growth rates (Ricklefs 1969b; Ricklefs et 

al. 1998). In contrast, a negative correlation between nest 

mortality rates and duration of the nestling period has been 

consistently observed (Ricklefs 1969b; Bosque and Bosque 

1995; Martin 1995; Ricklefs et al. 1998). These contrasting 

results may suggest that nest mortality rates influence duration 

of the nestling period, but does not influence rate of 

growth. However, previous analyses may have been compromised 

by a confounding between growth rate and duration 

of the nestling period. In particular, nestlings of species with 

low mortality remain in the nest longer and generally achieve 

either a longer period of asymptotic growth or weight re-

2506 V. REMESˇ AND T. E. MARTIN 

TABLE 1. Summary of factors that were hypothesized to drive the 

evolution of growth and development in birds together with the conceived 

direction. A � means higher growth rates and longer nestling 

periods, a � means slower growth rates and shorter nestling periods, 

and 0 means no effect. 

Factor Growth 

Body size 1 

Developmental mode (altricial or 

precocial) 2 

Clutch size (energetic limitation) 3 

Sibling competition 4 

Time-dependent mortality 5 

Foraging ecology 6 

Latitude 7 

Nutritional limitation 8 

Growth rate (K) 9 

� 

� 

� 

� 

� or 0 

� 

� 

� 

n/a 

Nestling 

period 

1 Ricklefs 1968a; Starck and Ricklefs 1998d. 

2 From altricial to precocial species, that is, slower growth and shorter to 

lacking nestling period; Ricklefs 1973, 1979a; Starck and Ricklefs 1998c. 

3 Lack 1968; Ricklefs 1986a. 

4 Werschkul and Jackson 1979; Ricklefs 1982; Bortolotti 1986; Royle et al. 

1999. 

5 Williams 1966; Lack 1968; Ricklefs 1984 (positive effect), 1969b, 1979a 

(no effect). 

6 Unpredictability of food, Case 1978; aerial foragers, O’Connor 1978; seabirds, 

Ricklefs 1979a, 1982. 

7 Ricklefs 1976; Oniki and Ricklefs 1981. 

8 Concerns tropical frugivorous birds, Ricklefs 1983. 

9 We can expect that species growing at higher rates (K) will also fledge earlier. 

cession than for species that leave the nest earlier. These 

differences can cause overestimation of growth rate for species 

that remain in the nest longer (see Fig. 1 and Methods) 

and, thereby, may obfuscate relationships between growth 

rates and nest mortality rates. 

Here we re-examine the potential relationships between 

growth rates and nest mortality, as well as other potential 

environmental selection pressures for a sample of 115 North 

American passerines. We used original data and recalculated 

growth rates to standardize for analytical approach and to 

examine the possible interaction of nestling period on growth 

rate estimates. We controlled for the influence of developmental 

mode (altricial–precocial spectrum) by including only 

altricial passerines. Our primary ecological factors of interest 

were time-dependent nest mortality caused by predation, latitude, 

and foraging ecology. Foraging ecology was suggested 

to influence growth and developmental strategies in seabirds 

(Ricklefs 1979a). So, we examined aerial versus nonaerial 

foragers because aerial foragers differ from other passerine 

groups by unpredictability of their food supply over evolutionary 

time, which seems to influence life-history strategies 

(O’Connor 1978; Martin 1995). We also examined clutch 

size, which may influence the per capita rate of food delivery. 

We use a comparative approach, which cannot test causal 

hypotheses, but can provide insight into possible selective 

forces by examining which factors explain variation in 

growth and developmental rates among species. 

METHODS 

Dataset 

We collected data for as many species of passerines as we 

could find in the literature (see Appendix 1). Data on growth 

� 

� 

� 

� 

� 

� 

� 

� 

� 

FIG. 1. Schematic depiction of the influence of the pattern of 

growth data (either with weight recession in later phases of the 

growth or without it) on the estimation of parameters of the logistic 

growth curve. Both datasets have the same initial rate of the weight 

increase up to day 18. The growth curve parameters for the growth 

data without recession (full circles with growth curve) are: K � 

0.5, A � 20, ti � 6. The same parameters for the data with recession 

(open circles, lower growth curve) are K � 0.54, A � 18.3, ti � 

5.5. The difference is caused by the recession data forcing the 

asymptote of the best fit curve to be lower and K to be higher. 

Consequently, we get two different values of K for the exact same 

rate of increase because of differing patterns of development after 

reaching the maximum weight in the nest (see Fig. 2). 

rates, latitudes, and nest predation rates are from original 

sources. We began with data on nestling growth listed by 

Starck and Ricklefs (1998b), but we checked and reanalyzed 

all growth rate estimates (see below). In addition, we were 

able to locate additional data, yielding primary growth data 

for 183 populations of 115 species of North American passerines. 

Of these, we were able to locate data on nest predation 

rates (proportion of nests taken by predators) for 107 

species. Data on the duration of nestling period, clutch size, 

and foraging category (aerial vs. nonaerial foragers) were 

from Poole and Gill (1992–2002). Data on adult body mass 

were from Dunning (1993). Where separate data on adult 

mass are given for males and females, we took the average. 

Time-dependent mortality is the only part of nest mortality 

that can be expected to favor elevated growth and developmental 

rates (Ricklefs 1969a; Case 1978). Other sources of 

mortality that do not bear direct relation to the length of nest 

cycle are not relevant for the evolution of growth rates. Timedependent 

mortality of nests can be caused, for example, by 

inclement weather but its main source is nest predation (Ricklefs 

1969a; Martin 1993). Percent of nests lost to predation 

was the common form of data available from primary sources 

and they are a compound result of the rate of time-dependent 

mortality caused by nest predation and the duration of nesting 

cycle. Thus, we transformed our data on nest predation to 

daily nest mortality rates by the formula: dmr ��(ln S)/T, 

where dmr is daily nest mortality rate caused by nest predation, 

S is proportion of nests that were successful (1— 

proportion depredated), and T is the duration of the nest cycle 

(for discussion on this transformation procedure see Ricklefs 

1969a; Ricklefs et al. 1998). 

Growth rate can be characterized as an increase in body

mass over time, whereas development is characterized by 

timing of developmental events during ontogeny of an individual. 

For example, nestlings of species that fledge earlier 

develop their body functions more quickly than nestlings in 

later-fledging species (Ricklefs 1967b, 1979b; Austin and 

Ricklefs 1977). This different rate of maturation of body 

functions is certainly connected to the demands of life outside 

the nest, and we tried to analyze which ecological factors 

might be responsible for timing of nest leaving (i.e., duration 

of the nestling period), while controlling for the effect of 

growth rate. In addition, we analyzed relative fledging mass, 

defined as the ratio of body mass at fledging to adult body 

mass as an estimate of the relative stage of development at 

fledging. Body mass at fledging was defined as the average 

mass of nestlings at the last day in the nest and was taken 

from the original studies. 

Body mass increases in passerines in an S-shaped function, 

which is expected on theoretical grounds (West et al. 2001). 

Many S-shaped mathematical curves could potentially fit 

growth data, but the logistic growth curve is traditionally 

used (Ricklefs 1967a, 1968a). Although Brisbin et al. (1987) 

criticized its use and suggested the use of more complex 

curves that are able to describe shape as well as rate, the 

advantage of the logistic growth curve is that it produces 

only three parameters that are readily biologically interpretable. 

The logistic growth curve has a form of W(t) � A/{1 

� e [�K(t�t i)] 

}, where W(t) denotes body mass of a nestling at 

time t, A is the asymptotic body mass that the nestling approaches, 

t i is the inflection point on the time axis in which 

growth changes from accelerating to decelerating, e is the 

base of natural logarithm, and K is a constant scaling rate of 

growth. Because the value of K indexes growth rate independently 

of absolute time of growth (in time �1 ), it is a convenient 

measure for comparative purposes (Ricklefs 1968a). 

Estimates of K (growth rate) are problematic for comparative 

purposes because of a negative correlation between K 

and A (asymptotic body mass). The most serious problem 

arises in species with nestlings that remain in the nest longer 

and experience weight recession in later phases of the nestling 

period (see Ricklefs 1968b). Here the downward hook of the 

data, after a maximum value is achieved, forces A to be 

estimated lower than it would be without this hook, and consequently 

the estimate of K is artificially inflated (see Fig. 

1). Consequently, species with the exact same growth during 

the initial growth period (illustrated by the vertical line in 

Fig. 1) yield different estimates of K when they remain in 

the nest for differing periods of time (Fig. 1; V. Remesˇ, 

unpubl. data). Recently, a new modification of the logistic 

growth curve was developed to deal with this problem in 

seabirds (Huin and Prince 2000). It has two K parameters, 

one for mass increase (K1) and the other for the rate of mass 

decrease during weight recession phase (K2). However, it 

suffers from the same shortcoming as the traditional method. 

Although this new approach fits growth data with weight 

recession well, the value of K1 (which equals K of the traditional 

model and is of interest to us) still depends on the 

pattern and extent of weight recession (V. Remesˇ, unpubl. 

data). 

To overcome these problems and obtain growth estimates 

that are comparable across different ecological groups of 

GROWTH STRATEGIES OF PASSERINES 

2507 

FIG. 2. Estimates of growth rate constant K (mean � SE) for aerial 

(n � 20) and nonaerial (n � 95) foragers for raw data and when 

adjusted for weight recession. In general, K is higher for unadjusted 

data than when data were truncated at the highest mass reached by 

the young. This effect was pronounced only in aerial foragers due 

to their typical growth pattern with strong weight recession in the 

nest (see also Fig. 1). Paired t-tests: t 1,188 ��4.27, P � 0.0001, n 

� 190 for nonaerial foragers; t 1,38 ��4.10, P � 0.0006, n � 40 

for aerial foragers. 

birds, we used two approaches. First, we fit the traditional 

logistic curve to the growth data truncated at the highest mass 

achieved by nestlings in species that remained in the nest 

past a maximum mass; truncation was necessary for 61 populations 

of 39 species, including 15 (of 20) species of aerial 

foragers, and 24 (of 95) species of nonaerial foragers. The 

effect of this adjustment on the estimation of K was marked 

only in aerial foragers, because these have a high incidence 

of weight recession (see above and Fig. 2). However, even 

after this adjustment, estimations of K could still be confounded 

by differences in ages and relative mass at fledging. 

Consequently, our second approach was to fit the logistic 

curve to the growth series truncated at 70% of adult body 

mass. This approach completely standardizes the relative 

nestling mass over which growth rates are estimated. We 

chose 70% as a compromise between retaining as much of 

the growth curve as possible and the maximum number of 

species possible (some species leave the nest at a lighter mass 

than 70% of adult body mass and consequently had to be 

excluded). This procedure led to loss of 18 species but still 

yielded highly standardized data on 97 species. Both these 

adjustments yield more standardized and appropriate estimation 

of K than in previous analyses. Analyses based on K 

fit to the growth data without either of the two adjustments 

produced virtually identical results to adjusted data, with exception 

that the explanatory power of aerial foraging for K 

was significantly reduced. Given the redundancy of these 

analytical results, we report only results of the analyses with 

the two standardized sets of K. 

For our analyses we used average K for species. However, 

growth rate of nestlings can be adversely affected by poor 

environmental, especially food, conditions during rearing 

(e.g., Martin 1987; Gebhardt-Henrich and Richner 1998;


TABLE 2. Multiple regression analyses of the constant K of the logistic growth curve in North American passerines in relation to potential 

ecological factors and covariates. Sample sizes are numbers of species for raw species data (not corrected for phylogenetic relationships) and 

of phylogenetically independent contrasts for PICs (corrected for phylogenetic relationships). For partial regression and correlation coefficients 

from the analysis of raw species data, see Figure 3. 

Adult body mass 

Clutch size 

Dmr 3 

Foraging mode 4 

Latitude 

�6.50 

�1.14 

4.49 

�1.90 

1.69 

Full model 

t P 

Raw species data (n � 107) PICs (n � 103) 

�0.0001 

0.2587 

�0.0001 

0.0604 

0.0940 

�6.54 

— 

5.11 

�1.97 

— 

Best model 2 

t P 

�0.0001 

— 

�0.0001 

0.0515 

— 

1 Fit to the growth data that were truncated at the highest mass reached by the young in the nest. 

2 Selected by the backward selection procedure in the multiple regression model of SPSS. 

3 Dmr is daily nest mortality rate caused by nest predation. 

4 Foraging mode is aerial foragers (coded 1) and nonaerial foragers (coded 0). 

Schew and Ricklefs 1998). As a result, we repeated analyses 

with maximum K for species, which might better reflect evolutionary 

responses of growth rates than average values. Nevertheless, 

results of these two analyses did not differ. Consequently, 

only the results of the analyses with average values 

of K are reported because we used average values for all 

other variables. 

Phylogenetic Analyses 

We analyzed raw species data, but also employed the method 

of phylogenetically independent contrasts to control for 

possible phylogenetic influences (Felsenstein 1985; Harvey 

and Pagel 1991) based on the CAIC software package (Purvis 

and Rambaut 1995). We analyzed the independent contrasts 

in a phylogenetic regression framework (Grafen 1989), in 

which contrasts computed by CAIC (CRUNCH algorithm) 

were analyzed with standard multiple linear regressions 

forced through the origin (see Garland et al. 1992). 

For analyses adjusting for phylogenetic relationships, we 

used a working phylogeny depicted in Martin and Clobert 

(1996), which is based on Sibley and Ahlquist’s (1990) DNA- 

DNA hybridization phylogenetic hypothesis, and supplemented 

it with more recent molecular phylogenetic information 

(details are available from the authors upon request). 

We did not have consistent estimates of branch lengths 

and so we used equal branch lengths (Garland et al. 1993). 

Previous analyses comparing equal branch lengths versus 

variable ones found little effect on results (Martins and Garland 

1991). Estimation of branch lengths is an empirical issue 

and their performance should be statistically tested (Garland 

et al. 1992). We checked the performance of equal branch 

lengths by plotting the absolute values of the standardized 

contrasts against their standard deviations (Garland et al. 

1992). In all cases, performance of equal branch lengths was 

good and much better than that of another option, Grafen’s 

(1989) branch lengths. 

Statistics 

Logistic growth curves were fit in nonlinear regression in 

SPSS (1996). We used the Levenberg-Marquardt estimation 

method, sum of squared residuals loss function, and no pa- 

K 1 

�2.34 

�0.16 

4.53 

0.49 

0.94 

Full model 

t P 

0.0214 

0.8764 

�0.0001 

0.6257 

0.3509 

�2.39 

— 

4.48 

— 

— 

Best model 2 

t P 

0.0188 

— 

�0.0001 

— 

— 

rameter constraints. The ability of various factors to explain 

interspecific variation in the growth rate constant K, duration 

of nestling period, relative fledging mass, and premature 

fledging was tested by multiple linear regressions in SPSS. 

The best models were selected by the backward selection 

procedure. Our P-to-enter and P-to-remove values were 0.05 

and 0.1, respectively (Sokal and Rohlf 1995). Selection of 

the final model in backward selection procedure depends in 

part on these P-values. Thus, we also validated the models 

by the means of Akaike’s information criterion (AIC), which 

is computed as nln(SSE/n) � 2p, where n is the number of 

observations, SSE is the sum-of-squares error, and p is the 

number of model parameters. This is a general criterion for 

choosing the best number of parameters to include in a model. 

We chose a model with the minimum number of parameters 

from the set of models for which the difference between 

AIC(i) and AIC(min) was lower than two, where AIC(i) is 

AIC of the particular model and AIC(min) is minimum AIC 

of all the possible models (see Anderson et al. 2000). Marginal 

means for raw species data were estimated by AN- 

COVAs in SPSS. Phylogenetic regressions were performed 

on independent contrasts as ordinary multiple linear regressions 

forced through the origin. 

Relative fledging mass was distributed normally. Other 

variables were transformed to meet the assumption of normal 

distribution. Adult body mass, clutch size, K, latitude, and 

premature fledging were log 10 transformed and daily mortality 

rates caused by nest predation were square-root transformed. 

RESULTS 

Growth Rate (K) 

Growth rates (K) fit to the data truncated at the highest 

mass achieved by nestlings were significantly correlated with 

adult body mass and daily nest predation rates (full model: 

R 2 adj. � 0.45, F 5,101 � 18.07, P � 0.0001; reduced model: 

R 2 adj. � 0.44, F 3,103 � 28.81, P � 0.0001). The influence 

of foraging mode was only marginally significant (Table 2, 

Fig. 3). Results were essentially the same for phylogenetically 

independent contrasts, with the exception that foraging 

mode was dropped from the model (Table 2).

FIG. 3. Scattergrams of the standardized residuals from the multiple 

regression model of raw species data for K fit to the growth 

data that were truncated at the highest mass reached by the young 

in the nest (see Table 2). (A) K versus adult body mass (full circles, 

Corvidae; open circles, others), both corrected for daily predation 

rates and foraging mode; (B) K versus daily predation rates, both 

corrected for adult body mass and foraging mode; and (C) marginal 

means for foraging mode (black bars, all species; white bars, without 

four species of Empidonax), adjusted for adult body mass and 

daily predation rates. K relates to adult body mass, daily predation 

rates, and foraging mode (coded as nonaerial foragers � 0[n � 

89], aerial foragers � 1[n � 18]) by the equation: log(K [day �1 ]) 

��0.256 (0.040) � 0.128 (0.020) log(body mass [g]) � 0.976 

(0.191) square root (dpr [day �1 ]) � 0.039 (0.020) foraging mode. 

Parameters are partial regression coefficients (SE). r p-values are 

partial correlation coefficients. 


2509 

In the analyses of raw species data, the relationship between 

K and daily predation rate was curvilinear (Fig. 3B), 

as indicated by a significant nonlinear quadratic term (t 1,104 

��3.44, P � 0.0008), when effects of adult body mass and 

foraging mode were controlled. No such effect was apparent 

in the analyses of phylogenetically independent contrasts 

when adult body mass was controlled (t 1,101 ��0.02, P � 

0.9807). 

Although foraging mode was marginally significant in the 

analysis of raw species data (Table 2), this influence was due 

to four species of the genus Empidonax (see Fig. 3C), and 

after adjusting for phylogenetic effects it disappeared (Table 

2). A strong phylogenetic effect was also apparent in the 

scaling of growth rate with adult body mass due to the influence 

of the family Corvidae (see Fig. 3A). After accounting 

for the effects of phylogeny, the influence of adult body mass 

on growth rate was much smaller (Tables 2, 3). Moreover, 

when the contrast between Corvidae and Vireonidae was excluded, 

the effect of adult body mass on growth rate completely 

disappeared (t 1,100 ��0.99, P � 0.3257, r p ��0.10) 

and daily predation rate became the best predictor (t 1,100 � 

4.69, P � 0.0001, r p � 0.43). 

Very similar results were obtained from the analyses of 

growth rates (K) fit to the data truncated at 70% of adult body 

mass. The difference was that both clutch size and latitude 

also entered the model in both raw species data (full model: 

R 2 adj. � 0.44, F 5,83 � 13.23, P � 0.0001; reduced model: 

R 2 adj. � 0.44, F 4,84 � 16.52, P � 0.0001) and phylogenetically 

independent contrasts (Table 3). 

Nestling Period 

Adult body mass, daily nest predation rates, foraging mode, 

and K were significantly related to the length of the nestling 

period when estimates of K were fit to the data truncated at 

the highest mass achieved by the nestlings (R2 adj. � 0.72, 

F5,101 � 56.15, P � 0.0001; Table 4, Fig. 4). The same results 

were found when analyses were performed on phylogenetically 

independent contrasts (Table 4). When the covariate K 

was based on data truncated at 70% of adult body mass, the 

analyses of factors influencing length of the nestling period 

yielded virtually identical results. 

Premature Fledging 

We also examined the extent to which nestlings were willing 

to fledge prematurely, defined as duration of the normal 

nestling period minus duration of the nestling period when 

nestlings fledge prematurely. The extent of premature fledging 

decreased with daily predation rates (t1,30 ��3.93, P � 

0.0005, n � 33) after accounting for the effect of adult body 

mass (t1,30 � 3.70, P � 0.0009, n � 33; Fig. 5; whole model: 

R2 � 0.43, F2,30 � 13.16, P � 0.0001). This was the same 

for phylogenetically independent contrasts. Neither clutch 

size nor the growth rate constant K entered these models. 

Relative Fledging Mass 

Relative fledging mass (� SE, n), defined as the ratio of 

the mass at fledging to adult body mass, averaged 0.819 

(� 0.015, 115) over all species. Both daily predation rates


TABLE 3. Multiple regression analyses of the constant K of the logistic growth curve in North American passerines in relation to potential 

ecological factors and covariates. Sample sizes are numbers of species for raw species data (not corrected for phylogenetic relationships) and 

of phylogenetically independent contrasts for PICs (corrected for phylogenetic relationships). 


Clutch size 

Dmr 3 


Latitude 

�5.78 

�2.57 

2.41 

�0.70 

3.13 

Full model 

t P 


�0.0001 

0.0121 

0.0181 

0.4885 

0.0024 

�5.76 

�2.59 

2.81 

— 

3.18 

Best model 2 

t P 

�0.0001 

0.0114 

0.0061 

— 

0.0021 

1 Fit to the growth data that were truncated at 70% of adult body mass. 




and foraging mode were significantly related to relative 

fledging mass, with adult body mass controlled (Table 5, 

Fig. 6; whole model: R 2 adj. � 0.46, F 3,102 � 30.24, P � 

0.0001). Examination of phylogenetically independent 

contrasts, with adult body mass controlled, still yielded 

significant effects of daily predation rates, but foraging 

mode was dropped from the stepwise selection procedure 

(Table 5). 

Relative fledging mass was curvilinearly related to daily 

predation rate, as reflected by a significant nonlinear quadratic 

term (t 1,103 � 2.46, P � 0.0154), when the effects of 

adult body mass and foraging mode were controlled (see Fig. 

6B). However, no such term was apparent in phylogenetically 

adjusted data, when the effect of adult body mass was taken 

into account (t 1,100 � 0.86, P � 0.3928). All analyses of the 

relative fledging mass were performed without the strongly 

outlying Leucosticte tephrocotis (relative fledging mass � 

1.54; see also Appendix 1). 

K 1 

�3.86 

�2.19 

2.15 

0.08 

2.45 

Full model 

t P 

0.0002 

0.0317 

0.0342 

0.9374 

0.0163 

�3.90 

�2.20 

2.17 

— 

2.47 

Best model 2 

t P 

0.0002 

0.0307 

0.0327 

— 

0.0157 

TABLE 4. Multiple regression analyses of the duration of the nestling period in North American passerines in relation to potential ecological 

factors and covariates. Sample sizes are numbers of species for raw species data (not corrected for phylogenetic relationships) and of phylogenetically 

independent contrasts for PICs (corrected for phylogenetic relationships). For partial regression and correlation coefficients from 

the analysis of raw species data, see Figure 4. 


Clutch size 

Dmr 2 


K 4 

DISCUSSION 

Previous comparative studies of variation in growth rates 

found little in the way of environmental correlates (see Ricklefs 

et al. 1998). In direct contrast, we found very strong 

effects of predation, with smaller but significant contributing 

3.95 

1.92 

�3.55 

6.08 

�5.74 

Full model 

t P 

Nestling period 


0.0001 

0.0570 

0.0006 

�0.0001 

�0.0001 

3.73 

— 

�4.34 

5.98 

�5.74 

Best model 1 

t P 

0.0003 

— 

�0.0001 

�0.0001 

�0.0001 




4 Fit to the growth data that were truncated at the highest mass reached by the young in the nest. 

effects of foraging mode, clutch size, and latitude on both 

growth rate and developmental strategies. These results show 

that growth and development of altricial nestlings are shaped 

by extrinsic environmental forces. 

Daily predation rates scaled positively with growth rates 

and negatively with duration of the nestling period (after 

controlling for growth rates; Tables 2–4; Figs. 3B, 4B). Furthermore, 

species with higher nest predation fledged at lighter 

relative mass (Table 5, Fig. 6B). These results suggest that 

multidimensional aspects of the growth strategies of altricial 

nestlings are strongly influenced by risk of nest predation. 

Such results are in line with theoretical arguments made by 

Lack (1968) and Case (1978), and with empirical studies of 

Kleindorfer et al. (1997) and Hałupka (1998). 

However, the benefits of growing faster and shortening the 

nestling period may reach a point of diminishing returns. 

Indeed, the curvilinear relationship between nest mortality 

versus growth rates (Fig. 3B; Results) and relative fledging 

mass (Fig. 6B; Results) could arise if costs (ecological: Martin 

1992; Martin et al. 2000a,b; physiological: Metcalfe and 

Monaghan 2001) of even faster growth exceed the benefits. 

Alternatively, leveling off of growth rates could be caused 

by reaching the maximum growth rate possible within cellular 

and physiological constraints (see Ricklefs 1969b). More- 

3.91 

1.23 

�2.64 

2.54 

�2.56 

Full model 

t P 

0.0002 

0.2220 

0.0095 

0.0126 

0.0121 

3.80 

— 

�2.88 

2.61 

�2.52 

Best model 1 

t P 

0.0003 

— 

0.0048 

0.0105 

0.0134


2511 

FIG. 4. Scattergrams of the standardized residuals from the multiple regression model of raw species data for the duration of the nestling 

period, with K fit to the growth data that were truncated at the highest mass reached by the young in the nest (see Table 4). (A) Duration 

of the nestling period versus adult body mass, both corrected for daily predation rates, foraging mode, and K; (B) nestling period versus 

daily predation rates, both corrected for adult body mass, foraging mode, and K; (C) nestling period versus K, both corrected for adult 

body mass, daily predation rates, and foraging mode; (D) marginal means for foraging mode adjusted for adult body mass, daily predation 

rates, and K. Nestling period relates to adult body mass, daily predation rates, K, and foraging mode (coded as nonaerial foragers � 0 

[n � 89], aerial foragers � 1[n � 18]), by the equation: log(nestling period [days]) � 0.862 (0.053) � 0.097 (0.026) log(body mass 

[g]) � 1.038 (0.239) square root (dpr [day �1 ] � 0.634 (0.110) log(K [day �1 ]) � 0.135 (0.022) foraging mode. Parameters are partial 

regression coefficients (SE). r p-values are partial correlation coefficients. 

over, because birds that grow quicker also fledge and mature 

earlier, the trade-off between growth and maturation could 

play a role (Ricklefs et al. 1994, 1998; but see Krijgsveld et 

al. 2001). 

Several studies have shown that duration of the nestling 

period scales negatively with nest predation rates (Lack 1968; 

Martin and Li 1992; Bosque and Bosque 1995; Martin 1995; 

Yanes and Suárez 1997). We show that this is true even after 

accounting for pure growth rates (Table 4, Fig. 4B). Thus, 

shorter nestling periods result not only from faster growth, 

but also from earlier timing of nest-leaving at an earlier stage 

of development. The latter is clearly reflected by the lower 

relative fledging mass in species with higher predation rates 

(Table 5, Fig. 6B). However, the precocity of fledging is 

clearly limited. This limit is evident from the negative relationship 

between the extent of premature fledging relative 

to nest predation rates. Whereas species with high predation 

already leave at an early stage of development and cannot 

leave many days earlier, species with low predation stay in 

the nest to a much later stage of development and have the 

capability of fledging many days earlier than they do (see 

Fig. 5). In addition, the reduction in fledging mass when 

fledging prematurely can be so high and costs connected with 

it so strong (Lindén and Møller 1989; Magrath 1991; Gebhardt-Henrich 

and Richner 1998; Lindström 1999) that it can 

be advantageous to keep this strategy as optional for the 

circumstances of real predation danger. 

Predation was not the only environmental correlate of 

growth and development. Food limitation has long been proposed 

to explain the evolution of growth and development 

of birds through energetic expensiveness of large clutch sizes 

(Lack 1968), shorter time available for feeding offspring at 

lower latitudes (Ricklefs 1976), and unpredictability of food 

supply (O’Connor 1978). Of these three, growth rates were 

associated with aerial foraging when using K fit to the growth 

data truncated at the highest mass (Table 2) and with clutch 

size and latitude when using K fit to the growth data truncated 

at 70% of adult body mass (Table 3). Nestling period duration 

differed strongly between aerial and nonaerial foragers (Table 

4, Fig. 4D). In contrast, the influence of aerial foraging on 

relative fledging mass was rather weak in the raw species 

data (Table 5, Fig. 6C), and disappeared after adjusting for


FIG. 5. Scattergram of the standardized residuals of premature 

fledging (reduction of the duration of the nestling period when 

fledging prematurely as compared to normal fledging, in days) versus 

daily predation rates, both corrected for adult body mass, for 

raw species data. Log(premature fledging [day]) � 0.333 (0.159) 

� 3.444 (0.876) square root (dpr [day �1 ]) � 0.336 (0.091) log(body 

mass [g]), n � 33. Parameters are partial regression coefficients 

(SE). r p-value is partial correlation coefficient. 

phylogeny (Table 5). This is not surprising, however, because 

aerial foragers show a high incidence of weight recession 

(see Methods), and thus rather long nestling periods do not 

translate to very high mass of fledglings relative to adult body 

mass. 

Aerial foragers seem to be food limited in general (Martin 

1987, 1995). Their food supply is temporally unpredictable 

and there is an ample evidence that adverse climatic conditions 

can lead on a proximate level to longer incubation and 

nestling periods and impaired growth, condition, and survival 

of nestlings (e.g., Bryant 1975; O’Connor 1978; McCarty 

and Winkler 1999). Nestlings of aerial foragers must survive 

relatively long periods without parental attendance during 

inclement weather conditions, whose incidence and duration 

are unpredictable. Adaptations to sustain these periods include 

extensive fat deposition and early thermal independence 

of the nestlings (Ricklefs 1967b; O’Connor 1978). It 

is possible to speculate that these adaptations aimed at decreasing 

susceptibility to starvation could also have led to or 

have been connected with adjustments in growth strategies 

TABLE 5. Multiple regression analyses of the relative fledging mass (fledging mass/adult body mass) in North American passerines in relation 

to potential ecological factors and covariates. Sample sizes are number of species for raw species data (not corrected for phylogenetic relationships) 

and of phylogenetically independent contrasts for PICs (corrected for phylogenetic relationships). For partial regression and correlation 

coefficients from the analysis of raw species data, see Figure 6. 


Dmr 2 


Relative fledging mass 


Full and best model 1 

t P 

�6.01 

�5.14 

2.62 

�0.0001 

�0.0001 

0.0101 

Full model 

t P 

�4.58 

�2.92 

�0.34 




of nestlings (O’Connor 1978), including lower growth rates 

and longer nestling periods. On the other hand, the strongest 

result concerning aerial foragers (i.e., longer nestling periods) 

can be explained alternatively—that they must wait in the 

nest until the maturation of their flight muscles that are critical 

for their demanding flight life. Thus, results concerning 

aerial foragers must be interpreted with caution and more 

analyses are clearly needed, but the positive results found 

here with relatively low power hold promise for future analyses. 

Lack (1968) proposed a trade-off between clutch size and 

growth rate (see also Ricklefs 1968a). Our data illustrate 

negative covariation among species; species with larger 

clutch size show slower growth of nestlings for the same 

adult body mass, nest predation, and latitude. Such a covariation 

could be merely a correlated response to the same 

factor—lower nest predation risk may simply favor both larger 

clutches (Martin 1995) and slower growth (Case 1978) 

independently. However, because we control for nest predation 

by multiple regression, it may be that this is cause 

and effect (i.e., a trade-off). A positive relationship between 

growth rate of nestlings and latitude was predicted by Ricklefs 

(1976). Although he suggested this effect to explain 

slower growth of tropical species, we show that this can be 

true even on a much smaller geographical scale. However, 

because both the effect of clutch size and latitude were apparent 

only when using K fit to the growth data truncated at 

70% of adult body mass, they should be taken with caution 

and be subject to additional analyses. 

Adult body mass can have an allometric influence on lifehistory 

traits. Because K indexes growth rate independently 

of body mass (Ricklefs 1968a; Starck and Ricklefs 1998b), 

however, there is no a priori reason to expect its negative 

scaling with growth rate. The negative scaling observed here 

may suggest basic design constraints of large body size (West 

et al. 2001). Alternatively, slower growth rates and longer 

nestling periods of larger birds could be a result of lower 

parental investment in offspring. Larger birds have higher 

adult survival rates (Sæther 1987, 1988, 1989; Martin 1995), 

and species with higher adult survival are expected to invest 

less in progeny (Charnov and Schaffer 1973; Martin 2002). 

Adult body mass could work here just as a reflection of adult 

survival rate. To test this hypothesis, comparative studies of 

�0.0001 

0.0043 

0.7383 

Best model 1 

t P 

�4.58 

�2.91 

— 

�0.0001 

0.0044 

—

FIG. 6. Scattergrams of the standardized residuals from the multiple 

regression model of raw species data for the relative fledging 

mass (fledging mass/adult body mass; see Table 5). (A) Relative 

fledging mass versus adult body mass, both corrected for daily 

predation rates and foraging mode; (B) relative fledging mass versus 

daily predation rates, both corrected for adult body mass and foraging 

mode; and (C) marginal means for foraging mode, adjusted 

for adult body mass and daily mortality rates. Relative fledging 

mass relates to adult body mass, daily predation rates, and foraging 

mode (coded as nonaerial foragers � 0[n � 88], aerial foragers � 

1[n � 18]) by the equation: relative fledging mass � 1.244 (0.062) 

� 0.182 (0.030) log(adult body mass [g]) � 1.524 (0.296) square 

root (dpr [day �1 ]) � 0.080 (0.031) foraging mode. Parameters are 

partial regression coefficients (SE). r p-values are partial correlation 

coefficients. 


2513 

growth strategies including adult survival rates and feeding 

rates are needed. 

In sum, we show that growth rates, duration of nestling 

period, and relative developmental stage at nest-leaving covary 

in altricial nestlings with nest predation rates, aerial foraging, 

clutch size, and latitude after taking into account adult 

body mass and phylogenetic effects. This is in line with the 

view that growth strategies of altricial nestlings are finely 

tuned to environmental conditions typical of each species 

(Lack 1968; Kleindorfer et al. 1997) and lends support to an 

adaptive view of variation in growth and development 

(Arendt 1997). Moreover, we show that studies addressing 

evolution of growth strategies should simultaneously examine 

both pure growth and timing of developmental events 

(here, fledging), because these two show independent evolution 

in relation to extrinsic selective forces. 

ACKNOWLEDGMENTS 

During the work on this manuscript, VR was supported by 

a grant from J. W. Fulbright Commission, and TEM by grants 

from National Science Foundation (DEB-9707598, DEB- 

9981527). We thank R. E. Ricklefs and an anonymous reviewer 

for comments that greatly improved quality of the 

manuscript. 

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2515 

APPENDIX 1 

Species means used in the analyses for adult body mass (Abm, g), clutch size (Cl, no. of eggs per nest), duration of nestling period (Nstl, 

days), duration of shortened nestling period when fledging prematurely (Nstl prem, days), growth rate constant K of the logistic curve fit to 

the growth data truncated at the highest mass achieved by nestlings (K, day �1 ), data truncated at 70% of adult body mass (K 70%, day �1 ), body 

mass at fledging (Fl mass, g), latitude of the study of growth (Lat, �N), number of primary growth series for the species (No. stud), foraging 

mode (Forag; n, nonaerial; a, aerial foragers), proportion of nests taken by predators (Pred, %), and references for primary sources (Ref ). 

Species Abm Cl Nstl 

Agelaius phoeniceus 

Aimophila botterii 

Aimophila carpalis 

Aimophila cassinii 

Ammodramus bairdii 

Ammodramus caudacutus 

Ammodramus maritimus 

Ammodramus savannarum 

Amphispiza bellli 

Anthus spinoletta 

Anthus spragueii 

Aphelocoma coerulescens 

Auriparus flaviceps 

Bombycilla cedrorum 

Calamospiza melanocorys 

Calcarius lapponicus 

Calcarius mccownii 

Calcarius ornatus 

Calcarius pictus 

Campylorhynchus 

bruneicapillus 

Cardinalis cardinalis 

Carduelis flammea 

Carduelis pinus 

Carpodacus mexicanus 

Catharus bicknelli 

Catharus fuscescens 

Catharus guttatus 

Catharus ustulatus 

Chondestes grammacus 

Cinclus mexicanus 

Cistothorus palustris 

Cistothorus platensis 

Corvus brachyrhynchos 

Corvus caurinus 

Dendroica discolor 

Dendroica kirtlandii 

Dendroica petechia 

Dendroica striata 

Dendroica virens 

Dolichonyx oryzivorus 

Dumetella carolinensis 

Empidonax difficilis 

Empidonax minimus 

Empidonax oberholseri 

Empidonax traillii 

Eremophila alpestris 

Euphagus cyanocephalus 

Geothlypis trichas 

Gymnorhinus 

cyanoephalus 

Hirundo pyrrhonota 

Hirundo rustica 

52.6 

19.9 

15.3 

18.9 

17.5 

19.3 

23.3 

17.0 

18.9 

20.9 

25.3 

83.3 

6.8 

31.9 

37.6 

27.3 

23.2 

18.9 

26.4 

38.9 

44.7 

13.6 

14.6 

21.4 

28.1 

31.2 

31.0 

30.8 

29.0 

57.8 

11.3 

9.0 

448.0 

391.5 

7.7 

13.8 

9.5 

13.0 

8.8 

31.6 

36.9 

10.9 

10.3 

10.4 

13.4 

31.4 

62.7 

10.1 

103.0 

21.6 

18.6 

47.4 

3.28 

3.65 

3.80 

3.97 

4.33 

3.90 

3.39 

4.30 

3.28 

4.60 

4.50 

3.27 

3.70 

4.06 

3.80 

5.25 

3.43 

4.29 

4.08 

3.37 

3.00 

4.20 

3.80 

4.26 

3.53 

4.00 

3.46 

3.57 

4.09 

4.30 

4.92 

6.59 

5.00 

4.02 

3.89 

4.63 

4.27 

4.32 

4.00 

5.00 

3.64 

3.30 

3.86 

3.78 

3.48 

3.10 

5.13 

4.00 

4.00 

3.50 

4.53 

3.25 

11.5 

10.0 

8.5 

8.0 

9.0 

9.7 

10.0 

8.0 

95. 

14.1 

11.2 

18.0 

19.0 

15.5 

8.5 

9.0 

10.0 

10.0 

8.1 

21.0 

9.5 

11.0 

15.0 

15.0 

11.6 

11.0 

12.0 

13.0 

11.5 

25.4 

14.0 

13.0 

33.3 

31.7 

9.4 

9.4 

8.4 

9.5 

10.0 

10.5 

10.5 

15.5 

14.3 

17.8 

13.5 

9.5 

13.3 

9.8 

21.0 

22.7 

20.3 

13.5 

Nstl 

prem K K 70% 

— 

8.0 

— 

— 

— 

8.0 

8.0 

7.5 

— 

12.0 

— 

12.0 

— 

— 

7.0 

— 

— 

— 

— 

— 

7.0 

— 

12.0 

13.0 

— 

— 

— 

10.0 

7.0 

— 

12.5 

11.0 

— 

25.0 

— 

8.0 

— 

— 

9.0 

8.8 

— 

— 

— 

— 

— 

— 

— 

8.0 

— 

— 

— 

— 

Yanes, M., and F. Suárez. 1997. Nest predation and reproductive 

traits in small passerines: a comparative approach. Acta Oecol. 

18:413–426. 

0.533 

0.489 

0.555 

0.515 

0.410 

0.564 

0.579 

0.462 

0.492 

0.491 

0.515 

0.302 

0.337 

0.439 

0.456 

0.545 

0.480 

0.511 

0.486 

0.396 

0.598 

0.435 

0.375 

0.627 

0.519 

0.646 

0.448 

0.510 

0.675 

0.312 

0.466 

0.408 

0.216 

0.264 

0.507 

0.547 

0.579 

0.538 

0.736 

0.511 

0.516 

0.433 

0.452 

0.434 

0.388 

0.522 

0.501 

0.537 

0.309 

0.442 

0.431 

0.529 

0.585 

0.484 

0.580 

— 

0.461 

0.546 

0.565 

— 

0.490 

0.448 

— 

0.296 

0.463 

0.479 

— 

0.535 

0.492 

0.462 

0.594 

0.380 

— 

0.548 

0.427 

— 

0.694 

0.632 

0.467 

0.518 

— 

0.242 

0.487 

0.342 

0.236 

0.272 

0.469 

0.398 

0.562 

0.499 

0.566 

0.511 

0.495 

0.413 

0.474 

0.394 

0.499 

0.559 

0.519 

0.598 

0.355 

0.455 

0.429 

0.550 

Fl 

mass Lat 

35.6 

14.7 

11.9 

12.0 

13.6 

15.4 

16.8 

10.5 

13.9 

18.6 

15.5 

59.8 

6.7 

33.2 

23.6 

22.1 

18.7 

14.9 

22.0 

31.3 

27.1 

9.2 

18.6 

11.8 

24.8 

24.5 

24.8 

28.9 

14.0 

53.0 

11.5 

7.4 

390.0 

303.0 

6.3 

12.7 

8.9 

11.3 

7.7 

22.1 

28.5 

10.5 

10.8 

10.1 

12.5 

22.3 

46.0 

10.3 

81.3 

20.8 

18.0 

36.7 

41.6 

31.5 

32.0 

31.5 

50.8 

40.3 

40.3 

45.0 

43.8 

45.0 

50.8 

27.3 

33.4 

41.6 

40.5 

69.5 

45.7 

49.0 

58.8 

32.2 

40.4 

59.8 

40.8 

46.9 

44.0 

40.9 

44.1 

44.6 

34.0 

46.9 

44.3 

44.0 

45.9 

49.7 

39.2 

44.0 

50.6 

— 

45.5 

43.3 

45.0 

36.4 

50.2 

37.9 

44.6 

48.9 

41.7 

43.5 

35.2 

37.9 

43.5 

40.9 

Corresponding Editor: T. Smith 

No. 

stud Forag Pred Ref 

3 

1 

1 

1 

1 

2 

2 

1 

1 

1 

1 

1 

1 

1 

1 

2 

4 

4 

1 

1 

2 

2 

1 

1 

1 

1 

2 

1 

1 

1 

1 

1 

2 

2 

1 

1 

4 

1 

1 

1 

1 

2 

1 

1 

2 

5 

1 

2 

1 

2 

2 

1 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

a 

a 

a 

n 

n 

n 

n 

a 

a 

n 

44.40 

29.20 

68.00 

30.77 

43.00 

36.20 

24.53 

58.90 

43.20 

33.33 

32.75 

29.50 

19.50 

21.43 

54.14 

32.40 

38.46 

44.74 

25.32 

28.60 

54.00 

— 

— 

45.80 

68.53 

55.00 

60.10 

35.14 

38.70 

3.80 

41.39 

22.70 

45.61 

49.10 

61.80 

40.10 

34.20 

— 

— 

29.80 

31.20 

58.90 

53.30 

55.50 

44.20 

24.90 

45.50 

14.50 

41.20 

13.62 

10.35 

52.50 

1, 4 

5, 6 

7 

6, 8 

9, 10 

11, 12 

11, 13 

1, 14 

1, 15 

16 

9, 17 

1, 18 

19 

20 

21 

1, 22 

9, 23, 23a 

9, 24 

25 

1, 26 

1, 27, 32 

28 

29 

1, 30 

31 

2, 27 

1, 33 

34 

1, 35 

36 

1, 37 

1, 38 

39 

1, 40 

1, 41 

1, 42 

1, 43 

44 

45 

1, 46 

1, 47 

1, 48 

1, 49 

1, 50 

1, 51 

1, 9, 52 

1, 53 

1, 54 

1, 55 

56 

23a, 57 

1, 27


Species Abm Cl Nstl 

Hylocichla mustelina 

Icteria virens 

Junco phaeonotus 

Lanius ludovicianus 

Leucosticte tephrocotis 

Melospiza lincolnii 

Melospiza melodia 

Mimus polyglottos 

Myioborus pictus 

Myiodynastes luteiventris 

Oporornis formosus 

Oreoscoptes montanus 

Parus atricapillus 

Passerculus sandwichensis 

Passerina amoena 

Pheuticus melanocephalus 

Pica pica 

Pipilo aberti 

Pipilo erythrophthalmus 

Piranga olivacea 

Piranga rubra 

Plectrophenax nivalis 

Pooecetes gramineus 

Progne subis 

Protonotaria citrea 

Quiscalus major 

Quiscalus mexicanus 

Quiscalus quiscula 

Riparia riparia 

Sayornis nigricans 

Sayornis phoebe 

Seiurus aurocapillus 

Seiurus motacilla 

Setophaga ruticilla 

Sialia currucoides 

Sialia mexicana 

Sialia sialis 

Spizella arborea 

Spizella breweri 

Spizella pallida 

Spizella passerina 

Spizella pusilla 

Stelgidopteryx serripennis 

Sturnella neglecta 

Tachycineta bicolor 

Tachycineta thalassina 

Toxostoma curvirostre 

Toxostoma longirostre 

Toxostoma rufum 

Troglodytes aedon 

Turdus migratorius 

Tyrannus forficatus 

Tyrannus tyrannus 

Tyrannus verticalis 

Vermivora peregrina 

Vireo atricapillus 

Vireo belli 

Vireo griseus 

Vireo olivaceus 

Xanthocephalus 

xanthocephalus 

Zonotrichia albicollis 

Zonotrichia atricapilla 

Zonotrichia leucophrys 

Zonotrichia querula 

25.3 

20.4 

47.4 

26.3 

17.4 

20.8 

48.5 

7.9 

46.3 

14.0 

43.3 

10.8 

20.1 

15.5 

44.5 

177.5 

46.0 

40.5 

28.6 

29.0 

42.2 

25.7 

49.4 

16.2 

166.5 

149.0 

113.5 

14.6 

18.7 

19.8 

19.4 

20.3 

8.3 

29.0 

28.1 

31.6 

20.1 

10.4 

12.0 

12.3 

12.5 

15.9 

97.7 

20.1 

14.2 

79.4 

69.9 

68.8 

10.9 

77.3 

43.2 

39.5 

39.6 

10.0 

8.5 

8.5 

11.4 

16.7 

64.6 

25.9 

28.8 

28.1 

36.3 

3.69 

3.54 

5.40 

4.48 

4.24 

3.88 

3.70 

3.40 

3.42 

4.12 

3.80 

7.00 

4.13 

3.37 

3.40 

6.48 

2.85 

3.30 

3.53 

3.20 

5.71 

4.00 

4.52 

4.61 

2.77 

3.50 

4.80 

4.38 

4.16 

4.58 

4.20 

5.00 

3.92 

5.39 

5.00 

4.40 

4.96 

3.27 

4.00 

3.60 

3.83 

6.25 

5.10 

5.45 

4.40 

3.12 

3.75 

3.60 

6.34 

3.30 

4.58 

3.40 

3.89 

5.50 

4.00 

3.40 

3.74 

3.20 

3.78 

4.15 

4.48 

3.90 

3.92 

8.9 

11.5 

18.1 

18.5 

10.5 

10.8 

12.0 

13.0 

17.0 

8.5 

11.9 

16.0 

9.3 

10.0 

11.5 

28.4 

12.5 

10.5 

10.0 

9.5 

12.8 

10.5 

28.5 

10.0 

13.5 

12.0 

13.5 

19.4 

19.5 

17.0 

8.5 

10.0 

9.0 

19.5 

21.4 

18.0 

9.0 

8.00 

8.00 

10.5 

7.5 

19.3 

11.0 

20.0 

23.5 

15.0 

13.0 

10.8 

17.0 

13.0 

15.4 

16.5 

16.0 

11.5 

10.5 

11.3 

10.0 

11.0 

10.5 

8.5 

9.8 

9.3 

9.1 

APPENDIX 1 

Continued. 

Nstl 

prem K K 70% 

— 

— 

— 

— 

— 

— 

10.0 

10.0 

— 

7.0 

— 

12.0 

8.0 

8.0 

— 

— 

— 

7.5 

— 

— 

— 

— 

— 

9.0 

— 

— 

— 

— 

14.5 

12.0 

— 

9.0 

7.5 

— 

— 

— 

— 

— 

— 

8.0 

5.0 

— 

8.0 

17.0 

— 

— 

— 

— 

— 

— 

— 

— 

— 

— 

— 

— 

— 

— 

— 

— 

— 

7.8 

— 

0.528 

0.457 

0.370 

0.363 

0.574 

0.484 

0.514 

0.557 

0.355 

0.680 

0.480 

0.402 

0.519 

0.480 

0.358 

0.223 

0.497 

0.519 

0.431 

0.704 

0.569 

0.612 

0.306 

0.654 

0.403 

0.422 

0.458 

0.377 

0.450 

0.425 

0.473 

0.590 

0.613 

0.369 

0.487 

0.463 

0.543 

0.543 

0.532 

0.558 

0.656 

0.478 

0.469 

0.511 

0.357 

0.403 

0.5456 

0.471 

0.515 

0.519 

0.382 

0.438 

0.413 

0.654 

0.412 

0.576 

0.486 

0.554 

0.492 

0.513 

0.636 

0.564 

0.541 

— 

0.472 

0.416 

0.479 

0.492 

0.480 

0.504 

0.478 

0.386 

0.530 

0.463 

0.431 

0.511 

0.604 

0.385 

0.324 

0.497 

— 

0.477 

— 

0.556 

0.600 

0.364 

0.520 

— 

— 

— 

0.277 

0.416 

0.414 

0.550 

0.575 

0.575 

0.541 

0.476 

0.436 

0.539 

0.631 

0.497 

0.539 

0.677 

0.449 

— 

0.491 

0.589 

— 

— 

— 

0.471 

0.466 

0.388 

0.457 

0.459 

0.558 

0.357 

0.737 

0.526 

0.559 

0.495 

0.506 

0.557 

0.567 

— 

Fl 

mass Lat 

16.3 

17.7 

44.3 

40.5 

14.7 

17.8 

35.0 

8.8 

39.0 

12.5 

38.0 

11.3 

15.4 

12.6 

33.0 

180.5 

31.3 

0.24.7 

20.5 

18.2 

30.4 

17.5 

52.0 

12.7 

77.7 

100.5 

60.5 

14.5 

18.7 

17.5 

13.5 

17.1 

7.7 

25.8 

25.1 

27.2 

16.7 

9.6 

10.3 

10.6 

8.9 

14.1 

40.0 

20.6 

16.5 

46.6 

37.3 

41.5 

10.2 

56.9 

30.1 

31.8 

36.0 

7.3 

8.2 

8.1 

10.4 

13.8 

45.1 

20.3 

23.1 

20.2 

24.5 

40.0 

31.9 

39.5 

51.5 

40.5 

40.0 

28.2 

36.0 

32.0 

38.0 

44.0 

42.4 

43.3 

42.0 

37.6 

49.7 

33.6 

39.6 

42.5 

39.0 

71.4 

43.1 

42.3 

42.2 

28.0 

30.6 

40.9 

42.7 

36.0 

39.0 

41.6 

42.4 

43.5 

46.3 

46.0 

42.5 

58.5 

42.8 

53.5 

43.3 

41.6 

42.2 

50.8 

45.1 

48.8 

30.2 

28.2 

39.0 

49.5 

42.4 

39.0 

40.2 

39.0 

49.8 

32.0 

32.0 

36.8 

43.5 

42.9 

45.8 

55.2 

47.9 

63.7 

No. 

stud Forag Pred Ref 

1 

1 

2 

1 

1 

1 

1 

1 

1 

1 

2 

1 

4 

1 

1 

2 

1 

2 

1 

1 

1 

2 

1 

1 

1 

1 

3 

2 

1 

1 

2 

1 

1 

3 

1 

2 

1 

3 

1 

4 

4 

1 

1 

3 

1 

2 

1 

1 

3 

2 

1 

3 

1 

1 

1 

1 

1 

1 

5 

1 

1 

6 

1 

n 

n 

n 

n 

n 

n 

n 

n 

a 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

a 

n 

n 

n 

n 

a 

a 

a 

n 

n 

n 

a 

a 

a 

n 

n 

n 

n 

n 

a 

n 

a 

a 

n 

n 

n 

n 

n 

a 

a 

a 

n 

n 

n 

n 

n 

n 

n 

n 

n 

n 

52.90 

26.00 

19.40 

16.70 

42.00 

28.10 

47.10 

37.90 

— 

30.00 

32.00 

19.70 

43.40 

35.00 

34.00 

29.00 

63.80 

51.90 

32.60 

64.00 

27.90 

52.90 

6.25 

31.00 

41.24 

31.08 

19.35 

— 

7.29 

15.90 

24.50 

— 

37.80 

10.81 

29.59 

48.60 

— 

20.50 

54.20 

41.20 

60.40 

19.00 

46.90 

29.44 

5.70 

40.20 

62.00 

29.00 

28.50 

40.20 

25.95 

32.70 

37.60 

40.00 

39.10 

38.50 

17.02 

24.90 

34.40 

41.30 

37.00 

51.10 

30.00 

1, 58 

2, 59 

1, 23a, 60 

61 

2, 62 

1, 63 

1, 64 

65 

66 

1, 67 

68 

1, 69 

1, 9, 70 

71 

1, 72 

73 

1, 74 

1, 27, 75 

1, 76 

77 

1, 78 

1, 79 

80 

1, 81 

82 

83 

84 

85 

86 

1, 87 

1, 27, 88 

89 

1, 90 

91, 92 

92 

1, 93 

94 

1, 23a, 95 

1, 96 

1, 97 

1, 98 

2, 99 

1, 9 

1, 100 

101 

1, 102 

103 

1, 104 

1, 105 

1, 106 

107 

1, 87, 108 

1, 108 

109 

110 

1, 110 

111 

1, 112 

1, 113 

1, 114 

115 

1, 116, 117 

1, 117

APPENDIX 2 

Sources of growth rate (G) and nest predation (P) data in Appendix 

1 (for sources of other variables, see Methods). 

(1) Martin 1995 (P); (2) Conway and Martin 2000 (P); (3) Starck 

and Ricklefs 1998b (G); (4) Williams 1940, Holcomb and Twiest 

1968, Cronmiller and Thompson 1980 in 3 (G); (5) Webb and Bock 

1996, no. 216 in 118 (P); (6) Maurer, B. A., E. A. Webb, and R. 

K. Bowers. 1989. Nest characteristics and nestling development of 

Cassin’s and Botteri’s sparrows in southeastern Arizona. Condor 

91:736–738 (G); (7) Lowther et al. 1999, no. 422 in 118 (P); Austin 

and Ricklefs 1977 in 3 (G); (8) Dunning et al. 1999, no. 471 in 

118 (P); (9) Maher, W. J. 1972. Growth of ground-nesting passerine 

birds at Matador, Saskatchewan, Canada. Pp. 85–102 in S. C. Kendeigh 

and J. Pinowski, eds. Productivity, population dynamics and 

systematics of granivorous birds. Polish Scientific Publishers, Warszawa 

(G); (10) Davis, S. K., and S. G. Sealy. 1998. Nesting biology 

of the Baird’s sparrow in southwestern Manitoba. Wilson Bull. 110: 

262–270 (P); (11) Woolfenden 1956 in 3 (G); (12) Greenlaw and 

Rising 1994, no. 112 in 118 (G, P); (13) Post and Greenlaw 1994, 

no. 127 in 118 (G); Post, W. 1981. The influence of rice rats Oryzomys 

palustris on the habitat use of the seaside sparrow Ammospiza 

maritima. Behav. Ecol. Sociobiol. 9:35–40 (P); Post, W. 1974. Functional 

analysis of space-related behavior in the seaside sparrow. 

Ecology 55:564–575 (P); (14) Walkinshaw, L. H. 1940. Some Michigan 

notes on the grasshopper sparrow. Jack-Pine Warbler 18:50– 

59 (G); (15) Petersen et al. 1986 in 3 (G); (16) Verbeek, N. A. M. 

1970. Breeding ecology of the water pipit. Auk 87:425–451 (G, P); 

(17) Maher, W. J. 1973. Birds. I. Population dynamics. Canadian 

Committee for the International Biological Programme (Matador 

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breeding biology study of the Verdin, Auriparus flaviceps (Sundevall) 

in Arizona. Am. Midl. Nat. 85:289–328 (G, P); Austin, G. 

T. 1977. Production and survival of the Verdin. Wilson Bull. 89: 

572–582 (P); (20) Putnam 1949 in 3 (G); Young, H. 1949. A comparative 

study of nesting birds in a five-acre park. Wilson Bull. 61: 

36–47 (P); (21) Shane 2000, no. 542 in 118 (G, P); (22) Maher 

1964, Fox et al. 1987 in 3 (G); (23) Mickey 1943 in 3 (G); With 

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pawnee site in 1972. Grassland biome US International Biological 

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longspur eggs and young. J. Field Ornithol. 67:275–280 

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longspur. Wilson Bull. 80:123–149 (G); Briskie 1993, no. 34 in 

118 (P); (26) Anderson and Anderson 1961 in 3 (G); (27) Norris 

1947 in 3 (G); (28) Grinnell 1943 in 3 (G); Walkinshaw, L. H. 

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50:64–70 (G); (29) Perry 1965 in 3 (G); (30) Badyaev, A. V., and 

T. E. Martin. 2000. Individual variation in growth trajectories: phenotypic 

and genetic correlations in ontogeny of house finch (Carpodacus 

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1939 in Rimmer, C. C., K. P. McFarland, W. G. Ellison, and J. E. 

Goetz. 2001. Bicknell’s thrush (Catharus bicknelli) in A. Poole and 

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North America, Inc., Philadelphia, PA (G); BBIRD database, 

MCWRU, Univ. of Montana, Missoula (P); (32) Eckerle, K. P., and 

R. Breitwisch. 1997. Reproductive success of the northern cardinal, 

a large host of brown-headed cowbirds. Condor 99:169–178 (G); 

(33) Perry, E. M., and W. A. Perry. 1918. Home life of the vesper 

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thrushes, kinglets and their allies. Dover Publications, Inc., 

New York (G); (34) Stanwood 1913 in 3 (G); Evans Mack and 

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488 in 118 (G); (36) Sullivan, J. O. 1973. Ecology and behavior 

of the dipper: adaptations of a passerine to an aquatic environment. 

Ph.D. diss., University of Montana, Missoula (G, P); (37) Welter, 

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Wilson Bull. 47:3–34 (G, P); (38) Crawford, R. D. 1977. Polygy- 


2517 

nous breeding of short-billed marsh wrens. Auk 94:359–362 (G); 

(39) Parmalee 1952 in 3 (G); Ignatiuk, J. B., and R. G. Clark. 1991. 

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Correlates of reproductive success in cooperatively breeding western 

American crows: if helpers help, it’s not by much. Condor 102: 

333–341 (P); (40) Butler et al. 1984 in 3 (G); Verbeek, N. A. M. 

1995. Body temperature and growth of nestling northwestern crows, 

Corvus caurinus. Can. J. Zool. 73:1019–1023 (G); (41) Nolan, V., 

Jr. 1978. The ecology and behavior of the prairie warbler Dendroica 

discolor. Ornithological Monographs no. 26. The American Ornithologists’ 

Union (G); (42) Mayfield 1960 in 3 (G); (43) Schrantz 

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feeding of nestling yellow warblers in relation to brood size and 

prey availability. Auk 99:332–341 (G); Weatherhead, P. J. 1989. 

Sex ratios, host-specific reproductive success, and impact of brownheaded 

cowbirds. Auk 106:358–366 (G); Briskie, J. V. 1995. Nesting 

biology of the yellow warbler at the northern limit of its range. 

J. Field Ornithol. 66:531–543 (G); (44) Hunt and Eliason 1999, no. 

431 in 118 (G); (45) Pitelka, F. A. 1940. Breeding behavior of the 

black-throated green warbler. Wilson Bull. 52:3–18 (G); (46) Martin, 

S. G. 1974. Adaptations for polygynous breeding in the bobolink, 

Dolichonyx oryzivorus. Am. Zool. 14:109–119 (G); (47) Zimmerman, 

J. L. 1963. A nesting study of the catbird in southern 

Michigan. Jack-Pine Warbler 41:142–160 (G); (48) Davis et al. 

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flycatcher. Auk 106:269–278 (G); (50) Pereyra, M. E., and M. L. 

Morton. 2001. Nestling growth and thermoregulatory development 

in subalpine dusky flycatchers. Auk 118:116–136 (G); (51) King 

1955 in 3 (G); Walkinshaw, L. H. 1966. Summer biology of Traill’s 

flycatcher. Wilson Bull. 78:31–46 (G); (52) Pickwell 1931, Beason 

and Franks 1973, Cunnings and Threlfall 1981 in 3 (G); Maher, 

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study of the yellowthroat, Geothlypis trichas. Proc. Minn. Acad. 

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nestling yellow-eyed juncos. Condor 93:138–146 (G); (60) Miller, 

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shrikes (Lanius). Univ. Calif. Publ. Zool. 38:11–242 (G); (61) Yarbrough, 

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Fischer 1983 in 3 (G); (65) Marshall, J., and R. P. Balda. 1974. 

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(68) Reynolds et al. 1999, no. 463 in 118 (G); Rotenberry, J. T., 

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the hermit thrush. Auk 35:310–321 (G); Dawson and Evans 1960 

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The evolution of fledging age in songbirds 

D. A. ROFF,*V.REMES ˇ &T.E.MARTINà 

*Department of Biology, University of California, Riverside, CA, USA 

Laboratory of Ornithology, Faculty of Science, Palacky´ University, Olomouc, Czech Republic 

àUS Geological Survey Biological Resources Discipline, Montana Cooperative Wildlife Research Unit, University of Montana, Missoula, MT, USA 

Keywords: 

age at maturity; 

evolution; 

fledging age; 

logistic growth; 

nest mortality; 

optimality; 

passerines. 

Introduction 

Abstract 

The central equation of life history theory is the Euler or 

characteristic equation, which comprises the age-schedules 

of reproduction and mortality and the Malthusian 

parameter, r, the last parameter being a measure of 

fitness (Fisher, 1930; Charlesworth, 1994; Roff, 2002). 

An analysis of the Euler equation shows that an important 

determinant of fitness is the age at first reproduction 

(Lewontin, 1965; reviewed in Roff, 1992). The evolution 

of this trait is the result of the integration of two other 

classes of traits, namely growth rate parameters and agespecific 

mortality rates. The importance of age-specific 

mortality rates on the evolution of the age at first 

reproduction is obvious. However, the influence of 

growth rate parameters is indirect, with their effects, in 

large measure, acting via effects on mortality rates and 

body size. The latter trait does not itself enter directly into 

the Euler equation but is important because it typically 

Correspondence: Derek A. Roff, Department of Biology, University of 

California, Riverside, CA 92521, USA. 

Tel.: 951 827 2437; fax: 951 827 4286; e-mail: Derek.roff@ucr.edu 

doi:10.1111/j.1420-9101.2005.00958.x 

In birds with altricial young an important stage in the life history is the age at 

fledging. In this paper we use an approach proven successful in the prediction 

of the optimal age at maturity in fish and reptiles to predict the optimal age of 

fledging in passerines. Integrating the effects of growth on future fecundity 

and survival leads to the prediction that the optimal age at fledging is given by 

a function that comprises survival to maturity, the exponent of the fecunditybody 

size relationship and nestling growth. Growth is described by the logistic 

equation with parameters, A, K and ti. Assuming that the transitional mortality 

curve can be approximated by the nestling mortality, M n, the optimal fledging 

age, tf, is given by a simple formula involving the three growth parameters, 

nestling mortality (Mn) and the exponent (d) of the fecundity-body size 

relationship. Predictions of this equation underestimate the true values by 

11–16%, which is expected as a consequence of the transitional mortality 

function approximation. A transitional mortality function in which mortality 

is approximately 0.3–0.4 of nesting mortality (i.e. mortality declines rapidly 

after fledging) produces predictions which, on average, equal the observed 

values. Data are presented showing that mortality does indeed decline rapidly 

upon fledging. 

determines reproductive success, either by increasing 

mating probability (e.g. dominance/territoriality in either 

males or females) or by increasing the number of 

propagules produced (reviewed in Roff, 1992, p. 126– 

128). A positive covariation between fecundity and body 

size is well demonstrated within ectotherm species 

(reviewed in Roff, 1992, p. 126) and has been frequently 

observed in those mammal species that produce relatively 

large litters (reviewed in Roff, 1992, p. 127). 

Using a model based on the observed growth curve, 

the fecundity-body size relationship and adult mortality 

rates, Roff (1984) produced a simple model that predicted 

with considerable accuracy the age at first reproduction 

in a wide range of fish species. This model was later 

successfully extended to turtles, snakes and lizards (Roff, 

2002, p. 217–220). All taxa thus far examined are 

ectotherms and thus the question arises whether this 

model is also appropriate for endotherms in general or at 

least for some classes of endotherms. In the present paper 

we adopt the same modelling approach to address this 

question with respect to age at maturity and fledging age 

in passerines. For this purpose we make use of the 

extensive database compiled by Remesˇ & Martin (2002) 

J. EVOL. BIOL. 18 (2005) 1425–1433 ª 2005 EUROPEAN SOCIETY FOR EVOLUTIONARY BIOLOGY 1425

1426 D. A. ROFF ET AL. 

on the growth characteristics and nestling mortality rates 

of 107 North American passerines. 

The model 

Assumptions and database 

(1) Following the empirical analyses of Ricklefs (1968), 

Starck & Ricklefs (1998) and Remesˇ & Martin (2002), we 

assume that nestling growth follows the logistic equation 

A 

WðtÞ ¼ 

ð1Þ 

1 þ e Kðt tiÞ 

where W(t) is mass at age t, A is the asymptotic size, K is a 

measure of ‘growth rate’ and ti is the inflection point on 

the growth curve. Where there were data from multiple 

populations of the same species, Remesˇ & Martin (2002) 

averaged across populations to produce species-specific 

parameter values. Similarly, where data on both sexes 

were available, Remesˇ & Martin (2002) used the average 

value. Because some species show a weight recession just 

prior to or after leaving the nest, Remesˇ & Martin (2002) 

estimated the growth parameters in two ways: first they 

fitted the growth function with the data truncated at the 

maximum nestling mass and, second they estimated 

parameter values using data truncated at 70% of adult 

mass. The results presented by Remesˇ & Martin (2002) 

did not differ qualitatively between these two estimation 

approaches, but to ensure that the present analysis is 

robust to the method of estimation we here present 

results using both sets of estimates. 

The data of Remesˇ & Martin (2002) shows that fledging 

mass is approximately 80% of adult mass on average and 

that the two are highly correlated (Fig. 1, Table 1). 

Adult wt (gm) 

500 

400 

300 

200 

100 

0 

0 

0 20 40 60 80 100 

0 100 200 300 400 500 

Fledging wt (gm) 

Fig. 1 Plots of adult mass on fledging mass, showing that adult mass 

is proportional to fledging mass (inset shows data spread excluding 

the three largest species). Data are from Remesˇ & Martin (2002). 

200 

150 

100 

50 

Table 1 Summary of regressions relating observed adult mass, 

observed fledging mass, and two asymptotic masses (A 1 is the 

maximum mass achieved in the nest, A 2 is 70% of adult mass). 

Response 

variable 

Predictor 

variable r 2 

However, the regression slopes (i.e. 1.18 and 1.17) of 

adult mass on the asymptotic mass parameters (see 

Table 1) indicate that these parameters are not direct 

estimates of adult mass but are proportional to it. The 

regression slopes of asymptotic mass parameters on 

fledging mass are very close to, and not significantly 

different from, unity (1.02 and 1.00), indicating that 

these parameters estimate the asymptotic mass at fledging 

not adult mass. The difference in slopes between 

adult mass vs. asymptotic mass on fledging mass implies 

that growth subsequent to fledging is not simply a 

continuation of the nestling logistic growth function. 

(2) Survival during the nestling period can be specified 

as 

Rt f 

MðxÞdx 

e 0 ¼ e Mntf ð2Þ 

where M(x) is the age-specific nestling mortality rate and 

tf is the age of fledging. Because of the limitations of the 

available data, we shall assume that this mortality can be 

reasonably approximated by a constant instantaneous 

mortality rate of Mn (Fig. 2: Note that an increase in Mn, 

as indicated by dotted line in Fig. 2, will favour a 

decreased fledging age, as already observed by Remesˇ & 

Martin, 2002). Being unable to fend for itself and avoid 

predators during early development, the mortality rate of 

the chick that leaves the nest, M0, exceeds the mortality 

rate of a chick that remains within the nest. Initially, the 

survival rate of an altricial hatchling displaced from the 

nest is zero (i.e. M0 ¼ ¥), but as development proceeds, 

chicks become increasingly capable of mobility outside 

the nest and more likely to evade predation away from 

the nest than within the nest. At some age, tf, the 

mortality rate inside the nest exceeds that outside the 

nest and selection favours fledging. The rate of mortality 

declines following the fledging to an adult instantaneous 

mortality rate of Ma (Deevey, 1947; Sullivan, 1989; 

Anders et al., 1997; Perkins & Vickery, 2001; Gardali 

et al., 2003). 

(3) At some age ta ‘adult’ mass is achieved and no further 

growth takes place. The age of adult mass consists of the 

time to fledging, tf, and the time from fledging to ta, 

denoted as t0 (Fig. 2): hence ta ¼ tf + t0. No data are 

available on ta but, following from the assumption/ 

n 

Intercept 

(SE) Slope (SE) 

Adult mass Fledging mass 0.96 115 2.55 (1.33) 1.21 (0.02) 

Adult mass A1 0.96 115 0.80 (1.30) 1.18 (0.02) 

Adult mass A 2 0.98 97 )1.29 (1.09) 1.17 (0.02) 

A 1 Fledging mass 1.00 115 1.47 (0.27) 1.02 (0.005) 

A2 Fledging mass 0.98 97 1.83 (0.87) 1.00 (0.015) 

J. EVOL. BIOL. 18 (2005) 1425–1433 ª 2005 EUROPEAN SOCIETY FOR EVOLUTIONARY BIOLOGY

Mortality rate 

Increased M n 

Mortality 

in nest, M n 

Mortality rate of chick outside the nest, MO 

Age 

t o 

g′ (t f,t a) = Mn 

g′ (t f,t a) = bMn 

"Adult" mortality, Ma 

Age of fledging, t f Age at "maturity", t a= t f+t o 

Fig. 2 A schematic plot of the relationship between the mortality 

rate within and outside the nest as a function of the age of the 

nestling. The mortality curve for chicks outside the nest is 

hypothetical and simply illustrates the assumption that, for altricial 

birds, leaving the nest cannot be achieved until development has 

proceeded to the point at which the chicks have reasonable mobility 

and thermoregulatory ability. Dotted line illustrates that the effect of 

increasing nest mortality is to decrease fledging age. The dot-dashed 

lines show the two transitional mortality functions used as approximations 

to the declining function indicated by the solid line. 

observation that adult mass is proportional to fledging 

mass (Table 1), we can write the mass at age t a, G(t a)as 

CA 

GðtaÞ ¼CWðtfÞ ¼ 

ð3Þ 

Kðtf 1 þ e tiÞ 

where, as described above, W(t) is the nestling growth 

function and C is the proportionality constant. 

(4) In ectotherms fecundity is typically an allometric 

function of size, with an exponent of approximately 3 for 

linear body measures and 1 for mass measures (Roff, 

1992, p. 126). Data for passerines is complicated by the 

general observation that egg mass increases with female 

size, both within and among species (Christians, 2002; 

Martin et al., 2004). The reasons for this increase are 

unknown, though a larger egg produces a larger hatchling, 

which probably increases both pre- and postfledging 

survival (Magrath, 1991; Roff, 1992, p. 350; 

Williams, 1994; Barbraud et al., 1999). This does not 

explain why egg size should covary with body size. 

However, because of this covariation, the total clutch 

mass increases with body size (Saether, 1987; Visman 

et al., 1996; Barbraud et al., 1999; T.E. Martin, unpublished 

data). We shall assume that increased clutch mass 

effectively increases fecundity by increasing the survival 

probability of chicks, and that this ‘effective fecundity’ is 

an allometric function of adult body size 

FðtaÞ ¼cGðtaÞ d ¼ c½CWðtfÞŠ d 

ð4Þ 

where c is a constant, characteristic of the population or 

species. We shall assume that the exponent d lies 

between 0.75, the exponent obtained from an interspecific 

analysis (Visman et al., 1996) and also the 

exponent for metabolic rate and productivity (Charnov, 

2000), and 1.00, which is the value typically observed for 

ectotherms and can be justified on geometric considerations 

(Roff, 1984,1992). 

(5) We assume that population size is stable, in which 

case the appropriate measure of fitness depends upon 

how density-dependence acts (Charnov, 1993; Charlesworth, 

1994; Mylius & Diekmann, 1995; Benton & 

Grant, 2000; Roff, 2002). Provided that there is no 

genetic variation for the characteristics that favour 

survival through the period during which densitydependence 

acts, or that the traits under study are 

genetically uncorrelated with such characteristics, the 

appropriate fitness measure is the expected lifetime 

fecundity of a female, R0. Even in the absence of these 

conditions being met, R 0 may often be a reasonable 

measure (Benton & Grant, 2000). In the absence of 

sufficient information to include density-dependent 

functions, we shall assume that one or both of the 

foregoing conditions hold and use R 0. If this measure is 

inappropriate then prediction should be compromised. 

Analysis 

Under the above scenario, and assuming an equal sex 

ratio, we have 

R0 ¼ 0:5cGðtaÞ d e 

J. EVOL. BIOL. 18 (2005) 1425–1433 ª 2005 EUROPEAN SOCIETY FOR EVOLUTIONARY BIOLOGY 

Evolution of fledging age 1427 

Z 

Mntf gðtf;taÞ 

1 

0 

e Max dx ð5Þ 

where x is adult age scaled to commence at the age at first 

reproduction, and e )g(tf ,ta) is the survival between tf and 

t a (the period labelled t 0 in Fig. 2), which is a function of 

the ‘M0’ curve. Note that this age is here set to the point 

at which growth ceases. In most cases there will be a 

period during which ecological conditions, such as 

winter, do not favour reproduction and do not permit 

further growth: these periods affect all life histories 

equally and hence can be ignored in this analysis. We are 

interested in determining the optimal value of t a. Because 

terms involving ta do not enter into the integral, we can 

write fitness, w, as 

wðtaÞ ¼0:5GðtaÞ d e Mntf gðtf;taÞ ¼ 0:5cGðtaÞ d e hðtaÞ 

ð6Þ 

To find the optimal value of ta ¼ tf + t0we differentiate 

with respect to ta and find the value at the differential 

equals zero (see appendix), which is when 

h 0 ðtaÞ ¼ dG0ðtaÞ GðtaÞ ¼ dW 0ðtaÞ WðtaÞ 

ð7Þ 

where W 0ðtfÞ ¼ @WðtfÞ 

@tf and h0ðtaÞ ¼ @hðtaÞ 

: In the absence of 

@ta 

data on the transition function for mortality upon 

fledging to a constant adult mortality rates we shall 

assume, as a first approximation, g(tf,ta) Mnt0 ¼


Mn(ta ) tf), from which we obtain h¢(ta) ¼ Mn. This ‘step’ 

function will overestimate the transitional mortality rate 

(Fig. 2). We can more closely approximate the transitional 

mortality by g(tf,ta) bMnt0 ¼ bMn(ta ) tf), where 

b is a proportionality factor that is population- or speciesspecific. 

Assuming this, we have h(ta) ¼ bMnta + 

Mntf(1 ) b). Because tf is much smaller than ta, we can 

use the approximation h(t a) bM nt a, from which we 

obtain h¢(t a) ¼ bM n. In the absence of estimates of b we 

shall use a single value. In doing this we merely imply 

that the distribution of b values is not very wide: if this 

assumption is incorrect it should not be possible to find a 

value of b that improves the fit between predicted and 

observed ages at fledging. 

Substituting the appropriate values in eqn 7 and 

rearranging gives 

tf ¼ 1 BðdK MnÞ 

ln 

K Mn 

¼ ti þ 1 

K ln 

dK 

Mn 1 

; ð8Þ 

where B ¼ e Kt i . Predictions for the alternate transitional 

mortality rate are obtained by substituting b Mn for Mn . 

The above equation predicts the optimal age at fledging 

and hence also the optimal age at first reproduction, 

excluding any time period, such as the winter, when 

growth and reproduction are ecologically unfavourable. 

Testing the model 

Predicted vs. observed ages at fledging 

We test the predictions of the above model by comparing 

the predicted values of tf with the observed age at the end 

of the nestling period using the two sets of parameter 

estimates (i.e. maximum nestling mass, and 70% adult 

mass) based on data reported in Remesˇ & Martin (2002). 

For both parameter sets there is a highly significant 

correlation between observed and predicted values, with 

the fecundity exponent, d, having little effect on the 

correlation or regression parameters (Fig. 3, Table 2). 

The percentage deviation between observed and predicted 

vary from 0.1 to 55.7% across individual species, with 

a mean ranging from 16 to 17%, a SD of 11% (Table 2) 

and a SE of 1%. The predictions using the first 

transitional mortality function g¢(ta,tf) ¼ Mn, are proportional 

to the observed values but tend to underestimate 

them by 11–16%. In the first estimate set this underestimation 

is significant both for the regression model 

(slope significantly greater than 1, Table 2) and a paired 

t-test (Table 2), but it is significant only for the paired ttest 

in the second estimate set (Table 2). The underestimation 

can be accounted for by the overestimation of the 

transitional mortality, as shown by slopes obtained using 

values of b less than unity (Fig. 4). Values of b less than 

approximately 0.29 produce slopes that overestimate tf, 

whereas values of b greater than approximately 0.39 

underestimate tf (Fig. 4). These results predict that there 

Observedt f (age at fledging) 

Observedt f (age at fledging) 

30 

20 

10 

d = 1.00 

Regression for d = 1.00 

1 : 1 line 

d = 0.75 

Regression for d = 0.75 

0 

0 10 20 30 

30 

20 

10 

Predicted t f 

0 

0 10 20 30 

Predicted t f 

Fig. 3 A comparison of observed and predicted ages at fledging in 

passerines. The left panel shows results for growth parameters 

estimated using data truncated at maximum nestling mass. The right 

panel shows results for growth parameters estimated using data 

truncated at 70% adult mass. 

is a significant decline in mortality rate between fledging 

and the cessation of growth. 

Contributions of parameters to the predicted age at 

fledging 

There are four parameters in the predictive equation 

(ti, K,Mn, d), one of which, d, little influences the 


Table 2 A comparison of the predicted age 

at fledging with the observed age at fledging 

using the first transitional mortality function 

g(t a,t f) ¼ M nt 0. 

Slope 

1.20 

1.15 

1.10 

1.05 

1.00 

0.95 

0.90 

0.85 

Max nestling mass, d = 1.00 

Max nestling mass, d = 0.75 

70% adult mass, d = 1.00 

70% adult mass, d = 0.75 

Slope = 1.00 

Growth parameters 

estimated by truncation at d* 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 

Fig. 4 The relationship between the slope of the regression of 

observed on predicted t f and b. 

prediction within the limits over which it is likely 

to range (Fig. 4). To examine the potential contribution 

of the other three parameters we first rewrite eqn 

8as 

tf ¼ ti þ 1 

1 

ðlnðdK MnÞþlnðMn ÞÞ: ð9Þ 

K 

The growth parameter K, ranges from 0.22 to 0.74 

(0.165 < dK < 0.74), with a median value of 0.49, 

whereas daily nestling mortality rate, Mn, ranges from 

0.0009 to 0.05, with a median of 0.016. Consequently, 

we can write 

tf ti þ 1 

1 

ðlnðdÞþlnðKÞþlnðMn ÞÞ: ð10Þ 

K 

The ln(d) ranges only from 0 to )0.29, which explains 

why it has relatively little effect on fledging age. The 

inflexion parameter ti varies approximately between 3 

and 6, which is similar to the range of lnðM 1 

n Þ (Fig. 5), 

but the effect of the latter is modulated by the reciprocal 

β 

Regression of observed on predicted 

Intercept (SE) Slope (SE)§ r 

of the growth parameter K, which varies largely within 

the range 1.5–2.5 (Fig. 5). Fledging age ranges from 7.5d 

to 33.25d, with a median of 11.5d, and thus all three 

parameters can potentially contribute significantly to its 

variation. This is confirmed by the correlations between 

each parameter and fledging age: 0.80 for ti, )0.66 for K 

and )0.52 for Mn (Table 3). 

The effect of asymptotic size on the optimal age at 

fledging 

Asymptotic size does not enter into the optimality 

solution but this does not mean that this trait cannot 

influence the optimal age at fledging/maturity. Changing 

the asymptotic size will change fecundity and very likely 

will also change survival rates (Saether, 1987,1988,1989; 

Martin, 1995). Further, if the growth parameters are not 

independent traits, changing one parameter will produce 

a change in the other parameters. In the present data set 

all three growth parameters significantly covary 

(Table 3), which could be a result of independent 

optimizations that happen to generate covariation, or 

more likely, and as indicated below, is a consequence of 

functional constraints among the three traits. 

West et al. (2001) working from fundamental principles 

derived a growth curve applicable to a wide range of 

taxa, including invertebrates, fish, mammals and birds: 

" # 

0:25 

W0 

at 

4W WðtÞ ¼Wmax 1 1 

e 

0:25 

( ) 4 

max ð11Þ 

Wmax 

Deviationà 

Maximum mass 0.75 )0.33 (0.76) 1.16 (0.06) 0.76 16.7 (10.6) 

Maximum mass 1.00 )0.60 (0.77) 1.12 (0.06) 0.78 15.8 (10.6) 

70% adult mass 0.75 0.07 (1.08) 1.15 (0.09) 0.67 17.5 (11.8) 

70% adult mass 1.00 )0.19 (1.10) 1.11 (0.08) 0.67 17.3 (10.9) 

*Exponent of the fecundity function. 

All regressions highly significant (P < 0.0001). 

àDeviation ¼ 100 · (Predicted ) Observed)/Observed. 

§Test of deviation from a slope of 1, from top to bottom: t 105 ¼ 2.62, P ¼ 0.0100; t 105 ¼ 2.06, 

P ¼ 0.0417; t 87 ¼ 1.72, P ¼ 0.0883; t 87 ¼ 1.33, P ¼ 0.1882. Results, from top to bottom, for 

paired t-test using observed and predicted ages: t 106 ¼ 6.26, P < 0.0001; t 106 ¼ 3.71, 

P ¼ 0003; t 88 ¼ 5.37, P < 0.0001; t 88 ¼ 3.52, P ¼ 0.0007. 

SD: standard deviation. 



which, like the logistic has just three parameters, W max, 

which is the asymptotic size, W0, which is the initial 

mass, and a parameter ‘a’, which controls the rate at 

which the asymptotic value is approached. Unlike the 

logistic used in the present paper, the asymptotic mass is 

embedded within the growth equation and is not simply 

a scaling parameter. The growth curve generated by the 

above function can be readily fitted by the logistic 

equation, as illustrated in the case of the robin, Erithacus


Numbers 

Numbers 

Numbers 

40 

30 

20 

10 

14 

12 

10 

0 

1 2 3 4 5 6 7 

1/K 

8 9 10 11 12 

8 

6 

4 

2 

0 

25 

20 

15 

10 

5 

1 2 3 4 5 6 7 8 9 10 11 12 

t i 

0 

1 2 3 4 5 6 7 8 9 10 11 12 

ln(1/M n) 

Fig. 5 Distributions of the three parameters, ti, K and Mn. The latter 

two are transformed to reflect their contribution to t f. Parameters 

estimated using the ‘first’ data set (maximum nesting mass). One t i 

value (15.86) not shown. 

rubecula (Fig. 6). If only the asymptotic value in equation 

is changed all three fitted values of the logistic equation 

also change, demonstrating that these components are 

not completely separate entities (Fig. 6). Thus selection 

that acted on asymptotic size alone would, according to 

the model of West et al. (2001), create a change in the 

other growth parameters, which would then induce an 

evolutionary change in the optimal age at fledging. 

Table 3 Correlations between growth parameters (estimated using 

nestling mass up to 70% of adult mass), observed fledging and adult 

masses, fledging age and nest mortality rate. 

Further, as shown in Fig. 6, we can predict that A should 

be negatively correlated with K but positively correlated 

with t i, and that K should be negatively correlated with t i. 

All three predictions are supported by the data of Remesˇ 

& Martin (2002), Table 3). 

Discussion 

The predictive model for fledging age in passerines 

developed in this paper is remarkably simple, but 

produces an excellent correspondence between predicted 

and observed fledging ages (Fig. 3, Table 2). The first 

transitional mortality function, (g(tf,ta) ¼ Mnt0), which is 

a step function that necessarily overestimates the mortality 

rate during the transitional period (Fig. 2), produces 

predictions that are 11–16% too small (Fig. 3). To 

estimate the average change in mortality rate required to 

eliminate this discrepancy we used an intermediate step 

function (g(tf,ta) ¼ bMnt0) that requires an additional 

parameter, b. This analysis indicated that a transitional 

mortality function in which mortality is approximately 

0.3–0.4 of nesting mortality produces predictions which, 

on average, equal the observed values (Fig. 4). These 

results suggest that the mortality rate averaged over the 

nestling period is significantly higher than that immediately 

following fledging. Support for this hypothesis is 

given by a comparison of juvenile mortality rates in 

passerines (data from Saether, 1989) with nestling 

mortality rates (data from Remesˇ & Martin, 2002): 

juvenile mortality rates average 0.003 (SE ¼ 0.0001, 

n ¼ 17), whereas nest mortality rates average 0.017 

(SE ¼ 0.0009, n ¼ 115), a difference that is highly 

significant (t122 ¼ 5.92, P < 0.001; Kruskal–Wallis test, 

v 2 1 

K A ti tf Mn 

Adult 

weight 

Fledging 

weight 

K 0* 0 0 0 0 0 

A )0.50 0 0 0.193 0 0 

t i )0.80 0.75 0 0.001 0 0 

t f )0.66 0.63 0.73 0 0 0 

Mn 0.40 )0.14 )0.34 )0.52 0.456 0.197 

Adult weight )0.47 0.99 0.69 0.55 )0.07 0 

Fledging weight )0.48 0.99 0.71 0.62 )0.13 0.98 

Correlations are shown below the diagonal, probabilities above the 

diagonal. 

*0 Indicates P < 0.001. 

¼ 36:1; P < 0.001). Moreover, studies that tracked 

young after fledging also indicate a rapid decrease in 

mortality with age following fledging (e.g. Sullivan, 

1989; Anders et al., 1997; Perkins & Vickery, 2001; 

Gardali et al., 2003). Finally, these results also suggest 

an interesting relationship that is previously undescribed: 

selection favours an age of fledging where post-fledging 


Fig. 6 Upper left panel shows the growth 

curve (•) generated by fitting data from the 

robin (E. rubecula) to equation 11. Parameter 

values (W max ¼ 22g, W 0 ¼ 1g, a ¼ 1.9) from 

West et al. (2001). The solid line shows the 

logistic equation fitted by nonlinear least 

squares regression (A ¼ 21.557, K ¼ 0.3506, 

t i ¼ 5.824). The remaining three panels 

show the changes in the fitted values of A, K 

and t i when only W max is varied. 

mortality is roughly 0.3–0.4 that experienced in the nest. 

This relationship encompasses observations that birds 

fledge at younger ages and developmental stages when 

nest mortality is greater (i.e. Remesˇ & Martin, 2002), but 

provides a more quantitative predictor of the actual age 

this should occur based on relative rates of pre- and postfledging 

mortality. 

Mass at fledging is generally less than the adult mass 

and the nestling growth curve shows that this adult mass 

cannot be estimated simply by projecting the nestling 

growth curve. This indicates that there is a significant 

change in growth following fledging, which, given the 

radical change in the thermal (i.e. no longer surrounded 

by warm siblings) and energetic (i.e. increased movement) 

environment of the fledgling, is not surprising. To 

further develop the model we require estimates of the 

change in mass between fledging and the attainment of 

adult mass. The model predicts the optimal age at 

fledging based upon the premise that the mass at this 

age is proportional to the mass at maturity (¼cessation of 

growth). This premise is supported by observation but 

the lack of data on growth subsequent to fledging hinders 

theoretical development. 

Whereas parental provisioning rate will reduce the 

likelihood of nestling starvation, nestling mortality due to 

predation may be largely beyond the control of the 

parents or chicks. Reduced begging by chicks and 

reduced provisioning rates may decrease mortality rates 

from predators that can use such cues, but many 

predators use alternate modes of detection for which 

there are few, if any, defenses (Montgomerie & Weatherhead, 

1988; Briskie et al., 1999; Martin et al., 2000; 

Ghalambor & Martin, 2002). A reduction in clutch size 

Wt 

K 

25 

20 

15 

10 

5 

0 

0 5 10 15 20 25 30 

Age 

0.30 

10 15 20 25 30 35 40 


0.42 

0.40 

0.38 

0.36 

0.34 

0.32 

Wmax 

A 

45 

40 

35 

30 

25 

20 

15 

10 

10 15 20 25 30 35 40 

7.5 

7.0 

6.5 

6.0 

ti 5.5 

5.0 

4.5 

4.0 


Wmax 

3.5 

10 15 20 25 30 35 40 

Wmax 

may increase the survival probability of the parent but an 

increase in provisioning rate may decrease parental 

survival (reviewed in Roff, 2002, p. 134–139). The 

optimal evolutionary response will depend upon how 

these components interact. To illustrate the complexity of 

this evolutionary response, consider the consequences of 

a reduction in clutch size at a given age at maturity, ta. 

Such a reduction can be modelled by changing the 

constant, c, in the fecundity eqn 4 (i.e. F(ta) ¼ cG(ta) d ). 

Because this coefficient does not enter the optimality 

equation (e.g. eqn 7), this change per se has no direct 

effect on the optimal age at fledging/maturity. However, 

changing c changes fecundity and hence changes the 

provisioning rate per nestling if total provisioning rate 

remains unchanged. Such a change would alter the 

nestling growth rate, which would then favour a change 

in the optimal age at fledging/maturity. On the other 

hand the parents might alter their total provisioning rate, 

which would most likely affect their mortality rate, 

which would then favour a change in the optimal age at 

maturity. 

Using stepwise multiple regressions Remesˇ & Martin 

(2002) found that fledging age to be a function of adult 

body mass, K, Mn and foraging mode (aerial vs. 

nonaerial). Rearrangement of the logistic growth eqn 1 

1 A 

gives time to fledging as tf ¼ ti K ln WðtfÞ 1 and 

hence in a multiple regression it would not be surprising 

to find significant contributions by K and by adult mass 

(which is highly correlated to both A and fledging mass: 

Table 3). Unlike these growth parameters the entry of Mn 

is not dictated by any necessary algebraic relationship 

and its retention in the final regression model is predicted 

by the model presented in this paper. Further, this model


provides no indication that nest mortality rate should be 

a function of body size measures, except in as much as 

they are correlated with the growth parameters. Thus the 

lowest correlations should appear between nest mortality 

rate and the three body mass measures, A, adult and 

fledging mass, which is indeed the case, none even being 

significant (Table 3). 

There remain a number of questions to be addressed in 

the future: (1) what is the growth function subsequent to 

fledging, (2) what trade-offs dictate the optimal combination 

of growth parameters, (3) what is the age-specific 

mortality curve and what proximate mechanisms generate 

this trajectory, (4) what is the mortality curve 

associated with fledging at an earlier age, (5) what is 

the relationship between clutch mass and body mass, (6) 

what are the consequences of changes in egg size upon 

growth and survival? The present analysis highlights the 

importance of these components of the life history in the 

evolution of fledging age in passerines and similarly 

highlights the importance of gathering further data. 


The data for this work was obtained and generated in 

part when VR was supported by a grant from J. W. 

Fulbright Commission and GACR No. 206/05/P581, and 

TEM by grants from National Science Foundation (DEB- 

9707598, DEB-9981527). Support to DAR was provided 

by an IC grant from UCR. 

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Appendix 

Derivation of the optimal ages at maturity and 

fledging 

The fitness function is 

Mntf gtf;ta ð Þ 

w ¼ 0:5cGðtaÞ d e 

Letting h(ta) ¼ Mntf + g(tf,ta) we have 

ð1Þ 

@w 

¼ dGðtaÞ 

@ta 

d 1 0:5cG 0 ðtaÞe hðtaÞ 

h 0 ðtaÞe hðtaÞ 0:5cGðtaÞ d 

¼ 0:5cGðtaÞ d 1 e hðtaÞ ðdG 0 ðtaÞ h 0 ðtaÞGðtaÞÞ ð2Þ 

where G 0 ðtaÞ ¼ @GðtaÞ 

@ta 

optimum value of ta we set @w 

@ta 

requires that 

and h 0 ðtaÞ ¼ @hðtaÞ 

@ta 

h 0 ðtaÞ ¼ dG0 ðtaÞ 

GðtaÞ ¼ dW 0 ðtfÞ 

WðtfÞ 

. To find the 

¼ 0; which from eqn 2 

ð3Þ 

Now, if g(tf,ta) ¼ Mn(ta)tf) then h(ta) ¼Mntf + Mn(ta ) tf) ¼ 

M nt a. Letting B ¼ e Kt i we have W(tf) ¼ A(1 + Be )Kt f ) )1 

and hence W¢(tf) ¼ AKBe )Kt f (1 + Be )Kt f) )2 . Substituting in 

eqn 3 and rearranging 

Mn ¼ dW 0ðtfÞ WðtfÞ ¼ dAKBe Ktfð1 þ Be Ktf Þ 2 

Að1 þ Be Ktf 

Ktf dKBe 

¼ 

1 þ Be Ktf 

Mnð1 þ Be Ktf Þ¼dKBe Ktf 

e Ktf Mn 

¼ 

BðdK MnÞ 

tf ¼ 1 BðdK MnÞ 

ln 

K Mn 



Received 11 January 2005; revised 8 April 2005; accepted 

13 April 2005 

Þ 1 

ð4Þ

Evolution, 60(8), 2006, pp. 1692–1700 

GROWTH STRATEGIES OF PASSERINE BIRDS ARE RELATED TO BROOD 

PARASITISM BY THE BROWN-HEADED COWBIRD (MOLOTHRUS ATER) 

VLADIMÍR REMES˘ 

Department of Zoology, Faculty of Science, Palacky University, Tr. Svobody 26, 77146 Olomouc, Czech Republic 

E-mail: vladimir.remes@upol.cz 

Abstract. Sibling competition was proposed as an important selective agent in the evolution of growth and development. 

Brood parasitism by the brown-headed cowbird (Molothrus ater) intensifies sibling competition in the nests 

of its hosts by increasing host chick mortality and exposing them to a genetically unrelated nestmate. Intranest sibling 

competition for resources supplied by parents is size dependent. Thus, it should select for high development rates 

and short nestling periods, which would alleviate negative impacts of brood parasitic chicks on host young. I tested 

these predictions on 134 North American passerines by comparative analyses. After controlling for covariates and 

phylogeny, I showed that high parasitism rate was associated with higher nestling growth rate, lower mass at fledging, 

and shorter nestling periods. These effects were most pronounced in species in which sibling competition is most 

intense (i.e., weighing over about 30 g). When species were categorized as nonhosts versus old hosts (parasitized for 

thousands of years) versus new hosts (parasitized the last 100–200 years), there was a clear effect of this parasitism 

category on growth strategies. Nestling growth rate was the most evolutionarily flexible trait, followed by mass at 

fledging and nestling period duration. Adjustments during incubation (incubation period length, egg volume) were 

less pronounced and generally disappeared after controlling for phylogeny. I show that sibling competition caused by 

brood parasites can have strong effects on the evolution of host growth strategies and that the evolution of developmental 

traits can take place very rapidly. Human alteration of habitats causing spread of brood parasites to new areas thus 

cascades into affecting the evolution of life-history traits in host species. 

Key words. Brood parasitism, comparative analysis, development, growth, habitat change, sibling competition. 

One of the major challenges in evolutionary biology is to 

explain interspecific phenotypic variation. Growth is an integral 

part of a species’ life history; thus, knowing the factors 

responsible for its evolutionary diversification is a key issue. 

Many factors were hypothesized to be responsible for driving 

the evolution of growth and development rates (review in 

Starck and Ricklefs 1998; Remes˘ and Martin 2002). However, 

despite substantial research attention, we still have an 

incomplete knowledge of the factors critical in shaping the 

evolution of growth. 

One of the factors with strong potential to drive the evolution 

of juvenile growth is sibling competition. Strong sibling 

competition over limited resources should lead to elevated 

growth rates, provided rapid growth ensures monopolization 

of the resources (Werschkul and Jackson 1979; 

Ricklefs 1982). Despite the clear logic of this theoretical 

concept, empirical studies have generated mixed results 

(Werschkul and Jackson 1979; Ricklefs 1982; Bortolotti 

1986; Royle et al. 1999; Lloyd and Martin 2003). An emerging 

consensus seems to have been that juvenile growth rates 

are rather insensitive to sibling competition or at most that 

the potential relationship is unclear and obscured by confounding 

variables (Ricklefs 1982; Lloyd and Martin 2003; 

but see Royle et al. 1999). However, the role of sibling competition 

as a selective agent depends on the evolutionary 

flexibility of growth rates in response to mortality (Ricklefs 

1982). An overview of growth variation in diverse taxa 

showed that growth rates are often tuned to prevailing environmental 

conditions (Arendt 1997). More importantly, Remes˘ 

and Martin (2002) showed that bird growth and development 

can be evolutionarily flexible in relation to juvenile 

mortality. Thus, it seems appropriate to rigorously re-examine 

the potential role of sibling competition in the evolution 

of growth and development rates. 

� 2006 The Society for the Study of Evolution. All rights reserved. 

Received March 17, 2006. Accepted May 31, 2006. 

1692 

The brood parasitic system of the brown-headed cowbird 

(Molothrus ater) and its avian hosts in North America is an 

ideal model for addressing this issue. The feasibility of this 

study system depends on two assumptions: (1) brood parasitism 

leads to stronger sibling competition in the host, and 

(2) elevated growth provides selective advantage to its bearer 

in sibling contest for resources. 

First, the intensity of sibling competition is set by an interplay 

between the benefits of obtaining more resources and 

both direct and indirect costs (Godfray 1991; Mock and Parker 

1997). Direct costs include, for example, the danger of 

luring nest predators by conspicuous begging behavior (Haskell 

1994; Dearborn 1999). Indirect costs include harm to 

gene-sharing nestmates (intrabrood conflict) and future offspring 

of the parents (interbrood conflict; Trivers 1974) incurred 

by nestlings monopolizing food brought by parents. 

Young of interspecific brood parasites have zero genetic relatedness 

to both their immediate nestmates and future offspring 

of the caring parents. Although selfishness of the parasitic 

chick is limited by positive effects of host young on 

its well-being (Kilner et al. 2004), cowbird young can afford 

more unequal monopolization of resources without bearing 

indirect costs (i.e., costs to their inclusive fitness; Hamilton 

1964). This lower indirect cost leads to the escalation of 

sibling competition (more precisely, nestmate competition) 

between cowbird and host young and may cascade to affect 

the evolution of begging display (e.g., more vigorous begging 

by brood parasites and hosts; Briskie et al. 1994; Dearborn 

1998, 1999), and host growth rates (Royle et al. 1999). If 

the presence of a more selfish nestmate (i.e., cowbird chick) 

in the nest leads to higher host young mortality (Lorenzana 

and Sealy 1999) and growth rate is evolutionarily sensitive 

to nestling mortality (Remes˘ and Martin 2002), this would

also lead to the evolution of more rapid growth (Ricklefs 

1982). 

Second, the young of brood parasites depend fully on their 

foster parents and thus the ability to acquire adequate resources 

from them. More importantly, nonevicting brood parasites 

(including the brown-headed cowbird) must procure 

these resources in the presence of competing host young (Davies 

2000). The interaction of parasitic and host young in the 

parasitic system of the brown-headed cowbird is strongly 

height dependent. Who gets fed depends critically on the 

height to which the nestling reaches with its gape (Dearborn 

1998; Lichtenstein and Sealy 1998), and young of similarsized 

hosts are not out-competed by cowbird nestlings (Dearborn 

and Lichtenstein 2002). Thus, it seems reasonable to 

speculate that elevated growth rate with resulting larger body 

size for a given age bears a clear advantage in this system 

(see also Friedmann 1963; Rothstein 1975). It should be noticed 

that chick discrimination (preferential feeding of their 

own young by host parents) could invalidate this argument. 

However, although it is known in several hosts of related 

species of cowbird (Fraga 1998; Lichtenstein 2001), it has 

not been found in any host of the brown-headed cowbird (see 

Grim 2006). 

Brown-headed cowbirds (body mass � 45 g, incubation 

period 11.5 days) successfully parasitize passerine species 

differing widely in adult body mass (Kilpatrick 1999). In 

small hosts, the cowbird chick is able to monopolize the 

majority of food brought by parents (see above). Consequently, 

the competition between other nestlings (i.e., true 

siblings) is very intense and every increase of the growth 

rate could bring a strong advantage. However, the smaller 

the host species, the higher the host young mortality, reaching 

even 100% (Lorenzana and Sealy 1999; Hauber 2003a; Kilner 

2003). Thus, in small hosts, there is little selection on growth 

strategies because a large majority of host young experiencing 

the presence of the cowbird young die and do not pass 

on their genes into future generations. In contrast, nestlings 

of large species fare well even in the company of parasitic 

nestmates, who die within a couple of days, thus eliminating 

any selection pressure on growth strategies of the host’s 

chicks (Scott and Lemon 1996). In intermediate hosts, the 

cowbird chick is a strong competitor, and at the same time 

host chicks survive to fledge. This combination of factors 

should bring the strongest selection on growth strategies in 

this size category of host species. Consequently, I predicted 

that the effect of the intensity of parasitism on the evolution 

of host growth rates would vary with host body mass, with 

the highest effect in the intermediate host mass, roughly 

equaling the mass of the cowbird. 

There are also other possible adjustments of host growth 

strategies for coping with the young cowbird in the nest, or 

at least for mitigating negative effects of its presence. Host 

young may increase their size, and consequently their competition 

potential, by hatching from larger eggs for a given 

adult body mass, or by shortening the incubation period to 

gain a head start in the development. Alternatively, they may 

escape direct competition with the cowbird chick by leaving 

the nest earlier and with lower body mass. However, since 

cowbirds beg vigorously even after leaving the nest (Hauber 

and Ramsey 2003), the strength of selection for early nest 

AVIAN GROWTH AND BROOD PARASITISM 

1693 

leaving will depend on the ability of the host young to compete 

with the cowbird young while in the nest as compared 

to after fledging. I also tested these hypotheses. 

Recent expansion of the cowbird breeding range in North 

America adds both complexity and appeal to this study system. 

Natural breeding habitat for the brown-headed cowbird 

is short grass vegetation with low or scattered trees (Lowther 

1993). Thus, before European settlement they were mainly 

restricted to the grasslands of the Great Plains. Today, cowbirds 

breed throughout the whole United States and southern 

Canada (Sauer et al. 2005). Their expansion eastwards (from 

about the late 1700s) and westwards (from about 1900) was 

enabled by transformation of woodlands to pastures and 

fields, which are readily adopted for breeding (Mayfield 1965; 

Ortega 1998). This led to the contact of the brown-headed 

cowbird with a number of new hosts (woodland species) with 

no previous experience with brood parasitism. At present, 

brown-headed cowbirds parasitize more than 200 passerine 

species (Ortega 1998; Davies 2000). New hosts (woodland 

species) have been exposed to parasitism for the last 100– 

200 years and thus differ from open-country species (either 

old hosts or nonhosts) in length of coevolution with the cowbird. 

This enables us to test not only the flexibility of avian 

growth and development in relation to sibling competition, 

but also the tempo of their evolution. 

In summary, parasitic young compete directly for food with 

host young, cause host young mortality (the strength of which 

depends on host size; Lorenzana and Sealy 1999), and can 

afford stronger sibling conflict because of its lower indirect 

cost for them. Furthermore, competition for resources is size 

dependent. These processes should result in selection for 

higher growth rates of host young. Alternative developmental 

strategies could include a short incubation period, laying 

large eggs, and shortening the nestling period by fledging 

soon and with low body mass. I tested these predictions on 

a sample of 134 species of North American passerines victimized 

to a certain degree by the brown-headed cowbird. I 

predicted rapid growth, low fledging mass, short nestling and 

incubation periods, and large eggs with increasing parasite 

pressure. If the tempo of the evolution of adaptation in growth 

and development to brood parasitism is slow, I predicted that 

old hosts would differ from nonhosts, whereas new hosts 

would not. On the contrary, if the tempo of this evolution is 

rapid, I predicted that both old and new hosts would differ 

from nonhosts. 

METHODS 

Dataset 

General procedures for collection and preparation of lifehistory 

data were as in Remes˘ and Martin (2002). After conducting 

an exhaustive search of databases, I added growth 

and life-history data for an additional 19 species (see Appendix 

1 available online only at http://dx.doi.org/10.1554/ 

06-170.1.s1), making the total of 134 species. I collated data 

on growth rate, fledging mass, incubation and nestling periods, 

clutch size, adult body mass, nest predation rate, foraging 

mode (aerial vs. nonaerial forager), and latitude of the 

growth study. As a measure of growth rate for subsequent 

analyses, I used the constant K of the logistic growth equation

1694 VLADIMÍR REMES˘ 

fit both to the growth data truncated at the highest mass 

reached in the nest (K max) and at 70% of adult body mass 

(K 70), according to analytical procedures developed by Remes˘ 

and Martin (2002). 

For every species I searched data on percentage of nests 

parasitized by the brown-headed cowbird (Ortega 1998, appendix 

C, D; Poole and Gill 1992–2002), breeding habitat 

and nest site (Ehrlich et al. 1988), and rejection rate of parasitic 

eggs (Rothstein 1975; Peer and Sealy 2004). I took the 

mean parasitism rate for each species by calculating average 

weighed by sample size of nests (together there were 844 

studies; median number of studies per species: 4; interquartile 

range: 1–8). For 590 studies, the years in which they were 

conducted were known. Of these studies, 49 were done before 

1900, 112 between 1901 and 1950, and the remaining 429 

studies after 1950. There was large intraspecific variation in 

reported parasitism rates. In species where there were at least 

two studies and mean larger than zero, mean coefficient of 

variation was 106%, and it decreased with mean parasitism 

rate (r ��0.70, P � 0.001, n � 81). 

Percentage of nests parasitized by a cowbird is not an ideal 

measure of the real impact of parasitism on the host growth 

strategies. First, some parasitized clutches are deserted/buried 

by hosts; thus, their nestlings are not exposed to cowbird 

chicks. Second, the cost of parasitism at the nestling stage 

(i.e., percentage of nestlings dying in nests with a cowbird 

chick as compared to nests without it) varies widely between 

species (Lorenzana and Sealy 1999). I was able to collate 

data on percentage of nests deserted/buried for 33 species 

(Hosoi and Rothstein 2000) and on the cost of parasitism for 

54 species (Lorenzana and Sealy 1999; Hauber 2003a; Kilner 

2003). This generated data on the real impact of parasitism 

on the growth strategies of host nestlings (i.e., [% parasitized 

� (1 � proportion deserted or buried)] � cost of parasitism) 

for only 29 species, which is too few for a rigorous analysis. 

However, if desertion of a parasitized nest is a rapid event 

(Hosoi and Rothstein 2000), then the majority of nests found 

and reported in the literature as parasitized do not include 

nests later deserted/buried. Thus, the percentage of nests reported 

as parasitized may be a rather good predictor of the 

force of selection on hosts. Therefore, I used percentage of 

nests parasitized as a proxy for the exposure of host nestlings 

to cowbird chicks and host body mass as a proxy for the 

impact of parasitism on host offspring (Lorenzana and Sealy 

1999; see above). 

Based on the information on parasitism rate I also categorized 

every species as either a host or a nonhost. Species 

with parasitism below 2% were categorized as nonhosts and 

other species as hosts, with three exceptions. Carduelis tristis 

and Bombycilla cedrorum were treated as nonhosts despite 

their parasitism rate of about 4%, because they feed their 

young with food unsuitable for cowbird chicks. Toxostoma 

rufum, in spite of its 4.5% parasitism rate, is a rejecter species 

(rejection of cowbird eggs � 96%; Rothstein 1975) and was 

also categorized as nonhost. For reasons explained in the 

introduction, I also categorized every host species as either 

an old or a new host. This categorization was based on breeding 

habitat, because that reveals long-term exposure to cowbird 

parasitism (for justification, see Hosoi and Rothstein 

2000). Forest hosts were categorized as new hosts whereas 

hosts breeding in edge, shrub, marsh, and open habitats were 

categorized as old hosts. Data are summarized in Appendix 

2 available online only at http://dx.doi.org/10.1554/06-170. 

1.s2 (for other life-history data for these species, see Remes˘ 

and Martin 2002). There was no difference in nest predation 

rate among habitats (F 4,124 � 1.15, P � 0.335). 

Among nonhost species, there are both rejecters (species 

ejecting cowbird eggs from their nests at rates � 75%) and 

acceptors (species rejecting � 25% of parasitic eggs). Acceptors 

are not parasitized for other reasons (e.g., no range 

overlap with cowbird, cavity nesting etc.). Species are either 

rejecters or acceptors with almost no intermediates (Rothstein 

1975, 1992). Also in my sample of nonhosts for which I was 

able to collate data on rejection of natural parasitic eggs, 

species fell either above 87% (rejecters, n � 16) or below 

20% ejection (acceptors, n � 6). Since these two categories 

of nonhosts may differ in the length of their coevolutionary 

interaction with cowbird, I compared all analyzed traits between 

them. There was no difference in either trait (F 2,19 � 

2.38, P � 0.140; body mass always controlled). Thus, these 

two groups of nonhosts may safely be assumed to have been 

correctly grouped. 

Analyses 

Based on prior knowledge of the factors related to avian 

growth and development rates (Starck and Ricklefs 1998; 

Remes˘ and Martin 2002) I fitted models with growth rate 

(constant K of the logistic equation; day �1 ), relative fledging 

mass (mass at fledging/adult body mass), nestling period duration 

(day), incubation period duration (day), and egg volume 

(cm 3 ) as response variables, and adult body mass (g), 

nest predation rate (daily rate of nest loss due to predation; 

day �1 ), clutch size (no. of eggs), and foraging mode (aerial 

vs. nonaerial forager) as predictors. In the analysis of growth 

rate, I also included latitude of the particular growth study 

(degrees north). To this baseline model, I added a variable 

expressing either the level or the length of cowbird parasitism. 

It was either parasitism rate (% of nests parasitized) or 

parasitism status (nonhost vs. new host vs. old host). To 

minimize the number of statistical tests, I tested interaction 

of these parasitism variables only with adult body mass (see 

above), nest predation, and the quadratic effect of parasitism 

rate and nest predation (to detect possible nonadditive effects 

of mortality factors). I selected final models based on backward 

elimination of nonsignificant variables (at ��0.05). 

In all models, I checked plots of residuals for any deviations 

from normality of error, linearity of effects, and homogeneity 

of variance (Grafen and Hails 2002). Variables were appropriately 

transformed, if necessary. 

In all analyses, species were treated as datapoints. Common 

descent of species may cause problems in the analysis of 

interspecific data. Species are historically related, which 

causes nonindependence of varying strength among datapoints. 

This violates assumptions of standard statistical techniques 

(Harvey and Pagel 1991). To overcome this problem, 

I applied the phylogenetic regression of Grafen (1989). This 

method is based on generalized least squares and adjusts the 

statistical analysis for nonindependence among species. This 

method is very flexible and enables fitting of standard sta-

FIG. 1. Relationship between growth rate (K max, day �1 ; top), relative 

fledging mass (mass at fledging/adult mass; middle), and nestling 

period (day; bottom), and adult host body mass (g) and parasitism 

rate (%). Shown are predicted relationships for average 

values of continuous variables controlled for in the analyses 


← 

1695 

tistical models, including interactions and categorical predictors. 

I used the PHYREG macro for SAS written by A. 

Grafen (Grafen 2005). I tested significance of either parasitism 

rate or parasitism category (or their interaction with a 

factor, if necessary) against other covariates that were selected 

as significant in the raw species data analyses. 

I assembled a working phylogeny of the studied species 

based on Sibley and Ahlquist (1990), Martin and Clobert 

(1996); and Remes˘ and Martin (2002), supplemented by the 

most recent molecular phylogenies (see Appendix 3 available 

online only at http://dx.doi.org/10.1554/06-170.1.s3). Since 

the phylogeny was assembled from many sources, I had no 

consistent estimates of branch lengths. I adopted uniform 

branch lengths. However, another arbitrary branch lengths 

option, Grafen’s (1989) branch lengths, generated qualitatively 

identical results. 

RESULTS 

Growth Rate 

Growth rate (K max) was positively related to parasitism rate, 

but this effect depended on adult body mass of the host (interaction 

[IN hereafter]: F 1,120 � 6.18, P � 0.014; Fig. 1) 

and nest predation rate (IN: F 1,120 � 6.39, P � 0.013). There 

was also a positive effect of latitude (F 1,120 � 7.43, P � 

0.007; whole model: R 2 � 0.48, F 6,120 � 18.56, P � 0.001). 

Similar results were obtained when K 70 was used as a response. 

Growth rate was again positively related to parasitism 

rate, and this effect depended on adult host body mass (IN: 

F 1,101 � 9.24, P � 0.003). There was also a simple positive 

effect of nest predation rate (F 1,101 � 7.78, P � 0.006) and 

latitude (F 1,101 � 16.14, P � 0.001), and a negative effect 

of clutch size (F 1,101 � 7.39, P � 0.008; whole model: R 2 

� 0.45, F 6,101 � 13.60, P � 0.001). Phylogenetically adjusted 

analyses generated the same results. The effect of parasitism 

rate on growth still depended on body mass in both K max (IN: 

F 1,117 � 8.15, P � 0.005) and K 70 (IN: F 1,99 � 5.96, P � 

0.020). 

Growth rate (K max) differed between parasitism categories, 

but this effect depended on host body mass (IN: F 2,117 � 

3.08, P � 0.050; Fig. 2) and nest predation rate (IN: F 2,117 

� 6.45, P � 0.002). There was also a significant positive 

effect of latitude (F 1,117 � 6.68, P � 0.011; whole model: 

R 2 � 0.50, F 9,117 � 13.16, P � 0.001). Analyses of K 70 

generated similar results. There was a difference between 

parasitism categories, but this effect depended on host body 

mass (IN: F 2,99 � 4.83, P � 0.010). There was also a simple 

positive effect of nest predation rate (F 1,99 � 5.69, P � 0.019) 

and latitude (F 1,99 � 13.87, P � 0.001), and a negative effect 

of clutch size (F 1,99 � 6.42, P � 0.013; whole model: R 2 � 

0.46, F 8,99 � 10.56, P � 0.001). Again, the interaction of 

parasitism category and adult body mass remained significant 

reported in Results (latitude � 42�N, nest predation rate � 0.127) 

and aerial foragers. Black dots are observations projected on the 

predicted plane, which should highlight parts of the plane that are 

most supported by data and thus best interpretable.


FIG. 2. Least squares means (�1 SE) of developmental characteristics 

(growth rate [day �1 ], relative fledging mass, nestling period 

[day], incubation period [day], and egg volume [cm 3 ]) estimated 

separately for nonhosts, new hosts, and old hosts, for three host 

body masses. These were chosen to represent minimum body mass 

found among hosts (10 g), body mass close to the mass of the brownheaded 

cowbird (50 g), and body mass twice as large (100 g). There 

is no estimate for 100 g for the new hosts, because no new host 

was as heavy; no old host was heavier than 100 g. Least squares 

means were estimated from analyses reported in Results. Error bars 

are hardly visible for egg volume, because standard errors are too 

small. 

even after adjusting for phylogeny in both K max (IN: F 2,114 

� 3.59, P � 0.031) and K 70 (IN: F 2,97 � 4.66, P � 0.012). 

Relative Fledging Mass 

Relative fledging mass was negatively related to parasitism 

rate, but this effect depended on adult body mass of the host 

(IN: F1,122 � 19.65, P � 0.001; Fig. 1). There was also a 

negative effect of nest predation rate (F1,122 � 18.59, P � 

0.001) and a difference between aerial foragers (least squares 

mean [SE] � 0.89 [0.023], n � 20) and nonaerial foragers 

(0.81 [0.009], n � 108; F1,122 � 10.84, P � 0.001; whole 

model: R2 � 0.56, F5,122 � 31.61, P � 0.001). The effect of 

parasitism rate on relative fledging mass still depended on 

host body mass in the phylogenetically adjusted analyses (IN: 

F 1,119 � 5.87, P � 0.017). 

Relative fledging mass differed between parasitism categories, 

and this effect depended on adult body mass (IN: 

F 2,120 � 7.46, P � 0.001; Fig. 2). There was again a significant 

negative effect of nest predation (F 1,120 � 18.86, P � 

0.001) and a significant effect of foraging mode (F 1,120 � 

9.23, P � 0.003; whole model: R 2 � 0.57, F 7,120 � 22.63, 

P � 0.001). The effect of parasitism category still interacted 

with host body mass in phylogenetic analyses (IN: F 2,117 � 

3.34, P � 0.039). 

All the analyses of the relative fledging mass were done 

without the strongly outlying Leucosticte tephrocotis (relative 

fledging mass � 1.54). 

Nestling Period 

Length of the nestling period was negatively related to 

parasitism rate, but this effect depended on adult body mass 

(IN: F1,122 � 5.85, P � 0.017; Fig. 1) and nest predation rate 

(IN: F1,122 � 12.97, P � 0.001). There was also a difference 

between aerial foragers (least squares mean [SE] � 16.2 

[1.05] days, n � 20) and nonaerial foragers (11.8 [1.02] days, 

n � 108; F1,122 � 38.12, P � 0.001; whole model: R2 � 

0.68, F6,122 � 43.49, P � 0.001). In phylogenetically adjusted 

analyses, the interaction of parasitism rate with host body 

mass became slightly nonsignificant (IN: F1,119 � 2.79, P � 

0.097), whereas the interaction with nest predation remained 

significant (IN: F1,119 � 8.33, P � 0.005). When the interaction 

of parasitism rate with nest predation was removed 

from the model, the simple negative effect of parasitism rate 

became marginally nonsignificant (F1,121 � 3.23, P � 0.075). 

Length of the nestling period differed between parasitism 

categories (Fig. 2), but this effect depended on nest predation 

rate (IN: F2,121 � 6.54, P � 0.002). However, parasitism 

category remained significant even when the interaction with 

nest predation rate was removed (F2,123 � 4.06, P � 0.020). 

There was a significant positive effect of adult body mass 

(F1,121 � 38.04, P � 0.001) and a significant effect of foraging 

mode (F1,121 � 35.10, P � 0.001; whole model: R2 � 

0.68, F7,121 � 36.92, P � 0.001). When phylogeny was controlled, 

the interaction of parasitism category with nest predation 

was still significant (IN: F2,118 � 3.83, P � 0.024). 

However, when this interaction was removed, the simple effect 

of parasitism category became nonsignificant (F2,120 � 

1.08, P � 0.342). 

Incubation Period 

Length of the incubation period was curvilinearly related 

to parasitism rate (F 1,122 � 7.86, P � 0.006), and this effect 

depended on adult body mass (IN: F 1,122 � 6.04, P � 0.015; 

Fig. 3). There was also a negative effect of nest predation 

rate (F 1,122 � 14.09, P � 0.001) and a difference between 

aerial foragers (least squares mean [SE] � 14.5 [1.02] days, 

n � 20) and nonaerial foragers (12.9 [1.01] days, n � 108; 

F 1,122 � 25.93, P � 0.001; whole model: R 2 � 0.52, F 6,122 

� 21.87, P � 0.001). In phylogenetic analyses, the interaction 

of parasitism rate with body mass was no longer significant 

(IN: F 1,119 � 0.03, P � 0.856). Moreover, curvilinear effect 

of parasitism remained only marginally significant (F 1,120 �

FIG. 3. Relationship between incubation period (day; top) and egg 

volume (cm 3 ; bottom), and parasitism rate (%) and adult host body 

mass (g). Shown are predicted relationships for average values of 

continuous variables controlled for in the analyses reported in Results 

(nest predation rate � 0.127, clutch size � 4.1) for aerial 

foragers. Black dots are observations projected on the predicted 

plane, which should highlight parts of the plane that are most supported 

by data and thus best interpretable. 

3.86, P � 0.052), and when removed from the model, parasitism 

rate had no linear effect on the incubation period 

whatsoever (F 1,121 � 0.04, P � 0.836). 

Length of the incubation period differed between parasitism 

categories, but this effect depended on adult body mass 

(IN: F 2,121 � 4.55, P � 0.012; Fig. 2). There was again a 


1697 

significant negative effect of nest predation rate (F 1,121 � 

16.67, P � 0.001) and a significant effect of foraging mode 

(F 1,121 � 18.93, P � 0.001; whole model: R 2 � 0.50, F 7,121 

� 17.38, P � 0.001). In phylogenetically corrected analyses, 

the interaction of parasitism category with host body mass 

disappeared (IN: F 2,118 � 1.85, P � 0.162), and there was 

also no effect of the sole parasitism category (F 2,120 � 0.67, 

P � 0.511). 

Egg Volume 

Egg volume was positively related to parasitism rate, but 

this effect depended on adult body mass (IN: F1,128 � 5.05, 

P � 0.026; Fig. 3). There was also a negative effect of clutch 

size (F1,128 � 9.96, P � 0.002; whole model: R2 � 0.96, 

F4,128 � 838.89, P � 0.001). The interaction of parasitism 

rate with host body mass disappeared in phylogeny-adjusted 

analyses (IN: F1,125 � 2.00, P � 0.160), and there was also 

no simple effect of parasitism rate (F1,126 � 1.82, P � 0.180). 

Egg volume differed between parasitism categories, but 

this effect depended on adult body mass (IN: F2,126 � 4.41, 

P � 0.014; Fig. 2). There was also a significant negative 

effect of clutch size (F1,126 � 8.12, P � 0.005; whole model: 

R2 � 0.97, F6,126 � 595.59, P � 0.001). Also in the case of 

parasitism category, both the interaction with host body mass 

(IN: F2,123 � 0.52, P � 0.593) and simple effect of parasitism 

category (F2,125 � 1.27, P � 0.285) were not significant in 

analyses adjusted for phylogeny. 

All the analyses of egg volume were performed without 

Calcarius ornatus, which was strongly outlying (standardized 

residual � 5.9). 

DISCUSSION 

Brood parasitism caused by the brown-headed cowbird affected 

growth and development of its North American hosts. 

At the nestling stage, growth rate increased, mass at fledging 

decreased, and nestling period duration shortened with increasing 

parasitism pressure. All these effects were only present 

in medium and large hosts (roughly above 30 g; Fig. 1). 

These results were not confounded by relevant covariates 

(adult body mass, latitude, clutch size, nest predation, foraging 

technique), which were all controlled for in multivariate 

analyses. Moreover, these results were also robust to the 

incorporation of phylogeny into the analyses. The effects 

were less clear at the incubation stage. Incubation period 

length was shortest at intermediate levels of parasitism, 

whereas egg volume increased with parasitism rate (Fig. 3). 

However, these effects largely disappeared when the analyses 

were adjusted for phylogeny. These results together lend 

strong support for the role of sibling competition caused by 

brood parasitism in shaping growth and development strategies 

of North American passerine birds. However, it is important 

to note that although the effects were clear and thus 

support the general effect of brood parasitism on host growth, 

they concerned a minority of the studied species. Among 68 

hosts, there were only 14 species heavier than 30 g and 31 

species heavier than 20 g. 

One of the most interesting aspects of these results is the 

pervasive presence of the interaction between the effects of 

parasitism rate and adult host body mass on growth and de-


velopment. The general trend at the nestling stage is for a 

minimum or no effect of parasitism rate in the smallest hosts 

(up to about 30 g), with the effect increasing up to the largest 

host body masses (Fig. 1). This agrees with the patterns of 

host mortality in the presence of the cowbird chick. The 

smaller the host species, the higher the host young mortality. 

In the smallest species (mass about 10–20 g), host mortality 

often reaches 100% (Lorenzana and Sealy 1999; Hauber 

2003a; Kilner 2003). The small young are easily outcompeted 

by much larger cowbird chicks in the begging contest for 

resources brought by parents (Dearborn 1998; Lichtenstein 

and Sealy 1998). Since this mortality cannot be escaped by 

adjusting growth strategies, it has no relevance for the evolution 

of growth rate (Ricklefs 1969). Similarly, no host 

chicks of the smallest species are exposed to competition 

with cowbird young for food (since they all die); thus, there 

is presumably no selection for early nest leaving. On the 

contrary, young of the hosts that are comparable to the cowbird 

in their size are not outcompeted by cowbird nestlings 

(Dearborn and Lichtenstein 2002), and they fare comparably 

well even in the company of parasitic nestmates (Davies 

2000). Thus, young of these species are exposed to direct 

competition for resources with the cowbird chick. Moreover, 

there is still on average about 20% more nestling mortality 

caused by the presence of the cowbird chick in hosts of this 

weight category (i.e., 45 g; V. Remes˘, unpublished analyses 

of data from Lorenzana and Sealy 1999; Hauber 2003a; Kilner 

2003). Since this mortality can be escaped or alleviated 

by increasing growth rate (see introduction), it has direct 

relevance for the evolution of growth strategies. However, 

cowbird-caused nestling mortality in large hosts (above about 

80 g) is negligible, and mortality of the cowbird chick in 

these hosts is high (Kilner 2003). I would accordingly predict 

weak to absent effects of cowbird parasitism on growth strategies 

with host mass substantially outreaching cowbird body 

mass. Instead, the effect increased up to the highest host 

masses. This may be caused by the low sample of large hosts 

that are heavily parasitized, which may have distorted the 

fitted relationship. Alternatively, there might really be no 

such weakening, for which fact there seems to be currently 

no explanation. 

Developmental adaptations during incubation were less 

clear. There was a shortening of the incubation period with 

increasing parasitism pressure in hosts above about 40 g, 

whereas in smaller hosts the relationship of incubation period 

to parasitism was curvilinear (Fig. 3). However, when adjusted 

for phylogeny, these effects disappeared. This seems 

surprising since incubation period has clear consequences for 

host mortality in this system (Hauber 2003a; Kilner 2003) 

and is as sensitive to another important selective factor, nest 

predation, as growth rate and nestling period duration (e.g., 

Bosque and Bosque 1995; V. Remes˘, unpubl. data). There 

are at least two possible explanations. (1) Incubation period 

in the brown-headed cowbird (10.5 days) is already very short 

for its body and egg mass. In fact, in the sample of species 

analyzed here, it lies outside the ranges for this body mass 

(45 g) or egg volume (3.46 cm 3 ), and is thus by far the shortest 

incubation period. This may make any significant shortening 

of the incubation period in the host species as compared to 

the cowbird physiologically impossible. Moreover, in species 

where the incubation period could be shorter than that of the 

cowbird (i.e., in very small species), this may bring no advantage 

since their mortality in the presence of the cowbird 

chick is extremely high anyway (see above). (2) Further 

shortening of the incubation period may be precluded by costs 

associated with it. For example, long incubation may bring 

significant advantages to the development of the immune 

system (Ricklefs 1992, 1993; Palacios and Martin 2006). 

Effects of parasitism rate on egg volume were rather weak 

(Fig. 3) and they disappeared completely after adjusting for 

phylogeny. This could be explained based on the finding that 

egg mass variation usually has small effects on subsequent 

chick mass (reviewed in Williams 1994; Krist et al. 2004). 

Moreover, egg mass is not evolutionarily sensitive to another 

major selective factor, nest predation rate (Martin et al. 2006; 

V. Remes˘, unpubl. data). 

When host species were categorized as nonhosts versus old 

hosts versus new hosts, the results were similar, as with simple 

parasitism rate, but enabled an insight into the tempo of 

the adjustment of growth strategies in relation to brood parasitism. 

New hosts have been parasitized for the last 100– 

200 years (Mayfield 1965), whereas old hosts have been parasitized 

for at least several thousands of years (see also Hosoi 

and Rothstein 2000). At the nestling stage, parasitism category 

interacted with host body mass in affecting growth rate 

and relative fledging mass, whereas there was a simple effect 

of parasitism category on nestling period duration (Fig. 2). 

These effects were largely supported by phylogenetically adjusted 

analyses. In the case of the nestling period and relative 

fledging mass, there was a clear effect of the length of coevolution 

with a cowbird parasite. Old hosts had the shortest 

nestling period and lowest fledging mass, in new hosts these 

characteristics were intermediate, and in nonhosts nestling 

period was longest and fledging mass largest. In the case of 

relative fledging mass, this was true for host body mass of 

50 g and higher, which agrees with body mass–dependent 

host mortality caused by the cowbird. Growth rate was higher 

in old hosts than in nonhosts in species weighing 50 g and 

above, but, surprisingly, it was highest in new hosts (Fig. 2). 

The effects of parasitism category were less clear during 

incubation, and completely disappeared after adjusting for 

phylogeny. Again, developmental characteristics were more 

flexible at the nestling stage than during incubation. Adjustments 

of the nestling period duration and relative fledging 

mass agreed with the length of coevolution with the brood 

parasite, whereas in growth rate this was not obvious. 

There are at least two complications hindering clear-cut 

interpretation of the results obtained here. First, I analyzed 

the evolution of growth and developmental allometries in 

relation to interspecific parasitism; that is, in all analyses, 

host adult body mass was controlled for. However, it is possible 

that host species under strong parasitism pressure could 

adjust adult body mass evolutionarily. Large species do not 

suffer much from cowbird parasitism (Lorenzana and Sealy 

1999); thus, this would be a logical outcome. However, here 

I was concerned with the evolution of developmental allometries 

and was not able to analyze possible shifts in host adult 

body mass. Second, cowbird host selection could play a role. 

If cowbird females selected hosts with, for example, rapid 

nestling growth (which could signal high food provisioning),

the relationship between rapid host growth and cowbird parasitism 

would emerge. However, such an argument would 

not easily apply to other traits (nestling period, relative fledging 

mass). Moreover, cowbird females are extreme generalists, 

laying many eggs during the breeding season to the 

nests of many hosts (reviewed in Ortega 1998; Davies 2000). 

Thus, it seems that behavioral adaptations on the side of 

parasite could not explain results obtained here. 

The evolutionary adjustment of growth strategies of cowbird 

hosts has a direct bearing on the evolution of other 

antiparasite strategies. The majority of cowbird hosts accept 

parasitic eggs (Ortega 1998). Antiparasite defenses at the 

nestling stage may include refusal to feed (Payne et al. 2001), 

chick desertion (Grim et al. 2003; Langmore et al. 2003), or 

direct killing (Redondo 1993). However, no such behavior 

is known in cowbird hosts, and relatively infrequent occurrence 

of these nestling-stage adaptations in hosts of brood 

parasites in general has been an enigma and provoked the 

development of multitude of explanations (reviewed in Grim 

2006). No study so far has considered elevated growth rates 

as a possible host defense against negative effects of parasitism. 

Moreover, adjustment of growth strategies may even 

partly explain the puzzling absence of other defenses at the 

nestling stage. If these growth adjustments mitigate negative 

effects of parasitic chicks on host young, they may decrease 

the strength of selection on the evolution of alternative defenses 

(similar to how egg rejection reduces selection pressure 

on the host adaptive response to parasitic chicks, see 

Grim 2006). Then the question remains why the adjustment 

of growth and development should evolve instead of other 

defenses. Obviously, it is free of recognition errors, which 

could be a key advantage over chick discrimination. Moreover, 

it may be less costly or more evolutionarily flexible. 

These hypotheses remain to be tested. 

This study showed that brood parasitism by the brownheaded 

cowbird affected the evolution of growth strategies 

of its hosts. Thus, in addition to juvenile mortality caused 

by nest predation (Remes˘ and Martin 2002; Roff et al. 2005), 

sibling competition caused by the presence of a genetically 

unrelated nestmate emerged as an important selective factor. 

This agrees with some previous comparative analyses demonstrating 

effects of sibling competition on pre- and postnatal 

development rates in birds (Royle et al. 1999; Lloyd and 

Martin 2003) and other studies demonstrating effects of parasitism 

on host life histories (Martin et al. 2001; Møller 

2005). Adjustments during nestling stage were more pronounced 

than during incubation. Growth strategies were surprisingly 

evolutionarily flexible. The adjustment of growth 

rate had to take place during the last 100–200 years, because 

new hosts grew at least as fast as old hosts. In the case of 

nestling period duration and relative fledging mass, new hosts 

were intermediate between old hosts and nonhosts, which 

suggests that these characteristics evolve more slowly and 

currently lag in their evolutionary response. The recent expansion 

of the brown-headed cowbird has been enabled by 

human alteration of habitats, especially forest fragmentation 

(Lloyd et al. 2005). Human alteration of habitats can thus 

have strong cascading effects on the evolution of life-history 

traits in birds (see also Martin and Clobert 1996; Hosoi and 

Rothstein 2000; Hauber 2003b). 


ACKNOWLEDGMENTS 

1699 

This study was supported by the Grant Agency of the Czech 

Republic (no. 206/05/P581) and the Ministry of Education, 

Youth and Sports (MSM6198959212). The dedication of 

many field workers who contributed over the decades to the 

collection of data on which this study is based is also acknowledged. 

I am obliged to T. Day, T. Grim, M. E. Hauber, 

M. Krist, and two anonymous reviewers for discussion or 

comments on the manuscript, and to B. Gruberová for ongoing 

support. 

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Corresponding Editor: T. Day

Avian growth and development rates and age-specific mortality: the 

roles of nest predation and adult mortality 

V. REMESˇ 

Department of Zoology, Palacky University, Olomouc, Czech Republic 

Keywords: 

age-specific mortality; 

development; 

growth; 

life-history theory; 

maternal effects; 

nest predation. 

Introduction 

Abstract 

Growth and development rates are an essential component 

of the life history of every species. Moreover, they 

vary widely among species and many hypotheses have 

been advanced to explain this variation (Starck & 

Ricklefs, 1998). One hypothesis states that high mortality 

of juveniles selects for rapid growth (Williams, 1966; 

Lack, 1968; Ricklefs, 1969b; Case, 1978). In birds it is 

now well supported empirically: high nest predation 

covaries with rapid nestling growth and short incubation 

periods (e.g. Bosque & Bosque, 1995; Martin, 1995; 

Remesˇ & Martin, 2002). 

Life-history theory predicts that high adult mortality 

selects for high parental investment into the current 

brood at the expense of future reproductive bouts. 

Parents of species with high extrinsic mortality are 

Correspondence: Vladimír Remesˇ, Department of Zoology, Faculty of 

Science, Palacky University, Tr. Svobody 26, 77146 Olomouc, Czech 

Republic. 

Tel.: ++420 585634221; fax: ++420 585225737; 

e-mail: vladimir.remes@upol.cz 

doi: 10.1111/j.1420-9101.2006.01191.x 

Previous studies have shown that avian growth and development covary with 

juvenile mortality. Juveniles of birds under strong nest predation pressure 

grow rapidly, have short incubation and nestling periods, and leave the nest at 

low body mass. Life-history theory predicts that parental investment increases 

with adult mortality rate. Thus, developmental traits that depend on the 

parental effort exerted (pre- and postnatal growth rate) should scale positively 

with adult mortality, in contrast to those that do not have a direct relationship 

with parental investment (timing of developmental events, e.g. nest leaving). 

I tested this prediction on a sample of 84 North American songbirds. Nestling 

growth rate scaled positively and incubation period duration negatively with 

annual adult mortality rates even when controlled for nest predation and 

other covariates, including phylogeny. On the contrary, neither the duration 

of the nestling period nor body mass at fledging showed any relationship. 

Proximate mechanisms generating the relationship of pre- and postnatal 

growth rates to adult mortality may include increased feeding, nest attentiveness 

during incubation and/or allocation of hormones, and deserve further 

attention. 

expected to provide more resources to their offspring 

(Williams, 1966; Charlesworth, 1994; Roff, 2002). Thus, 

development characteristics that are sensitive to the 

amount of resources supplied by parents should covary 

with adult mortality rates. Age-specific mortality has 

been shown to drive the evolution of life histories in 

other taxa (e.g. Reznick et al., 1990). However, the 

application of this approach in bird studies lagged behind 

because of the traditional emphasis on food limitation 

(Lack, 1954, 1968; Martin, 1987). Thus, it seems timely 

to apply this theoretically well-supported approach to 

birds as an important model in evolutionary research (see 

Martin, 2002). 

High parental investment may be connected with 

juvenile growth by several proximate pathways. First is 

obviously food: growth rate of songbird nestlings depends 

on the amount of food brought by parents (Martin, 

1987). Consequently, high supply of food by parents may 

lead to rapid growth. Secondly, parents with elevated 

death rates may brood nestlings more often and in this 

way save their thermoregulatory energetic costs. Saved 

resources may be channelled to growth. These are 

ª 2006 THE AUTHOR 20 (2007) 320–325 

320 JOURNAL COMPILATION ª 2006 EUROPEAN SOCIETY FOR EVOLUTIONARY BIOLOGY

proximate relationships and reflect phenotypic plasticity 

of growth (Schew & Ricklefs, 1998). However, they 

provide the opportunity for the evolutionary response 

and for the fixation of the relationship between adult 

mortality and juvenile growth rate on the interspecific 

scale. 

The proximate link between parental investment and 

prenatal development rate (inverse of the incubation 

period duration) is more obvious. Egg development is 

sensitive to temperature (Webb, 1987). Parents investing 

more time into nest attentiveness (time spent on the nest 

incubating) achieve higher incubation temperature. This 

leads to shorter incubation periods (Martin, 2002). 

Moreover, females put androgens into egg yolk. 

Although the evidence is mixed, at least in some species 

more yolk androgens lead to more rapid growth or more 

vigorous begging (reviewed in Groothuis et al., 2005). 

If higher levels of maternally derived androgens translate 

into higher circulating levels in nestlings, this may also 

lead to more rapid growth because high circulating levels 

of testosterone are related to more vigorous begging 

(Goodship & Buchanan, 2006). 

On the other side, developmental traits that are less 

sensitive to parental investment need not be related to 

adult mortality rates. Timing of nest leaving is related to 

nest predation rate, i.e. short nestling periods are related 

to high nest predation as predicted by a life-history model 

(Roff et al., 2005). However, nestlings are able to leave 

the nest even several days before normal fledging time 

(Remesˇ & Martin, 2002). This decision seems to be 

related to immediate danger of nest predation and thus 

cannot be easily related to the level of parental investment. 

As body mass of the young at fledging also depends 

on the timing of nest leaving, the argument is similar also 

for this developmental trait. On the other side, there are 

developmental processes the rate of which depends on 

the amount of resources supplied by parents and have 

direct bearing to timing of nest leaving (e.g. development 

of feathers). Thus, there could be a relationship between 

adult mortality, parental investment and timing of nest 

leaving. 

I tested these ideas on a sample of 84 North American 

songbirds and collated published data on nestling growth 

rate, incubation and nestling period length, and body 

mass of the young at fledging. As control variables, adult 

body mass, latitude, clutch size, foraging ecology, and 

nest predation intensity were used. After controlling for 

these variables, and the phylogeny of the species, I tested 

for covariation between annual adult mortality rate and 

species-specific development characteristics (i.e. nestling 

growth rate, incubation and nestling period length, and 

body mass at fledging). It was predicted that nestling 

growth would covary positively and incubation period 

duration negatively with adult mortality. I expected no 

relationship of nestling period duration or fledging mass. 

These predictions were supported by the comparative 

analyses made. 

ª 2006 THE AUTHOR 20 (2007) 320–325 

JOURNAL COMPILATION ª 2006 EUROPEAN SOCIETY FOR EVOLUTIONARY BIOLOGY 

Materials and methods 

Avian growth and age-specific mortality 321 

I worked with data from the literature and collated data 

on growth rates of nestlings from original studies as in 

Remesˇ & Martin (2002). Latitude of the growth study 

was also taken. Growth rate was quantified by the 

constant K of the logistic growth curve fitted to the 

growth data truncated at the highest mass achieved by 

chicks in the nest (see Remesˇ & Martin, 2002). The 

logistic growth curve has a form of W(t) ¼ A/{1 + 

exp [)K(t)t i)]}, where W(t) denotes body mass of a 

nestling at time t, A is the asymptotic body mass that the 

nestling approaches, ti is the inflection point on the time 

axis in which growth changes from accelerating to 

decelerating, exp represents the exponential function, 

and K is a constant scaling rate of growth. Because the 

value of K indexes growth rate independently of absolute 

time of growth (in time )1 ), it is a convenient measure for 

comparative purposes (Ricklefs, 1968). Relative fledging 

mass was calculated as mass at fledging divided by adult 

body mass. Adult body mass was taken from Dunning 

(1993). Data on incubation and nestling period duration, 

clutch size and foraging ecology was taken from ‘The 

Birds of North America’ series (Poole & Gill, 1992–2002). 

Species were categorized as aerial foragers (species 

feeding on flying insects) vs. non-aerial foragers (other 

foraging techniques). Data on nest predation and adult 

mortality rates were taken from original studies (summarized 

in Martin, 1995; Remesˇ & Martin, 2002) and the 

general reference (Poole & Gill, 1992–2002). Only mortality 

that accumulates with time is relevant for the 

evolution of growth rates (Ricklefs, 1969a). Nest predation 

was available as percentage of nests taken by 

predators. As it is the product of daily nest predation 

rate and the duration of the nestling period, I converted it 

to daily nest predation rate according to: Dpr ¼ )( lnS)/ 

T, where D pr is daily nest mortality rate caused by nest 

predation, S is proportion of nests that were successful 

(1 ) proportion depredated), and T is the duration of the 

nest cycle (Ricklefs, 1969a). The parameters most limiting 

the number of species were growth and adult 

mortality. I had growth data on 134 species but found 

adult mortality rates and other relevant factors for 84 of 

them. Data are summarized in Appendix 1 of Supplementary 

Material. 

Data were analysed by using multiple regression based 

on previous studies (e.g. Martin, 1995; Remesˇ & Martin, 

2002) for which a set of covariates – adult body mass (g), 

clutch size (no. of eggs in the clutch), foraging mode 

(aerial vs. non-aerial foragers) and latitude (°N, only in 

the analysis of K) – were selected and included in the 

analysis. Against these covariates two mortality factors 

were tested: nest predation and adult mortality. Response 

variables were growth rate (K, day )1 ), incubation period 

duration (i.e. inverse of prenatal development rate, day), 

nestling period duration (day), and relative fledging mass 

(mass at fledging/adult body mass). I selected effects

322 V. REMESˇ 

based on their significance level in a backward stepwise 

manner. 

Common descent of species may cause problems in the 

analysis of interspecific data. Species are historically 

related, which causes non-independence of varying 

strength among data points. This violates assumptions 

of standard statistical techniques (Harvey & Pagel, 1991). 

To overcome this problem, the phylogenetic regression of 

Grafen (1989) was applied. This method is based on 

generalized least squares and adjusts the statistical 

analysis for non-independence among species. This 

method is very flexible and enables fitting of standard 

statistical models, including interactions and categorical 

predictors. PHYREG macro for SAS (SAS Institute, 2005) 

written by Alan Grafen (Grafen, 2005) available at http:// 

users.ox.ac.uk/ grafen was used. 

A working phylogeny of the studied species based on 

Sibley & Ahlquist (1990), Martin & Clobert (1996) and 

Remesˇ & Martin (2002), supplemented by the most 

recent molecular phylogenies (details are available from 

the author upon request) was assembled. As the 

phylogeny was assembled from many sources, I had no 

consistent estimates of branch lengths. I adopted uniform 

branch lengths. However, another arbitrary branch 

lengths option, Grafen’s (1989) branch lengths, generated 

qualitatively identical results. 

Results 

Growth rate of nestlings increased with both annual 

adult mortality rate and daily nest predation rate. Other 

factors were also significant and this model explained 

52.3% of variation in growth rate (F6,76 ¼ 13.89, 

P < 0.001; Table 1; Fig. 1). Incubation period duration 

decreased with both annual adult mortality rate and daily 

nest predation rate. Other factors were also significant 

and this model explained 45.3% of variation in incubation 

period duration (F 4,79 ¼ 16.33, P < 0.001; Table 1, 

Fig. 1). There was no relationship between either nestling 

period duration or relative fledging mass and adult 

mortality. However, both were negatively related to nest 

predation rate (Table 1). The models explained 61.1% of 

variation in nestling period (F4,79 ¼ 31.01, P < 0.001) 

and 61.3% of variation in relative fledging mass (F5,78 ¼ 

24.73, P < 0.001). 

In the phylogenetic regression, I tested the significance 

of the two mortality factors while controlling for the 

Table 1 Results of the raw species data analyses relating development characteristics to mortality factors (in bold) and relevant covariates 

among 84 species of North American songbirds. 

Factor 

Growth rate Nestling period Relative fledging mass Incubation period 

F1,76 P F1,79 P F1,78 P F1,79 P 

Body mass 8.51 0.005 12.17

covariates. All the relationships between developmental 

traits and mortalities remained qualitatively the same, 

including their direction, as in cross-specific analyses 

without phylogeny: growth rate [adult mortality (AM): 

F 1,76 ¼ 8.55, P < 0.0051; nest predation (NP): F 1,76 ¼ 

16.94, P < 0.001], incubation period (AM: F1,79 ¼ 7.56, 

P < 0.01; NP: F1,79 ¼ 11.37, P < 0.001), nestling period 

(AM: F 1,79 ¼ 0.40, P ¼ 528; NP: F 1,79 ¼ 14.65, 

P < 0.001), and relative fledging mass (AM: F 1,78 < 0.01, 

P ¼ 0.952; NP: F1,78 ¼ 14.44, P < 0.001). There was no 

correlation between adult mortality and nest predation 

rate across species (r ¼ )0.11, P ¼ 0.299, n ¼ 84). 

Discussion 

Nestling growth rate scaled positively and incubation 

period length negatively with annual adult mortality 

rate, even when other relevant factors together with 

phylogenetic relationships among species were controlled 

for. This seems to be in accordance with the lifehistory 

theory predicting higher parental investment into 

current offspring when adult mortality is high (Williams, 

1966; Charlesworth, 1994; Roff, 2002). On the contrary, 

there was no relationship between either nestling period 

duration or relative mass at fledging and adult mortality 

rates. Timing of fledging is strongly driven by nest 

predation (Roff et al., 2005). It is still not clear whether 

parents or offspring determine the length of nestling 

period on the proximate level (Nilsson & Svensson, 1993; 

Johnsen et al., 1994; Johnson et al., 2004), but this 

analysis shows that there is little potential for adult 

mortality to drive the evolution of this trait. 

One possible factor could have confounded this analysis: 

if parents with high mortality invested more into 

nest defence, this could lead to lower nest predation rates 

in these species. Consequently, as nest predation is a 

strong determinant of nestling growth (Remesˇ & Martin, 

2002), growth could have been slowed in these species. 

However, as there was no correlation between nest 

predation and adult mortality (see Results), and effects of 

adult mortality on development rates were in fact 

positive, this seems unlikely. Parents either were unable 

to defend nests effectively or directed their extra 

investment into other activities. Nest predators may also 

behaviourally constrain higher investment into the 

brood. Parental activity around the nest leads to higher 

risk of nest predation (Martin et al., 2000b; Eggers et al., 

2005; Fontaine & Martin, in press). Risk of nest predation 

shaped evolutionarily both incubation feeding by males 

(Martin & Ghalambor, 1999) and incubation behaviour 

of females (Conway & Martin, 2000). Increasing nest 

attentiveness while reducing activity around the nest 

may thus be achieved by lengthening on-nest bouts 

(Conway & Martin, 2000). Similarly, increasing food 

supply while keeping feeding frequency at low levels 

may be achieved by increasing food load per feeding trip 

(e.g. Martin et al., 2000a). 

ª 2006 THE AUTHOR 20 (2007) 320–325 



The effect of adult mortality on prenatal development 

during incubation may have been mediated by egg yolk 

hormones. Mother birds allocate androgens into egg yolk 

during vitellogenesis and these may have positive effects 

on begging and development of the young (e.g. Schwabl, 

1996; Eising et al., 2001; Eising & Groothuis, 2003; 

Tschirren et al., 2005). Short incubation and nestling 

periods covary with high levels of egg androgens 

(Gorman & Williams, 2005). In this way, behavioural 

constraints imposed by nest predation could also have 

been avoided. Moreover, if high egg yolk androgen levels 

translate into high circulating levels in nestlings, this may 

also lead to rapid growth, as young with high testosterone 

levels beg more vigorously (Goodship & Buchanan, 

2006). However, egg hormones may also have detrimental 

effects on the young (e.g. Sockman & Schwabl, 

2000; Navara et al., 2005). 

Comparative analyses of course cannot distinguish 

between cause and effect. Thus, it is possible that the 

causality of the correlations revealed in this study is 

reversed. Incubation periods and chick growth rates may 

be evolutionarily driven by some hitherto unknown 

selection pressure. Then, if there is a causal link between 

these developmental traits and adult mortality, adult 

mortality may in fact be driven by this selection pressure 

through the developmental traits. It has been shown that 

mortality caused by both parasites and pathogens 

(Møller, 2005) and brood parasitism (Remesˇ, in press) 

can influence growth strategies in passerines. Thus, in 

further analyses it will be important to control also for 

this factor. However, recent results of comparisons of 

avian life histories across latitudes suggest that the causal 

path from adult mortality down to offspring developmental 

traits is at least an acceptable explanation 

(Ghalambor & Martin, 2001; Martin, 2002, 2004). 

Nevertheless, this issue will have to receive more 

attention in future work. 

Besides benefits rapid development can also have its 

costs, mediated for instance by poorly developed immune 

system (Ricklefs, 1992, 1993; Tella et al., 2002; Soler 

et al., 2003; Palacios & Martin, 2006). Slow development 

with extra energy invested into the maturation of critical 

physiological functions (e.g. immune or neural system) 

could bring important advantages later in life (see 

Ricklefs, 1993; Ricklefs et al., 1998). Thus, the extra 

resources provided by parents did not necessarily have to 

be channelled to more rapid growth. Moreover, there are 

other energetically demanding processes going on during 

the individual development in the nest. For example, 

development of feathers could use up the extra energy 

supplied by parents (Murphy, 1996), which could lead to 

earlier functional maturation of wings and consequently 

earlier fledging. However, it seems that neither of these 

alternatives took place. The first predicts no relationship 

of any developmental characteristic to adult mortality, 

whereas the second one, a negative relationship between 

nestling period and adult mortality. However, there was

324 V. REMESˇ 

a positive relationship between adult mortality and 

growth rate, but not with either timing of nest leaving 

or relative fledging mass. It seems that the extra 

resources were mainly channelled to higher tempo of 

tissue synthesis and growth, although at least partial 

channelling into improving some juvenile physiological 

functions cannot be ruled out. To validate this hypothesis, 

we will have to analyse potential relationships 

between adult mortality and juvenile pre- and postfledging 

physical and physiological performance. 

In sum, I show that both pre- and postnatal growth 

rates scale positively with adult mortality rates in birds, 

which accords with life-history theory. More generally, 

this study shows that life-history theory has the power to 

predict patterns of relationships between suites of lifehistory 

and developmental traits (see also Roff et al., 

2005). Developmental traits have recently emerged as an 

important model system for the investigation of the 

evolution of avian life histories and provide a new 

perspective on this attractive issue (Martin, 2002, 2004). 

Comparative analysis presented here generates a set of 

questions that should be explored based on both published 

and new field data. In future work, it will be 

critical to explore relationships of mortality rate (both of 

adults and nests) to growth rate, incubation period, 

feeding frequency, food load per feeding trip, incubation 

attentiveness and temperature, hormones in egg yolk 

and blood of nestlings, and physical and physiological 

performance of offspring (see Martin, 2002; Gorman & 

Williams, 2005). 


This study was supported by grants from the Grant 

Agency of the Czech Republic (206/05/P581) and MSMT 

(MSM6198959212). I gratefully acknowledge the dedication 

of many field workers who contributed over the 

decades to the collection of data on which this study is 

based. I also thank two anonymous reviewers for 

stimulating comments. 

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Supplementary Material 

The following supplementary material is available for this 

article online: 

Appendix S1. Species mean values used in the analyses 

for adult body mass, clutch size, duration of incubation 

and nestling period, growth rate constant K of the logistic 

curve, body mass at fledging, latitude of the study of 

growth, foraging mode, daily nest mortality rate caused 

by predation, and annual adult mortality rate. 

This material is available as part of the online article 

from http://www.blackwell-synergy.com 

Received 5 April 2006; revised 7 June 2006; accepted 18 June 2006

Naturwissenschaften (2010) 97:331–335 

DOI 10.1007/s00114-009-0635-5 

SHORT COMMUNICATION 

Explaining postnatal growth plasticity in a generalist 

brood parasite 


Received: 9 June 2009 /Revised: 27 November 2009 /Accepted: 30 November 2009 /Published online: 19 December 2009 

# Springer-Verlag 2009 

Abstract Selection of a particular host has clear consequences 

for the performance of avian brood parasites. 

Experimental studies showed that growth rate and fledging 

mass of brood parasites varied between host species 

independently of the original host species. Finding correlates 

of this phenotypic plasticity in growth is important for 

assessing adaptiveness and potential fitness consequences 

of host choice. Here, I analyzed the effects of several host 

characteristics on growth rate and fledging mass of the 

young of brown-headed cowbird (Molothrus ater), a 

generalist, non-evicting brood parasite. Cowbird chicks 

grew better in fast-developing host species and reached 

higher fledging mass in large hosts with fast postnatal 

development. A potential proximate mechanism linking fast 

growth and high fledging mass of cowbird with fast host 

development is superior food supply in fast-developing 

foster species. So far, we know very little about the 

consequences of the great plasticity in cowbird growth for 

later performance of the adult parasite. Thus, cowbird 

species could become interesting model systems for 

investigating the role of plasticity and optimization in the 

evolution of growth rate in birds. 

Keywords Brood parasitism . Molothrus . Cuckoo . 

Growth . Plasticity 

Electronic supplementary material The online version of this article 

(doi:10.1007/s00114-009-0635-5) contains supplementary material, 

which is available to authorized users. 

V. Remeš (*) 

Department of Zoology and Laboratory of Ornithology, 

Faculty of Science, Palacky University, 

Tr. Svobody 26, 



Introduction 

Avian brood parasitism is an excellent model for 

investigating coevolutionary interactions between species. 

Multiple adaptations were revealed on the side of both 

parasites and hosts (Davies 2000). Whereas hosts are 

selected to avoid parasitism, one of the key components of 

the success of the parasite is to choose a suitable host 

(Krüger 2007). 

Selection of a particular host species has clear consequences 

for the fitness of the brood parasite. In the first 

line of defense, hosts differ in the level of aggression near 

the nest (Røskaft et al. 2002). Similarly, hosts differ in their 

ability to eject parasitic eggs (Peer and Sealy 2004) and to 

desert the parasitized nest (Hosoi and Rothstein 2000). 

Further, hatching and fledging success of the parasite differ 

strongly among host species in both cuckoo (Kleven et al. 

2004) and cowbird (Kilner 2003). Even if the parasitic 

chick successfully fledges, it might do so in different body 

mass and condition with important fitness consequences 

(Gebhardt-Henrich and Richner 1998). Brood parasite's 

growth rate and fledging mass differed strongly among 10 

hosts of common cuckoo (Cuculus canorus; Kleven et al. 

1999; Grim 2006; Grim et al. 2009) and 20 hosts of brownheaded 

cowbird (Molothrus ater; Kilpatrick 2002). Consequently, 

the challenge is to find ecological and life-history 

characteristics of host species that explain different growth 

performance of parasites in their nests. 

Studies done so far used host body size (Kleven et al. 

1999; 2004; Kilpatrick 2002; Grim 2006) or hatching 

synchrony between parasite and host (Kilner 2003; Tonra et 

al. 2008) as predictors of the performance of parasitic 

chicks. However, other host traits might be decisive for 

parasite's performance, and all potentially important traits 

should be studied simultaneously to weigh their relative

332 Naturwissenschaften (2010) 97:331–335 

importance. Here, I assessed potential influence of multiple 

host traits on the growth and fledging mass of the nestlings 

of brown-headed cowbird, a generalist, non-evicting brood 

parasite. I studied host body mass, hatching synchrony 

between host and cowbird, host postnatal developmental 

rate, number of host young reared alongside with the 

cowbird young, and the length of historical co-occurrence 

between host and cowbird (Kilpatrick 2002; Kilner 2003; 

Remeš 2006; Tonra et al. 2008), and statistically separated 

their independent effects. 


To test alternative hypotheses about factors decisive for 

growth and development of cowbird young, I modeled 

effects of multiple explanatory variables on cowbird 

growth rate and fledging mass. Based on previous 

research, I included host body mass (Kilpatrick 2002), 

hatching synchrony between cowbird and host (Kilner 

2003; Tonraetal.2008), and the number of host young 

raised together with the cowbird chick (Kilner et al. 2004). 

I included two other host characteristics. First, length of 

the historical co-occurrence of cowbird with a host might 

have led to coevolutionary interactions affecting suitability 

of a particular host (Hosoi and Rothstein 2000;Hauber2003; 

Kilner et al. 2004; Remeš 2006). Second, fast host 

development might indicate superior parenting abilities 

(Saether 1994) and thus affect cowbird chick growth. I also 

included quadratic effects of the following four factors: host 

body mass (Rivers 2007), number of host young raised 

together with the cowbird chick (Kilner 2003; Kilneretal. 

2004), development rate, and hatching synchrony (Tonra et 

al. 2008). 

I assembled data for this study from published sources. I 

took data for cowbird growth rate (day −1 ), cowbird mass at 

8 days of age (as a proxy for fledging mass, g), host growth 

rate (day −1 ), and host nestling period (days) from Kilpatrick 

(2002). Growth rate was quantified as constant K of the 

logistic growth model (see Remeš and Martin 2002; Remeš 

2007). I took host adult body mass (g) from Dunning 

(1993). Host species vary in the average number of own 

young that is raised with the parasitic chick, with potential 

consequences for cowbird growth (Kilner et al. 2004). I 

took this number from Trine et al. (1998), Lorenzana and 

Sealy (1999), Kilner et al. (2004), and the BBIRD database 

(2005). I calculated the difference between mean cowbird 

incubation time (10.5 days; Hauber 2003) and host 

incubation time (days; from Tonra et al. 2008) as a rough 

estimate of cowbird-host hatching synchrony (Hauber 

2003; Tonra et al. 2008). I based the categorization of 

hosts into those with long vs. short co-occurrence with 

cowbird on habitat association (Hosoi and Rothstein 2000). 

Forest species were considered to have had short 

co-occurrence, whereas hosts from other habitats were 

considered to have had long co-occurrence. I took this 

information from Hosoi and Rothstein (2000), Peer and Sealy 

(2004), and Remeš (2006). The dataset is presented in 

Electronic supplementary material (Appendix S1). 

Host growth rate and nestling period length were highly 

correlated, Pearson's r=−0.54 (P=0.014, N=20). When 

these two variables were combined together by a 

principal components analysis, the first component 

explained 77.1% of variability. Component loadings were 

0.88 for growth rate and −0.88 for nestling period. Thus, 

high values of this new composite variable (“development 

rate”) indicated fast growth and short nestling period. 

Nestling period duration and host body mass were log10 

transformed in all the analyses. In all models, I checked 

residuals for any deviations from normality, equal variance, 

and linearity. First, I present results of univariate analyses of 

individual factors. Second, I fit multivariate models of 

cowbird growth and fledging mass. Subsequently, I removed 

nonsignificant factors (at α=0.05) from multivariate models 

starting with the least significant one until I ended with the 

minimum adequate model (Grafen and Hails 2002). To 

ensure that the results of this procedure were not biased, I 

also built the final model by forward addition of individual 

factors. I ended up with the same models as in the backward 

elimination procedure. Significance level was set at α=0.05. 

β statistics are standardized regression coefficients. All 

statistical analyses were done in JMP 7.0.1 software (SAS 

Institute Inc., Cary, USA). 

Results 

Growth rate of the cowbird chick varied from 0.3 to 

0.7 day −1 (mean ± SD=0.536±0.080 day −1 , N=19) and 

fledging mass from 14.8 to 28.2 g (23.6±3.52 g, N=20). 

Cowbird growth rate and fledging mass were highly 

correlated (r=0.76, P

Naturwissenschaften (2010) 97:331–335 333 

Table 1 Univariate relationships of cowbird growth rate and fledging mass (mass at 8 days of age) to several host traits 

Predictor Cowbird growth rate Cowbird fledging mass 

(F1,16=5.78, P=0.029) and quadratic, nonlinear effect of 

host development rate (F2,16=6.19, P=0.024; whole model: 

F3,16=9.44, P=0.001, R 2 =0.64; Fig. 1b). 

Cowbird growth rate (day -1 ) 

Cowbird fledging mass (g) 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

30 

28 

26 

24 

22 

20 

18 

16 

A 

B 

F P DF β R 2 

14 

-3 -2 -1 0 1 2 3 

Host development rate 

Fig. 1 Relationships of a growth rate and b fledging mass of the 

cowbird chick to host development rate, characterized as the first 

component of PCA on host growth rate and nestling period length (see 

“Results”) 

Discussion 

F P DF β R 2 

Host body mass (log) 0.01 0.940 1, 17 0.02

334 Naturwissenschaften (2010) 97:331–335 

postnatal development rate (r=0.76, P

Naturwissenschaften (2010) 97:331–335 335 

Schew WA, Ricklefs RE (1998) Developmental plasticity. In: Starck 

JM, Ricklefs RE (eds) Avian growth and development. Oxford 

Univ Press, Oxford, pp 288–304 

Tonra CM, Hauber ME, Heath SK, Johnson MD (2008) Ecological 

correlates and sex differences in early development of a 

generalist brood parasite. Auk 125:205–213 

Trine CL, Robinson WD, Robinson SK (1998) Consequences of 

brown-headed cowbird brood parasitism for host population 

dynamics. In: Rothstein SI, Robinson SK (eds) Parasitic birds 

and their hosts: studies in coevolution. Oxford Univ Press, 

Oxford, pp 273–295 

Weathers WW (1996) Energetics of postnatal growth. In: Carey C (ed) 

Avian energetics and nutritional ecology. Chapman & Hall, New 

York, pp 461–496 

Wiley JW (1986) Growth of Shiny Cowbird and host chicks. Wilson 

Bull 98:126–131

SHORT COMMUNICATION 

Domestic chickens defy Rensch’s rule: sexual size dimorphism 

in chicken breeds 

V. REMESˇ * & T. SZÉKELY* 

*Biodiversity Laboratory, Department of Biology and Biochemistry, University of Bath, Bath, UK 

Laboratory of Ornithology, Faculty of Science, Palacky University, Olomouc, Czech Republic 

Keywords: 

allometry; 

domestication; 

Gallus gallus; 

Phasianidae; 

sexual size dimorphism; 

sexually antagonistic. 

Introduction 

Abstract 

Differences between sexes in various morphological, 

ecological and behavioural traits have puzzled biologists 

for a long time. It was a subject of substantial interest for 

Darwin himself (Darwin, 1871). Although recent 

research has discovered a great deal about patterns of 

variation in sexual dimorphism among taxa at various 

phylogenetic levels, the genetic and physiological mechanisms 

(Bonduriansky & Chenoweth, 2009) as well as 

the selective processes that generate these patterns 

remain poorly understood (Shine, 1989; Andersson, 

1994; Blanckenhorn, 2005; Fairbairn et al., 2007). 

Correspondence: Vladimír Remesˇ, Laboratory of Ornithology, 

Faculty of Science, Palacky University, Olomouc, Czech Republic. 

Tel.: +420 585634221; fax: +420 585634002; 


doi:10.1111/j.1420-9101.2010.02126.x 

Sexual size dimorphism (SSD), i.e. the difference in sizes of males and females, 

is a key evolutionary feature that is related to ecology, behaviour and life 

histories of organisms. Although the basic patterns of SSD are well 

documented for several major taxa, the processes generating SSD are poorly 

understood. Domesticated animals offer excellent opportunities for testing 

predictions of functional explanations of SSD theory because domestic stocks 

were often selected by humans for particular desirable traits. Here, we analyse 

SSD in 139 breeds of domestic chickens Gallus gallus domesticus and compare 

them to their wild relatives (pheasants, partridges and grouse; Phasianidae, 53 

species). SSD was male-biased in all chicken breeds, because males were 

21.5 ± 0.55% (mean ± SE) heavier than females. The extent of SSD did not 

differ among breed categories (cock fighting, ornamental and breeds selected 

for egg and meat production). SSD of chicken breeds was not different from 

wild pheasants and allies (23.5 ± 3.43%), although the wild ancestor of 

chickens, the red jungle fowl G. gallus, had more extreme SSD (male 68.8% 

heavier) than any domesticated breed. Male mass and female mass exhibited 

positive allometry among pheasants and allies, consistently with the Rensch’s 

rule reported from various taxa. However, body mass scaled isometrically 

across chicken breeds. The latter results suggest that sex-specific selection on 

males vs. females is necessary to generate positive allometry, i.e. the Rensch’s 

rule, in wild populations. 

One of the most widespread patterns is an allometric 

relationship between body sizes of males and females, 

termed the Rensch’s rule (Rensch, 1950; Fairbairn, 

1997), whereby the extent of male-biased sexual size 

dimorphism (SSD) increases with overall body size across 

species, whereas the extent of female-biased SSD 

decreases with body size. Although well documented 

across diverse taxa, it is by no means universal (Abouheif 

& Fairbairn, 1997; Fairbairn, 1997; Cox et al., 2003; 

Blanckenhorn et al., 2007; Fairbairn et al., 2007) and is 

particularly lacking in taxa with females larger than 

males (Webb & Freckleton, 2007; but see Fairbairn, 2005; 

Stuart-Fox, 2009). 

The extent of SSD within species is usually viewed as 

resulting from sex-specific equilibrium of sexual, fecundity 

and viability selections (Andersson, 1994; Blanckenhorn, 

2005; Cox & Calsbeek, 2009) possibly constrained 

by cross-sex genetic correlations (Poissant et al., 2010). 

ª 2010 THE AUTHORS. J. EVOL. BIOL. 23 (2010) 2754–2759 

2754 JOURNAL COMPILATION ª 2010 EUROPEAN SOCIETY FOR EVOLUTIONARY BIOLOGY

However, why the extent of SSD systematically changes 

with body mass across related species is less clear, and 

many hypotheses were advanced to explain the occurrence 

and strength of the allometry (Abouheif & 

Fairbairn, 1997; Fairbairn, 1997, 2005). The most 

supported hypothesis at present is sexual selection, 

whereby intense sexual selection drives the evolution 

of body size of the selected sex, usually the males, with 

weaker correlated selection on body size in the other sex 

(Székely et al., 2004, 2007; Raihani et al., 2006; Dale 

et al., 2007). However, the latter studies focused on a 

single animal clade, the birds, under natural conditions. 

Studies of Rensch’s rule, as far as we are aware, 

focused exclusively on animal populations in their 

natural environment with the exception of insects that 

have been investigated in the laboratory (e.g. water 

strider Aquarius remigis, dung flies Scatophaga spp and 

fruit flies Drosophila spp; reviewed in Fairbairn et al., 

2007) and domestic goats and sheep (Polák & Frynta, 

2009). However, domesticated animals offer largely 

untapped resources for studies of SSD. First, excellent 

data exist on the body sizes of males and females from a 

large range of breeds. Second, the breeds underwent 

substantial diversification during their cohabitation with 

humans (Montgomerie, 2009), sometimes surpassing 

phenotypic diversification of their wild ancestors (Drake 

& Klingenberg, 2010). Third, in many domestic breeds, 

the males, females or both sexes were selected for a 

particular set of traits, and therefore, the extent and 

direction of SSD and allometry should reflect different 

artificial selection regimes. 

Here, we investigate SSD and size-related allometry 

across domestic chicken breeds. By comparison with 

their wild relatives (pheasants, partridges, grouse; 172 

species; family Phasianidae; del Hoyo et al., 1994), we are 

contrasting the patterns between domestic breeds and 

Phasianidae. We have two specific objectives: (i) to test 

whether the extent and allometry of SSD differ between 

chicken breeds and wild pheasants and allies (Aves: 

Phasianidae) and (ii) to test whether chicken breeds and 

their wild counterparts exhibit the Rensch’s rule. We 

expected that if sexual selection had the primary role in 

generating Rensch’s rule under natural conditions (Dale 

et al., 2007; Székely et al., 2007), the allometry of SSD 

consistent with Rensch’s rule would be absent in chicken 

breeds. This expectation is based on two lines of argument. 

Firstly, in domestic breeds, humans select for 

desired traits that are often unrelated to sexual selection, 

for instance milk production, meat quality or egg 

production. Therefore, sexual selection is expected to 

be weak in domestic stocks. For instance, in traditional 

extensive poultry farming systems, the farmer may 

choose one (or a few) cocks so that female poultry can 

only exert a limited, if any, choice (Verhoef-Verhallen & 

Rijs, 2009). Secondly, artificial selection is unlikely to 

mimic sexually antagonistic selection – a suspected driver 

of Rensch’s rule in wild populations – because humans 



are using directional selection to obtain the desired traits, 

such as increased egg or meat production. Therefore, the 

nontargeted sex is allowed to track changes in the 

targeted sex. 


We collected data on chicken breeds and wild pheasants, 

partridges and grouse from the literature. We obtained 

data on adult male and female body mass, and the 

number of eggs produced per year in traditional chicken 

breeds from Europe, Americas and Asia (Pavel & Tuláček, 

2006). As a wild avian group for comparison, we used 

family Phasianidae as reconstructed by modern molecular 

phylogenetics (Crowe et al., 2006a,b; Kriegs et al., 

2007). This family includes the wild ancestor of chicken 

breeds, the red jungle fowl (Gallus gallus; Liu et al., 2006), 

and also a congeneric species, the grey jungle fowl (Gallus 

sonneratii), which was a potential source of introgression 

into domestic chicken lines (Eriksson et al., 2008). We 

collected male and female body mass from a comprehensive 

source on avian life histories (Lislevand et al., 

2007): mean body masses of adult males and females 

preferentially taken during the breeding season. We 

calculated sexual dimorphism index (SDI) as follows: we 

divided the mass of the heavier sex by the mass of the 

lighter sex, subtracted one and made the resulting figure 

negative for breeds (or species) in which the males were 

the larger sex whereas we let it positive in breeds (or 

species) where the females were the larger sex (Lovich & 

Gibbons, 1992). SDI is a convenient and readily interpretable 

measure of sexual dimorphism (Fairbairn et al., 

2007); for instance, a value of )0.3 indicates the males 

are by 30%, or 1.3 times, larger than females, whereas a 

zero value indicates monomorphism. The distribution of 

SDI significantly departed from normality in both 

chicken breeds and wild species (Fig. 1), and it was not 

Species or breeds (%) 

60 

50 

40 

30 

20 

10 

Sexual size dimorphism in chicken breeds 2755 

Wild species 

Breeds 

* 

M > F 

F > M 

0 

–1.2 –1.0 –0.8 –0.6 –0.4 

SDI 

–0.2 0.0 0.2 

Fig. 1 Frequency distribution of sexual size dimorphism as measured 

by SDI (see Materials and methods for explanation) in 

pheasants, partridges and grouse (53 species), and in chicken breeds 

(139 breeds). Asterisk indicates the ancestor of domestic chicken, 

red jungle fowl. Arrows indicate average SDI of pheasants and 

allies (black) and chicken breeds (grey). M > F: male heavier than 

female and vice versa.

2756 V. REMESˇ AND T. SZÉKELY 

possible to normalize it by any transformation. Therefore, 

we used nonparametric Kruskal–Wallis tests to compare 

median values of SDI between classes. 

We categorized chicken breeds according to Pavel & 

Tuláček (2006) based on desired characteristics that have 

been targeted by the breeders in different breeds. First, 

fighting breeds (n = 19) were traditionally kept and bred 

for cock fighting. The desired characteristic was fighting 

ability in cocks, somehow analogous to the outcome of 

male–male competition in the wild. Second, ornamental 

breeds (n = 15) were kept for decorative purposes and 

pleasure. Here, selection for aesthetic characteristics in 

both sexes, perhaps analogous to the outcome of mate 

choice acting on both sexes in the wild, prevailed. Third, 

there were dual-purpose breeds (n = 105) kept for smallscale 

subsidy of eggs and meat. Although individual 

breeds in this group differ in their primary use (i.e. 

production of eggs, meat or both), most of them are used 

as universal breeds and many of them include both egg 

and meat producing lines within the breed. Thus, we 

made no attempt to split this category into breeds used 

predominantly for the production of eggs vs. meat. The 

common theme among dual-purpose breeds is artificial 

selection for female fecundity and meat production. 

These two traits (egg production per year and female 

body mass) were positively related in dual-purpose 

breeds (r = 0.74, P < 0.001, n = 98) indicating no tradeoff 

between fecundity and body mass. The desired 

characteristics of the latter breeds appear to include the 

analogue of the outcome of fecundity selection in wild 

species. Our final data set included 139 chicken breeds 

and 53 wild species of pheasants, partridges and grouse. 

To test for Rensch’s rule, we fitted major axis regression 

(MAR) of log10 male mass against log10 female mass 

both for chicken breeds and wild pheasants and allies in 

smatr package for R (Warton et al., 2006). We tested the 

deviation of the slope from isometry (i.e. slope = 1; smatr 

test statistic r), and for heterogeneity of slopes among 

breed categories (smatr likelihood ratio test statistic LR). 

We also adjusted the analysis of wild pheasants and allies 

for phylogenetic relationships among species using phylogenetically 

independent contrasts (Felsenstein, 1985) 

as suggested by Abouheif & Fairbairn (1997). We fitted 

MAR to independent contrasts forced through zero in the 

smatr package. For the calculation of phylogenetically 

independent contrasts, we used a working phylogeny 

based on the most recent molecular phylogenies 

(Bloomer & Crowe, 1998; Crowe et al., 2006b; Lislevand 

et al., 2009). Our phylogenetic hypothesis for the family 

Phasianidae is presented in Appendix S1. The phylogenetic 

positions of three species (Francolinus lathami, 

Francolinus nathani and Francolinus psilolaemus) were 

uncertain, and therefore, these species were excluded 

from the phylogenetic analyses. We used a composite 

phylogeny with all branches set to the same length. We 

calculated the independent contrasts in the CAIC package 

for R (Orme et al., 2009) and checked that there was 

no relationship between the absolute values of contrasts 

and (i) the estimated nodal values and (ii) the square root 

of the expected variance at the node (Garland et al., 

1992). Because the CAIC package for R does not allow 

polytomies in the phylogeny, we resolved the polytomies 

(two polytomies in total) randomly. 

Results 

Body mass dimorphism in chickens and wild 

pheasants and allies 

In all chicken breeds, the male was heavier than the 

female (Table 1, Fig. 1). The median SDI was not different 

among chicken breed categories (Kruskal–Wallis test, 

v 2 = 4.1, P = 0.127), although cock fighting breeds 

tended to be the most dimorphic (SDI = )0.248 ± 0.020, 

mean ± SE), followed by ornamental ()0.224 ± 0.018) 

and dual-purpose breeds ()0.208 ± 0.006). 

In wild pheasants and allies, however, both malebiased 

and female-biased SSD occurred (Table 1, Fig. 1). 

The median SDI of chicken breeds and wild pheasants 

and allies did not differ (Kruskal–Wallis test, v 2 = 3.0, 

P = 0.082). However, SDI was significantly more variable 

in wild species than in chicken breeds (Bartlett test: 

F1,190 = 155.9, P < 0.001). The red jungle fowl had one 

of the most extreme male-biased dimorphisms (SDI = 

)0.688, Fig. 1). 

Rensch’s rule 

Chicken breeds exhibited an isometric relationship in 

body mass, because the confidence interval of the slope 

of MAR included one (b = 1.011, 95% CI = 0.9959– 

1.0257, n = 139; Fig. 2). Furthermore, the MAR slopes 

were not significantly different among chicken breed 

categories (LR = 2.4, P = 0.308). Wild pheasants and 

allies, however, exhibited strong allometry consistent 

with the Rensch’s rule using both species-level data 

(b = 1.151, 95% CI = 1.1000–1.2049, n = 53; Fig. 2) and 

phylogenetically independent contrasts (b = 1.148, 95% 

CI = 1.0706–1.2309, n = 48). These patterns remained 

Table 1 Body mass and sexual size dimorphism as measured by 

SDI (see Materials and methods for explanation) in chicken breeds 

and wild pheasants, partridges and grouse. 

Male body 

mass (g) 

Female body 

mass (g) SDI 

Chicken breeds (n = 139) 

Mean ± SE 2103.2 ± 102.65 1727.3 ± 83.71 )0.215 ± 0.006 

Range 600–5650 500–4650 )0.089 to )0.469 

Pheasants and allies (n = 53) 

Mean ± SE 876.2 ± 161.00 636.9 ± 90.62 )0.235 ± 0.034 

Range 41–7400 35.6–4222 0.118 to )1.136 

SDI, sexual dimorphism index. 



SSD 

0.4 

0.3 

0.2 

0.1 

0.0 

–0.1 

consistent when we restricted body mass variation to that 

exhibited by chicken breeds (from 500 to 5650 g), which 

more than halved the sample size (species-level data: 

b = 1.209, 95% CI = 1.0206–1.4412, n = 23; independent 

contrasts: b = 1.205, 95% CI = 0.9883–1.4813, 

n = 21). One may argue that extreme SDI indicates 

intense sexual selection: indeed, restricting the analyses 

of wild phasianids to SDI exhibited by chicken breeds 

()0.089 to )0.469) produced isometric relationships 

(species-level data: b = 1.018, 95% CI = 0.9757–1.0616, 

n = 35; independent contrasts: b = 1.003, 95% CI = 

0.9602–1.0481, n = 31). 

Discussion 

Isometry 

Wild species 

Breeds 

35 60 120 250 500 1000 2000 4000 

Female mass (g) 

Fig. 2 Sexual size dimorphism (SSD) (defined here as log(male 

mass ⁄ female mass)) against log(female mass) for chicken breeds 

(continuous line) and wild pheasant, partridges and grouse (hatched 

line). The dotted horizontal line indicates no scaling of SSD with size. 

For illustration, shown are ordinary least squares lines of the 

relationship between SSD and log(female mass). Note that this 

plot is equivalent to plotting log(male mass) against log(female 

mass), but it is easier to see differences between chicken breeds 

and wild pheasants on this scale. The major axis regression of 

log(male mass) on log(female mass) in chicken breeds was not 

different from isometry (r = 0.12, P = 0.158), whereas in wild 

pheasants, partridges and grouse it was different for both 

species-level data (r = 0.66, P < 0.001) and phylogenetically 

independent contrasts (r = 0.50, P < 0.001). The arrow indicates 

the ancestor of domestic chicken, the red jungle fowl. 

Our study provided two major results. First, we showed 

that the body masses of chicken breeds were not 

consistent with the Rensch’s rule, and the lack of 

allometry was consistent across breed categories. Second, 

in pheasants and allies, the allometry of SSD was 

significantly positive, consistent with previous studies of 

Phasianidae (Drovetski et al., 2006; Lislevand et al., 2009; 

Fig. 2). 

Both the extent of SSD and its isometry across chicken 

breed categories remained remarkably conservative 




regardless of the selection targets (cock fighting, ornamentation, 

or egg and meat production) and whether the 

selection was aimed to improve male traits (cock fighting), 

female traits (egg and meat production) or traits in 

both sexes (ornamentation). Thus, despite varying selective 

forces, scaling of SSD with body mass did not diverge 

across breed categories. The regression slope (male mass: 

female mass) among chicken breeds was 12.2% less than 

the comparable slope among wild pheasants and allies. 

The reduced slope among domestic chickens is comparable 

to domesticated goats and sheep, in which the 

slopes decreased by 16.5% and 8.6%, respectively, when 

compared to the wild relatives, although both domestic 

goats and sheep still showed positive allometry, i.e. the 

Rensch’s rule (Polák & Frynta, 2009). 

We propose two reasons for the reduced allometry in 

domestic breeds. First, male–male competition is usually 

relaxed in captivity because breeders often allocate 

particular sires to dams. Selection on strong, heavy males 

would be thus much reduced. Second, sex-specific or 

sexually antagonistic selection might be relaxed or 

lacking in captivity. In the wild, males and females are 

exposed to natural and sexual selections of different 

strengths, resulting in different net selection acting in 

males and females on the same traits (Cox & Calsbeek, 

2009) that may be either sex-specific (different strengths 

but the same direction) or sexually antagonistic (different 

direction; Bonduriansky & Chenoweth, 2009). On the 

contrary, humans use directional selection to achieve 

desired characteristics in the targeted sex and allow for a 

phenotypic response in the other sex. This response will 

be likely in the same direction in a homologous trait (e.g. 

body mass) as in the targeted sex because of high genetic 

correlations between the sexes (Lande, 1980; Poissant 

et al., 2010). Strong sexual selection for large males leads 

under natural conditions to large male body size, with 

weaker, correlated selection on female body mass (Kolm 

et al., 2007), resulting in allometry of SSD consistent with 

Rensch’s rule (Dale et al., 2007). The same is true for 

selection for small males (Székely et al., 2004). Full 

correlated response in female body mass is often, 

although not always, prevented by sex-specific selective 

optima. A recent review of selection in wild animal 

populations found out that males and females often 

differed substantially in the direction and magnitude of 

the selection on shared traits (Cox & Calsbeek, 2009). In 

captive populations, however, full correlated phenotypic 

responses in homologous traits in males and females may 

manifest themselves given the lack of opposing selective 

forces. 

As chicken breeds exhibited a small range of body mass 

and SDI compared to wild species (Figs 1 and 2), we 

carried out two types of sensitivity analyses. First, we 

restricted body mass variation in wild species to fall 

within the range of domestic breeds and found that wild 

species still exhibited positive allometry. Second, we 

restricted SDI and found that allometry was no longer

2758 V. REMESˇ AND T. SZÉKELY 

significant. The latter analysis, where all species with 

extreme SSD of either direction were removed (Fig. 2), 

shows that extreme SSDs are critical for generating 

positive allometry. To some extent, this is not too 

surprising because these are the very species subject to 

intense sex-specific selection – a process suspected to 

underlie Rensch’s rule. 

Although other aspects of selection on body mass also 

change with domestication (loss of resource competition, 

lack of predators), we suggest that changes in 

sexual selection regimes were more important. The 

reason is that the role of differential sex-specific selection 

pressures in generating diverse SSD in wild populations 

has been recently supported by various studies 

(Blanckenhorn, 2005). First, strong sexually antagonistic 

selection was present in strongly sexually dimorphic 

species (Cox & Calsbeek, 2009). Thus, SSD responds to 

sex-specific selection, although it fails to resolve fully 

the intralocus sexual conflict (Bonduriansky & Chenoweth, 

2009). Second, sexual selection may act on either 

females or females to generate allometry of SSD. A key 

factor is the aspect of sexual selection related to body 

size, i.e. intrasexual competition for mating opportunities 

(Dale et al., 2007). Evolution of the allometry of SSD 

in domestic chickens is certainly not prevented by 

prohibitively high cross-sex genetic correlations, because 

they are similar in magnitude, or even slightly lower, in 

domestic chickens when compared to red jungle fowl 

(Poissant et al., 2010). What is perhaps missing in 

captivity is selection pressures connected with sexual 

competition and sex-specific reproductive roles. Thus, 

our study adds to the available evidence and points to 

the importance of sexual selection (Dale et al., 2007; 

Lislevand et al., 2009) and sex-specific selection pressures 

for generating positive allometry for SSD and its 

great variability in the wild (see also De Mas et al., 

2009). 

In conclusion, we argue that domestic stocks are 

excellent yet rarely used resources for testing hypotheses 

of SSD. Using domestic chicken breeds, we show that 

unlike their wild relatives, the domestic breeds do not 

show positive allometry in body mass. This is a somehow 

tantalising result, because domestic chickens originated 

from one of the most strongly allometric groups among 

all birds (Fairbairn, 1997; Dale et al., 2007; Székely et al., 

2007; Webb & Freckleton, 2007). We propose that 

domestic poultry fail to exhibit an allometric relationship 

because unlike in wild populations male and female 

poultry were not subject to different selection regimes 

towards different optima in body sizes of adult males and 

females. 


This study was supported by the Czech Ministry of 

Education (MSM6198959212) and INCORE (FP6-2005- 

NEST-Path, no. 043318). We thank N. Priest for fruitful 

discussion and B. Matysioková for comments of the 

manuscript. 

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Supporting information 

Additional Supporting Information may be found in the 

online version of this article: 

Appendix S1 Composite phylogenetic hypothesis of the 

family Phasianidae used in our analyses. 

As a service to our authors and readers, this journal 

provides supporting information supplied by the authors. 

Such materials are peer-reviewed and may be reorganized 

for online delivery, but are not copy-edited 

or typeset. Technical support issues arising from supporting 

information (other than missing files) should be 

addressed to the authors. 

Dryad Digital Repository doi: 10.5061/dryad.1965 

Received 17 May 2010; revised 2 September 2010; accepted 3 September 

2010

C. Mateřské efekty a nutriční ekologie

Oecologia (2004) 140: 52–60 

DOI 10.1007/s00442-004-1568-5 

POPULATION ECOLOGY 

Miloš Krist . Vladimír Remeš . Lenka Uvírová . 

Petr Nádvorník . Stanislav Bureš 

Egg size and offspring performance in the collared flycatcher 

(Ficedula albicollis): a within-clutch approach 

Received: 26 November 2003 / Accepted: 24 March 2004 / Published online: 29 April 2004 


Abstract Adaptive within-clutch allocation of resources 

by laying females is an important focus of evolutionary 

studies. However, the critical assumption of these studies, 

namely that within-clutch egg-size deviations affect offspring 

performance, has been properly tested only rarely. 

In this study, we investigated effects of within-clutch 

deviations in egg size on nestling survival, weight, 

fledgling condition, structural size and offspring recruitment 

to the breeding population in the collared flycatcher 

(Ficedula albicollis). Besides egg-size effects, we also 

followed effects of hatching asynchrony, laying sequence, 

offspring sex and paternity. There was no influence of egg 

size on nestling survival, tarsus length, condition or 

recruitment. Initially significant effect on nestling mass 

disappeared as nestlings approached fledging. Thus, there 

seems to be limited potential for a laying female to exploit 

within-clutch egg-size variation adaptively in the collared 

flycatcher, which agrees with the majority of earlier 

studies on other bird species. Instead, we suggest that 

within-clutch egg-size variation originates from the effects 

M. Krist (*) 

Museum of Natural History, 

nám. Republiky 5, 

771 73 Olomouc, Czech Republic 

e-mail: krist@prfnw.upol.cz 

Tel.: +420-585515128 

Fax: +420-585222743 

M. Krist . V. Remeš . S. Bureš 

Laboratory of Ornithology, Palacký University, 

tř. Svobody 26, 


L. Uvírová . P. Nádvorník 

Department of Cell Biology and Genetics, Palacký University, 

Šlechtitelů 11, 


P. Nádvorník . S. Bureš 

Department of Zoology, Palacký University, 

tř. Svobody 26, 


of proximate constraints on laying females. If true, 

adaptive explanations for within-clutch patterns in egg 

size should be invoked with caution. 

Keywords Cross-fostering . Intraclutch . Maternal 

effects . Nestling growth . Offspring fitness 

Introduction 

Within-clutch allocation of resources by a laying female is 

an important topic in evolutionary ecology. In addition to 

studies examining the allocation of resources in relation to 

laying order (e.g. O’Connor 1979; Slagsvold et al. 1984; 

Wiggins 1990; Williams et al. 1993a; Cichoń 1997; 

Viñuela 1997; Hillström 1999), increasing attention is 

being paid to possible adaptive allocation in relation to egg 

sex (Weatherhead 1985; Leblanc 1987a; Mead et al. 1987; 

Teather 1989; Andersson et al. 1997; Cordero et al. 2000, 

2001; Rutkowska and Cichoń 2002; Blanco et al. 2003; 

Cichoń et al. 2003; Magrath et al. 2003). By allocating 

resources differentially in relation to laying order, females 

may enhance/impair survival of the later hatching chicks 

(Slagsvold et al. 1984) or favour chicks with the highest 

reproductive value (Williams et al. 1993a). By targeting 

resources to eggs of a particular sex, the female may also 

obtain two types of benefits. First, in sexually dimorphic 

species, she may boost performance of the smaller sex to 

prevent it from starvation due to competition for food with 

the larger sib of the other sex (Anderson et al. 1997). 

Second, when in good condition she may increase her 

fitness by investing selectively resources to the sex with 

larger variance in reproductive success (Trivers and 

Willard 1973). 

The critical assumption of the adaptive allocation of 

resources within a given clutch is that the amount of the 

invested resources has consequences for offspring fitness. 

For example, it is usually assumed that the larger the egg, 

the higher the fitness of the offspring that hatches from this 

egg. This assumption has been most often tested by the 

cross-fostering approach when eggs/nestlings are swapped

etween nests and performance of offspring in relation to 

mean egg size of the clutch is analysed (we know of 16 

such studies; e.g. Bize et al. 2002; Pelayo and Clark 2003). 

However, to test the assumption of the within-clutch 

adaptive allocation specifically, it is better to examine 

effects of within-clutch deviations in egg size on the 

performance of individual offspring. First, variation in egg 

size is typically much greater between females than within 

clutches of individual females (Christians 2002). Thus, 

cross-fostering studies work with the egg-size variability 

that is most probably not available to laying females when 

allocating resources within a clutch. Second, direct 

competition between sibs for resources supplied by 

parents, monopolisation of these resources by dominant 

sibs and selective parental feeding in relation to offspring 

size are common within broods (Budden and Wright 

2001). Cross-fostering studies working on the betweenfemale 

level do not take into account these within-family 

relations and thus may not reliably estimate egg-size 

effects present on the within-brood level (see also Nilsson 

and Svensson 1993). Third, positive covariation between 

direct and maternal pathways of the determination of 

offspring phenotype may exist, which would lead to 

overestimation of egg-size effects in cross-fostering design 

(Krist and Remeš 2004). 

Studies testing effects of within-clutch deviations on 

offspring performance have been done less frequently than 

cross-fostering studies (we know of nine studies; e.g. 

Howe 1976; Amat et al. 2001). They often compared 

average egg size of surviving versus non-surviving 

siblings instead of looking at individual offspring. Moreover, 

they did not, for the most part, control for the factors 

that are known to affect offspring performance. First, all 

nest-mates may be affected in the same way by broodlevel 

factors (between-year variation in the quality of 

breeding conditions, advancement of breeding season, 

territory quality). These factors may be included in the 

analyses of egg-size effects on offspring performance to 

reduce unexplained variation and thus increase statistical 

power of the main test. Second, nest-mates may differ in 

performance due to hatching asynchrony (Magrath 1990), 

laying order (Ylimaunu and Järvinen 1987), sex (Becker 

and Wink 2003), paternity (Sheldon et al. 1997) or 

concentration of androgens in eggs (Schwabl 1993). These 

individual-level factors may be, in contrast to brood-level 

factors, correlated with within-clutch differences in egg 

size and as such directly confound any relationship 

between the latter and offspring performance. 

To test the assumption of adaptive within-clutch 

allocation of resources, we examined effects of withinclutch 

deviations in egg size on individual offspring 

performance in the collared flycatcher (Ficedula albicollis), 

a small migratory passerine. Besides egg-size effects, 

we also followed effects of other individual-level factors 

including hatching asynchrony, laying order, offspring sex 

and paternity, which makes our study well suited for 

separating an independent effect of egg size on offspring 

performance. We examined effects of these factors on 

nestling survival, weight, fledgling condition, structural 

size and offspring recruitment to the breeding population. 

In addition, we also controlled statistically for some 

brood-level covariates to render the analyses of egg-size 

effects more powerful. 


Field methods 

53 

The study was conducted in Velký Kosíř forest (49°32′N, 17°04′E, 

370–450 m a.s.l.), central Moravia, the Czech Republic, in 2001– 

2003. In the study area, there were five plots with the total number 

of about 350 nest-boxes. Three plots were located in coniferous 

(Picea abies) and the other two in deciduous (Quercus petrea) 

forest. Approximately 60 pairs of collared flycatchers bred in the 

nest-boxes each year. 

The study area was visited daily during the breeding season. Each 

egg was numbered with a waterproof felt-pen and measured to the 

nearest 0.01 mm with a digital calliper on the day it was laid. Egg 

volume was calculated using the formula: 

volume=0.51×length×width 2 (Hoyt 1979). Two measures of width 

were taken in two perpendicular directions and their average was 

used as a measure of width. After 10–13 days of initiation of 

incubation, eggs were taken from nests, put into a thermo-box and 

then within 10 min of transfer placed into individual compartments 

in an incubator. Plastic dummy eggs were put into the nests for 

females to incubate. The method was successful since only one out 

of 38 artificial clutches was abandoned. Temperature in the 

incubator ranged between 37 and 39°C, humidity between 40 and 

70%. The incubator was checked for newly hatched young at least 

every 3 h throughout the day and night. Hatching time was recorded 

for each chick. When hatching was not directly observed, hatching 

time was approximated as the midpoint between the check when the 

egg was hatched and the preceding check when the egg was still 

unhatched. As soon as possible, hatchlings were returned to their 

nest of origin. The mean time (±SD) which elapsed between 

hatching and the return of the hatchling to the nest was 2.95±2.33 h 

(range 10 min–10 h). The longer time periods occurred when the 

young hatched in the evening and starved until sunrise, which is also 

the case under natural conditions. To ensure that the delay did not 

affect our results, we included the time elapsed between hatching 

and returning the hatchling to the nest (“time to return” hereafter) as 

a covariate into our models (see below). Before their return, the 

claws of hatchlings were marked by nail-varnish to enable 

individual recognition. Nestlings were checked daily until they 

were 13 days old, i.e. close to fledging. Every day, nestlings were 

weighed to the nearest 0.25 g with a Pesola spring balance and remarked 

if needed. Nestlings were ringed when about 7 days old, 

blood sampled (about 25 μl) by brachial venipuncture at 10–13 days 

and their tarsi were measured (to the nearest 0.01 mm) at 13 days. 

Blood samples were transferred to 1 ml of Queen’s lysis buffer 

(Seutin et al. 1991). Dead nestlings were taken from nests and 

conserved in 70% ethanol. Putative parents were caught with nestbox 

traps while feeding nestlings and their blood was sampled in the 

same way as for nestlings. 

Each year nearly all adults breeding in the study area were 

captured and checked for rings. Thus for the young fledglings in 

2001, 2 years of potential recapture as breeding adults were 

available, but only 1 year for the young fledgling in 2002. It is 

certain that some individuals that had ultimately recruited to the 

breeding population were not discovered by us and thus we 

erroneously treated them as non-recruits. However, under the 

assumption that the dispersal and the probability of starting breeding 

in the second year of life are not biased with respect to egg size, our 

subsample of the recruits is representative. The first part of the 

assumption seems to be realistic as breeding dispersal is unbiased 

with respect to other offspring traits such as fledgling weight or 

tarsus length in this species (Pärt 1990). Concerning the second part 

of the assumption, it is possible that superior individuals already

54 

start breeding in the second year of life while individuals in bad 

condition are “floaters” at this time but are recruited a year later, in 

their third year of life. This would lead to over-representation of 

individuals in good condition in our sample of recruits and thus 

overestimation of egg-size effects on recruitment (to the extent that 

egg size positively affects condition and probability of early 

breeding). However, this possibility makes our conclusions even 

more conservative (see below). 

Sex and paternity 

Nestling sex and parentage were determined using standard methods 

for the collared flycatcher (Sheldon and Ellegren 1996). In short, 

DNA was extracted from blood or tissue samples using the phenolchloroform 

method. Sex was determined by polymerase chain 

reaction amplification of the CHD gene using primers P2 and P8 

(Griffiths et al. 1998), followed by polyacrylamid electrophoresis. 

The method was completely accurate: sex of about 60 adults of 

known sex was determined rightly in all cases. Parentage was 

determined by comparing genotypes of putative parents and 

nestlings at three microsatellite loci: FhU2, FhU3 and FhU4. Their 

combined exclusion power is about 96% in the collared flycatcher 

(Sheldon and Ellegren 1996). This means that in about 4% of cases 

nestlings sired by an extra-pair male are erroneously concluded to be 

sired by the pair male. It was not possible to determine sex and 

parentage in three offspring due to their disappearance from the nest 

or decay of tissues. 

Samples and statistics 

Out of 224 artificially incubated eggs originating from 38 nests, 180 

hatched, which represents hatchability of 80.4%. Only nests in 

which either all or all but one young hatched were used in this study. 

This ensured a natural level of sibling competition in the studied 

nests. Mean egg volume of the clutch did not differ between the two 

groups of nests (nests with high hatchability, mean egg volume 

±SE=1620.9±23.5 mm 3 , n=29; nests with low hatchability, mean 

egg volume±SE=1635.3±42.1 mm 3 , n=9; t=0.3, P=0.77). Further, 

only nests where both parents were captured, allowing the 

determination of parentage, were used. Consequently, 121 chicks 

hatched in 22 nests remained for the analyses. Hatchability in these 

nests was 92.4%, which equals the natural level (Cramp and Perrins 

1993; M. Krist, unpublished data). Clutch sizes were six, five and 

seven eggs in 19, two and one nest, respectively. All clutch sizes 

were pooled for the analyses. Nevertheless, results were virtually the 

same when only six-egg clutches were used (results not shown). 

Analyses of tarsus length, nestling mass and fledgling condition 

(residuals from the regression of 13-day body mass on tarsus length; 

weight=0.852+0.669 tarsus, n=89, P=0.008, r 2 =0.079), were based 

only on nestlings that subsequently fledged, because nestlings that 

died did not exhibit normal growth for several days before death (i.e. 

their mass remained constant or even decreased when the mass of 

their sibs increased). The only exception were young from three 

nests that were abandoned at the end of the nestling phase, probably 

due to depredation of parents. These young grew normally before 

their abandonment and were included in the analysis of nestling 

mass up to the day before strong mass recession was recorded. 

Because the aim of this study was to analyse the effects of 

intraclutch egg-size variation, egg volume was converted to relative 

egg volume (hereafter termed “egg size”). This was computed as 

egg volume minus the mean egg volume of the clutch (i.e. centring). 

In this way, between-clutch variation is removed and relative egg 

volume then represents egg-size variation within clutches. To enable 

comparison between nests, hatching time was computed for every 

nestling as follows. The value of zero was assigned to the firsthatched 

young. Time (in hours) elapsed between hatching of the first 

young and every subsequent nest-mate was assigned to the latter. 

The resulting variable is hereafter termed “hatching asynchrony”. 

To assess the effect of egg size on offspring performance, five 

models were fitted. The response variables in these models were 

nestling survival (binomial variable; fledged versus not fledged), 

recruitment to the breeding population (binomial variable; recruited 

versus not recruited; only young that fledged successfully were used 

for this test), fledgling tarsus length, fledgling condition and nestling 

mass, respectively. The predictor variables were as follows. Firstly, 

egg size, hatching asynchrony, laying sequence, sex and paternity 

were retained in all the models as fixed effects of interest. The only 

exception was the model for nestling survival, which was fitted 

without paternity because all extra-pair young fledged. In this latter 

case, maximum likelihood estimates of effects may not exist and 

thus the validity of the model fit would be questionable. The reason 

for including the above variables in all final models was that their 

effects on offspring performance after controlling for the other 

factors are not known and that is why they may be of interest. 

Secondly, mean egg volume of the clutch, year, advancement of the 

breeding season (standardised between years by subtracting the 

median date of egg-laying in the particular year from the actual egglaying 

date), and the time elapsed between hatching and returning of 

the hatchling to the nest were included in initial models as fixedeffects 

covariates. To test also for the possibility that the effect of 

egg size on the response variables depends on brood-level variables, 

interactions of egg size with year, breeding season and mean egg 

volume of a clutch were initially fitted in all models. Covariates and 

their interactions with egg size were selected according to Akaike’s 

information criterion (AIC). The final model was that with the 

lowest number of parameters from the series of models which had 

AIC between the smallest value and the smallest value+2 (see 

Burnham and Anderson 1998). Thirdly, nest was included as a 

random effect to control for dependence of data points within nests. 

Denominator df were computed using Satterthwaite’s method. 

Recruitment and survival were analysed using GLIMMIX macro 

of SAS (generalised linear mixed model with binomial error and 

logit link), the other models were fitted using PROC MIXED. 

The model for nestling mass was more complex than the other 

models. First, nestlings were weighed each day until the brood was 

13 days old (brood age zero is the day the first egg of a clutch 

hatched). Hence, an individual nestling was treated as a second 

random factor nested within nest (i.e. higher-level factor) and age of 

the brood as an additional fixed effect (for the rationale of the model, 

see Singer 1998). Second, all the interactions of brood age with 

fixed effects of interest were initially included in the model to 

investigate whether the effect of independent variables changes as 

young grow. Interactions were selected according to AIC as 

described above. 

Hatching asynchrony, laying order and egg size were positively 

correlated (Fig. 1). Such correlations between independent variables 

in multiple regression (multicollinearity) could reduce the power of 

the analyses. To assess the influence of multicollinearity on our 

significance tests, we looked at variance inflation factors (VIF) for 

individual predictors. Predictors with VIF

Fig. 1 a Egg size as a function of the laying sequence. The 

regression equation is: egg size (mm 3 )=−52.19+14.96 laying 

sequence (F1,129=24.39, P9 days old (Table 1, Figs. 2a, b, 3). Weaker 

55 

correlation between the mass of 1-day-old nestlings and 

egg size is probably a methodological artefact caused by 

the fact that the mass was measured only to the nearest 

0.25 g. Therefore, the same mass was assigned to many 1day-old 

nestlings although in fact they differed in mass. 

From a brood age of 3 days onwards, this level of 

precision was fully adequate as nestlings were much 

heavier. 

In contrast to egg size, hatching asynchrony had a 

strong effect on offspring performance. Late-hatching 

young were smaller and had poorer survival than earlyhatching 

young (Table 1). On the other hand, the young 

from later eggs in the laying sequence were larger than the 

young from earlier eggs (Table 1). Time to return of 

offspring to the nest negatively affected offspring survival 

probability (Table 1). At the time of fledging, extra-pair 

young tended to be in better condition than young sired by 

social mates, and sons were in better condition than 

daughters (Table 1). As it was impossible to assess the 

effect of paternity on offspring survival while controlling 

for the effects of other predictors in the generalised linear 

model (see Materials and methods), we computed at least 

the probability that extra-pair and pair offspring differ in 

their survival by Fisher’s exact test. Extra-pair young 

tended to survive better than young sired by social mates 

(n=101, P=0.067). 

To assess the validity of the statistically non-significant 

results concerning egg size, we computed 95% confidence 

Fig. 2a, b Nestling mass as a function of the relative egg size in the 

course of the nestling period. Displayed are a partial regression 

(±95% confidence intervals) and b partial correlation coefficients 

between the two , partialled with respect to hatching asynchrony, 

laying order, offspring sex, paternity and nest. Brood age is in days. 

Brood age zero is the day the first egg of a clutch hatched

Table 1 Parameter estimates 

and type III F-tests of fixed 

effects for recruitment a , nestling 

survival a 56 

, mass, fledgling tarsus 

length and fledgling condition 

a In the models for recruitment 

and nestling survival the probabilities 

for recruitment/survival 

are modelled. The model for 

recruitment does not contain a 

random factor, all other models 

contain a random intercept for 

nest. The variables are coded as 

follows—sex: male = 0, female 

= 1; paternity: young sired by 

social male = 0, by extra-pair 

male = 1; year: 2002 = 0, 2001 = 

1 bNumerator df were always 1 

intervals for standardised effect sizes of egg size on 

fledgling mass, condition and tarsus length (Fig. 4). This 

method may be preferable to commonly used power 

analysis because confidence intervals have several advantages 

as compared to power analysis in evaluating nonsignificant 

results (e.g. Steidl et al. 1997; Hoenig and 

Heisey 2001). Effects were predicted for mean difference 

between the largest and the smallest egg in the clutch, i.e. 

177.2 mm 3 (11.6% of the smaller egg). We multiplied this 

value by the parameter estimate for the effect of egg size 

on a particular trait and its 95% confidence limits and 

standardised by dividing them by the SD of the particular 

trait. Cohen (1988) suggested a convention that the values 

of standardised effects of 0.2, 0.5 and 0.8 could be treated 

Estimate SE df b 

as small, medium and large effects, respectively, when two 

groups are compared. Thus, our data suggest that the 

difference between the largest and the smallest eggs within 

clutches could at most cause only small positive effects in 

fledgling mass and condition (Fig. 4). 

Discussion 

Egg-size effects 

F P 

Recruitment 

Intercept −2.976 1.198 83 

Relative egg size −0.00102 0.00703 83 0.02 0.886 

Laying sequence 0.1002 0.335 83 0.09 0.766 

Hatching asynchrony 0.0250 0.0460 83 0.30 0.589 

Sex −0.375 0.941 83 0.16 0.691 

Paternity 0.260 0.944 83 0.08 0.784 

Nestling survival 

Intercept 8.07 1.62 

Relative egg size 0.00868 0.00649 91.6 1.79 0.184 

Laying sequence 0.203 0.305 91.1 0.44 0.507 

Hatching asynchrony −0.263 0.0522 93.7 25.42

Fig. 3 Nestling mass (mean±2SE, grams) in relation to brood age 

(days) for nestlings from large (greater than clutch mean) (open 

circle) and small (smaller than clutch mean) (filled circle) eggs. 

Brood age zero is the day the first egg of a clutch hatched 

Fig. 4 Standardised effects (estimate±95% confidence interval) 

caused by the mean difference between the largest and smallest egg 

in the clutch (177.2 mm 3 ) for fledgling tarsus length, mass and 

condition. The dashed lines refer to small (0.2), medium (0.5) and 

large (0.8) standardised effects as suggested by Cohen (1988). 

Numbers by the confidence intervals are probabilities (in percent) 

that the size of the standardised effect is below that indicated by the 

dashed line 

deviations in egg size on recruitment, nestling survival, 

fledgling condition and fledgling tarsus length of the 

collared flycatcher. Similarly, the initially strong effect of 

the egg size on the nestling mass diminished steadily as 

young were growing, resulting in fledgling mass being 

independent of egg size. Moreover, the fact that interactions 

between egg size and some brood-level factors (year, 

advancement of breeding season and mean egg volume of 

the clutch) did not improve the model fit substantially, 

suggests that the lack of egg-size effects holds true in a 

wide range of external conditions. Of course, it is possible 

that within-clutch deviations in egg size affected some 

component of offspring fitness that we did not measure 

(e.g. immunocompetence). However, fledgling condition, 

mass and structural size (indicated by tarsus length) are 

generally assumed to be the traits with the greatest 

influence on fitness later in life (Gebhardt-Henrich and 

Richner 1998). This may be true in the collared flycatcher 

because tarsus length and condition at fledging were found 

to be under strong directional selection in this species 

57 

(Kruuk et al. 2001; Merilä et al. 2001). Furthermore, the 

narrow confidence intervals for egg-size effects suggest 

that the lack of effect was not caused by small sample size, 

at least in the three measures of offspring size for which 

such confidence intervals can be determined (as the 

response variable is distributed normally). 

As this is a correlative study, it is impossible to derive 

definite conclusions about causal relationships. However, 

we have suggested previously that the within-clutch 

approach is the most powerful one from the range of 

non-experimental approaches available for the analysis of 

egg-size effects (Krist and Remeš 2004). Our conclusions 

of no egg-size effects could be compromised only when 

other pre-laying maternal effects affecting offspring 

performance (e.g. concentration of carotenoids or steroids 

in egg yolk) are negatively correlated with within-clutch 

deviations in egg size thus tending to cancel each other out 

(see Krist and Remeš 2004). Although this problem is 

solvable only by experimental manipulation of egg size, 

we believe that such counteractive pre-laying maternal 

effects are unlikely. Thus although the definite answer to 

the question of egg-size effects on offspring performance 

can be derived only from a manipulative study, results of 

our detailed study suggest that within-clutch variation in 

egg size is unimportant for offspring performance in the 

collared flycatcher. 

So far only a few studies have examined effects of 

within-clutch differences in egg size on offspring survival 

(Leblanc 1987b; Grant 1991; Williams et al. 1993b; 

Dawson and Clark 1996; Amat et al. 2001) and even fewer 

on their effects on offspring mass in birds (Howe 1976; 

Anderson et al. 1997; Erikstad et al. 1998; Magrath et al. 

2003). Of the four studies on the effect of intraclutch eggsize 

variation on offspring mass, egg size was not 

influential in one study (Magrath et al. 2003), was fully 

confounded with laying order in another one (Erikstad et 

al. 1998) and influential in the remaining two studies 

(Howe 1976; Anderson et al. 1997). Out of the five studies 

on offspring survival, only one (Amat et al. 2001) found a 

positive effect of larger eggs. Thus, our finding of no 

effects of within-clutch deviations in egg size on 

components of offspring performance is in accordance 

with most previous studies, which suggests that adaptive 

explanations of intraclutch egg-size patterns should be 

invoked with caution. 

An alternative to adaptive explanations is that intraclutch 

egg-size patterns can be primarily generated by 

proximate constraints on egg development. For example, 

in studies that found the effect of embryo sex on egg size, 

the difference ranged from 1 to 3.5% of the smaller egg 

(Mead et al. 1987; Anderson et al. 1997; Cordero et al. 

2000, 2001; Blanco et al. 2003; Magrath et al. 2003). It 

seems unlikely to us that the 1.3% difference in egg size in 

favour of males found in the house sparrow (Passer 

domesticus) (Cordero et al. 2000) would be sufficient to 

improve offspring fitness significantly when, in the 

collared flycatcher, a species with similar nestling development 

and breeding biology, a 11.6% difference was 

unimportant to the offspring. Instead, the sex of the

58 

embryo could affect in some, yet unknown, way the 

deposition of albumen into the egg in the oviduct thus 

causing the slight difference in egg size. Similarly, a 

number of proximate constraints and processes could 

generate complex patterns of egg-size variation with 

laying sequence. These include the depletion of endogenous 

reserves in capital breeders (Pierotti and Bellrose 

1986), gearing up physiologically for egg production 

(Parsons 1976), changing hormonal levels at the onset of 

egg laying and incubation (Mead and Morton 1985), 

changing the ambient temperature before egg laying 

(Ojanen et al. 1981; Järvinen and Ylimaunu 1986), a 

time constraint for laying early (Slagsvold and Lifjeld 

1989; Nilsson and Svensson 1993) and a changing food 

supply in income breeders (Perrins 1970). A different 

degree of importance of individual proximate factors in 

different species/populations could be responsible for a 

rich diversity of intraclutch egg-size variation patterns 

found in birds. 

Moreover, the relative importance of individual proximate 

factors may change systematically with body size, 

breeding habitat or latitude. In an influential study, 

Slagsvold et al. (1984) showed that large bird species 

lay small last eggs (brood-reduction strategy) and small 

species lay big last eggs (brood-survival strategy). They 

argued that this pattern is adaptive because large birds are 

less vulnerable to nest predation than small species. 

However, the alternative explanation could be that this 

pattern is driven by proximate constraints. Large species 

are often capital breeders, whereas small species are 

income breeders (see Meijer and Drent 1999). Since 

endogenous reserves may be depleted during laying, large 

species could lay relatively small last eggs. In contrast, 

small species must forage for all nutrients that they deposit 

into eggs and since food supply increases at the time of 

laying from day-to-day (Perrins 1970), they may gather 

more resources at the end of laying resulting in relatively 

large last eggs. This hypothesis seems to explain the 

observed pattern, i.e. a gradual change in egg size with 

laying sequence (Howe 1976; Wiggins 1990; Cichoń 

1997; Hillström 1999; this study), better than the adaptive 

hypothesis, which predicts that only the last eggs hatching 

asynchronously should be larger/smaller (Howe 1976; 

Slagsvold et al. 1984). 

Hatching asynchrony, laying sequence and paternity 

As in many other studies (reviewed in Magrath 1990), 

hatching asynchrony was an important determinant of both 

nestling size and survival. This strong effect as compared 

to no effect of egg size was probably caused by much 

greater mass differences in nestling hierarchies, generated 

by hatching asynchrony, than by egg-size differences, as 

was found in several other species (e.g. Magrath 1992; 

Viñuela 1996). Nestlings hatching from ultimate, and to a 

lesser extent penultimate, eggs hatched usually later 

(Fig. 1) and thus were disadvantaged. On the other hand, 

laying sequence per se tended to affect positively both 

nestling mass and fledgling tarsus length suggesting that 

egg composition could be responsible. The concentration 

of several egg components can change with laying 

sequence, including antibodies and carotenoids (Saino et 

al. 2002a, b), or steroid hormones (e.g. Schwabl 1993). 

Steroid hormones are the most probable candidates 

causing the larger size of nestlings from later-laid eggs. 

First, their concentration has recently been found to 

increase in laying sequence in a number of species (see 

Whittingham and Schwabl 2002). Second, steroids 

enhance the development of muscles important for 

begging (Lipar 2001) and nestling growth (Schwabl 

1996). 

There was a marginally non-significant effect of 

paternity on fledgling condition with extra-pair young 

tending to be in better condition than young sired by the 

social mate (Table 1), which is in agreement with an earlier 

study on the same species by Sheldon et al. (1997). This 

finding supports the hypothesis that extra-pair mates are 

genetically better than social mates (Sheldon 2000; but see 

Colegrave 2001) which would have important implications 

for the theory of sexual selection (Griffith et al. 

2002). However, it is still not clear whether a difference in 

offspring performance in relation to paternity is directly 

caused by good genes inherited from extra-pair sires or 

female favouritism of extra-pair young (Gil et al. 1999; 

Cunningham and Russell 2000). Nevertheless, our study is 

the first in which some potential pathways of female 

favouritism (i.e. egg size, hatching asynchrony) were 

controlled on the within-clutch level. Recently two other 

studies have demonstrated that, on the between-female 

level, genetic benefits of mate choice (Parker 2003) and 

polyandry (Kozielska et al. 2004) exist even after 

controlling for female favouritism. Although extra-pair 

young tended to survive better in this study it is even more 

unclear whether this tendency is due to any genetic effect, 

because in this test no other factors were controlled for. It 

may be that this tendency was driven by the effect of 

hatching asynchrony, since extra-pair young hatch earlier 

than their half-sibs in this species (M. Krist, P. Nádvorník, 

L. Uvírová, S. Bureš, unpublished data). 

Conclusions 

It has been often claimed that females exploit intraclutch 

egg-size variation adaptively in relation to offspring sex 

and laying order. However, the assumption of increasing 

offspring performance with increasing egg size has been 

rarely properly tested. We found no influence of 

intraclutch egg-size variation on offspring performance. 

Although a definite conclusion can be made only after 

manipulative studies are performed, our results strongly 

suggest that the assumption does not hold in the collared 

flycatcher, and this finding is in agreement with most 

previous studies. Consequently, there seems to be limited 

potential for female birds in many species to exploit 

within-clutch variation in egg size adaptively. Instead, we 

suggest that intraclutch egg-size variation most often has

no adaptive significance and is caused by proximate 

constraints on laying females. 

Acknowledgements We thank E. Tkadlec, K. Weidinger and 

several anonymous referees for valuable comments on or discussion 

of the manuscript, and J. Stříteský for exceptional field co-operation. 

K. Weidinger kindly provided us with plastic eggs. We thank forest 

enterprise Čechy pod Kosířem for providing the nest-boxes and 

caravan needed for this work. M. K. thanks Kačenka for everyday 

support. This study was supported by a grant from the Czech 

Ministry of Education (MSM 153100012) and from GAČR 

(no. 206/03/0215). The study was approved and supervised by the 

Ethical Committee of Palacký University and complies with the 

current law of the Czech Republic. 

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FORUM 

FORUM 

FORUM 

Maternal effects and offspring performance: in search of the best 

method 

Milosˇ Krist, Museum of Natural History, nám. Republiky 5, CZ-771 73 Olomouc, Czech Republic 

(krist@prfnw.upol.cz). / MK and Vladimír Remesˇ, Laboratory of Ornithology, Palacky´ University, trˇ. Svobody 26, 

CZ-771 46 Olomouc, Czech Republic. 

Traditionally, maternal effects have been treated as a source of 

troublesome environmental variance that confounds our ability to 

accurately estimate the genetic basis of the traits of interest. 

However, the adaptive significance of maternal effects is currently 

at the centre of the attention of ecologists. Thus, in turn, the 

genetic basis of traits has become a troublesome source of the 

genetic resemblance that confounds our ability to accurately 

estimate the maternal effects of interest. This fact is, however, less 

widely realized among ecologists. We demonstrate this on the 

example of studies investigating egg-size effects on offspring 

performance in birds. Traditionally this relationship is being 

studied by cross-fostering of eggs or young and it is claimed that 

this design is able to separate the effects of egg size per se. 

However, a positive covariation between the direct effects of 

genes and the maternal effects exists for many studied traits, 

which may result in overestimation of the egg-size effects on 

offspring performance in cross-fostering studies. Within-clutch 

comparisons or direct experimental manipulation of the egg size 

are the approaches that do not suffer from such covariation and 

therefore give less biased estimates of the egg-size effects than 

cross-fostering studies. 

Maternal effects in animal ecology 

Offspring phenotype is determined by genes and the 

environment. Besides the direct effect of genes and the 

environment, maternal effects often play a significant 

role. Previously, maternal effects have been treated as the 

source of confusion in determining precisely quantitative 

genetic parameters (Falconer 1989). However, it is now 

widely appreciated that they can play an important role 

in driving the dynamics of evolution and population 

growth. Specifically, by introducing time lags into both 

these processes, they may lead to unpredictable evolutionary 

trajectories (Kirkpatrick and Lande 1989) and 

destabilization of population dynamics, e.g. in the form 

of population cycles or decaying oscillations (Ginzburg 

1998, Beckerman et al. 2002). Moreover, at the individual 

level, maternal effects may influence offspring 

fitness and thus serve both offspring and parents as 

adaptations. This adaptive significance of maternal 

effects has recently become a popular focus of ecological 

FORUM is intended for new ideas or new ways of interpreting existing information. It 

provides a chance for suggesting hypotheses and for challenging current thinking on 

ecological issues. A lighter prose, designed to attract readers, will be permitted. Formal 

research reports, albeit short, will not be accepted, and all contributions should be concise 

with a relatively short list of references. A summary is not required. 

and evolutionary studies (Mousseau and Fox 1998). 

Traditionally, maternal effects have been studied in 

domesticated species by complex analyses (Lynch and 

Walsh 1998), which is not easily applicable to freeranging 

animals. Moreover, these analyses just partition 

the variance in the focal trait and determine which part 

of this variance can be ascribed to maternal effects in 

general. Lande and Price (1989) devised a regression 

method based on Kirkpatrick and Lande (1989) that is 

able to isolate maternal effects specific for certain 

maternal traits. However, this method requires that all 

the maternal characters exerting maternal effects be 

included in the analyses (rather unrealistic condition) 

and is not readily applicable to sex-limited characters. 

Below we evaluate other methods for studying maternal 

effects employed in wild populations, including sexlimited 

characters, on the example of the effects of egg 

size on offspring performance in birds. 

Studying egg-size effects on offspring performance is 

important for two areas of evolutionary ecology. One, 

life-history theory predicts a trade-off between number 

and quality of offspring produced from limited resources. 

Two, potentially adaptive allocation of limited 

resources among siblings within a clutch is widely 

studied in a broad range of species. The critical assumption 

in both these cases is that the amount of resources 

allocated to an egg has an effect on offspring performance. 

Consequently, egg-size effects on offspring 

performance are among the most frequently studied 

topics in the area of maternal effects and birds are the 

taxon in which these effects have been studied most 

often. We are aware of at least 60 studies dealing with 

this question in birds, 41 of which were reviewed by 

Williams (1994). However, the approaches usually employed 

do not control for potential confounding factors. 

Consequently, despite high research attention, results of 

many studies estimating egg-size effects on offspring 

performance may be biased. 

422 OIKOS 106:2 (2004)

Quantitative genetic framework 

To demonstrate how the effect of egg size per se on 

offspring performance can be derived, it is useful to 

frame the problem in the quantitative genetics terms. 

From the quantitative genetics perspective, the phenotypic 

value of each trait can be partitioned into the 

components attributable to genes (genotypic value) and 

the environment (environmental deviation) (Falconer 

1989). The genotypic value can be further partitioned 

into the breeding value (determined by additive effect of 

genes), the dominance deviation (interactions of alleles 

within the same locus) and the interaction deviation 

(interactions of alleles between loci, i.e. epistasis). In this 

basic framework, maternal effects are subsumed under 

the environmental effects, because they are defined as the 

non-genetic influence of the maternal phenotype on the 

offspring’s phenotype (Kirkpatrick and Lande 1989). 

For our purposes, egg size is singled out as the 

maternal effect of interest, whereas all other maternal 

effects (e.g. parental feeding, concentration of testosterone 

in the egg) and pure environmental effects 

(e.g. weather, food supply) are grouped together as the 

offspring environment. Consequently, the phenotypic 

value of each offspring’s trait (z x) can be viewed as 

being determined by three sources: the direct effect of 

genes (Gox), the offspring’s environment Ex, and the egg 

size (Sm) which is itself compounded of an environmental 

(Emw) and a genetic (Gmw) component (Fig. 1). 

Here, subscript x denotes an offspring and w mother; o is 

direct pathway of determining offspring phenotype, m 

denotes maternal (indirect) pathway through egg size. 

Throughout, we assume that direct and indirect genetic 

Fig. 1. Path diagram depicting the determination of the 

phenotype zx of an individual x by direct genetic effects Gox, 

environmental effects E x (including also all parental effects 

except of egg size) and egg size S m. The mother of x is denoted 

by w. O denotes direct pathway, m denotes maternal pathway. 

Modified from Lynch and Walsh (1998). 

effects, Gox and Gmw, contain additive effects but do not 

exhibit dominance or epistasis. Note that Ex has neither 

o nor m subscript, because it includes both direct and 

maternal effects, and Sm has neither w nor x subscript, 

because it is the trait of both the mother and the 

offspring. 

If the three sources determining the traits of offspring 

were uncorrelated, a simple statistical technique such as 

the linear regression would give a reliable estimate of 

egg-size effects on any of offspring traits (except of 

daughter’s egg size). However, when these sources are 

correlated, experimental or statistical techniques will be 

needed to separate egg-size effects per se. 

A review of methods 

In 1990 it was suggested that the size of the egg a female 

lays might be positively correlated with the quality of her 

territory or subsequent parental feeding rate to young 

(i.e. positive CovExSm exists) and that this correlation 

can be removed by experimental swapping of clutches/ 

broods between nests / a technique known as crossfostering 

(Amundsen and Stokland 1990, Reid and 

Boersma 1990). In both these studies, the authors found 

that the size of the eggs which foster mothers originally 

laid was a more influential determinant of offspring 

traits at fledging than the size of the egg from which the 

offspring actually hatched. This suggests that when 

cross-fostering is not performed, egg-size effects on 

offspring performance are highly overestimated. Therefore 

cross-fostering became a very popular technique to 

study egg-size effects on offspring performance 

(we know of 16 such studies performed to date). 

However, although cross-fostering decouples much of 

correlation of egg size with offspring environment 

(CovExSm, below, Table 1), it does not deal with the 

possible covariation between direct effect of genes and 

egg size (CovG owS m). Yet, this covariation is likely to be 

large in many cases because of the choice of ‘fitness’ 

measure usually employed in studies of this kind. In 

practice, instead of studying directly the effects of egg 

size on offspring fitness, we usually study the effects on 

some correlate of fitness. Morphological traits of fledglings 

such as tarsus length or body weight are frequently 

used as these correlates. However, female size is frequently 

positively correlated with the size of eggs she lays 

(Christians 2002) and at the same time morphological 

traits are highly heritable (Merilä and Sheldon 2001). 

This means that the size of the trait in the offspring is 

highly influenced by direct effect of additive genes but 

this effect is ascribed (to the extent to which additive 

genes for mother body size are involved in the correlation 

between female size and egg size) to the effect of egg 

size in the cross-fostering design. In principle this may be 

a problem in every studied trait including offspring 

OIKOS 106:2 (2004) 423

Table 1. Summarization of relative merits and shortcomings of the individual approaches. CovE xS m is a covariance between 

environmental effects and egg size, CovGowSm is a covariance between direct additive genetic effects and egg size. 

Approach Controls for Remains uncontrolled Main use 

Observational Nothing CovExSm 

CovGowSm Do not use 

Cross-fostering Partially CovExSm CovExSm (pre-laying parental 

effects and differential 

feeding of young) 

Use with caution 

(egg size-number trade-off) 

CovG owS m 

Within-clutch Partially CovExSm Fully CovGowSm 

CovExSm (pre-laying parental effects and 

differential feeding of young) 

Within-clutch 

adaptive allocation of resources 

Manipulation Partially CovExSm Fully CovGowSm 

CovExSm (differential feeding of young) 

For all purposes 

survival. Cross-fostering thus does not reveal effects of 

egg size per se, which was realized only rarely (Magrath 

1992, Styrsky et al. 1999) and was not mentioned in the 

most recent cross-fostering studies (Hipfner et al. 2001, 

Bize et al. 2002, Pelayo and Clark 2003). The covariation 

CovGowSm can arise either through CovGowGmw if, for 

example, the same gene facilitates the conversion of food 

into yolk in a female and food into flesh in a nestling 

(Magrath 1992) or through CovG owE mw if, for example, 

larger females or males (more precisely individuals with 

larger breeding values for body size) attain better 

territories which enable females to produce larger eggs. 

These covariations have not been quantified in birds so 

far, however, in mammals it has been found that the 

genetic covariation between direct and maternal pathways 

of determining the offspring phenotype might be 

high (Riska et al. 1985, McAdam et al. 2002). Thus it 

does not seem reasonable to assume that a similar 

covariation does not exist in other taxa. 

The direct effect of additive genes may be controlled 

statistically by including the midparent value of the trait 

as a covariate in the analysis of egg-size effects on the 

same trait in offspring. Although this has been done 

with the maternal value of the trait in some observational 

studies (Larsson and Forslund 1992, Potti and 

Merino 1994), it has never been done in any crossfostering 

study investigating egg-size effects on offspring 

performance. In some cases addition of such a covariate 

may be relatively easy / for example when investigating 

egg-size effects on fledging tarsus length, which is fully 

grown at the time of fledging in many species. However, 

this might be very burdensome when investigating traits 

that can be measured only in offspring (e.g. growth rate) 

and impossible when investigating survival of offspring 

up to recruitment, because all parents were successfully 

recruited. 

However, two other approaches that do not suffer 

from CovGowSm can be used to investigate egg-size 

effects on offspring performance. First, effects of egg size 

on offspring performance may be compared within 

clutches. So far, this approach has been used less often 

than the cross-fostering approach (we know of nine 

studies using the within-clutch approach, e.g. Dawson 

and Clark 1996, Amat et al. 2001), and its advantage 

over cross-fostering was not mentioned in any of these 

studies. Among-clutch variation in egg size may be 

removed by centring egg sizes within clutches, i.e. by 

subtracting mean egg size of the clutch from the actual 

size of every egg in the clutch. Resulting values represent 

within-clutch variation and as such are then used in the 

statistical analyses. Given that chromosomes segregate at 

random in meiosis, CovG oxS m is zero among full-sibs. 

Non-zero CovG oxS m could arise if the female was able to 

recognize which allele of an allelic pair had come to the 

ovum in meiotic division and targeted resources accordingly 

or to control the outcome of meiosis in relation to 

the size of ovum to be ovulated. Such high female 

control, however, seems highly unlikely for alleles on 

autosomes or homologous parts of sex-chromosomes. 

On the other hand, targeting of resources might perhaps 

work in relation to genes that are located at nonhomologous 

parts of sex-chromosomes, such as sexdetermining 

genes, as suggested by studies demonstrating 

differences in egg size between the sexes (Cordero et 

al. 2000). 

In the within-clutch approach, territory and parents 

are the same for all sibs and that is why also CovE xS m is 

controlled to a similar degree as in cross-fostering design 

(Table 1). Strictly speaking, however, CovE xS m need not 

be zero both in within-clutch and cross-fostering design. 

Firstly, egg size may be correlated with other pre-laying 

maternal effects, for example concentration of hormones, 

antibodies or carotenoids in the egg. In this 

case the amount of these compounds would increase 

allometrically with egg size (slope of the regression of the 

amount of a compound on egg size would differ from 

one) contrary to the situation when it would increase 

isometrically with egg size (slope would equal one). In 

the latter case, the amount of these compounds may be 

treated as being a part of the egg size. Secondly, parents 

may feed more intensely small (or large) young which 

hatched from small (or large) eggs. This effect may be 

424 OIKOS 106:2 (2004)

stronger in within-clutch approach, because offspring 

are raised in the same nest and larger sibs may 

monopolize resources supplied by parents. Moreover, 

the problem that is specific for the within-clutch 

approach is that within-clutch (and also within-female) 

variation in egg size is usually much smaller than interfemale 

variation. On average, differences in egg size 

within clutches explain only 30% of the total egg-size 

variation (Christians 2002). Thus effects of great differences 

in egg size, which exist at the population level, 

cannot be directly estimated by within-clutch approach. 

On the other hand, there are many studies investigating 

adaptive allocation of resources among individual eggs 

within a clutch in relation to laying order (Slagsvold et 

al. 1984) and sex (Cordero et al. 2000). These studies rely 

on the assumption that within-clutch variability in egg 

size has some consequences for offspring performance. 

This assumption may be properly tested by the withinclutch 

approach outlined above. Possible monopolization 

of resources by larger siblings and small differences 

in egg size within clutches are not problems in this 

context, because they are inherent features of the 

relationships among young within a clutch. 

The second approach to remove CovGowSm is the 

direct manipulation of egg volume. This method is the 

best way to elucidate potential effects of egg size on 

offspring performance (Sinervo et al. 1992). In birds, it 

was used, to our best knowledge, only twice on 

domesticated species under laboratory conditions 

(Hill 1993, Finkler et al. 1998). In these studies, certain 

part of the albumen or yolk of unincubated eggs was 

removed by a syringe and a needle. Such an egg size 

manipulation removes also the potential correlation 

between egg size and other pre-laying maternal effects, 

which is an additional advantage compared to the other 

approaches. Given the strengths of this approach, it 

could become a powerful tool in elucidating effects of 

egg size on offspring performance also in populations of 

wild-ranging birds. However, although invasive egg 

sampling and manipulation have been successfully 

applied to some wild bird species (Lipar 2001, Saino et 

al. 2003), rather high egg mortality encountered in the 

study on hens mentioned above (Finkler et al. 1998) 

seems to question broad applicability of this method. 

Moreover, we have no information on how big changes 

in egg volume in comparison with the natural egg-size 

variability are within the reach of this method, while at 

the same time keeping egg mortality within acceptable 

limits. Both these issues remain to be addressed in 

studies on wild species. The manipulative approach 

also suffers from the possibility that parents may feed 

their young selectively with respect to their size, which 

also means to the size of the egg they hatched from. This 

could be controlled for by statistically controlling for the 

amount of food brought to individual offspring by their 

parents or by hand-rearing of the young (Anderson et al. 

1997). 

Conclusions 

In this comment we evaluated relative merits and 

shortcomings of the different approaches to the study 

of egg-size effects on offspring performance in birds 

(Table 1). It has been accepted that the cross-fostering 

design is better than the simple observational approach 

and thus it became a standard methodological tool. We 

argue that there are even better approaches that should 

give less biased estimates of egg-size effects: the withinclutch 

approach and the direct experimental manipulation 

of egg-size. These approaches are relatively readily 

applicable to free-ranging populations of animals and 

plants. Therefore, further studies using these approaches 

would be valuable for better understanding of the 

evolution and impact of maternal effects and also for 

the evaluation of how much the traditional approaches 

for studying adaptive maternal effects suffer from 

uncontrolled confounding factors. We focused our 

attention on egg-size effects in birds because this is one 

of the best-studied systems in the area of maternal effects 

and much effort has been devoted to it. However, general 

logic of our argument applies equally well to other traits 

and other taxa. 

Acknowledgements / We thank Loeske Kruuk, Ben Sheldon, 

Trevor Price and Emil Tkadlec for valuable comments or 

discussion on the manuscript. This study was supported by a 

grant from the Czech Ministry of Education (MSM 153100012) 

and from GACR (No. 206/03/0215). M. K. thanks Kačka for 

her continuous encouragement. 

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426 OIKOS 106:2 (2004)

Nest design and the abundance of parasitic 

Protocalliphora blow flies in two 

hole-nesting passerines 1 

Vladimír REMEŠ 2 , Laboratory of Ornithology, Palacky University, Tr. Svobody 26, CZ-771 46 Olomouc, 

Czech Republic, e-mail: remes@prfnw.upol.cz 

Miloš KRIST, Laboratory of Ornithology, Palacky University, Tr. Svobody 26, CZ-771 46 Olomouc, Czech 

Republic, and Museum of Natural History, nam. Republiky 5, CZ-771 73 Olomouc, Czech Republic. 

Ectoparasites dwelling in bird nests and feeding on the 

blood of nestlings and adults are an important group of parasites. 

In hole-nesting birds they include fleas (Siphonaptera), 

flies (Diptera), and mites (Acarina). Ectoparasites can cause 

lowered breeding performance and nest desertion in adults 

(Oppliger, Richner & Christe, 1994), negatively affect 

growth and condition of nestlings (Eeva, Lehikoinen & 

Nurmi, 1994; Merino & Potti, 1995; Puchala, 2004; review 

in Møller, Allander & Dufva, 1990), and reduce lifetime 

reproductive success of hosts (Fitze, Tschirren & Richner, 

2004). Moreover, they also serve as vectors of internal 

1 Rec. 2005-01-06; acc. 2005-04-12. 

Associate Editor: Marty Leonard. 

2 Author for correspondence. 

12 12 (4): 549-xxx 549-553 (2005) 

Abstract: Ectoparasites dwelling in bird nests regularly reduce reproductive success and condition of breeding birds. Thus, 

establishing the factors that determine the abundance of ectoparasites is important for better understanding of reproductive 

trade-offs and life history evolution in birds. A recent hypothesis states that interspecific differences in the abundance of 

ectoparasites may be caused by nest composition. For example, great tits (Parus major) have nests made of mosses and 

fur, whereas Ficedula flycatchers have nests made of grasses, bast, and bark, and tits are more infested by nest-dwelling 

ectoparasites than flycatchers. We swapped nests between pairs of great tits and collared flycatchers (F. albicollis) during 

egg-laying or early incubation and counted parasitic Protocalliphora blow flies at the end of breeding to test this hypothesis 

experimentally. We controlled statistically for habitat (oak versus spruce forest), brood size, season, year, and mean nestling 

weight before fledging. We found a significant effect of bird species (tit > flycatcher), habitat (oak > spruce), and year. There 

was no effect of nest type. Consequently, the hypothesis ascribing the different abundance of ectoparasites in great tits and 

collared flycatchers to different nest composition was not supported by our study. 

Keywords: blow fly, ectoparasites, Ficedula, nest design, Parus, Protocalliphora. 

Résumé : Les ectoparasites qui infestent les nids réduisent souvent le succès reproducteur et la condition physique des 

oiseaux nicheurs. Il est donc important d’identifier les facteurs qui influencent leur abondance pour mieux comprendre les 

caractéristiques de la reproduction et l’évolution du cycle vital chez les oiseaux. Selon une hypothèse récente, les différences 

interspécifiques dans l’abondance des ectoparasites pourraient être associées aux matériaux de construction des nids. Par 

exemple, les nids de la mésange charbonnière (Parus major) sont fabriqués de mousses et de poils alors que ceux des 

gobemouches (Ficedula spp.) sont faits de graminées, de liber et d’écorce. Or, on sait que les mésanges sont plus infestées 

par des ectoparasites que les gobemouches. Nous avons échangé les nids construits par des couples de mésanges charbonnières 

avec ceux construits par des gobemouches à collier (F. albicollis) pendant la ponte ou au début de l’incubation. Nous avons 

par la suite compté les mouches du genre Protocalliphora à la fin de la nidification pour tester l’hypothèse mentionnée plus 

haut. Nous avons pu contrôler de façon statistique l’habitat (forêt de chênes ou d’épicéas), la taille de la couvée, la saison, 

l’année et le poids moyen des jeunes avant l’envol. Nous avons trouvé un effet significatif pour l’espèce d’oiseau (mésange 

> gobemouche), l’habitat (chênes > épicéas) et l’année. Le type de nid n’a eu pour sa part aucun effet sur les ectoparasites. 

En conséquence, l’hypothèse d’un lien entre l’abondance d’ectoparasites et les matériaux de construction des nids n'est pas 

supporté par les résultats de cette étude. 

Mots-clés : design des nids, ectoparasites, Ficedula, mouches, Parus, Protocalliphora. 

Nomenclature: Sabrosky, Bennett & Whitworth, 1989; Cramp & Perrins, 1993. 

Introduction 

parasites and bacterial and viral diseases (Bowman et al., 

1997). Thus, knowledge of the factors that the determine 

abundance of these ectoparasites in nests is critical for better 

understanding of reproductive trade-offs and life history 

evolution in birds (Clayton & Moore, 1997). 

Abundance of nest-dwelling ectoparasites varies significantly 

among individuals within a given bird host species, 

but even bigger variation in both prevalence and 

parasite abundance is found between sympatric host species 

(Bennett & Whitworth, 1992; Whitworth & Bennett, 1992). 

One of the hypotheses suggested to explain interspecific 

differences in ectoparasite load posits that the differences 

are caused by differences in nest design (Bauchau, 1998), 

such as differences in details of nest construction and nest 

composition (Hansell, 2000). Fresh plant material in the

Remeš & KRist: Nest desigN aNd blow flies 

nest, for example, may have negative effects on parasite 

abundance (Clark & Mason, 1985; Petit et al., 2002; but see 

Dawson, 2004). Nest design may thus affect (1) demography 

of ectoparasite populations within nests (larval mortality, 

competition, growth), because demography is driven by 

various parameters of the living environment (e.g., humidity; 

Heeb, Kölliker & Richner, 2000) and the living environment 

depends on nest composition, and/or (2) attractiveness 

of the particular nest type for laying/dispersing to females of 

parasites, which may cue on specific features of the nest. 

In this study we tested the “nest design” hypothesis on 

great tits (Parus major) and collared flycatchers (Ficedula 

albicollis). The two species build nests of very different 

composition: while tit nests are composed of moss and 

feather/hair, flycatcher nests consist of dry grass, bast, and 

pieces of bark (Cramp & Perrins, 1993). At the same time, 

the abundance of larvae of parasitic flies (Protocalliphora 

spp.) is regularly higher in nests of the great tit than in 

Ficedula flycatchers (Bauchau, 1998). We experimentally 

switched nests between great tit and collared flycatcher 

pairs and followed the effects of this treatment on the abundance 

of parasitic flies in nests. In this way we were able to 

separate independent effects of nest design and species. We 

also statistically controlled for a number of other factors, 

including habitat, year, season, brood size, and mean nestling 

weight before fledging. 

In line with the “nest design” hypothesis we hypothesized 

that the lower number of ectoparasites in Ficedula 

flycatchers is caused by the composition of their nests (presence 

of bast and bark with toxic secondary compounds; 

Pearce, 1996) and, thus, predicted that there would be fewer 

parasitic flies in the Ficedula nests regardless of the host 

species actually dwelling in the nest. 

550 

Methods 

Great tit and collared flycatcher are small, hole-nesting, 

insectivorous passerines breeding widely in various types 

of woodlands. They readily accept nest-boxes for breeding. 

Great tits are year-round residents, whereas collared flycatchers 

are long-distance migrants wintering in Africa. These 

two species differ in brood size [great tit: median 11 (range 

7–14), collared flycatcher: 6 (4–7), timing of breeding (great 

tits start breeding at mid-April, collared flycatchers at the end 

of April), body mass (great tit: ca 18 g; collared flycatcher: 

ca 14 g, V. Remeš & M. Krist, unpubl. data)]. Otherwise their 

breeding ecology is similar. 

This study was conducted in 2002-2003 in the Velký 

Kosíř area in the eastern Czech Republic (49° 32' N, 17° 

04' e, 300-450 m asl). We studied great tits and collared flycatchers 

on six nest-box plots, of which three were placed in 

spruce (Picea abies) and the other three in an oak (Quercus 

spp.) forest, in both cases interspersed with birch (Betula 

pendula) and pine (Pinus silvestris). Each plot had 50-90 

nest-boxes. In early spring (before tits started nest building), 

nest-boxes were checked and cleaned (old nests were 

removed). From mid-April to mid-June, as a part of a larger 

study, we followed basic breeding biology of both species. 

To be able to separate independent effects of nest 

design and species per se on the abundance of nest-dwell- 

ing flies, we switched nests between great tit and collared 

flycatcher pairs. As a control treatment, we swapped nests 

between pairs of the same species. Thus, nests of all hosts 

were swapped. Two experimental (2 × flycatcher–tit) and 

two control (tit–tit, flycatcher–flycatcher) manipulations 

were made on the same day each time. We strove to make 

manipulations as early as possible in the breeding cycle. 

However, tits breed earlier than flycatchers. Consequently, 

manipulations on flycatcher nests were made during egg 

laying, whereas in tits we made the manipulations up to 

the fourth day after the clutch was complete (mean ± SD 

number of days from laying of the first egg to nest manipulation 

was 2.5 ± 2.97 in flycatchers and 12.5 ± 1.86 in tits). 

We increased our sample for flycatchers by also using some 

nests of tits in late incubation. In these experimental pairs, 

we followed nest-type effects only in the flycatcher, not in 

the great tit. 

In the week following fledging, we collected nests and 

placed them into plastic bags that were sealed so that no fly 

could escape. We collected only nests from which at least 

one young had fledged. Within four weeks of collection, 

we opened the bags, took the nests to pieces, and counted 

the number of larvae, pupae, and adult flies (if they had 

emerged from pupae in the meantime). Time between fledging 

of young and collection of nests was not the same for 

all the nests. However, collection of nests within one week 

of fledging is a standard procedure in ectoparasite research 

(Eeva, Lehikoinen & Nurmi, 1994; Birdblowfly.com). More 

importantly, even when some flies had dispersed immediately 

after fledging, before a nest was collected, they could 

be counted by counting empty pupae, which are very conspicuous 

and cannot be overlooked. 

Two species of parasitic flies were identified in the 

nests: Protocalliphora azurea and P. falcozi. Both species 

are regularly found in the nests of European cavity nesters 

(Hurtrez-Boussès et al., 1997; Wesołowski, 2001). 

All Protocalliphora species overwinter as adults and do 

not lay eggs in host nests until young birds hatch (Gold & 

Dahlsten, 1989; Sabrosky, Bennett & Whitworth, 1989). 

Thus, our experimental procedure of switching the nests 

during egg laying or early incubation was sufficient to 

separate independent effects of nest type. The two species 

of blow flies were lumped together for further analyses 

for two reasons (see also Hurtrez-Boussès et al., 1997). 

First, all Protocalliphora flies (except P. braueri; Eastman, 

Johnson & Kermott, 1989) are intermittent feeders that 

feed on the blood of nestlings and in the meantime dwell in 

the nest substrate (Sabrosky, Bennett & Whitworth, 1989). 

Moreover, P. azurea and P. falcozi are of similar body size 

(9-11 mm and 8-10 mm, respectively; Grunin, 1970), 

so their effects on hosts can be expected to be similar. 

Second, it was not possible to identify all the flies to species 

because not all individuals emerged from pupae and to our 

best knowledge only adults can be identified in European 

Protocalliphora flies. 

When analyzing the abundance of flies, we first fit a 

generalized linear model with Poisson error distribution and 

log link, which is usually suitable for count data. However, 

our data were strongly overdispersed (deviance/df = 23.26),

which is common in parasitology (Wilson & Grenfell, 1997). 

Thus, we used negative binomial error distribution and log 

link, which led to a reasonable dispersion index of data 

(deviance/df = 1.50). All these analyses were done in PROC 

GENMOD in SAS (SAS Institute, 2000). Initially, we fit a 

full model with the following explanatory effects: nest type 

(tit versus flycatcher nest), species (tit versus flycatcher), 

habitat (spruce versus oak forest), year (2002 versus 2003), 

brood size (number of hatched nestlings), season (Julian 

hatching date), and mean nestling weight before fledging 

(in grams, day 13 after hatching in flycatcher, day 15 in tit); 

we also included all two-way interactions between nest type 

and all other factors. The final model was selected by backward 

elimination of non-significant terms, except for the 

two main factors of interest (nest type and species), which 

were retained in the model regardless of their significance. 

Hatching date, brood size, and mean nestling weight were 

standardized by subtracting the value of a given nest from 

the mean of a given species (i.e., the values were standardized 

within species). However, the results were the same 

with non-standardized values. Test statistics (χ 2 -values) and 

P-values reported in Results for non-significant terms are 

from the backward elimination procedure just before the particular 

term (being the least significant) was removed from 

the model. Values for significant factors and/or factors of 

interest (i.e., nest type and species) are from the final model. 

The rationale for the inclusion of the above-mentioned 

variables was as follows. Nest type and species were the 

main factors of interest. Other factors were included as 

covariates to reduce unexplained variation and thus the 

power of the main test. Season and habitat could affect 

flying activity of the flies (through temperature, humidity, 

etc.). Brood size and mean nestling weight could affect survival 

and growth of larvae (by determining the amount of 

blood available for feeding). Alternatively, the latter factors 

could affect oviposition behaviour of fly females. 

Results 

In total, 13 experimental and 17 control tit pairs and 20 

experimental and 19 control flycatcher pairs were available 

for the analyses. Sample sizes differ between experimental 

and control treatments because some nests were abandoned 

or depredated. There was a significant effect of species 

(χ 2 = 5.54, P = 0.019), habitat (χ 2 = 9.00, P = 0.003), and 

year (χ 2 = 5.99, P = 0.014) on the abundance of parasitic 

Protocalliphora flies in nests (Figure 1). Neither nest type 

(χ 2 = 0.28, P = 0.595, Figure 1) nor the interaction of nest 

type with species (χ 2 = 1.29, P = 0.256, Figure 2) had a significant 

influence. Similarly, there was no significant effect 

of brood size (χ 2 = 0.01, P = 0.905), mean nestling weight 

(χ 2 = 0.85, P = 0.358), season (χ 2 = 0.94, P = 0.332), or any 

interaction of nest type with other factors (all χ 2 -values < 0.63, 

all P-values > 0.431). 

Discussion 

We experimentally tested the hypothesis that nest 

design is responsible for interspecific differences in ectoparasite 

infestation in two species of hole-nesting passerines, 

ÉCosCieNCe, vol. 12 (4), 2005 

figuRe 1. Number (least squares means ± 95% confidence limits) of 

parasitic Protocalliphora flies in the nests of great tit and collared flycatcher 

in relation to habitat, year, species, and nest type. FA = collared flycatcher, 

PM = great tit. Statistical tests are reported in Results. 

figuRe 2. Number (least squares means ± 95% confidence limits) of 

parasitic Protocalliphora flies in the great tit (Tit) and collared flycatcher 

(Flycatcher) according to nest type (FA = collared flycatcher, PM = great tit). 

Statistical tests are reported in Results. 

the great tit and the collared flycatcher (Bauchau, 1998). 

There was no influence of nest type on the intensity of 

infestation by parasitic Protocalliphora flies. We did, however, 

find a significant effect of species, habitat, and year. 

Females of parasitic Protocalliphora flies overwinter 

as adults and lay their eggs in nests during the nestling 

phase of the host breeding cycle (Gold & Dahlsten, 1989; 

Sabrosky, Bennett & Whitworth, 1989). Larvae hatch within 

2 d, feed on blood of nestlings while dwelling in the nest 

substrate, and after one to two weeks of growth pupate to 

complete the life cycle (Sabrosky, Bennett & Whitworth, 

1989; Bennett & Whitworth, 1991). Thus, in these flies both 

active choice of a certain nest type by females and demography 

of larvae within host nests (competition, growth rate, 

mortality) may play a significant role in determining their 

abundance in relation to nest type. 

Since there was no effect of nest type on fly abundance, 

it is likely that neither of the two possible processes played 

any role: fly females did not cue on nest composition when 

selecting their oviposition site, and demographic processes 

among larvae did not influence their abundance in relation 

551

Remeš & KRist: Nest desigN aNd blow flies 

to nest type. Alternatively, these two processes may have 

counteracted each other in determining the abundance of 

larvae: flies may have selected the type of nest in which their 

larvae had worse performance; however, such a maladaptive 

habitat choice seems unlikely to evolve (but see Remeš, 

2000). Nevertheless, the absence of any effect of nest type on 

the abundance of flies is rather puzzling. It is, for example, 

known that pine bark and bast contain toxic secondary compounds 

with a strong potential to negatively affect ectoparasites 

(Pearce, 1996; Bauchau, 1998). As flycatchers use this 

material to build their nests, this should have led to higher 

ectoparasite abundance in tit nests. 

In contrast to nest type, there was a strong effect of 

species per se on the abundance of parasitic flies: tits were 

more intensely infested regardless of nest type. Tits and 

flycatchers differ in brood size, timing of breeding, nestling 

weight, and the nestling period duration (see Methods), 

which could in principle cause the difference between species. 

For example, the greater number of young in the great 

tit and the longer time that great tit young remain in the nest 

could mean that more food is available for parasitic larvae, 

which could lead to their higher abundance. However, those 

factors that we measured and included in the models had no 

influence on the abundance of flies within species as evidenced 

by their non-significance when used as standardized 

factors in the analysis (see Results). Thus, it seems unlikely 

that any of these is the causal factor behind the effect of species. 

This effect may have several more subtle explanations. 

First, adult flycatchers may be more capable of behavioural 

anti-parasite defences, for example in the form of nest cleaning 

by catching laying females and/or parasitic larvae (see 

also Hurtrez-Boussès et al., 2000; Tripet, Glaser & Richner, 

2002). Second, flycatcher nestlings may be more resistant 

to parasitism and fly larvae suffer greater mortality because 

of more effective immune defence. Third, the preference 

of laying females for certain bird species may significantly 

alter patterns of ectoparasite infestation. The preference for 

certain species of hosts (here great tits) may have arisen, 

for example, from better performance of fly larvae on their 

nestlings (for whatever reason, e.g., different skin thickness, 

resistance to parasitism, length of the nestling period, etc.). 

Our study was not suited to revealing the proximate mechanism 

of the effect of species. However, given the strong 

effect of species per se, it would be interesting to find out 

which mechanism is responsible. 

Habitat was an important determinant of the abundance 

of flies: they were more abundant in the oak forest than in 

the spruce forest. All three oak plots were situated on warmer 

and drier southern slopes, whereas spruce plots were 

situated either on the top of the hill (two of them) or on the 

colder and more humid northern slope (one plot). Although 

known effects of weather (temperature and ambient humidity) 

on fly abundance are in accord with this difference 

(Merino & Potti, 1996), many uncontrolled factors differing 

between the two forest types may have had an influence. 

In summary, there was no effect of nest type (nest of 

great tit versus collared flycatcher) on the abundance of 

nest-dwelling parasitic Protocalliphora flies. Thus, the 

hypothesis ascribing different levels of ectoparasite infesta- 

552 

tion between the great tit and Ficedula flycatchers to nest 

design (Bauchau, 1998) was not supported by our experimental 

study. 

Acknowledgements 

We thank J. Stříteský and Lesy ČR for providing nest-boxes, 

and L. Mazánek for identifying flies. T. L. Whitworth and two 

anonymous referees provided valuable comments on an earlier 

version of this paper. This work was supported by grants from the 

Ministry of Education of the Czech Republic (MSM6198959212) 

and GAČR (No. 206/03/0215). 

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553

Functional 

Ecology 2007 

21, 

776–783 

© 2007 The Authors. 

Journal compilation 

© 2007 British 

Ecological Society 

Blackwell Publishing Ltd 

Maternal carotenoid supplementation does not affect 

breeding performance in the Great Tit ( Parus major) 

VLADIMÍR REMES,*† 

MILOS 

KRIST,*‡ VITTORIO BERTACCHE§ and 

RICCARDO STRADI§ 

* Department of Zoology, Palack& 

University, Tr. 

Svobody 26, 77146 Olomouc, Czech Republic, 

‡ Museum of Natural 

History, Nám. Republiky 5, 77173 Olomouc, Czech Republic, 

§ Istituto di Chimica Organica ‘A. Marchesini’ 

Università degli Studi di Milano, via Venezian 21, 20133 Milan, Italy 

Introduction 

Summary 

1. Carotenoids are micronutrients with many beneficial health-related effects. They are 

effective antioxidants and stimulants of the immune system. Carotenoids cannot be 

synthesized in animals and must be obtained from food. As such, they may limit reproductive 

output and performance, and on the proximate level mediate reproductive 

trade-offs. 

2. We studied carotenoid limitation in wild Great Tits ( Parus major) 

by supplementing 

prelaying and laying females with lutein, the most abundant carotenoid in this species. 

We followed the effects of this supplementation on egg yolk carotenoid composition, 

and offspring and parental performance. 

3. Females transferred the supplemented lutein into egg yolks, increasing lutein 

concentration to the upper limit of naturally occurring concentrations in control 

pairs. Concentrations of zeaxanthin, β-carotene 

and α-carotene 

did not differ between 

supplemented and control pairs. 

4. Effects on offspring and parental performance were generally absent or weak. There 

were no effects on timing of laying, clutch size, hatching success, nestling survival, 

nestling mass (day 6 and 14), tarsus length or T-cell mediated immune response. Males 

on supplemented nests fed their young more than those on control nests. There was no 

positive effect on female feeding or mass. 

5. Negligible effects of lutein supplementation on offspring and parental performance 

might be explained by high natural abundance of carotenoids or other antioxidants, 

where additional carotenoids bear no strong advantage to the birds. Additionally, 

conflicting results of different studies may be explained by species-specific features of 

their life-histories. 

Key-words: 

antioxidants, carotenoids, egg yolk, food supplementation, parental investment, resource 

allocation 

Functional Ecology (2007) 21, 

776–783 

doi: 10.1111/j.1365-2435.2007.01277.x 

Animals are expected to allocate limited resources 

among competing bodily functions so as to maximize 

fitness. During reproduction, mothers face a fundamental 

decision of how much resources to invest into 

current reproductive bout, and how much to retain for 

maintenance and future reproduction. Besides elaborate 

postnatal parental care females also invest heavily 

during the prenatal period into the fabrication of eggs. 

Besides energy needed for embryo development, eggs 

are packed with many valuable resources, including 

†Author to whom correspondence should be addressed. 

E-mail: vladimir.remes@upol.cz 

antibacterial enzymes, antibodies, hormones and 

carotenoids (Blount, Houston & Møller 2000). 

Carotenoids are a group of several hundred biologically 

active compounds with many important biological 

functions in signalling and physiology (Møller et al. 

2000). They are widely used in the colouring of bird 

plumage and bare parts (Olson & Owens 2005). 

Carotenoids enhance the intensity of both cell-mediated 

and humoral immune response (Chew & Park 2004). 

They are also effective scavengers of reactive oxygen 

species (ROS) that arise during metabolic processes 

(Krinsky 2001). ROS are free radicals and non-radical 

oxygen-containing molecules that are able to damage 

proteins, lipids and DNA (de Zwart et al. 

1999), a 

condition called oxidative stress that has been implicated 

776

777 

Carotenoids and 

breeding 

performance 




Ecological Society, 

Functional Ecology, 

21, 

776–783 

in the etiology of many diseases and ageing (Crimi 

et al. 

2006). ROS are removed by the multifaceted 

antioxidant system that includes enzymes (e.g. catalase, 

superoxide dismutase), water-soluble antioxidants 

(ascorbic acid, glutathione) and fat-soluble antioxidants 

(vitamin E, carotenoids; Sies & Stahl 1995). 

Carotenoids can be synthesized only by plants, certain 

bacteria and fungi, while animals must ingest them 

with their food. As a ‘diet-dependent’ resource used 

in both signalling and physiology, they are a good 

candidate for mediating life-history trade-offs (Blount 

2004). 

Birds deposit carotenoids into egg yolks and the 

amount varies markedly within and among species 

(Hargitai et al. 

2006). Some variation among yolks in 

the concentration of carotenoids can be explained by 

laying order (e.g. Blount et al. 

2002a; Saino et al. 

2002), 

year (Hargitai et al. 

2006) or habitat (Hõrak, Surai & 

Møller 2002; Cassey et al. 

2005). Studies on both 

domestic and wild birds demonstrated higher yolk 

carotenoid concentrations in mothers supplemented 

by carotenoid-rich diet (Blount et al. 

2002b; Bortolotti 

et al. 

2003; Biard, Surai & Møller 2005; McGraw, 

Adkins-Regan & Parker 2005; Ewen et al. 

2006; 

Berthouly, Helfenstein & Richner 2007). However, we 

still do not understand whether these patterns represent 

active deposition of carotenoids by mothers (Blount 

et al. 

2002a, Blount et al. 

2002b; Royle, Surai & Hartley 

2003) or simply reflect their supply in the diet (Partali 

et al. 

1987). 

Carotenoids are responsible for the typical yellow 

to orange colour of yolks and have important physiological 

functions. They reduce the susceptibility of yolk 

lipids to peroxidative damage (Blount et al. 

2002b) and 

later protect developing embryo from oxidative stress 

(Surai, Noble & Speake 1996). This is important because 

birds grow very fast and their intense metabolism 

makes them especially vulnerable to oxidative damage 

(Blount et al. 

2000). Upon hatching, yolk-derived 

carotenoids can affect the susceptibility of hatchling 

tissues to oxidative damage (Surai et al. 

1996), the 

ability of chicks to accumulate dietary carotenoids 

in their body (Koutsos et al. 

2003), or parameters of 

their immune function (Haq, Bailey & Chinnah 1996; 

Koutsos, López & Klasing 2006). All these studies were 

on domestic hens. Three studies on passerines suggest 

that similar effects may exist in this group of birds. 

McGraw et al. 

(2005) found that carotenoid supplementation 

of females enhances hatching and fledging 

success in captive Zebra Finches ( Taeniopygia guttata). 

Under wild conditions Biard et al. 

(2005) found that 

young hatching from the eggs of carotenoid-supplemented 

females had longer tarsi at hatching and more leukocytes 

in their blood during growth. Berthouly et al. 

(2007) found out that maternally derived carotenoids can 

help nestlings cope with stress. 

To advance our understanding of potential carotenoid 

limitation in wild birds, we performed a carotenoidsupplementation 

study in wild Great Tits ( Parus major 

Linnaeus 1758). Reproducing parents face a trade-off 

of how many limited resources to allocate into current 

reproductive bout vs self-maintenance and future 

reproduction. In this study we focused on the potential 

for carotenoid limitation in the current reproductive 

bout. We provided Great Tit pairs with a lutein-rich 

supplement before and during egg laying and followed 

the effects of this supplementation on yolk carotenoid 

concentrations, and reproductive and parental 

performance. We tested three possible scenarios: (1) 

Supplemented females do not increase yolk lutein 

concentration and the intensity of parental care does 

not change. This would mean that parents are not 

limited during current reproduction. (2) Supplemented 

females increase yolk lutein with no effects on offspring 

performance and no effects on the intensity of parental 

care. In this scenario, parents are not limited in their 

current reproductive bout and the female bird just 

channels surplus micronutrients into the eggs. (3) 

Supplemented females do increase yolk lutein concentration 

with positive effects on offspring performance 

and/or parents care more intensely. This would 

demonstrate carotenoid limitation during current 

reproduction. 

Methods 

field work 

Great Tits are small, insectivorous, resident passerines 

that breed in nest holes during April–June in various 

woodland types. We studied them in 2004 on six nestbox 

plots (400 nest-boxes in total) in the Velky 

Kos’ r 

area in the eastern Czech Republic (49° 

32′ 

N, 17° 

04′ 

E, 

300–450 m a.s.l.). Three plots were in a sessile oak 

( Quercus petraea) 

forest, the other three in a Norway 

spruce ( Picea abies) 

forest. Before birds started breeding 

nest-boxes were checked and cleaned. 

We visited nest-boxes daily to determine the start of 

nest building and egg laying. We marked eggs daily by 

a water-proof pen. Before and during egg laying we 

supplemented experimental tit pairs with 25 mg of 

CWS lutein (DSM Nutritional Products (Basel, 

Switzerland), composition: 7% of lutein, 1% DLα- tocopherol, 1% ascorbyl palmitate, 18% fish gelatine, 

46% sucrose, 2% sodium ascorbate and 25% corn starch), 

which means 1·75 mg of lutein daily. According to the 

information given in Partali et al. 

(1987; c. 3·3 µ g of 

carotenoids per one lepidopteran larva) this makes 

daily increase in carotenoid intake equivalent to c. 530 

lepidopteran larvae. Control pairs were supplemented 

with a placebo lacking lutein with otherwise identical 

composition. We started with 33 experimental and 27 

control pairs. Both lutein and placebo were enclosed in 

a pill made from animal fat ( c. 0·6 g) and put into a 

plastic cup (diameter 3 cm, height 2 cm). It was put 

inside the nest-box inhabited by the focal tit pair, c. 5 cm 

above the nest rim on one side of the nest-box. Supplemental 

units were freshly prepared every evening and

778 

V. Reme3 

et al. 






21, 

776–783 

stored at –20 ° C and in the dark until use the following 

day. To increase the attractiveness of the pill, we always 

added five meal-worms into the plastic cup. A pill was 

supplemented daily until egg laying was terminated. 

We started the supplementation on the day when tits 

started to bring animal fur into the moss base of the 

nest being built. We did not start the supplementation 

earlier, because females may switch between nestboxes 

in the earlier phases of nest building. All the 

pairs which we started to supplement continued in 

breeding. On average 2·4 pills (SD = 2·6, N = 60) 

had already been eaten on the day the first egg of 

the clutch was laid (supplemented: 2·4 ± 2·8, N = 33; 

control: 2·5 ± 2·3, N = 27; F < 0·1, P = 0·818). Supplementation 

was regularly taken by birds, only 7 out of 

812 pills remained uneaten the next day. We did not 

monitor the nest-boxes and thus we do not know 

the relative share of the sexes in the consumption of 

the supplement. When incubation commenced, apart 

from seven cases we collected the egg laid on that day 

(i.e. the last egg in the laying sequence that could have 

been collected without having been incubated for more 

than a few hours) and stored it at –20 ° C before further 

analysis. On average, 11·5 (SD = 2·8, N = 53) pills had 

been eaten by the birds in each nest before the collected 

egg was laid (supplemented: 11·5 ± 3·1, N = 28; control: 

11·5 ± 2·5, N = 25; F < 0·1, P = 0·984). Average 

position in the laying sequence of this egg was 10·0 

(SD = 2·0, N = 53; supplemented: 10·1 ± 2·1, N = 28; 

control: 9·9 ± 1·9, N = 25; F = 0·1, P = 0·726). 

To recognize the young hatching from late eggs that 

had the greatest probability of being affected by the 

supplementation, we frequently visited nests around 

the expected time of hatching. At most nests, we 

identified and marked nestlings hatched from late eggs 

in the laying sequence by clipping their dawn feathers. 

We weighed all the young when they were 6 days old 

and again when they were 14 days old. On day 14 we 

also measured their tarsi. On day 13, we measured the 

thickness of the right wing web of three young per nest 

(those that hatched from late eggs, if known) with a 

pressure-sensitive gauge (model PK-1012E, Mitutoyo, 

Tokyo, Japan) and then injected it with 0·09 mg of 

phytohaemagglutinin (L-8754, Sigma-Aldrich, St. 

Louis, MO, USA) in 25 µ L of phosphate buffered 

saline. We re-measured the wing web 24 h later (± 2 h). 

We always measured the wing web twice and took the 

average. T-cell mediated immune response was quantified 

as the difference in the wing web thickness 

measured 1 day after the injection and a day before. 

On average 8·8 pills (SD = 3·0, N = 46; supplemented: 

9·2 ± 3·0, N = 25; control: 8·1 ± 2·9, N = 21; F = 1·7, 

P = 0·194) had been eaten by parents before the eggs 

from which the young that were scored for immune 

response originated were laid (mean position in the 

laying sequence = 7·4, SD = 2·1, N = 46; supplemented: 

7·3 ± 2·0, N = 25; control: 7·4 ± 2·2, N = 21; 

F < 0·1, P = 0·985). In some nestlings, we did not 

know their exact position in the laying order. In such 

cases, we assigned the average of the possible positions 

for the chick (e.g. if we knew that the chick hatched 

either from egg 7 or 8, its position in the laying order 

was assigned to be 7·5). 

During incubation we captured females, weighed 

them on a spring Pesola balance (to the nearest 0·25 g), 

and measured their tarsus with a digital caliper (to the 

nearest 0·01 mm). We also quantified male and female 

feeding rate per hour when nestlings were 7–11 days 

old (median = 9 days). We set up a camera c. 5–10 m 

from the nest-box and filmed feeding activity for 75 min. 

We then discarded the first 15 min of the recording and 

counted number of feeds provided by male and female 

during subsequent 60 min. 

analysis of carotenoids 

In the yolk of the collected eggs we determined 

concentrations of lutein, zeaxanthin, α-carotene 

and 

β-carotene. 

To extract carotenoids, weighted amount 

of egg yolk (on an average of 200 µ L) was homogenized 

with 2 mL of a mixture of 4% NaCl solution and 

ethanol (1 : 1, v/v) followed by sonication for 7 min. 

We then added 3 mL of hexane and further homogenized 

for 5 min. Yolk was then drawn into the tubes and 

centrifuged at 6000 r.p.m. for 5 min. After centrifugation 

hexane was collected and the extraction was 

repeated three times. Hexane extracts were combined 

and evaporated under N2 

at room temperature, and 

the residue was dissolved in 1·5 mL of acetonitrile : 

dichloromethane (1 : 1, v/v) and centrifuged. The 

supernatant was used for carotenoid determination. 

Carotenoids were determined by high performance 

liquid chromatography equipped with a binary LC pump 

Model 250 (Perkin Elmer, Norwalk, CT, USA), using 

two sequential LICHROCARTTM PUROSPHERTM 

RP18 columns (250 × 4nn I.D) maintained at 40 ° C by 

a column block heater. A mobile phase of acetonitrile : 

methanol (85 : 15) and acetonitrile : dichloromethane : 

methanol (70 : 20 : 10, v/v) in linear gradient elution 

with PDA detection (Series 200, Perkin Elmer) at 450 nm 

was used. Peaks were identified and quantified using 

reference carotenoids kindly supplied by Carotenature 

(Lupsingen, Switzerland). 

data analysis 

Data were analysed by general linear models. For 

every response variable (i.e. offspring and parental 

traits), we fit a separate model with treatment as the 

main predictor of interest and other explanatory 

variables that have been previously shown to be important 

as covariates. Initially, we included habitat (oak vs 

spruce) and season (Julian date of the first egg, date 

1 = 1 January) as covariates to all models. We analysed 

these response variables (additional covariates in 

parentheses): carotenoid concentration, hatching 

success (clutch size), nestling survival until day 14 and 

nestling mass at day 6 (brood size at hatching), nestling

779 


breeding 

performance 






21, 

776–783 

tarsus length at day 14 (brood size at day 14, female 

tarsus length), nestling body mass at day 14 and T-cell 

mediated immunocompetence (brood size at day 14, 

tarsus length at day 14), clutch size (female tarsus 

length), female body mass (brood size, female tarsus 

length, day of the nest cycle when captured), and male 

and female feeding rate per hour (brood size at 

feeding, hour of day, age of the young). In the case of 

nestling mass and tarsus length we used the young 

originating from late eggs that had the greatest chance 

to be affected by supplementation (the three nestlings 

used for the PHA test, see above). However, analyses 

using mean values for all the young in the nest generated 

identical results (results not shown). Initially, we also 

included interactions between the treatment and all 

other factors. We gradually removed non-significant 

predictors beginning with interactions until only significant 

factors remained in the model (at α = 0·05), 

with the exception of treatment. It was always retained 

as the main factor of interest. Variables were checked 

for departures from normality and appropriately 

transformed if necessary. We checked the reliability 

of our results by calculating standardized effect sizes 

(difference in least squares means of the dependent 

variable between supplemented and control groups/ 

SD of the total sample) with their 95% confidence 

limits. All tests were performed in JMP software of 

SAS Institute, Cary, NC, USA. 

Results 

egg yolk carotenoids 

Egg yolk concentration of lutein was significantly 

increased in experimental nests ( F1,51 

= 20·4, P < 0·001; 

Fig. 1). This increase was within physiological levels 

experienced by birds in this population: average 

Fig. 1. Mean concentration (± 1 SE, in µg/g) of egg yolk 

carotenoids in the Great Tit in lutein-supplemented and 

control pairs. 

–1 

concentration in experimental eggs was 54·6 µ g g , 

whereas one-third (8 of 25) of control nests had lutein 

–1 

concentration very close to this value (49 µ g g or 

–1 

higher, maximum value in control eggs was 58·9 µ g g ). 

Although zeaxanthin tended to have higher concentration 

in experimental pairs, no other carotenoid differed 

between experimental and control pairs: zeaxanthin 

( F1,51 

= 3·4, P = 0·073), α-carotene (F1,47 = 2·2, P = 0·145) 

and β-carotene (F1,47 < 0·1, P = 0·944). Surprisingly, 

there was a negative relationship between the number 

of supplementation units eaten and lutein concentration 

in yolk in experimental pairs (r = –0·48, P = 0·009, 

N = 28; Fig. 2). However, the significance of this 

relationship was caused by one outlying nest and 

disappeared after its exclusion (r = –0·33, P = 0·097, 

N = 27). There was no significant relationship in 

control pairs (r = 0·13, P = 0·535, N = 25; Fig. 2). 

Since the overall effect of supplementation was 

positive (i.e. increased carotenoid concentration in 

experimental as compared to control pairs; Fig. 1), it is 

rather difficult to explain this negative correlation. It 

might be possible that in females that ate too many 

units and therefore had ingested a greater amount of 

lutein this interfered in some way with the incorporation 

into the egg yolk. However, this is only speculation and 

further research with precise doses of lutein would be 

needed to solve this puzzle. 

offspring traits 

There was no effect of supplementation on offspring 

performance-related traits, including hatching success 

(F 1,49 = 0·3, P = 0·597), nestling survival from hatching 

to day 14 of age (F 1,47 = 0·2, P = 0·684), nestling mass 

at day 6 (F 1,47 = 0·7, P = 0·402), nestling mass at day 14 

(F 1,42 = 0·3, P = 0·571), nestling tarsus length at day 14 

(F 1,42 = 0·5, P = 0·478) and T-cell mediated immunocompetence 

(F 1,42 = 0·5, P = 0·484; Table 1). 

On the other side, these traits were significantly 

related to some covariates. Hatching success was 

Fig. 2. Relationship between lutein concentration in egg yolk 

(µg/g) and number of supplemental units eaten fit separately 

for lutein-supplemented and control pairs. One extreme value 

(lutein concentration = 156·7 µg g –1 , no. of units eaten = 5) is 

omitted from the figure (see Results).

780 Table 1. Least squares means (1SE) of offspring and parental traits in lutein- 

V. supplemented Reme3 et al. and control pairs. LS means are from final models with only significant 

covariates retained 






21, 776–783 

positively related to clutch size (F 1,49 = 6·2, P = 0·016; 

whole model: F 2,49 = 3·3, P = 0·045, R 2 = 0·12), nestling 

survival was higher in the oak than in the spruce 

habitat (F 1,47 = 15·0, P < 0·001; whole model: F 2,47 = 7·5, 

P = 0·001, R 2 = 0·24) and body mass at day 6 was 

negatively related to brood size (F 1,47 = 4·3, P = 0·043; 

whole model: F 2,47 = 2·6, P = 0·089, R 2 = 0·10). 

Further, body mass at day 14 was higher in the oak 

than in the spruce habitat (F 1,42 = 9·2, P = 0·004) and 

positively related to tarsus length (F 1,42 = 42·7, P < 0·001; 

whole model: F 3,42 = 19·3, P < 0·001, R 2 = 0·58), tarsus 

length at day 14 was positively related to both brood 

size (F 1,42 = 5·9, P = 0·019) and female tarsus length 

(F 1,42 = 6·7, P = 0·013; whole model: F 3,42 = 5·5, 

P = 0·003, R 2 = 0·28) and T-cell immunocompetence 

was positively related to brood size (F 1,42 = 11·7, P = 

0·001; whole model: F 2,42 = 5·9, P = 0·006, R 2 = 0·22). 

parental traits 

LS means (SE) 

Supplemented Control 

Offspring traits 

Hatching success (proportion hatched) 0·86 (0·012) 0·88 (0·014) 

Nestling survival (per 14 days) 0·75 (0·055) 0·78 (0·058) 

Nestling mass day 6 (g) 8·9 (0·20) 9·2 (0·22) 

Nestling mass day 14 (g) 16·9 (0·22) 17·0 (0·22) 

Nestling tarsus length (mm) 22·6 (0·11) 22·7 (0·12) 

Nestling T-cell immunocompetence (mm) 0·52 (0·026) 0·54 (0·026) 

Parental traits 

Clutch size (no. of eggs) 10·2 (0·30) 10·3 (0·31) 

Laying date (Julian date) 107·2 (0·66) 108·6 (0·73) 

Female feeding rate (per hour) 9·2 (1·23) 8·9 (1·31) 

Male feeding rate (per hour) 16·2 (1·61) 11·1 (1·72) 

Female body mass (g) 19·0 (0·17) 19·3 (0·17) 

There was a significant effect of lutein supplementation 

on clutch size but it depended on season (interaction: 

F 1,50 = 8·3, P = 0·006). In control nests, clutch size 

decreased with season whereas in experimental nests it 

changed nonlinearly – at first it increased, whereas 

later (after Julian day 105) it decreased in a similar 

way to control clutches (Fig. 3). Clutch size was 

furthermore positively affected by female tarsus length 

(F 1,50 = 6·2, P = 0·017) and was larger in the oak as 

compared to the spruce forest (F 1,50 = 16·1, P < 0·001; 

whole model: F 5,50 = 6·1, P < 0·001, R 2 = 0·38). Experimental 

and control pairs did not differ in their timing 

of breeding (F 1,58 = 2·1, P = 0·157). 

Female feeding rate did not differ between treatments 

(F 1,45 < 0·1, P = 0·869, R 2 < 0·01) whereas males 

on experimental nests fed more frequently than males 

on control nests (F 1,45 = 4·7, P = 0·036, R 2 = 0·09; 

Table 1). No other factors were significant in the 

analysis of feeding rates. Male and female feeding 

Fig. 3. Relationship between clutch size and season (Julian 

date of the first egg, date 1 = 1 January) fit separately for 

lutein-supplemented and control pairs. 

frequencies were not intercorrelated (r = –0·17, P = 

0·242, N = 47). Female body mass did not differ between 

treatments (F 1,50 = 1·8, P = 0·184), whereas it was higher 

in the oak habitat than in the spruce habitat (F 1,50 = 

10·5, P = 0·002), scaled positively with female tarsus 

length (F 1,50 = 20·5, P < 0·001), and negatively with the 

day of the nest cycle at capture (F 1,50 = 32·6, P < 0·001; 

whole model: F 4,50 = 19·9, P < 0·001, R 2 = 0·61). 

checking the reliability of the 

results 

In four experimental nests, the concentration of egg 

yolk lutein was higher than the highest value in any 

control nest. In these nests, unnaturally high doses of 

lutein could have toxic effects on nestlings. Then, 

mixing of beneficial (physiologically high levels) and 

toxic (pharmacological levels) effects of egg carotenoids 

in one analysis could have prevented any beneficial 

effects showing up. Thus, we repeated all the above 

analyses without those four nests. However, the results 

did not change. It seems that any potentially harmful 

effects of very high doses of carotenoids did not 

compromise our analyses. 

For the standardized effect sizes (with confidence 

intervals) of carotenoid supplementation treatment on 

offspring and parental performance traits see Fig. 4. 

Discussion 

To investigate carotenoid limitation on egg formation 

and reproduction in wild birds, we supplemented 

prelaying and laying female Great Tits with lutein, the 

most abundant egg yolk carotenoid in this species 

(Partali et al. 1987). We showed that this supplementation 

had a clear effect on egg composition, because 

yolks of supplemented females had significantly more 

lutein than those of control females (Fig. 1). Strong 

effect of lutein supplementation on its concentration in 

egg yolk is not surprising. Increased concentrations of 

yolk carotenoids in females supplemented with carotenoids 

in their diet were demonstrated in both captive

781 


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performance 






21, 776–783 

Fig. 4. Standardized effects (with 95% CIs) of treatment on 

offspring and parental, performance-related traits. Vertical 

dashed lines denote small (0·2), medium (0·5) and large (0·8) 

effects, respectively, according to Cohen (1988). For the 

definition of the traits, see Methods. 

(summarised in Bortolotti et al. 2003; McGraw et al. 

2005) and wild birds (Blount et al. 2002b; Biard et al. 

2005; Ewen et al. 2006; Berthouly et al. 2007). 

On the other hand, subsequent effects on offspring 

performance were negligible, whereas the effects on 

parental traits were slightly stronger. Clutch size and 

male feeding responded significantly to the supplementation, 

and although non-significant, confidence 

intervals for timing of laying included a strong 

negative effect (i.e. advancement of laying date; see 

Fig. 4), which suggests that it might have gone 

undetected because of low statistical power. The effect 

on clutch size was only apparent in the interaction 

with season (Fig. 3). These results by and large conform 

to the scenario 2 (see Introduction). In this scenario, 

parents are not carotenoid limited in their current 

reproductive bout and the female bird just channels 

surplus micronutrients into the eggs. However, males 

on supplemented nests fed their offspring more than 

males on control nests. This could mean that males 

might have been limited in the intensity of parental 

care, which would conform to the scenario 3. Parents 

may also have been limited by carotenoids in their selfmaintenance 

and future reproduction. However, based 

on our data we were not able to test this possibility and 

it remains an interesting challenge for future work. 

We showed that clutch size decreased with the 

advancement of the laying season in control pairs 

whereas it changed nonlinearly in supplemented pairs 

(Fig. 3). This pattern of clutch size change with the 

season is quite puzzling. Whereas one study found a 

beneficial effect of carotenoid supplementation on 

laying potential of females (in Lesser Black-backed 

Gulls, Larus fuscus, Blount et al. 2004) other experiments 

did not demonstrate any effects (Biard et al. 2005; 

McGraw et al. 2005). Moreover, since there was no 

significant effect of carotenoid supplementation on 

laying date, we have currently no explanation for the 

pattern found. 

Another interesting result is the higher feeding rate 

of males on supplemented nests as compared to 

control nests. This may have been caused by better 

male condition if they also consumed the supplement, 

in which case they would be carotenoid limited in their 

current reproductive bout. Alternatively, they may 

have been willing to increase paternal investment 

in supplemented broods where supplementation may 

have made either females or offspring more attractive 

and worthy of increased investment. In this case this 

result would not be indicative of carotenoid limitation 

in males but rather of differential allocation of parental 

effort (see Sheldon 2000). However, the plausibility of 

this explanation is decreased by the finding in a recent 

study of the Great Tit that the young supplemented by 

carotenoids are not more attractive to parents and the 

parents do not increase their investment (Tschirren, 

Fitze & Richner 2005). If proved by further studies, 

higher feeding rate of males on supplemented nests 

would be an interesting observation since we currently 

know virtually nothing about possible relationships 

between male carotenoid supply, health and physiology, 

and paternal investment in birds (Blount 2004). 

There are several nonexclusive explanations for 

weak to absent positive effects of our supplementation 

on offspring performance. Confidence intervals for the 

effect of supplementation on offspring traits did not 

embrace either middle or large positive effects (standardised 

effects of 0·5 and 0·8, respectively, according 

to Cohen 1988; see Fig. 4). There is a possibility that 

there were small positive effects (standardized effect 

size of 0·2) that we were not able to detect with our 

sample size. However, if there were any important (i.e. 

middle or large) positive effects of extra carotenoids in 

eggs on offspring performance, we would have been 

able to detect them. 

Three biologically interesting explanations seem 

to be worth discussing. First, the detectability of potential 

effects may depend upon the amount of carotenoids 

already present in the egg. All eggs may have been 

supplied with carotenoids to such an extent that any 

increase brought about by our supplementation had 

no detectable health and performance related benefits 

for the offspring. It is known that beneficial effects 

of carotenoids are dose-dependent, increasing with 

increasing amounts supplemented but later reaching a 

plateau (Alonso-Alvarez et al. 2004). 

Second, the antioxidant system of birds consists of 

an integrated system of substances, including enzymes, 

water-soluble and fat-soluble antioxidants. Vitamin E 

is a fat-soluble antioxidant present in bird egg yolk, 

including the Great Tit (Hõrak et al. 2002). It is 

transferred from egg yolk to the developing young 

and increases resistance to oxidative damage of tissues 

(Surai, Noble & Speake 1999). Our supplementation 

included small amounts of α-tocopherol (see Methods). 

It could be possible that α-tocopherol enhanced 

the antioxidant system of developing chicks in both 

experimental and control nests to such an extent that 

its further enhancement by lutein in experimental nests 

was not detectable. However, in such a case, young

782 

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21, 776–783 

birds would have to be much more sensitive to αtocopherol 

than to lutein. Great Tit yolk (c. 0·35 g, V. 

Remes, unpublished data) contains about 54 µg of 

α-tocopherol (c. 155 µg g –1 ; Hõrak et al. 2002) and about 

13 µg of lutein (c. 35–38 µg g –1 ; this study, average for 

control pairs; Hõrak et al. 2002). We supplemented 

about 250 µg of α-tocopherol (4·6 × the amount in one 

yolk) and 1750 µg of lutein (135 × the amount in one 

yolk) daily. Thus, we supplemented about 29 times 

more intensely with lutein than with α-tocopherol. 

Accordingly, this explanation does not seem likely. 

However, factorial experiments supplementing laying 

mothers with different antioxidant system-enhancing 

micronutrients (e.g. Surai 2000) in the wild will be 

needed to resolve this issue. 

Third, conflicting results of our study and previous 

ones could be explained by different study organisms. 

For instance, Biard et al. (2005) studied Blue Tits 

(P. caeruleus). In these smaller birds clutch mass 

comprises a relatively larger proportion of female 

body mass than in the closely related Great Tit. Thus, 

these authors suggest that laying females in this species 

need relatively more carotenoids and are thus more 

carotenoid limited than Great Tits (see also Biard, 

Surai & Møller 2006). This view concerns limitation 

during acquisition of resources. On the other hand, 

species differ in their resolution of the trade-off between 

current and future reproduction based on their 

position on the slow-fast life-history continuum 

(Ghalambor & Martin 2001). This might drive the 

species-specific patterns of allocation of acquired 

(supplemented) micronutrients between offspring and 

self-maintenance. Life-history differences between 

species together with carotenoid supply in the environment 

might thus be responsible for conflicting results 

of carotenoid-supplementation experiments. For 

further development of this area, it will be critical to 

perform similar supplementation studies on various 

species differing in their life-history strategies, while at 

the same time also following allocation of supplemented 

carotenoids to self-maintenance and future reproduction. 

In general, carotenoid-supplementation studies 

that generated clear and strong positive effects on 

offspring performance were either performed in 

captivity (McGraw et al. 2005) or carotenoids were 

injected directly into the eggs in the wild (Saino et al. 

2003). Food supplementation studies made in the wild 

up to now generated weak and unconvincing results 

(Biard et al. 2005; Berthouly et al. 2007; this study). 

Decisiveness of these weak results becomes even 

lower in the light of the number of statistical tests often 

performed with inherently increased probability of 

statistical error and finding false relationships (see 

also de Ayala, Martinelli & Saino 2006). This is surprising 

given the many beneficial health-related effects of 

carotenoids (see above, but see McCall & Frei 1999). 

It may be more difficult to detect beneficial effects 

of carotenoids in the wild because of less well controlled 

experimental conditions. Wild birds may also have 

enough carotenoids to provide to their young with 

resulting sufficient antioxidant protection. Further 

experimental increase of carotenoids may then operate 

in the plateau region of the dose-dependent relationship 

between carotenoid concentration and beneficial 

effects (Alonso-Alvarez et al. 2004). Experiments with 

carotenoid-deplete and carotenoid-replete eggs in seminatural 

conditions could help to resolve this issue 

(e.g. Koutsos et al. 2003). Alternatively, the antioxidant 

system of the young may be supported by other antioxidants 

to a sufficient level, again precluding any 

potentially beneficial effects of carotenoids to show up. 

These thoughts are in line with weak beneficial effects 

of direct supplementation of nestling food with dietary 

carotenoids (Biard et al. 2006) and with vitamin E 

(de Ayala et al. 2006) in the wild. Moreover, the effects 

detected by Biard et al. (2006) differed between species, 

and the authors suggested that the potential for 

beneficial effects of supplemental antioxidants might 

vary with the life-history strategy of the particular 

species. Similar species-specific effects were found in 

the relationships between carotenoids, immune function 

and male ornamentation in birds (summarized by 

Blount 2004). More studies on diverse species taking 

into account broader spectrum of antioxidants and 

employing more sophisticated study design are clearly 

needed to resolve these interesting issues. 


This study was supported by a grant from the Ministry 

of Education of the Czech Republic (MSM6198959212) 

and GACR (206/07/P485). We are obliged to Urseta, 

s.r.o. for providing lutein supplementation. This study 

was approved by the Ethical Committee of Palacky 

University and complies with the current law of the 

Czech Republic. 

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Received 5 October 2006; revised 16 December 2006; accepted 

19 March 2007 

Editor: Kevin McGraw

ACTA ORNITHOLOGICA 

Vol. 45 (2010) No. 1 

The design of artificial nestboxes for the study of secondary hole-nesting 

birds: a review of methodological inconsistencies and potential biases 

Marcel M. LAMBRECHTS*, Frank ADRIAENSEN, Daniel R. ARDIA, Alexandr V. ARTEMYEV, 

Francisco ATIÉNZAR, Jerzy BAŃBURA, Emilio BARBA, Jean-Charles BOUVIER, Jordi 

CAMPRODON, Caren B. COOPER, Russell D. DAWSON, Marcel EENS, Tapio EEVA, Bruno 

FAIVRE, Laszlo Z. GARAMSZEGI, Anne E. GOODENOUGH, Andrew G. GOSLER, Arnaud 

GRÉGOIRE, Simon C. GRIFFITH, Lars GUSTAFSSON, L. Scott JOHNSON, Wojciech KANIA, 

Oskars KEIŠS, Paulo E. LLAMBIAS, Mark C. MAINWARING, Raivo MÄND, Bruno MASSA, 

Tomasz D. MAZGAJSKI, Anders Pape MqLLER, Juan MORENO, Beat NAEF-DAENZER, 

Jan-Cke NILSSON, Ana C. NORTE, Markku ORELL, Ken A. OTTER, Chan Ryul PARK, 

Christopher M. PERRINS, Jan PINOWSKI, Jiri PORKERT, Jaime POTTI, Vladimir REMES, Heinz 

RICHNER, Seppo RYTKÖNEN, Ming-Tang SHIAO, Bengt SILVERIN, Tore SLAGSVOLD, Henrik 

G. SMITH, Alberto SORACE, Martyn J. STENNING, Ian STEWART, Charles F. THOMPSON, Piotr 

TRYJANOWSKI, Janos TÖRÖK, Arie J. van NOORDWIJK, David W. WINKLER & Nadia ZIANE 

*Centre d’Ecologie Fonctionnelle et Evolutive, UMR 5175, CNRS, 1919 route de Mende, 34293 Montpellier Cedex 5, 

FRANCE, e-mail: marcel.lambrechts@cefe.cnrs.fr 

All authors’ affiliations — see appendix 2 

Lambrechts M. M., Adriaensen F., Ardia D. R., Artemyev A. V., Atiénzar F., Bańbura J., Barba E., Bouvier J.-C., 

Camprodon J., Cooper C. B., Dawson R. D., Eens M., Eeva T., Faivre B., Garamszegi L. Z., Goodenough A. E., Gosler 

A. G., Grégoire A., Griffith S. C., Gustafsson L., Johnson L. S., Kania W., Keišs O., Llambias P. E., Mainwaring M. C., 

Mänd R., Massa B., Mazgajski T. D., Mrller A. P., Moreno J., Naef-Daenzer B., Nilsson J.- Å., Norte A. C., Orell M., 

Otter K. A., Park Ch. R., Perrins Ch. M., Pinowski J., Porkert J., Potti J., Remes V., Richner H., Rytkönen S., Shiao M.- 

T., Silverin B., Slagsvold T., Smith H. G., Sorace A., Stenning M. J., Stewart I., Thompson Ch. F., Török J., Tryjanowski 

P., van Noordwijk A. J., Winkler D. W., Ziane N. 2010. The design of artificial nestboxes for the study of secondary 

hole-nesting birds: a review of methodological inconsistencies and potential biases. Acta Ornithol. 45: 1–26. 

DOI 10.3161/000164510X516047 

Abstract. The widespread use of artificial nestboxes has led to significant advances in our knowledge of the ecology, 

behaviour and physiology of cavity nesting birds, especially small passerines. Nestboxes have made it easier to perform 

routine monitoring and experimental manipulation of eggs or nestlings, and also repeatedly to capture, identify and 

manipulate the parents. However, when comparing results across study sites the use of nestboxes may also introduce 

a potentially significant confounding variable in the form of differences in nestbox design amongst studies, such as their 

physical dimensions, placement height, and the way in which they are constructed and maintained. However, the use 

of nestboxes may also introduce an unconsidered and potentially significant confounding variable due to differences 

in nestbox design amongst studies, such as their physical dimensions, placement height, and the way in which they are 

constructed and maintained. Here we review to what extent the characteristics of artificial nestboxes (e.g. size, shape, 

construction material, colour) are documented in the ‘methods’ sections of publications involving hole-nesting passerine 

birds using natural or excavated cavities or artificial nestboxes for reproduction and roosting. Despite explicit previous 

recommendations that authors describe in detail the characteristics of the nestboxes used, we found that the 

description of nestbox characteristics in most recent publications remains poor and insufficient. We therefore list the 

types of descriptive data that should be included in the methods sections of relevant manuscripts and justify this by 

discussing how variation in nestbox characteristics can affect or confound conclusions from nestbox studies. We also 

propose several recommendations to improve the reliability and usefulness of research based on long-term studies of 

any secondary hole-nesting species using artificial nestboxes for breeding or roosting. 

Key words: methods, nestboxes, nest sites, passerines, secondary cavity-nesting birds, field experiments, tit, flycatcher, 

Ficedula, Parus, Cyanistes 

Received — Aug. 2009, accepted — April 2010

2 M. M. Lambrechts et al. 

INTRODUCTION 

Half of the avian orders use some form of cavity 

for nesting or roosting (Gill 2007), with up 

to 30% of the bird species present in some locations 

being cavity-nesters (Newton 1994, Bai & 

Mühlenberg 2008). While primary hole-nesters 

such as woodpeckers are able to excavate their 

own nest holes in trees, obligate secondary holenesters 

do not have the physical force to drill large 

holes in hard wood, although some are capable of 

modifying or enlarging existing cavities in dead or 

decaying trees (Newton 1994, Schepps et al. 1999, 

Martin & Norris 2007, Atienzar et al. 2009). 

Consequently, secondary hole-nesters must rely 

upon natural tree cavities or unoccupied holes 

excavated by primary hole-nesters (Martin & 

Eadie 1999, Remm et al. 2006). Thus, the availability 

and characteristics of tree holes suitable for secondary 

hole-nesters is partly influenced by the 

activity and abundance of primary hole-nesting 

species, which in turn depend upon the characteristics 

of the tree species present (e.g. size and age, 

architecture, hardness) as well as the activity of 

other taxonomic groups, such as micro-organisms 

(including fungi), insects, amphibians, reptiles, 

and mammals (Conner 1977, Wilson et al. 1991, 

Bednarz et al. 2004, Jackson & Jackson 2004, Ojeda 

et al. 2007, Wesołowski 2007, Camprodon et al. 

2008, Koch et al. 2008, Lambrechts et al. 2008, 

Matsuoka 2008). Fluctuations in external factors 

such as ambient temperature and the intensity 

and direction of wind or rain can also influence 

the availability and characteristics of holes (e.g. 

East & Perrins 1988, Walankiewicz 1991, 

Wesołowski et al. 2002). For instance, persistent 

strong winds or rain may increase the erosion of 

existing small cavities, blow branches from trees 

so that new cavities are created, or blow down 

dead trees containing cavities. 

Each natural hole found in a dead or living tree 

probably has a unique combination of variables, 

such as the position, orientation and shape of the 

entrance hole, and the cavity’s material, wall 

thickness, depth, diameter, floor area, shape, 

colour, volume, internal surface, light conditions 

and age (van Balen et al. 1982, Nilsson 1984, East 

& Perrins 1988, Rendell & Robertson 1989, Carlson 

et al. 1998, Czeszczewik & Walankiewicz 2003, 

Wesołowski & Rowiński 2004, Mazgajski 2007b; 

Table 1). The size or other characteristics of cavities 

occupied by secondary hole-nesting birds can 

vary within and between species (Table 1). Studies 

of these cavities can provide the necessary 

background data for the better design of research 

that uses man-made nestboxes (e.g. 

Nilsson 1975, 1984, Mrller 1989, 1992, Wesołowski 

2007). 

The use of nestboxes to study secondary 

hole-nesters 

Many secondary hole-nesting species readily 

breed in man-made artificial cavities, most of 

which are nestboxes placed against tree trunks, 

fences or walls, or erected on posts (von 

Haartman 1969, Kibler 1969, Perrins 1979, Pikula & 

Beklova 1980, Newton 1994, Lesiński 2000, Zingg 

et al. 2010). The use of such artificial cavities in 

avian research has greatly advanced our understanding 

of breeding behaviour in cavity-nesting 

species. Nestboxes allow researchers to perform 

routine monitoring and experimental manipulation 

of eggs or nestlings, as well as repeatedly capture, 

identify and manipulate the parents or offspring 

(e.g. Sanz 1998, Visser et al. 2003, Both et al. 

2004, Griffith et al. 2008). The use of nestboxes 

often increases the local population of secondary 

hole-nesters available for study, and it may also 

help to better control, reduce, or eliminate stochastic 

effects associated with abiotic factors or 

predation, thus increasing sample sizes and facilitating 

data analyses or interpretation. Since the 

pioneering investigations of G. Wolda in the 

Netherlands (Kluyver 1951, Lack 1955), artificial 

nestboxes have been used to aid research in a 

range of sub-disciplines of the behavioural and 

environmental sciences e.g. behavioural ecology, 

cognitive ecology, conservation biology, ecotoxicology, 

evolutionary ecology, functional ecology, 

molecular ecology, population ecology (e.g. Busse 

& Olech 1968, Perrins 1979, Lundberg & Alatalo 

1992, Koenig et al. 1992, Newton 1994, Schlaepfer 

et al. 2002, Blondel et al. 2006, Seppänen & 

Forsman 2007, Slagsvold & Wiebe 2007, Mazgajski 

2008, Liedvogel et al. 2009, Mänd et al. 2009, 

Holveck et al. 2010, Van den Steen et al. 2010, 

Zingg et al. 2010). Nestbox studies have also contributed 

significantly to the development of lifehistory 

theory in free-ranging organisms (Clut - 

ton-Brock 1988, Newton 1989) because of the ease 

with which manipulations can be performed. 

Consequently, secondary hole-nesting passerines 

have become one of the most intensively investigated 

free-living bird groups of the world (e.g. 

Riddington & Gosler 1995), and occupy top positions 

with respect to the total number of papers 

returned by two ISI Web of Science searches of 

passerine studies by common and scientific name

Table 1. Mean (± SD) dimensions of natural nest holes in three European hole-nesting species. Ranges in brackets. N — sample size. † — data from different seasons, * — calculated 

from figures. 

Source 

Height above entrance diameter holes depth bottom area 

the ground (m) (smallest value, cm) (cm) (cm2 ) 

Species Habitat Country N 

Parus major 

deciduous UK 10–11 2.67 ± 1.53 4.9 ± 2.0* Edington & Edington 1972 

deciduous UK 7–10 5.9 5.7 ± 2.6; 35.8 ± 25.1; East & Perrins 1988 

(0.4–11) 5.9 ± 1.6† 35.8 ± 29.3† 

NL 33 2.6 ± 0.9 3.9 ± 1.4 139.2 ± 64.3 van Balen et al. 1982 

deciduous/mixed SE 10–16 2.3 3.5 ± 0.6* 21.0 ± 6.0 (11–32) Nilsson 1984 

deciduous SE 20 5.2 ± 2.2 4.5 ± 2.0 15 ± 15 249 ± 189 Carlson et al. 1998 

ash-alder PL 10 5.2 Wesołowski 1989 

oak-hornbean PL 27 8.7 Wesołowski 1989 

riverine EE 14–17 6.5 (2.2–12) 3.8 (2.5–5.5) 20.9 (8.0–45.0) Remm et al. 2006 

boreal/riparian MN 16 5.0 ± 2.7 4.1 ± 1.3 Bai et al. 2005 

Design of artificial nestboxes 3 

Cyanistes caeruleus 

deciduous UK 10 3.24 ± 1.73 3.1 ± 1.2* Edington & Edington 1972 

deciduous UK 13–17 5.5; 7.4 † 5.4 ± 2.6; 22.6 ± 6.96; East & Perrins 1988 

(0.3–10.3) 5.5 ± 2.5† 24.1 ± 5.71† 

NL 20 4.0 ± 2.4 4.0 ± 1.6 88.5 ± 66.4 van Balen et al.1982 


deciduous/mixed SE 10-11 4.4 3.2 ± 0.8* 13.7 ± 7.1 Nilsson 1984 


oak-hornbean PL 81 8.3 

oak-hornbean PL 53 8.8 (1.5–18.7) 2.6 15.3 Hebda 2007 

riverine EE 14–20 5.6 (0.5–12) 2.7 (2.1–3.2) 21.4 (3.5–19.0) Remm et al. 2006 

Ficedula hypoleuca 

deciduous UK 17–18 2.37 ± 1.32 3.9 ± 1.2* Edington & Edington 1972 


deciduous/mixed SE 3.3 Nilsson 1984 

deciduous SE 105 (33–735) Alatalo et al. 1988 


oak-hornbean PL 18 6.3 Wesołowski 1989 

oak-hornbean PL 102–180 8.2 ± 4.6 3.5 ±1.1 19.3 ± 7.6 205 ± 525 Czeszczewik & 

(1.2–23) (1.5–8.5) (6–51) (28–2826) Walankiewicz 2003 

riverine EE 24–27 6.7 (2.0–12.5) 3.8 (2.5–5.7) 16.6 (3.0–45.0) Remm et al. 2006


(Average number of publications of the two 

searches in 2010 for cavity-nesters: Parus major — 

1807, Sturnus vulgaris — 1293, Passer domesticus — 

999, Ficedula hypoleuca — 880, Cyanistes caeruleus — 

756, Tachycineta bicolor — 744, Sialia sialis — 414 

versus non-cavity nesters: Hirundo rustica — 663, 

Melospiza melodia — 609, Agelaius phoeniceus — 488, 

Carpodacus mexicanus — 335). 

However, studies involving birds breeding in 

nestboxes have been criticized on the grounds 

that the boxes differ in several ways from natural 

or excavated cavities, and that avian research 

in free-ranging populations is overwhelmingly 

dominated by a few ‘classic’ model systems. 

Consequently results derived from these studies 

may fail to reflect natural variation potentially 

reducing their general validity or applicability 

(e.g. Nilsson 1975, Mrller 1989, 1992, but see 

Koenig et al. 1992, Wesołowski 2007). For instance, 

van Balen et al. (1982) cites the main difference 

between natural holes and artificial nestboxes 

erected in the same location as a higher rate of 

nest failure in natural holes because of water logging 

or competition from bigger animals, factors 

that are often excluded when nestboxes are used. 

Whether nestboxes are safer than natural cavities 

from nest predators or competitors may depend 

on differences in height or positioning between 

artificial and natural cavities (Nilsson 1984, 

McCleery et al. 1996), or whether protective devices 

have been added to the nestboxes to reduce predation. 

For instance, metal plates or wire mesh fitted 

around the entrance hole of nestboxes may 

prevent the hole being enlarged by woodpeckers 

or mammals, and pipes placed in the entrancehole 

may prevent predators from reaching the 

contents. Also, treating wooden nestboxes with 

chemicals, such as preservatives or pesticides, 

may prevent them from decay due to fungal rot or 

burrowing arthropods (e.g. Kibler 1969, Nilsson 

1984, McCleery et al. 1996, Miller 2002, Main - 

waring & Hartley 2008, Skwarska et al. 2009). 

Nestboxes are also designed so that researchers 

can frequently inspect their contents which may 

cause rapid changes in the chemical environment 

within the nest (e.g. CO 2 concentrations influencing 

attractiveness of flying insects exploiting avian 

hosts — see Tomás et al. 2008) or micro-climate 

that do not occur in natural or excavated holes. 

Nestbox studies may also be prone to biased sampling 

because they may only be occupied by individuals 

or species that accept artificial cavities, 

while the remainder retain a preference for 

natural or excavated cavities (Dhondt 2007). The 

proportion of individuals that use either natural 

or artificial holes may therefore depend on the difference 

in quality between the two or the distribution 

of individual preferences for different breeding 

sites. The interval between the placement of 

nestboxes and the start of a study may also result 

in biased sampling. For instance, if nestboxes are 

initially erected just a few weeks before the start 

of the breeding season of resident species, nonterritorial 

first-year birds may be attracted to 

study sites that do not provide opportunities for 

winter roosting in natural cavities. 

While there are many benefits of the intensive 

study of model species, there is an obvious danger 

of basing our general understanding of birds on 

such a small number of cavity-nesting species that 

share a similar ecology and are mostly located in 

just one region of the world (Europe). To date, 

while there are many studies of a variety of holenesters 

in North America they have not dominated 

the literature to the same extent as have the 

European studies (e.g. see also Chamberlain et al. 

2009). There are whole regions of the world in 

which nestboxes have never been used, although 

studies have recently been published on avian 

nestbox exploiters in Australia (Griffith et al. 2008), 

Argentina (Massoni et al. 2006, Cockle & Bodradi 

2009, Llambias & Fernández 2009), Chile (Moreno 

et al. 2005, 2007) and China (Wang et al. 2008) (but 

see for instance older studies in Tryjanowski et al. 

2006 and Evans et al. 2009 for New Zealand or 

Eguchi 1980 for Japan). It should also be pointed 

out that nestboxes in some countries are widely 

distributed throughout the countryside by 

forestry administrations or environmental organizations 

(e.g. Poland, Spain). For instance, thousands 

of nestboxes of a specific design have been 

erected for >50 years in many Polish and Spanish 

forests, constituting an unavoidable part of the 

environment for hole-nesters that does not 

depend on researchers and that may have 

induced significant selective pressures on populations. 

There exist biases in the material used (e.g. 

nestbox design), the choice of the model species, 

and the environment (e.g. European woodland) 

in which research is conducted. The extent to 

which all this is really a problem is difficult to 

evaluate since few studies have compared biological 

aspects of birds which breed in nestboxes 

and those which breed in natural or excavated 

cavities at the same location (but see e.g. East & 

Perrins 1988, Johnson & Kermott 1994, Miller 2002, 

Llambias & Fernández 2009 and references

therein). However, this issue can be evaluated 

indirectly by examining whether the characteristics 

of artificial nestboxes and their occupants 

differ from those of natural or excavated nestholes 

(e.g. Dhondt 2007), and if there are differences, 

whether these would affect the likelihood 

of supporting or rejecting a particular hypothesis. 

The ecological significance of variation in nestbox 

characteristics 

Several studies indicate that the characteristics 

of each nestbox influence the physical environment 

(e.g. ambient temperature, humidity, light 

reflectance) or biotic environment (e.g. presence 

and biology of other organisms, parent-offspring 

interactions) within the nest-chamber, and therefore 

the development or survival of the eggs or 

nestlings, or the survival or physical condition of 

adults using nestboxes for reproduction or roosting 

(Löhrl 1973, van Balen 1984, Slagsvold & 

Amundsen 1992, Mazgajski 2007a, Dhondt et al. 

2010). The internal size of the nest cavity may also 

influence clutch size, depending on the size 

ranges of the nest-chamber or species involved 

(e.g. Karlsson & Nilsson 1977, Moeed & Dawson 

1979, Löhrl 1980, van Balen 1984, Gustafsson & 

Nilsson 1985, Slagsvold & Amundsen 1992). In 

Great Tits Parus major, for instance, an experiment 

in which nestboxes were replaced with a different-sized 

box shortly after the onset of egg laying 

found that size differences in subsequently-produced 

clutches may be as large as the variation in 

clutch size observed across distinct forest types 

(e.g. > 2 eggs, Löhrl 1973, 1980, Sanz 1998). In Pied 

Flycatchers Ficedula hypoleuca, this may not be 

related to an adjustment of clutch size to cavity 

size per se, but could for example result from 

females laying larger clutches selecting larger cavities 

for breeding or spending more time to find a 

large nest cavity (Slagsvold 1987). 

Several other variables seem intuitively likely 

to affect whether a nestbox is occupied, and if so, 

the success of each breeding attempt. For example, 

erecting boxes of varying dimensions at the 

same location has shown that different individuals 

or species use cavities with different characteristics. 

The size and position (e.g. height, orientation) 

of the entrance hole appears to determine 

which individual or species will occupy nestboxes 

and how their life-history traits will be expressed 

in the presence of other organisms (e.g. Kluyver 

1951, Nilsson 1984, Barba & Gil-Delgado 1990, 

Dhondt & Adriaensen 1999, Zingg et al. 2010). For 

example, large individuals or species cannot enter 


small entrance holes or nestbox chambers simply 

because of physical constraints. Furthermore, 

smaller individuals or species may prefer to breed 

in nestboxes with small entrance holes, particularly 

those located high in trees, to reduce risks related 

to predation or competition (Löhrl 1970, 1977, 

Slagsvold 1975, Nilsson 1984, Newton 1994, Sorace 

& Carere 1996). 

A given nestbox type may be preferred in one 

environment and yet disfavoured in another. For 

instance, local meteorological effects may influence 

the preferences for certain nest-cavities 

with birds avoiding those with holes oriented in 

the direction of prevailing wind or rain (e.g. 

Goodenough et al. 2008). Orientation of nests has 

been found to influence site selection of natural 

cavities and artificial cavities in Tree Swallows 

Tachycineta bicolor (Rendell & Robertson 1994, 

Ardia et al. 2006), probably because internal nest 

temperatures differ as a function of orientation 

(Ardia et al. 2006). Orientation of occupied nestboxes 

may also be influenced by the location of 

conspecifics (Mennill & Ratcliffe 2004). According 

to Kluijver (1951), cavity preferences may differ 

between males and females and vary seasonally. 

Birds may only avoid nestboxes with larger holes 

in areas with high perceived predation risk (e.g. 

Sorace & Carere 1996). The cleaning practices of 

nestboxes may also affect the choice of nestboxes. 

(Mazgajski 2007a). For example, Pied Flycatchers 

may gain time benefits in nest building if the old 

nest is not removed (Orell et al. 1993, Mappes et 

al. 1994), House Wrens Troglodytes aedon may prefer 

nestboxes containing old nests because they 

provide evidence of previous success at that location 

(Johnson 1996, Pacejka & Thompson 1996), or 

Great Tits may avoid nestboxes containing old 

nests with many fleas (Rytkönen et al. 1998). Thus, 

the choice and location of nestbox type may interact 

with other environmental factors, such as the 

availability and properties of natural cavities, and 

significantly influence the outcome of ecological 

field investigations. 

To examine whether scientists working with 

secondary hole-nesting passerines have acknowledged 

the significance of variation in nestbox 

design, we assessed whether nestbox characteristics 

have been documented in the ‘methods’ sections 

of publications or incorporated into statistical 

analyses of long-term databases. We also discuss 

how variation in nestbox characteristics can 

affect or confound conclusions from nestbox studies 

and propose several recommendations to 

improve future research.


METHODS 

Three papers published over 15 years ago discussed 

the potential artefacts associated with the 

use of nestboxes, but also emphasised that many 

scientists simply did not report what they did 

with their nestboxes (Mrller 1989, 1992, Koenig et 

al. 1992). Field ornithologists were urged to: 1) 

improve the design of their nestboxes so that they 

mimic more closely the characteristics of natural 

or excavated holes and 2) describe the characteristics 

of their boxes and the procedures used for 

maintaining boxes to allow for the exact replication 

of protocols across studies (see also Kelly 

2006). To assess whether subsequent investigations 

followed these recommendations, we examined 

the methods section of publications involving 

the most commonly investigated hole-nesting 

birds. We divided the papers into two groups: 

those published before the recommendations of 

Koenig et al. (1992) and Mrller (1992) (“older” 

publications), and those published from 1992 

onwards (“more recent” publications). Based on 

the recommendations of Mrller (1989), we predicted 

that descriptions of nestbox characteristics 

would be more frequent among articles published 

from 1992 onwards. We located relevant publications 

by using the common or scientific (current or 

older) names of secondary hole-nesting passerines 

as key words in the leading electronic databases 

(ISI Web of Science, Biblio-Vie, BiblioSHS) 

and also searching the extensive collection of 

reprints possessed by P. Isenmann (CEFE- 

Montpellier). We searched the reference section of 

each of these publications to identify other relevant 

publications that we had not previously 

encountered because they were not electronically 

indexed. We are aware that publications are often 

not independent units, but we have chosen this 

entity because it does give the probability that a 

new reader misses the relevant information in the 

first paper they read. We surveyed a total of 696 

publications from 108 different journals, of which 

594 (85%) involved a single species. These publications 

concerned 12 frequently investigated secondary 

hole-nesting species, most of which were 

Eurasian tits (Paridae) or old world flycatchers 

(Muscicapidae) (Fig. 1). 

RESULTS AND DISCUSSION 

Our review produced four main results, 

leading to the overall conclusion that nestbox 

characteristics are variable and often unreported 

in the scientific literature and that the significance 

of these characteristics is often either ignored or 

underappreciated. 

1. Detailed descriptions of nestbox properties are 

often lacking in recent publications 

Around 40% of the older publications (

Parus 

major 

Cyanistes 

caeruleus 

Periparus 

ater 

Poecile 

palustris 

nestlings addressed to parents or social mates (e.g. 

Holveck et al. 2010). Since natural or excavated 

tree holes used by secondary hole-nesting birds 

can be described using at least 10 different parameters 

(Kibler 1969, van Balen et al. 1982, East & 

Perrins 1988, Carlson et al. 1998, Remm et al. 2006), 

perhaps these same parameters could be defined 

and standardized, then used to describe artificial 

nestboxes in widely understood details, as architects 

do when designing buildings. 

2. Nestboxes often represent only a small fraction 

of the properties of natural holes 

The nestboxes erected in most study sites are 

usually identical in shape and dimensions. 

Although the nestboxes were probably made to a 

consistent design in order to minimize potential 

confounding variables and maximize sample sizes 

(e.g. Drilling & Thompson 1988, McCleery et al. 

1996, Llambias & Fernández 2009), the scientific 

rationale for using a particular type of nestbox, for 

mounting and positioning them in a particular 

way, or for protecting them from predators is 

often not provided. This is a particular problem 

when nestboxes possess physical properties that 

significantly differ from those of tree cavities (e.g. 

Nilsson 1975, Karlsson & Nilsson 1977, Moeed & 

Dawson 1979, Alatalo et al. 1988, Mrller 1989, 1992, 

Purcell et al. 1997) and nestbox characteristics 

selected to initiate a long-term study significantly 

influence the results of ecological investigations 

(e.g. McCleery et al. 1996, García-Navas et al. 2008). 


Ficedula 

hypoleuca 

Ficedula 

albicollis 

Passer 

domesticus 

Sturnus 

vulgaris 

Sturnus 

unicolor 

Troglodytes 

aedon 

Tachycineta 

bicolor 

Fig. 1. Percentage of publications describing or citing 0 or more nestbox characteristics in analysed papers in two publication 

periods: a — < 1992, b — ≥ 1992. The percentage of publications with information on none (0) one (1), two (2) or more than 

two (>2) nest-box properties are indicated, which includes the publications citing references providing the information in the 

methods section. Number of papers in parenthesis. 

Sialia 

sialis 

Total 

It should be noted though that the continuity of 

nestbox characteristics and placements in some 

study sites can lead to in-depth research where 

confounding (changeable) factors are controlled 

(e.g. nestbox orientation, Goodenough et al. 2008). 

3. Different research teams often do not use the 

same nestbox designs and research protocols 

Different research groups studying hole-nesting 

birds in different locations rarely use the same 

type or size of nestbox, even when studying the 

same species (see Appendix 1). There may be several 

economic or scientific reasons for such variation 

in nestbox design. The material used to construct 

nestboxes in each location may depend on 

local availability or price (Moeed & Dawson 1979), 

or on decisions taken by forestry administrations 

or environmental organisations without consulting 

researchers regarding the massive distribution 

of a certain type of nestbox (e.g. Spain), or may 

have been proven to be optimal by local ornithological 

organisations (e.g. Baucells et al. 2003). 

Conse quently, if nestboxes are designed with particular 

characteristics because of local environmental 

conditions, such as the use of thick-walled 

boxes in areas with more extreme weather conditions, 

then nestboxes should be more variable in 

species with a larger distributional range. 

Although we did find large-scale variation in nestbox 

characteristics among research teams studying 

the same species, there was little scientific justification 

for this, as this variation was not intend-


ed to better understand biological consequences 

of ‘nestbox-environment’ interactions at broad 

spatiotemporal scales. Replicates in nestbox 

design across study sites are currently often segregated 

instead of properly interspersed. Further - 

more, nestbox characteristics may also differ within 

local populations, as some nestboxes are re - 

placed after several years or different boxes may 

be erected as new breeding plots are established. 

However, it has to be noted that nestbox design or 

placement may be constrained by the need to 

avoid human predation or destruction of nests in 

certain countries. For instance, in Spain nestboxes 

are erected at >3 m above the ground to avoid citizens 

collecting nests or eggs (but see e. g. nestbox 

research in orange monocultures in eastern Spain, 

Monrós et al. 1999), in other areas people collect 

nestboxes to adorn houses and gardens, and in 

Kenya people search nestboxes to collect honey. 

There are several potential consequences of 

research teams failing to use the same nestbox 

design. For example, different nestbox types 

necessitate the use of different devices for capturing 

birds. A box opened through the roof (e.g. 

Wageningen box, van Balen 1984) probably will 

require a different trapping device than a box 

opened through a front door (Schwegler type, see 

www.schwegler-natur.de, Blondel 1985) (Gosler 

2004, M. Lambrechts pers. obs.). The use of distinct 

types of boxes may also influence the time 

devoted to measuring or manipulating certain 

traits such as nest characteristics. There may be 

biological consequences of the different capture or 

monitoring techniques required for each type of 

box, since some may be more stressful to the captured 

birds than others. Furthermore, avian personalities 

or other phenotypic traits may differ 

between studies because certain types of nestbox 

may influence whether they attract relatively 

“shy” or “bold” individuals (see Garamszegi et al. 

2009). In addition, the activity of researchers, such 

as the frequency of box-monitoring, parent capture 

and release behaviour (i.e. relative to chick 

age, and using mist nets or box traps), may all 

affect the level of desertion or the rate at which 

parents feed their chicks (and hence growth rates 

and offspring condition). Direct researcher effects 

may be limited in populations in which adults are 

more resistant to disturbance, as a result of conditioning 

(where individuals become habituated to 

nestbox design-related research methods over 

their lifetimes), or even potentially, and perhaps 

more interestingly, by selection over generations 

(e.g. Mrller 2010). 

Wooden nestboxes have to be treated more 

frequently with chemicals than those built from 

concrete or a mix of wood and concrete (e.g. 

Schwegler boxes), and may also have to be 

replaced more frequently. In long-term studies, 

replacement boxes should be erected in the same 

exact location as the previous box or else there 

may be a change in their local environment, for 

instance changing the risk of predation (e.g. 

Sonerud 1989, Sorace et al. 2004) or exposure to 

weather conditions (e.g. Goodenough et al. 2008). 

However, keeping the box environment un - 

changed may not be possible in commercial 

forests due to frequent habitat alterations by forest 

practices (see below). 

Some unwanted variation in experimental 

design complicating data interpretation could be 

avoided using consistent nestbox characteristics 

across study populations or periods (e.g. “exact” 

replication of nestbox design, Hurlbert 1984, 

Hairston 1989, Kelly 2006). For instance, if different 

study sites containing identical nestboxes, differ 

significantly in average clutch size, as is the 

case in Blue Tits (e.g. Isenmann 1987), it is unlikely 

that the smaller clutch sizes were physically 

constrained by the size of the nest-cup (e.g. 

Slagsvold 1989), assuming that geographic variation 

in clutch size does not result from spatial variation 

in monitoring protocols. However, the same 

nestbox set up does not allow us to exclude the 

hypothesis that the size of the nest cup, physically 

limited by the size of the nestbox chamber, 

limits the production of clutches larger than those 

observed in the nestboxes used. Perhaps a 

design replicating more than one nestbox type, 

and standardising monitoring protocols, across 

study populations or periods could better test 

some hypotheses dealing with causal relationships 

between nestbox design and life-history 

traits. 

4. Variation in nestbox characteristics is often 

ignored in statistical analyses 

Nestbox properties may act as confounding 

factors that should be considered as covariates in 

statistical analyses of variation in individual or 

population characteristics. However, most previous 

comparative studies of phenotypic traits in 

secondary avian hole-nesters have not included 

nestbox design as a factor in statistical analyses 

(e.g. Järvinen 1989, Sanz 1998, 2003, Encabo et al. 

2002, Both et al. 2004). This was apparently 

because variation in nestbox characteristics was 

assumed to constitute random noise or because

information on nestbox design was unavailable. 

For instance, the nestboxes used since the 1950s to 

study tits in Wytham Wood, Oxford, were altered 

in the 1970s to make them more resistant to damage 

from Great Spotted Woodpeckers Dendrocopos 

major and to significantly reduce the impact of 

weasels Mustela nivalis entering nestboxes (Perrins 

1979). These changes in how nestboxes were constructed 

(e.g. first from wood then a mixture of 

wood and concrete) and positioned (initially 

attached directly on the trunks, later removed 

from the trunks and suspended from branches) 

resulted in a rapid temporal shift in life-history 

traits in Great Tits both at the individual and population 

level (McCleery et al. 1996, Julliard et al. 

1997), a finding apparently ignored in more recent 

analyses of the long-term database from Oxford. 

The biological consequences of variation in nestbox 

design or position could be investigated if 

future publications would provide details on nestbox 

characteristics. The non-exhaustive list of 

designs and positions of nestboxes presented in 

Table 3 could thus be exploited in future comparative 

analyses to test for the relative importance of 

nestbox design and other environmental factors 

(e.g. population density or composition, presence 

of other organisms) in the expression of individual 

life-history traits. Perhaps variation in nestbox 

characteristics (e.g. nest-chamber size) across most 

study sites used are often too narrow to become 

significant confounding factors in many comparative 

analyses that combine data from different 

research teams. However, this does not exclude 

the possibility that variation in nestbox design 

may become a significant confounding variable 

when study systems would better reflect the 

observed variation in characteristics of exploited 

natural or excavated cavities (see Introduction). 

CONSTRAINTS ON REPLICATION OF ‘NEST- 

BOX DESIGN — ENVIRONMENT’ INTERAC- 

TIONS 

There are many environmental factors associated 

with nesting cavities that affect the life-history 

or phenotypic traits of adults or offspring 

directly or result from an interaction with the 

characteristics or position of the nest cavity. For 

instance, exposure to wind may be a dominant 

factor in coastal sites, whereas in mountainous 

terrain or northern regions, variation in altitude 

and solar exposure may create major environmental 

differences over small distances. Moreover, the 


continual growth of trees and forests means that 

the environment can change significantly during 

long-term investigations at the same study site. 

Forest management practices, particularly the 

removal of old or dead trees that contain excavated 

or natural holes, will have major effects on the 

bird species present due to rapid changes in cavity 

availability, which may influence other biological 

interactions at intraspecific and interspecific 

levels (e.g. Newton 1994, Quine et al. 2007, Webb 

et al. 2007, Wesołowski 2007, Camprodon et al. 

2008, Cornelius et al. 2008, Blondel et al. 2010). 

Habitats shared with humans (e.g. city parks, suburban 

gardens) during the breeding season may 

be more likely to be occupied by individuals not 

disturbed by the presence of human activity 

(Remacha & Delgado 2009, Mrller 2010). The fact 

that characteristics of the habitat and densities of 

excavators and secondary hole-nesters may be 

influenced by anthropogenic activities means that 

the characteristics of natural cavities used may not 

(always) mirror the situation in areas that are relatively 

unaffected by humans, where they still 

exist. Black-capped Chickadees Poecile atricapillus, 

for example, will readily utilize nestboxes in urban 

landscapes where natural cavities are scarce, and 

even accept fairly unusual nesting substrates (e.g. 

hollow fenceposts, Smith 1991). This same species 

shows low acceptance of these same nestboxes in 

mature forests where ample natural nesting sites 

exist (K. Otter, unpublished data). The same is 

true for Willow Tits Poecile montanus, which do not 

accept nestboxes in northern Finland (i.e. they 

excavate new holes in decaying stumps each 

year, Orell & Ojanen 1983), but readily use 

special nestboxes filled with coarse saw-dust 

in southern Sweden (von Brömssen & Jansson 

1980). Thus studies of birds nesting in cavities 

in anthropogenically modified habitat may 

not be an ideal basis for generalisations about 

the best design, placement and management of 

nestboxes. Thus, there are limits to replicating 

studies in different areas and to what can be 

and should be included in the description of the 

study. 

Perhaps the existence of logistic constraints on 

exactly replicated research in free-ranging populations 

may justify the use of experiments in seminatural 

conditions better controlling for possible 

interactions between nestbox characteristics and 

the social or physical environment surrounding 

the boxes (e.g. Kempenaers & Dhondt 1991, Velky 

et al. 2010, but see Lambrechts et al. 1999). The recommendations 

presented below are intended to


be a minimum list, and the main purpose of the 

list is to reduce the likelihood that differences 

between studies are not an artefact of differences 

in the characteristics of the nestboxes used. 

Authors are therefore urged to describe all those 

aspects of their study sites they consider relevant 

in comparison to other sites. 

List of recommendations 

We urge authors, referees and editors to ensure 

that the following information is accessible or provided 

in future publications, or at least as an 

online supplement: 

1. Nestbox dimensions (e.g. Table 3). Include a 

minimum amount of information on nestbox 

design, including size and position of the entrance 

hole, thickness and material of nestbox walls, and 

width, breath, and height of the internal chamber; 

2. Location of nestboxes. Report the position of 

nestboxes (e.g. placed against tree trunks or hanging 

from a branch attached to a cable or metal 

hook), including height, supporting structure 

(tree, wall, post), orientation, the average distance 

between neighbouring boxes and their density 

(number of boxes per ha); 

3. Maintenance procedures of nestboxes. Report 

whether and when old nests are removed, and 

whether nestboxes or nests are treated to remove 

parasites or microorganisms (e.g. pesticides, 

micro-wave treatments, rot prevention chemicals). 

Perhaps most importantly, researchers 

should report whether nestboxes are cleaned 

out before each new nesting season (e.g. to 

remove winter nests from secondary hole-nesting 

mammals). The use of weather protection devices 

should also be mentioned. It should be noted if 

boxes are removed from the study sites between 

each breeding season. Because old or damaged 

boxes are often replaced by new boxes, a study site 

can contain nestboxes differing widely in age, 

which should be noted. 

4. Protection of nestbox occupants. If the nestboxes 

incorporate any anti-predator features, including 

devices to reinforce entrance holes (e.g. metal 

plates), or to prevent predators from entering the 

nestbox chamber (e.g. pipes to lengthen the 

entrance) they should be mentioned. 

5. Inspection of nestboxes. Report how frequently 

(and for how long) nestboxes are opened to allow 

the contents to be inspected, whether they open 

at the front, side or the roof, and whether any 

nestbox traps are used to capture the occupants. 

In addition, it would be useful to know how 

long the study has been operating in this way, the 

overall level of desertion of reproductive attempts 

and the degree of adult and natal philopatry, as 

these will all potentially contribute to the strength 

of conditioning or selection on the breeding 

adults at the site. 

6. Study-site characteristics. Report abiotic and 

biotic factors at local study sites which could conceivably 

affect ‘nestbox-environment’ interactions. 

Report the frequency of artificial holes relative 

to the frequency of natural or excavated holes, 

or if this is impossible (e.g. for holes >10 m high or 

in forest with difficult topography or dense 

understory) provide at least data about the dominant 

tree species, age of tree stands, and the proportion 

of nestboxes occupied. The availability of 

similar or different nestboxes in the general geographic 

area should also be given if possible, as 

people other than researchers erect nestboxes in 

many countries. To assess the representativeness 

of the biological samples taken from the nestboxes, 

a reference survey to assess properties of natural 

holes could be carried out in each nestbox plot. 

Research is needed through which information 

on the phenotypic traits of interest is obtained 

from breeders in natural or excavated holes and 

these traits are compared with that of birds breeding 

in nestboxes. It should also be noted whether 

there are any particular habitat management practices 

(e.g. selective logging of trees) or patterns of 

habitat use (common or occasional recreational 

areas during the breeding season), which could 

affect the birds’ biology. 

PERSPECTIVES 

Future research projects dealing with biological 

consequences of variation in one or more nestbox 

characteristics could either focus on preferences 

of birds for particular nestboxes or on the 

biological consequences of the choice of particular 

types of boxes. For instance, insectivorous birds 

which are capable of laying many eggs may prefer 

to breed in large nestboxes if hole size provides 

reliable information about tree trunk diameter 

and canopy size, which could influence the availability 

of defoliating insects required to rear the 

chicks. Birds may reduce their clutch size in small 

nestboxes in order to adjust the number of eggs 

laid to the size of the nest cup, thereby improving 

incubation behaviour, reducing sibling competition 

for limiting space within the nestbox-chamber 

or reducing problems related to hyperthermia 

in hot environments (e.g. van Balen 1984). Testing

these competing hypotheses about nestbox choice 

or adjustment of life-history traits after a nestbox 

has been accepted will require different types of 

experimental design, which have been conducted 

or proposed in former investigations (e.g. Löhrl 

1973, 1980, García-Navas et al. 2008). Studies looking 

at preferences could erect two or more nestbox 

types close together, such as on the same tree (e.g. 

Bortolotti 1994) , to better control for variation in 

environmental factors other than nestbox design. 

Quantifying parental or territory characteristics at 

the same time may test whether preferences for 

particular nestboxes (e.g. of a given orientation) 

are adjusted to particular phenotypic characteristics 

of the parents or the territory (e.g. dominant 

wind direction). Studies looking at the consequences 

of occupying a particular nestbox type 

while controlling for biases due to nestbox preferences 

could allow birds to settle on a territory then 

change nestbox types. These field studies should 

ideally avoid experimentally induced biases 

caused by nest desertion as much as possible. 

Research protocols should also be adjusted to the 

life-histories of the biological models involved, 

which may require preliminary investigations 

before adequate experimental protocols can be 

performed. For instance, the consequences of differences 

in nestbox design for roosting can only be 

studied in populations where roosting in nestboxes 

has been confirmed (e.g. Dhondt et al. 2010). 

Nestbox characteristics (>10), parental phenotypes 

(age, behaviour, physiology, morphology, 

genetics), habitat charactertistics (e.g. presence of 

other organisms, local climate, light conditions, 

type of habitat, social factors, resources, population 

density and composition) and fitness components 

(e.g. laydate, clutch size, egg characteristics, 

egg hatching success, brood size, fledging success, 

offspring phenotypes) may interact in a complex 

manner, so there are ample opportunities for 

development of research projects in different 

parts of the world aimed to better understand 

these complex relationships. What follows is a 

short list of topics that could be developed in 

future research or education projects. 

1. The biological consequences of existing variation 

in nestbox design 

Different research teams use replicates in nestbox 

design that are currently often segregated, so 

that biological consequences of existing spatial 

variation in nestbox design cannot always be 

properly investigated. Existing spatial variation in 

nestbox design could be redistributed across study 


sites so that design replicates become properly 

interspersed. For instance, the effect of differences 

in nestbox material commonly used (wood versus 

a woodconcrete mix) could be investigated in 

local study plots following the experimental 

designs proposed above (e.g. García-Navas et al. 

2008). 

2. The biological consequences of nestbox size in 

heterogeneous environments 

Previous field investigations focusing on the 

life-history consequences of nest-chamber size 

have usually only compared two nestbox size 

classes (e.g. Löhrl 1977, van Balen 1984, Slagsvold 

& Amundsen 1992, but see e.g. Moeed & Dawson 

1979, Korpimäki 1985) often without taking environmental 

or social factors into account. If bigger 

nestbox chambers would result in larger broods, 

then smaller nest-chambers which cause birds to 

produce smaller clutches may improve fledging 

success because of a reduction in brood size, especially 

in poor habitat or younger forest patches. If 

larger boxes would always be preferred regardless 

of the richness of the habitat, the hypothesis that 

large nestboxes can become severe ecological or 

evolutionary traps in poor habitats can be experimentally 

tested, both at the individual (Schlaepfer 

et al. 2002, Robertson & Hutto 2006) and population 

level (Schlaepfer et al. 2002, Mänd et al. 2009). 

These investigations require simultaneous 

studies of the proximate mechanisms involved in 

nestbox choice (e.g. genetic, environmental or historical 

basis) and the fitness consequences of the 

choice of a particular nestbox type in different 

environmental conditions (e.g. many versus few 

food resources). 

3. The biological consequences of the nestbox 

chemical environment 

The biological consequences of variation in the 

types of wood used for the construction of nestboxes 

have to our knowledge not been experimentally 

investigated. In North America, Western 

Red Cedar Thuja plicata is often advocated for 

nestboxes on hobbyist websites. This wood is 

commonly used to line trunks and wardrobes due 

to the aromatics it omits deterring moths, 

and it likely has an effect on insect colonization 

of nestboxes as well. The different types of 

wood (e.g. oak versus pine versus exotic) and the 

chemical compounds they contain may influence 

the species assemblage of invertebrates and 

microorganisms that colonize nestboxes, and 

thereby potentially interact with the avian


occupants. These effects may be intensified in 

older nestboxes that are not cleaned by 

researchers or are rarely opened for monitoring. 

4. Nestbox design, perceived microclimate and 

biological consequences 

Several nestbox characteristics, such as wall 

thickness, or the material (e.g. García-Navas et al. 

2008) or colours used for the construction of 

nestboxes, may influence the nature and stability 

of the nest microclimate and therefore the functioning 

and interactions amongst organisms occupying 

these cavities. The nestbox characteristics 

which are more likely to insulate parents, eggs 

and offspring against these environmental fluctuations 

will likely differ across latitudes or altitudes, 

which could be investigated with both 

observational and experimental approaches, also 

within the framework of climate change. If the 

presence of old nests favours roosting conditions 

in winter (Pinowski et al. 2006), then nestbox 

maintenance procedures may influence climatebird 

interactions at the time of nestbox occupation 

(e.g. García-Navas et al. 2008 for arguments in the 

methods). 

5. Effects of different combinations of nestbox 

characteristics 

An experimental design using different nestbox 

characteristics as treatments should ideally 

alter box characteristics in such a way that 

the ‘optimal’ nestbox shape for given individuals, 

territories, populations or habitats can be 

identified with high precision. This approach 

calls for field experiments combining different 

nestbox properties to better reflect variation in 

natural or excavated holes exploited. It would 

require a large increase in the number of nestbox 

types to be erected within or across study 

plots. As an example, a design combining three 

classes in wall thickness, cavity depth and 

cavity width (small, intermediate, and large 

values), varying independently of each other 

among nestboxes, would require 27 different 

nestbox types. In addition, each type should be 

sufficiently replicated either within or across 

study populations for the quantification of 

intra-nestbox type (random) variation. These 

boxes should ideally be erected “randomly” in a 

local plot following the designs A1-A3 described 

in Hurlbert (1984) or experimental designs 

proposed above. Such an experiment would 

provide unique opportunities for the study of 

phenotypic plasticity in life-history traits, with 

nest-cavity characteristics perhaps to be considered 

as physical constraints imposed on the plasticity 

of nest building-dependent life-history 

traits. Such a project has been conducted before 

(Korpimäki 1985). 

6. Nestbox design and nest building 

Secondary hole-nesters accepting cavities 

initially created by other species often have more 

complex nests than do primary hole-nesters. If 

nests from secondary hole-nesters are adjusted 

in response to shortcomings in nest-cavity characteristics, 

the consequences of interactions between 

nest structure or composition and nestbox 

characteristics could also be investigated. For 

instance, bigger nests may be built in artificial 

nestboxes with a thinner or colder nestbox wall 

(e.g. Nager & van Noordwijk 1992), perhaps to 

provide more efficient protection against meteorological 

fluctuations in colder environments 

(Pinowski et al. 2006). Larger nests appear to 

be sexually selected in birds in general (e.g. 

Soler et al. 1998), and small hole chambers may 

prevent the full expression of this phenotypic 

trait. Larger nests built in larger/deeper nestboxes 

(cf. Mazgajski & Rykowska 2008) may also contain 

and develop larger populations of ectoparasites 

(e.g. Tripet & Richner 1997) or other invertebrates. 

All this suggests that causal relationships between 

nestbox characteristics, nest characteristics and 

avian characteristics may be complex, deserving 

further study. 

7. Research protocols, avian selection and conditioning 

Different research teams do not always use the 

same procedures for catching birds or monitoring 

nestbox contents. What are the effects of researchgroup 

dependent behaviours on bird populations 

breeding or roosting in artificial nestboxes? Do different 

regimes of catching and monitoring result 

in different levels of brood desertion or condition? 

If adults are caught in a nestbox while feeding 

chicks, are they more cautious when entering the 

nest in the future, relative to birds caught in a 

mist-net outside the box? Perhaps these researchgroup 

effects may lead to consistent differences in 

fitness components across study sites or research 

groups. Avian conditioning to researcher effects 

over the lifetime of an individual, or selection over 

time, with adults that better cope with researcher 

disturbance having higher reproductive success 

and passing on these personalities to their offspring, 

could also be investigated.

8. Increased standardization and communication 

With the development of the internet and the 

global real-time availability of information across 

all researchers, much more attention should be 

paid to the construction of standardized protocols 

over large geographic areas (e.g., http://golondrinas.cornell.edu). 

Project-specific websites and listserves 

can help in all aspects of broadly based collaborations, 

from standardization of nestbox plans 

and construction to the design and execution of 

experiments to objectively evaluate changes to 

those designs. 

ACKNOWLEDGEMENTS 

We are grateful to all team members, colleagues 

and students for constructive discussions and suggestions. 

Jacques Blondel, Patty Gowaty, Kate 

Lessells and Tomasz Wesołowski kindly provided 

extensive remarks on an earlier version of the 

manuscript. Kristina Cockle, Peter Dunn, Paul 

Isenmann, Rimvydas JuÓkaitis, Kathy Martin, Eric 

Matthysen, Roger Prodon, Michael Schaub, Jaume 

Soler-Zurita and Linda Whit tingham sent data on 

the design of their nestboxes or indicated interesting 

reprints or books. 

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STRESZCZENIE 

[Modele skrzynek lęgowych używanych do 

badań dziuplaków wtórnych: ich wpływ na uzy - 

skiwane wyniki oraz niespójności metodyczne] 

Szerokie wykorzystywanie sztucznych miejsc 

gniazdowych, jakimi są skrzynki lęgowe w badaniach 

dziuplaków, szczególnie drobnych gatun - 

ków wróblowych, doprowadziło do znacznego 

zwiększenia naszej wiedzy o ich ekologii, fizjo - 

logii i zachowaniu. Skrzynki lęgowe ułatwiają 

kontrole lęgów, eksperymentalne zabiegi, którym 

poddawane są jaja lub pisklęta oraz chwytanie, 

identyfikowanie i eksperymenty na ptakach 

dorosłych. Z drugiej strony tak częste wykorzystywanie 

sztucznych miejsc lęgowych prowa - 

dzi do powstania jak do tej pory pomijanego, ale 

potencjalnie istotnie wpływającego na uzyskiwane 

wyniki, efektu samych skrzynek — ich 

wymiarów, wysokości umieszczenia, sposobu 

konstrukcji, otwierania czy konserwacji. 

W pracy podsumowano, w jakim zakresie 

publikacje naukowe dotyczące dziuplaków wtór - 

nych dokumentują charakterystykę skrzynek 

lęgowych wykorzystywanych do badań (wymia - 

ry, kolor, materiał, z którego są wykonane itd.). Do 

analizy publikacje podzielono na te opublikowane 


przed i po roku 1992, ponieważ w tym roku 

ukazały się drukiem dwie prace zalecające m. in. 

dokładne opisywanie stosowanych skrzynek 

lęgowych. Należało zatem przyjąć, że w now - 

szych pracach (po 1992) właściwości skrzynek 

(wymiary, materiał, z którego są wykonane itd.) 

powinny być opisywane dokładniej. Jednakże 

analiza rozdziałów opisujących metody w łącznie 

696 publikacjach wykazała, że w pracach publi - 

kowanych po 1992 informacje dotyczące wykorzystywanych 

w badaniach skrzynek są jeszcze 

bardziej fragmentaryczne (Fig. 1). 

W pracy zwrócono uwagę, że skrzynki lęgowe 

są najczęściej ujednolicone, a ich charakterystyka 

odzwierciedla tylko część cech dziupli naturalnych. 

Należy pamiętać, że w przypadku dziupli 

naturalnych, zmienność, szczególnie wymiarów 

wewnętrznych, czy wysokości, na której są 

umieszczone, jest bardzo duża zarówno pomię - 

dzy poszczególnymi gatunkami dziuplaków, jak 

i w obrębie tego samego gatunku (Tab. 1). Prócz 

tego analiza cech skrzynek lęgowych używanych 

przez różne zespoły badaczy wykazała, że nawet 

przy prowadzeniu badań na tym samym gatunku, 

stosowane skrzynki i metody mogą bardzo się 

różnić. Dużą wartością pracy jest zestawienie 

charakterystyk skrzynek lęgowych stosowanych 

do badań najpospolitszych dziuplaków, przede 

wszystkim w Europie (Apendyks 1). 

W pracy podano listę typowych cech, charakteryzujących 

używane w badaniach skrzynki 

lęgowe, które powinny być zamieszczane w publikacjach. 

W pracy wyjaśniono konieczność 

prezentowania takich informacji, podając przy - 

kłady, jak charakterystyka skrzynek lęgowych 

wpływa na dziuplaki. Na zakończenie przedstawiono 

kilka generalnych zagadnień związanych 

ze zmiennością wykorzystywanych do tej pory 

skrzynek lęgowych, które należałoby poddać 

badaniom.


Appendix 1. Characteristics of artificial nestboxes exploited by hole-nesting passerine birds for breeding or roosting. Abbreviations: Nestbox position: H — height above the ground 

(in m), O — entrance orientation (r = random, a = all same direction, with direction in parentheses, o = other), ST — nestbox substrate (t = tree, f = fence, b = building, p = post, 

aw = attached to a branch with a hook or wire away from hard substrate); Nestbox characteristics (all in cm): EHD — entrance hole diameter, WT — wall thickness; NBCS — nestbox 

chamber size (internal height x width x depth, in the case of circular bottom diameter (diam.) is provided), D — distance between lower edge of entrance hole to bottom of nest 

chamber, M — material used for construction, Source — citation or publication referring to a nestbox type or citation of people that provided data.– — lack of data. Species: P.m. — Parus 

major, P. v. — Parus varius, C.c — Cyanistes caeruleus, P.a — Periparus ater, P.p. — Poecile palustris, F.h. — Ficedula hypoleuca, F.a. — Ficedula albicollis. 

Species Period Study site Nestbox position Nestbox design characteristics Source 

H O ST EHD WT NBCS D M 

Paridae 

C.c. 2001–2008 Brabtia, DZ 1.5–2 o t 2.6 2 17.5 x 9 x 11 9 Wood-brown N. Ziane; Chabi & 

Isenmann 1997, 

Ziane et al. 2006 

C.c. 2001–2004 EL-Ghorra, DZ 2 o t 2.6 2 17.5 x 9 x 11 9 Wood-brown N. Ziane; Chabi & 

Isenmann 1997; 

Ziane et al. 2006 

P.m., C.c. 1959–2010 Ghent, BE 2.5 - t 3.0–3.2 - 23 x 9 x 12 16.5 Wood F. Adriaensen; 

Dhondt et al. 1984 

P.m., C.c. 1978–mid 1980s Antwerp, BE 2.5/1.5 - t 2.6/3.2 1.5 23 x 9 x 12 16.5 Pine F. Adriaensen; 

Dhondt 2010 

P.m., C.c. Mid 1980s–2010 Antwerp, BE 2.5/1.5 - t 2.6/3.2 1.5 23 x 9 x 12 16.5 Plywood F. Adriaensen; 

Dhondt 2010 

P.m., C.c. - Antwerp, BE - - t 2.6/3.0 1.5 23.5 x 9 x 12.5 16.5 Wood (green) M. Eens 

C.c. 1982–2004 Hrvatsko Zagorje, HR - - - 2.9 - 23 x 12 x 12 - - Dolenec 2007 

P.m., C.c. 2005–2010 Olomouc, CZ ~1.6 a (S) t 3.2 2 22.5–25.5 x 11.5 x 13 17.5 Spruce V. Remes; 

Matysiokova & 

Remes 2010 

P.m. 1995–2010 Kilingi-Nõmme, EE 1.5–2 o t 3.5–4.0 2.4 30 x 11 x 11 17–19 Wood R. Mänd; 

Mänd et al. 2005 

P.m., C.c., - Rødhus, DK 1–2 r t 2.8/3.2 1.5 25 x 15 x 15 20 Spruce A. P. Møller; 

P.a., P.p. Møller et al. 2010 

P.m., C.c. 1976-2010 Oxford, UK - - aw 2.6/3.2 3 2 17–18 x 12 diam 13/14 Schwegler 2M A. Gosler; 

Perrins 1979, 

McCleery et al. 1996 

P.m., C.c. 1988–1993 Sussex, UK - - - ≤ 3.18 2.2 13.8–17.8 x 8.5 Pine M. Stenning; 

(1991–93:2.86) 10 x 11 Stenning 2008 

P.m., C.c., 1949–1990 Gloucestershire - - - 2.8 1.9 17.8–25.4 x 10.8 Rough sawn, Plank M. Stenning; 

P.a., P.p. (Dean), UK 12.7 x 10.2 Campbell 1968; 

Stenning et al. 1988 

P.m., C.c., 1990–2010 Gloucestershire ~3 r t 2.8 1.8 21 x 11 x 17 14.2–15.2 5-plywood A. Goodenough; 

P.a., P.p. (Dean), UK Sloping roof Goodenough 

et al. 2008 

C.c. 1997–2010 Lancaster, UK ~1 r t 2.5 2 20 x 15 x 15 12 Sitka spruce M. Mainwaring; 

Mainwaring & 

Hartley 2009 

Continued on the next page




P.m., C.c. 1991–2010 Harjavalta, FI ~2 r t 3.2 2.6 20–22 x 12 x 12 17 Spruce T. Eeva; 

(1991–96: Eeva et al. 1994 

3.6–3.8) 

P.m. 1969–2009 Oulu, FI 1.5 (2.5–4) 1 r t 3.2–3.4 2.0–2.2 24 x 12.5 x 12.5 16.5–17.5 Pine M. Orell; 

24 x 11 x 11 Orell & Ojanen 1983a 

C.c. 1998–2009 Oulu, FI 1.5 r t 2.8 2.2 23 x 8 x 12.5 14 Pine S. Rytkönen 

P.m. 2001–2010 Southeastern 2 a (SE) t 3.2 32 17–18 x 12 diam. 13/14 Schwegler B1 J. C. Bouvier; 

Avignon, FR Bouvier et al. 2005 

P.m., C.c. 1991–2010 Montarnaud, FR ~2–31 o1 t1 2.6/3.2 32 17–18 x 12 diam. 13/14 Schwegler B1 P. Perret; 

or concrete3 Dias & Blondel 1996, 

Lambrechts et al. 2008 

P.m., C.c. 1985–1997 Puéchabon, FR ~1.5–31 o1 t1 2.6/3.2 32 17–18 x 12 diam. 13/14 Schwegler B1 P. Perret; 

or concrete3 Dias & Blondel 1996, 

Lambrechts et al. 2008 

P.m., C.c., P.a. 1976–2010 Ventoux, FR ~2–31 o1 t1 2.6/3.2 32 17–18 x 12 diam. 13/14 Schwegler B1 P. Perret; 

or concrete3 Blondel 1985 

P.m., C.c. 1993–2010 Muro, Corsica, FR ~2–31 o1 t1 2.6/3.2 32 17–18 x 12 diam 13/14 Schwegler B1 P. Perret; 

or concrete3 Lambrechts et al. 2004 

P.m., C.c., P.a. 1976–2010 Pirio,Corsica, FR ~2–31 o1 t1 2.6/3.2 32 17–18 x 12 diam. 13/14 Schwegler B1 P. Perret; 

or concrete3 Blondel 1985 

P.m., C.c. 1982–2010 Pilis Mountains, HU 1.7 r t 3.2 3 24 x 11 x 11 15 Black Locust J. Török 

P.m.,C.c., 1993-2010 Sicily, IT - - - 3.4 1 15 x 10.5 x 15 7.8 Coniferous wood B. Massa; 

P.a., P.p. (esp. Fir) Massa et al. 2004 

P.m. 1990–1992 Tyrrhenian coast, ~3 - t 3.5/3.7/6 - 22 x 14 x 14 ; Wood A. Sorace; 

Central IT 35 x 25 x 255 - Sorace & Carere 1996 

C.c. 1996–2003 Sicani Mountains, - - - 3.2/5 - 20 x 15 x 15 ; Wood B. Massa; 

Madonie, IT 30 x 20 x 205 - Sarà et al. 2005 

P.m. Early various places, LV - - - 4.5–5.0 2.5 - x 12 x 12 14 Spruce, O. Keišs; 

1980s–2010 triangle Pine, Aspen Vilka 1999 

P.m., C.c., P.a. 1978–2010 5 districts, LT - - - 3.5 - 23 x 12 x 12 - Wood R. Juškaitis; 

Juškaitis 2006 

P.m., C.c. 1955–2010 Oosterhout, NL 2.5 - t 3.2 1.5 23 x 9 x 12 17 Wood A. J. van Noordwijk; 

van Balen 1984 

P.m., C.c. 1955–2009 Hoge Veluwe, NL 2 - t 3.2 1.5 23 x 9 x 12 17 Wood A. J. van Noordwijk; 


P.m., C.c., P.a. 1955–2010 Vlieland, NL 2.5 (since - t 3.2 1.5 23 x 9 x 12 17 Wood A. J. van Noordwijk; 

1996 partly 1) van Balen 1984 

P.m., C.c. 1975–2010 Warnsborn/ 1/2.5 - t 3.2 1.5 23 x 9 x 12 17 Wood A. J. van Noordwijk; 

Westerheide, NL van Balen 1984 

P.m., C.c. 1983–2010 Buunderkamp, NL 2.5 - t 3.2 1.5 23 x 9 x 12 17 Wood A. J. van Noordwijk; 






P.m., C.c., P.a. 1979–1984 Northern PL ~4 a (SE) t 3.5 2 4,5 29 x 10 x 11 17.5 Wood W. Kania 

(–1988) 

P.m., C.c., P.a. 1979–1984 Northern PL ~4 a (SE) t 4.7 2 4,5 37 x 13 x 14 23.3 Wood W. Kania 

(–1988) 

P.m., C.c. 1999–2010 Łódź, PL - - - 2.9 2.2 22 x 9 x 10; 12– 29 Pine J. Bańbura; 

30 x 11 x 11; Alabrudzińska 

36 x 13 x 13 et al. 2003 

P.m., C.c. 2005–2010 Sekocin, Warsaw, PL 2.5 a t 2.7/3.2 2.58 22 x 13 x 13 - Wood T. Mazgajski; 

Mazgajski & 

Rykowska 2008 

P.m. 2003–2007 Coimbra, PT 2–3 a t 2.8 1.5 18.5 x 13 x 12 16.5 Wood A. C. Norte; 

Norte et al. 2008, 2010 

P.m. 2004–2007 Coimbra, PT 2–3 a t 3 2 18 x 12 x 15 14 Wood A. C. Norte; 

Norte et al. 2008, 2010 

P.m. 1979–2010 South Karelia, RU 1.5–1.8 o t 3–3.4 2.4 26 x 10–12 x 10–12 15–16 Pine, Spruce A. Artemyev; 

Artemyev 2008 

P.m. 2007–2010 Hongreung, 1.2–1.8 r aw 3/3.5 2 25–30 x16 x 15 13.5 Douglas fir C. R. Park 

P. v. Seoul, KR 

P.m.. 2009–2010 Seogwipo City, KR 1.2–1.8 r aw 3/3.5 2 27.5 x 16 x 15 13.5 Douglas fir C. R. Park 

P.m. 2005–2010 Catalonia, ES 3–5 o aw 4 1.5–2 35 x 13 x 155 24 Scotch pine, J. Camprodon; 

recycled Baucells et al. 2003 

transport pall 

C.c., P. p. 2005–2010 Catalonia, ES 3–5 o aw 2.5 1.5-2 26 x 11 x 95 25 Scotch pine, J. Camprodon; 

Recycled Baucells et al. 2003 


P.m., C.c., 1999–2010 Catalonia, ES 3–5 o aw 2.6/3 1.0–2 25 x 10 x 15 18 Scotch pine, J. Camprodon; 

P.a., P.p. recycled Baucells et al. 2003 


P.m.,C.c., P.a. 1999–2004 Catalonia, ES 3–5 o aw 3.1 2 26.5 x 10.1 x 19.48 - GACO 2000 J. Camprodon; 

P. p. Pine, recycled Camprodon et al. 2008 


P.m. - Sagunto, ES - - - 3.2 1.5 18.5 x 10.6– 8.4–12.64 Pine E. Barba; 

11.6 x 10.6– Álvarez & Barba 2008 

12.14 P.m., C.c., P.a. 1984–2010 La Hiruela -Madrid, ES 2–4 r t 3.5 1.5 17.5–19.5 x 11.5 x 13 10 Pine J. Potti; Potti 2009 

P.m., C.c., P.a. 1989–2010 Valsain, ES 3–5 r aw 3.5 1.5 17.5–19.5 x 11.5 x 137 10 Pine J. Moreno; 

Merino et al. 2000, 

Sanz et al. 1993, 2000 

P.m., C.c., P.a. - Göteborg, SE - - - 3.2 1–3 17–18/21 x 12 diam. 15.5 Birch trunk B. Silverin 

C.c., P.p., 1983–2010 Revinge, SE ~1.5 r t 2.6 2.2 20 x 7.8 x 9.5 13 Spruce J. Å. Nilsson; 

Nilsson & Smith 1988 





P.m. 1991–2010 South Skåne, SE ~1.5 r t 3.4 2.2 24 x 9.5 x 12.4 16 Spruce J. Å. Nilsson; Nilsson & 

Källander 2006 

P.m., C.c. 1980–2010 Gotland, SE 1.5–1.7 r t 3.2 2 25–30 x 10 x 10 20 Pine L. Gustafsson 

P.m., C.c. 1989–1997 Basel, CH - - - 2.8/3.2 c. 1.8 22 x 12 x 15 20 Wood B. Naef- Daenzer; 

Naef-Daenzer & 

Keller 1999 

P.m., C.c. 1985–1992 Birsfelder Hard, 2.5 - t 3.2 1.2 23 x 9 x 12 17 Wood/ concrete A. J. van Noordwijk 

Basel, CH 

P.m., C.c., P.a. 1985–1992 Blauen, Basel, CH 2.5 - t 3.2 1.2 23 x 9 x 12 17 Wood A. J. Van Noordwijk 

P.m., C.c., P.a. 1992–1994 Celerina, CH - - - 2.6/3.2 32 18 x 12 diam. c.15 Schwegler, B. Naef-Daenzer; 

Wood-concrete Mattes et al. 1996 

P.m. 1990–2010 Bern, CH - - - 3 - - x 12.5 x 12.5 21 Wood Heeb et al. 1996 

P.a. 1999–2005 Guan-Yuan, Taiwan ~3 a t 3 0.9 18 x 13 x 135 13 Lauan, M. T. Shiao; 

Plywood Shiao et al. 2009 

P.a. 2004–2005 Guan-Yuan, Taiwan ~3 a t 4.5 0.9 18 x 13.5 x 13.56 11 Lauan M. T. Shiao; 

Plywood Shiao et al. 2009 

Muscicapidae 

F.h. Mid 80s–1987 Antwerp, BE 2.5 - t 3.2 1.5 23 x 9 x 12 16.5 Plywood Dhondt et al. 1987 

F.a. 2005–2010 Olomouc, CZ ~1.6 a (S) t 3.2 2 22.5–25.5 x11.5 x 13 17.5 Spruce V. Remes; 

Matysiokova 

& Remes 2010 

F.h. 1995–2010 Kilingi-Nõmme, EE 1.5–2 o t 3.5-4.0 2.4 30 x 11 x 11 17–19 Wood R. Mänd; 

Mänd & Tilgar 2003 

F.h. 1949–1990 Gloucestershire - - - 2.8 1.9 21.6x12.7x10.2 10.8 Rough sawn M. Stenning; 

(Dean), UK Roof (17.8-25.4) Plank Campbell 1968; 

Stenning et al. 1988 

F.h. 1990–2010 Gloucestershire ~3 r t 2.8 1.8 21 x 11 x 17 14.2–15.2 5-plywood A.Goodenough; 

(Dean), UK Sloping roof Goodenough et al. 

2008 

F.h. 1991–2010 Harjavalta, FI ~2 r t 3.2 (1991– 2.6 20–22 x 12 x 12 17 Spruce T. Eeva; 

96:3.6–3.8) Eeva et al. 1994 

F.a. 1982–2010 Pilis Mountains, HU 1.7 r t 3.2 3 24 x 11 x 11 15 Black Locust J. Török 

F.h. Early various places, LV - - - 4.5–5.0 2.5 - x 12 x 12 14 Spruce, Pine, Aspen O. Keišs; Vilka 1999 

1980s–2010 triangle 

F.h. 1978–2010 5 districts, LT - - - 3.5 - 23 x 12 x 12 - Wood R. Juškaitis; 

Juškaitis 2006 

F.h. 1955–2010 Hoge Veluwe, NL 2 - t 3.2 1.5 23 x 9 x 12 17 Wood A. J. van Noordwijk; 


F.h. 1975–2010 Warnsborn/ 2.5/1 - t 3.2 1.5 23 x 9 x 12 17 Wood A. J. van Noordwijk; 

Westerheide,NL van Balen 1984 

F.h. 1983–2010 Buunderkamp,NL 2.5 - t 3.2 1.5 23 x 9 x 12 17 Wood A. J. van Noordwijk; 






F.h. 1979–1984 Northern PL ~4 a (SE) t 3.5 25 29 x 10 x 11 17.5 Wood W. Kania 

(–1988) 

F.h. 1979-1984 Northern PL ~4 a (SE) t 4.7 25 37 x 13 x 14 23.3 Wood W. Kania 

(–1988) 

F.h. 2005–2010 Sekocin, Warsaw, PL 2.5 a t 3.2 2.58 22 x 13 x 13 - Wood T. Mazgajski; 

Mazgajski & 

Rykowska 2008 

F.h. 1979–2010 South Karelia,RU 1.5–1.8 o t 3–3.4 2.4 26 x 10/12 x 10/12 15–16 Pine, spruce A. Artemyev; 

Artemyev 2008 

F.h. 1984–2010 La Hiruela,Madrid, ES 2–4 r t 3.5 1.5 17.5–19.5 x 11.5 x 13 10 Pine J. Potti; Potti 2008 

F.h. 1989–2010 Valsain, ES 3–5 r aw 3.5 1.5 17.5–19.5 x 11.5 x 137 10 Pine J. Moreno; 

Moreno et al. 2006, 

2008 

F.a. 1980–2010 Gotland, SE 1.5–1.7 r t 3.2 2 25–30 x 10 x 10 20 Pine L. Gustafsson 

F.h. 1985–1992 Blauen, Basel, CH 2.5 - t 3.2 1.2 23 x 9 x 12 17 Wood A. J. van Noordwijk 

Passer domesticus 

- Lexington, 2–4 o b 3.9 1.9 28 x 14.1 x 14.1 16 Pine (white) I. Stewart; 

Kentucky, USA Westneat et al. 2002 

Sturnus vulgaris 

- Antwerp, BE - - - 5 1.5 30.25 x 13.5 x 16 19 Wood-brown M. Eens 

- Warsaw, PL 4 r t 4.7–5 2.58 - x 12–13 x 15 22 Wood T. Mazgajski; 

Mazgajski 2007 

1900–2009 Scania, SE ~2/~3.5 - t 5.0 2.0–2.4 36 x 13 x 13 24 Spruce H. G. Smith; Smith 

1995 

Troglodytes aedon 

1980–2010 McLean County, ~1.5 r p 3.2 1.855 22 x 8.4/8.8 x 9 13 Coniferous C. Thompson; 

Illinois, USA (brown/redwood) Drilling & 

Thompson 1988 

1989–2010 Big Horn, ~1.5 - p 2.54 2 18 x 10 x 10 13 Cedar L. S. Johnson; 

Wyoming, USA Johnson 1996 

Tachycineta bicolor 

1997–2010 UWM Field Station, ~1.5 o p 4 1 20.3–25 x 13 x 13 13 Plywood L. Whittingham, P. 

WI, USA Dunn; Whittingham & 

Dunn 2000 

- Queen’s, Ontario, CAN - - - 4 1 20.3–25 x 13 x 13 13 Plywood P. Dunn; Rendell & 

Robertson 1993 

- Prince George, - - - 3.2 1.5 23–30 x 13.5 x 12.5 13 Plywood R. D. Dawson; 

BC, CAN Dawson et al. 2005 

Details on nestboxes design for otherTachycineta can be found on http://golondrinas.cornell.edu/Data_and_Protocol/NestBoxDesignCentimeters.html

Comments: 

1 Height, orientation and placement of nestboxes may be influenced by the position of trees or branches used to erect nestboxes. Height of nestboxes may 

also differ between study sites or study periods in a given region (1969–1988, One nestbox colony in Oulu); 

2 Schwegler boxes have a removable front panel which is thinner (1.5–1.8 cm) than the wall thickness of the main body; 

3 Some self-made nestboxes consist of concrete or unstandardised mixtures of concrete and wood (P. Perret, Montpellier, pers. comm.); 

4 Self-made wooden nest-boxes are usually not “identical” in structure. For instance E. Barba measured a sample of 10 boxes of which the average values 

are provided in this table. Nestboxes may also differ in shape because they come from different suppliers (e.g. Álvarez & Barba 2008); 

5 Nestboxes all have a permanently mounted trapdoor (e.g. a sliding sheet-metal piece mounted under a redwood piece) over the entrance. The result is 

that the entrance hole is deeper than the nestbox wall (e.g. box wall thickness of 1.8 + 2.0 cm redwood trapdoor = 3.8, C. Thompson pers. comm.). 

Nestboxes all also have an elongated entrance channel in some study sites (one out of two nestboxes in northern Poland, W. Kania, pers. com.); 

6 Some boxes have been constructed to attract specific species (e.g. short-toed tree creeper Certhia brachydactyla, nuthatch Sitta europaea, Italian sparrows 

Passer italiae, common dormouse Muscardinus avellanarius, or green-backed tits Parus monticolus), but also exploited by other secondary hole-nesters (e.g. 

blue, great, or coal tit); 

7 Some nestboxes have a slit (1–1.5 cm) between the roof and the frontal lid increasing light conditions inside the nestbox chamber (J. Moreno pers. comm.); 

8 In some nestbox types, a small peace of wood has been added under the entrance hole inside the nestbox chamber, i.e. a small table reinforcing the nestbox 

also used by adults before visiting the nest; 

8 In some nestbox types, the front wall is thicker than the other walls (e.g. front wall 5 cm, other walls 2.5 cm, e.g. Mazgajski & Rykowska 2008); Most nest- 


boxes have circular entrance holes (but see e.g. O. Keišs). 

Source (In alphabetic order, full list available on request): 

Alabrudzińska et al. 2003. Acta Ornithol. 38: 151–154; Álvarez & Barba 2008. Acta Ornithol. 43: 3–9; Artemyev 2008. Moscow, Nauka, pp. 1–267 (In Russian); 

Baucells et al. 2003. Lynx Edicions, Barcelona; Blondel 1985. J. Anim. Ecol. 54: 531–556; Bouvier et al. 2005. Environ. Toxicol. Chem. 24: 2846–2852; Campbell 

1968. Forestry 41: 27–46; Camprodon et al. 2008. Acta Ornithol. 43: 17–31; Chabi Y., Isenmann P. 1997. Alauda 65: 13–18; Dawson et al. 2005. Oecologia 144: 

499–507; Dhondt et al. 1984. Ibis 126: 388–397; Dhondt et al. 1987. Gerfault 77: 333–339; Dhondt 2010. J. Anim. Ecol. 79: 257–265; Dias & Blondel 1996. Ibis 

138: 644–649; Dolenec 2007. Wilson J. Ornithol. 119: 299–301; Drilling & Thompson 1988. Auk 105: 480–491; Eeva et al. 1994. Can. J. Zool. 72: 624–635; 

Goodenough et al. 2008. Ethol. Ecol. Evol. 20: 375–389; Heeb et al. 1996. J. Anim. Ecol. 65: 474–484; Johnson 1996. J. Field Ornithol. 67: 212–221; Juškaitis 

2006. Folia Zool. 55: 225–236; Lambrechts et al. 2004. Oecologia 141: 555–561; Lambrechts et al. 2008. Russ. J. Ecol. 39: 516–522; Mainwaring & Hartley 2009. 

Behav. Proc. 81: 144–146; Mänd & Tilgar 2003. Ibis 145: 67–77; Mänd et al. 2005. Biodiv. Conserv. 14: 1823–1840; Massa et al. 2004. Ital. J. Zool. 71: 209–217; 

Mattes et al. 1996. Ornithol. Beob. 93: 293–314; Matysiokova & Remes 2010. Ibis 152: 397–401; Mazgajski 2007. Pol. J. Ecol. 55: 377–385; Mazgajski & 

Rykowska 2008. Acta Ornithol. 43: 49–55; McCleery et al. 1996. J. Anim. Ecol. 65: 96–104; Merino et al. 2000. Proc. Roy. Soc. Lond. 267: 2507–2510; Mrller et 

al. 2010. J. Anim. Ecol.79: 777–784; Moreno et al. 2006. J. Avian Biol. 37: 555–560; Moreno et al. 2008. Ethology 114: 1078–1083; Naef-Daenzer & Keller 1999. 

J. Anim. Ecol. 68: 708–718; Nilsson & Källander 2006. J. Avian Biol. 37: 357–363; Nilsson & Smith 1988. J. Anim. Ecol. 57: 917–928; Norte et al. 2008. Auk 125: 

943–952; Norte et al. 2010. Condor 112: 79–86; Orell & Ojanen 1983. Ann. Zool. Fenn. 20: 77–98; Perrins 1979. British Tits, Collins, London; Potti 2008. Acta 

Oecologica 33: 387–393; Potti 2009. J. Ornithol. 150: 893–901; Rendell & Robertson 1993. Ibis 135: 305–310; SarB et al. 2005. J. Zool. 265: 347–357; Sanz et al. 

1993. Ardeola 40: 155–161; Sanz et al. 2000. Oecologia 122: 149–154; Shiao et al. 2009. Auk 126: 906–914; Smith 1995. Proc. R. Soc. Lond. B 260:45–51; Sorace 

& Carere 1996. Ornis svecica 6: 173–177; Stenning 2008. Acta Ornithol. 43: 97–106; Stenning et al. 1988. J. Anim. Ecol. 57: 307–317; van Balen 1984. Ardea 

72: 163–175; Vilka 1999. Vogelwelt 120: 223–227; Westneat et al. 2002. Condor 104: 598–609; Whittingham & Dunn 2000. Mol. Ecol. 9: 1123–1129; Ziane et 

al. 2006. Acta Ornithol. 41: 163–169.


APPENDIX 2. Authors’ contact information: 

Marcel M. LAMBRECHTS 

Centre d’Ecologie Fonctionnelle et Evolutive, 

UMR 5175, CNRS, 1919 route de Mende, 34293 

Montpellier Cedex 5, FRANCE 

Frank ADRIAENSEN 

Evolutionary Ecology Group, University of 

Antwerp, Department of Biology, Campus CGB, 

B-2020 Antwerp, BELGIUM 

Daniel R. ARDIA 

Department of Biology, Franklin & Marshall 

College, PO Box 3003, Lancaster, PA 17604, USA 

Alexandr V. ARTEMYEV 

Russian Academy of Sciences, Karelian Res. Ctr., 

Inst. Biol., Petrozavodsk 185610, RUSSIA 

Francisco ATIÉNZAR, 

”Cavanilles” Institute of Biodiversity and 

Evolutionary Biology, University of Valencia, 

Apartado Oficial 2085, E-46071 Valencia, SPAIN 

Jerzy BAŃBURA 

Department of Experimental Zoology & 

Evolutionary Biology, University of Łódź, 

Banacha 12/16, 90–237 Łódź, POLAND 

Emilio BARBA 

Cavanilles” Institute of Biodiversity and 

Evolutionary Biology, University of Valencia, 

Apartado Oficial 2085, E-46071 Valencia, SPAIN 

Jean-Charles BOUVIER 

INRA, Plantes & Systčmes de Culture Horticoles, 

UR 1115, 84000 Avignon, FRANCE 

Jordi CAMPRODON 

Biodiversity Department, Forest Technology 

Center of Catalonia, 25280 Solsona, Catalonia, 

SPAIN 

Caren B. COOPER 

Cornell Laboratory of Ornithology, 159 Sapsucker 

Woods Rd., Ithaca, NY 14850, USA 

Russell D. DAWSON 

Ecosystem Science and Management Program, 

University of Northern British Columbia, Prince 

George, BC Canada V2N 4Z9, CANADA 

Marcel EENS 

Campus Drie Eiken, Department of Biology 

(Ethology), Building C, B–2610 Antwerp (Wilrijk), 

BELGIUM 

Tapio EEVA 

Section of Ecology, 20014 University of Turku, 

FINLAND 

Bruno FAIVRE 

Université de Bourgogne, UMR CNRS 5561 

BioGéoSciences, 6 Boulevard Gabriel, 21000 Dijon, 

FRANCE 

Laszlo Z. GARAMSZEGI 

Department of Evolutionary Ecology, Estación 

Biológica de Dońana – CSIC, 41092 Seville, SPAIN 

Anne E. GOODENOUGH 

Department of Natural and Social Sciences, 

University of Gloucestershire, Glos GL50 4AZ, UK 

Andrew G. GOSLER 

Edward Grey Institute of Field Ornithology, South 

Parks Road, Oxford OX1 3PS, UK 

Arnaud GRÉGOIRE 

Centre d’Ecologie Fonctionnelle et Evolutive, UMR 

5175, Université de Montpellier II, 1919 route de 

Mende, 34293 Montpellier Cedex 5, FRANCE 

Simon C. GRIFFITH 

Department of Brain, Behaviour and Evolution, 

Macquarie University, Sydney, NSW 2109, 

AUSTRALIA 

Lars GUSTAFSSON 

Animal Ecology/Department of Ecology and 

Evolution, Evolutionary Biology Centre, Uppsala 

University, Norbyvägen 18d, S-752 36 Uppsala, 

SWEDEN 

L. Scott JOHNSON 

Department of Biology, Towson University, 

Towson MD 21252, USA 

Wojciech KANIA 

Ornithological Station, Museum and Institute of 

Zoology, Polish Academy of Sciences, 80–680 

Gdansk, POLAND

Oskars KEIŠS 

Laboratory of Ornithology, Institute of Biology, 

University of Latvia; LV–2169 Salaspils, LATVIA 

Paulo E. LLAMBIAS 

Ecologia del Comportamiento Animal, CCT-MEN- 

DOZA CONICET, 5500 Mendoza, ARGENTINA 

Mark C. MAINWARING 

Lancaster Environment Centre, Lancaster 

University, Lancaster, LA1 4YQ, UK 

Raivo MÄND 

Department of Zoology, Institute of Ecology and 

Earth Sciences, University of Tartu, 46 Vanemuise 

Str., Tartu, 51014, ESTONIA 

Bruno MASSA 

Stazione Inanellamento c/o Dipartimento SEN- 

FIMIZO, Università di Palermo, I–90128 Palermo, 

ITALY 

Tomasz D. MAZGAJSKI 

Museum and Institute of Zoology, Polish 

Academy of Sciences, Wilcza 64, 00–679, 

Warszawa, POLAND 

Anders Pape MqLLER 

Laboratoire Ecologie, Systematique et Evolution, 

UMR 8079 CNRS-Université Paris-Sud XI- 

AgroParisTech, Batiment 362 Université Paris-Sud 

XI, F-91405 Orsay Cedex, FRANCE; Center for 

Advanced Study, Drammenveien 78, NO-0271 

Oslo, NORWAY 

Juan MORENO 

Departamento de Ecología Evolutiva, Museo 

Nacional de Ciencias Naturales-CSIC, J. Gutiérrez 

Abascal 2, E-28006 Madrid, SPAIN 

Beat Naef-DAENZER 

Swiss Ornithological Institute, CH-6204 Sempach, 

SWITZERLAND 

Jan-Cke NILSSON 

Ecology Building, Animal Ecology, Lund 

University, 22362 Lund, SWEDEN 

Ana C. NORTE 

Institute of Marine Research, Department of Life 

Sciences, University of Coimbra, Apartado 3046, 

3001-401 Coimbra, PORTUGAL 


Markku ORELL 

Department of Biology, University of Oulu, P. O. 

Box 3000 FI-90014, FINLAND 

Ken A. OTTER 

Ecosystem Science and Management Program, 

University of Northern British Columbia, Prince 

George, BC Canada V2N 4Z9, CANADA 

Chan Ryul PARK 

Warm-temperate Forest Research Center, Korea 

Forest Research Institute, Seogwipo City, Jejudo, 

SOUTH KOREA 

Christopher M. PERRINS 

Edward Grey Institute of Field Ornithology, South 

Parks Road, Oxford OX1 3PS, UK 

Jan PINOWSKI 

Center of Ecological Research, Polish Academy of 

Sciences, Dziekanów Leśny, PL 05 092 Lomianki, 

POLAND 

Jiri PORKERT 

Gocarova 542, 500 02 Hradec Kralove, CZECH 

REPUBLIC 

Jaime POTTI 

Department of Evolutionary Ecology, Estación 

Biológica de Dońana – Tr. Svobody 26, CSIC, 41092 

Seville, SPAIN 

Vladimir REMES 

Laboratory of Ornithology, Department of 

Zoology, Palacky University, CZ–77146 Olomouc, 

CZECH REPUBLIC 

Heinz RICHNER 

University of Bern, Institute of Ecology & 

Evolution (IEE), CH-3012 Bern, SWITZERLAND 

Seppo RYTKÖNEN 

Department of Biology, University of Oulu, P. O. 

Box 3000 FI-90014, FINLAND 

Ming-Tang SHIAO 

Wildlife laboratory, School of Forestry and 

Resource Conservation, National Taiwan Univer - 

sity, Taipei 106, TAIWAN 

Bengt SILVERIN 

Department of Zoology, University of Göteborg, 

Box 463, 405 30, SWEDEN


Tore SLAGSVOLD 

Centre for Ecological and Evolutionary Synthesis 

(CEES), Department of Biology, University of 

Oslo, P. O. Box 1066, Blindern, NO-0316 Oslo, 

NORWAY 

Henrik G. SMITH 

Ecology Building, Animal Ecology, Lund Univer - 

sity, 22362 Lund, SWEDEN 

Alberto SORACE 

SROPU, Via R. Crippa 60, Rome, ITALY 

Martyn J. STENNING 

School of Life Sciences, University of Sussex, 

Falmer, Brighton, Sussex BN1 9QG, UK 

Ian STEWART 

Department of Biology, University of Kentucky, 

Lexington KY 40506–0225, USA 

Charles F. THOMPSON 

Behavior, Ecology, Evolution, and Systematics 

Section, School of Biological Sciences, Illinois State 

University, Normal, IL 61790-4120, USA 

Janos TÖRÖK 

Behavioural Ecology Group, Dept. Syst. Zool. & 

Ecol., Eötvös University, H–1117, Budapest, 

Pázmány P. sétány 1/C, HUNGARY 

Piotr TRYJANOWSKI 

Institute of Zoology, Poznan University of Life 

Sciences, Wojska Polskiego 71 C, 60–625 Poznań, 

POLAND 

Arie J. VAN NOORDWIJK 

Netherlands Institute of Ecology, P. O. Box 40, NL 

6666 ZG Heteren, THE NETHERLANDS 

David W. WINKLER 

Cornell University Museum of Vertebrates, 

Laboratory of Ornithology and Department of 

Ecology & Evolutionary Biology, Ithaca, NY, 

14853, USA 

Nadia ZIANE 

Laboratoire d’Ecophysiologie animale, Départe - 

ment de Biologie, B. P. 12 2300 Annaba, ALGÉRIE

D. Ornamenty a rodičovské investice

Ibis (2010), 152, 397–401 

Short communication 

Assessing the usefulness 

of ptilochronology in the 

study of melanin- and 

carotenoid-based 

ornaments in the Great Tit 

Parus major 

BEATA MATYSIOKOVÁ* & VLADIMÍR REMESˇ 

Department of Zoology and Laboratory of 

Ornithology, Palacky´ University, Trˇ. Svobody 26, 


Keywords: feather growth, nutritional condition, 

signalling. 

Ptilochronology is a method for assessing the nutritional 

condition of birds based on the width of daily growth 

bars on feathers. Wide growth bars reflect fast feather 

growth and as feather growth is costly, the width of the 

bars reflects the condition of a bird during moult (Grubb 

1989). It is a very simple and inexpensive method, 

which makes it ideal for field research (Grubb 2006). In 

addition, as a sampled feather is soon replaced by a new 

feather, a process that would take place during natural 

moult, this method is also harmless to the bird. 

Ptilochronology has therefore become a popular 

method for assessing the nutritional state of birds in the 

wild (Grubb 2006). However, the efficacy of the method 

might differ, for example, between sexes (Grubb 1989, 

Takaki et al. 2001, Bostrom & Ritchison 2006) or age 

categories (Grubb et al. 1991). Kern and Cowie (2002) 

failed to find any relationship between the growth of different 

types of feathers taken from the same individual. 

Furthermore, other studies have concluded that the general 

validity of the method is unclear and that it can be 

used only under strictly controlled conditions (Murphy 

& King 1991). 

Despite these reservations, ptilochronology has been 

used in several studies of feather ornaments as an indicator 

of condition (Hill & Montgomerie 1994, Eeva et al. 

1998, Keyser & Hill 1999, Doucet 2002, Senar et al. 

2003, van Oort & Dawson 2005, Hegyi et al. 2007, 

Siefferman et al. 2008, Kimball 2009). The assumption 

*Corresponding author. 

Email: betynec@centrum.cz 

ª 2010 The Authors 

Journal compilation ª 2010 British Ornithologists’ Union 

is that these species moult body and contour feathers at 

the same time. Thus, if both ornaments and feather 

growth bars reflect condition (Griffith et al. 2006, 

Grubb 2006, Hill & McGraw 2006), then these two 

traits should covary. Carotenoid-based feather ornaments 

are expected to reflect a bird’s condition and there is 

evidence supporting this claim (von Schantz et al. 1999, 

Hill 2002, McGraw 2006a). Although melanin ornaments 

were thought not to reflect condition (McGraw 

2006b), recent evidence suggests that they might be as 

condition-dependent as carotenoid-based ornaments 

(Griffith et al. 2006, Galván & Alonso-Alvarez 2008). 

No clear-cut relationship between feather ornaments 

and feather growth has emerged from studies to date 

(see above). As ptilochronology is a very simple method 

and has great potential in field ornithology, we examined 

the relationships between both carotenoid- and melaninbased 

ornaments and feather growth in a large sample of 

individuals of a wild passerine. We chose the Great Tit 

Parus major because expression of its carotenoid-based 

(Hõrak et al. 2000, Tschierren et al. 2003, but see Fitze 

& Richner 2002) and melanin-based ornaments (Fitze & 

Richner 2002, Galván & Alonso-Alvarez 2008) is known 

to depend on condition during moult and feather 

growth. Thus, if feather growth also reflects condition 

during moult, we expected a positive correlation 

between the width of feather growth bars and the 

expression of both carotenoid- and melanin-based ornaments. 

METHODS 

This research was conducted at three adjacent nestbox 

plots (188 nestboxes in total) in a deciduous forest near 

the village of Grygov (49°31¢N, 17°19¢E) in eastern 

Czech Republic. Nestboxes were placed 1.5 m above 

the ground and, besides Great Tits, were also inhabited 

by Blue Tits Cyanistes caeruleus, Collared Flycatchers 

Ficedula albicollis and Nuthatches Sitta europaea. Fieldwork 

was carried out between 2005 and 2007 from early 

April until mid-June. 

During feeding of nestlings (median age of young 

females = 7 days, males = 9 days), we captured parents 

in the nestbox. We captured females at almost all the 

nests (n = 165). However, because of time constraints, 

we captured males only from a subset of nests 

(n = 109). We measured their tarsus-length with digital 

callipers (to the nearest 0.01 mm) and weighed them 

on a spring Pesola balance to the nearest 0.125 g. From 

each bird we took 10–15 yellow feathers from the 

upper right part of the breast for spectrophotometric 

analysis. We photographed the bird’s breast with a digital 

camera (Panasonic DMC-FZ5). When taking a 

picture of the breast, we held the bird outstretched by 

its tarsi and beak and photographed it together with a 

ruler from a standard distance following the protocol of

398 B. Matysioková & V. Remesˇ 

Figuerola and Senar (2000). All measurements and photographs 

were taken by V.R. We also plucked the second 

outer rectrix from the right side of the tail for later 

measurement of growth bars on feathers. We determined 

the age of the birds based on their plumage as 

1 year old or older (Jenni & Winkler 1994). For each 

bird, we calculated its condition as the residual from 

the linear regression of body mass on tarsus-length 

(Brown 1996). 

Analyses of samples 

We quantified reflectance spectra of yellow feathers sampled 

from the breast using standard procedures (Andersson 

& Prager 2006). We used 10–15 feathers from each 

bird, which is sufficient to obtain reliable values from 

our study species (Quesada & Senar 2006). We used an 

Avantes AvaSpec-2048 fibre-optic spectrometer together 

with an AvaLight-XE xenon pulsed light source and 

WS-2 white reference tile. The probe was used both to 

provide light and to sample reflected light and was held 

perpendicular to the feather surface. We took five readings 

from different parts of each feather. Feathers were 

arranged on a black, non-reflective surface so that they 

overlapped extensively. 

We obtained reflectance (%) from 320 to 700 nm in 

1-nm increments. We calculated carotenoid chroma as 

(R700–R450) ⁄ R700, where R700 is the reflectance at 

700 nm and R450 the reflectance at 450 nm. In statistical 

analyses we used the average carotenoid chroma calculated 

from the five readings from each set of feathers. 

To assess repeatability of our measurements, in a subsample 

of feathers we rearranged feathers and took 

another five readings, and again averaged the carotenoid 

chroma calculated from them. We calculated repeatability 

of these two average carotenoid chroma estimates as 

an intraclass correlation coefficient (Lessels & Boag 

1987), which was sufficiently high (ri = 0.85, P < 0.001, 

n = 55). We use carotenoid chroma here because it 

reflects the amount of yellow carotenoids (lutein and 

zeaxanthin) in breast feathers in the Great Tit (Isaksson 

& Andersson 2008, Isaksson et al. 2008). 

We analysed photographs of breast feathers in Adobe 

PHOTOSHOP CS3 Extended. We used the quick selection 

tool to roughly delimit the black stripe. Then we manually 

finished the selection so that it was as precise as possible 

and measured the surface area of the stripe. We 

used a standard in photographs of each bird to adjust the 

scale of each photograph and to obtain absolute surface 

area (cm 2 ). We defined stripe surface as the area of the 

black feathers between the point of inflexion, where the 

ventral stripe widens to a throat patch, and the posterior 

end of the stripe (Figuerola & Senar 2000). All measurements 

were taken by B.M. To assess repeatability, a different 

observer measured a subsample of photographs; 

repeatability was high (ri = 0.87, P < 0.001, n = 75). 



As it is not possible to use the standard technique 

(see Grubb 1989, 2006) to determine the width of 

feather growth bars in the Great Tit (Senar et al. 2003), 

we used the modification suggested by Carrascal et al. 

(1998). We measured the length of the feather and overall 

width of the first 10 measurable distal growth bars to 

the nearest 0.1 mm. Growth bars were not apparent in 

all the feathers and we excluded these feathers from the 

analyses. All measurements were taken twice. To obtain 

the width of one growth bar (mm) we divided the average 

of the two measurements by 10. Repeatability of the 

two measurements was high (ri = 0.99, P < 0.001, 

n = 210). All measurements were taken by B.M. 

Statistical analyses 

We analysed variation in growth bar width using general 

linear mixed models (GLMM). As we sampled some 

individuals repeatedly across years, we included individual 

identity as a repeated factor in the mixed procedure 

of SAS. First, we fitted a model with the following factors 

and covariates: year, sex, age, carotenoid chroma, 

black stripe, length of tail feather and condition. We subsequently 

removed non-significant factors (age, black 

stripe, condition) until we had only statistically significant 

variables at the level of a = 0.05 in the model. F 

and P values for non-significant factors given in the 

Results section are those immediately before the factor 

was removed from the model. Growth bar width was 

transformed to the power of four to normalize its originally 

left-skewed distribution and all the analyses were 

conducted using this transformation. Residuals from 

each linear model were checked to conform to the 

requirements of normal distribution, equal variance and 

linearity (Grafen & Hails 2002). 

RESULTS 

We obtained tail feathers from 238 birds over 3 years 

(146 females, 92 males). Average length of tail feathers 

was 65.9 mm (mean ± 3.15 sd, n = 238) and was larger 

in males than in females (F1,236 = 226.27, P < 0.001). 

Individual identity as a random repeated factor was 

significant (estimate = 5.089 ± 0.4685 se, z = 10.86, 

P < 0.001). 

The width of feather growth bars was 2.87 mm 

(mean ± 0.23 sd, n = 210), being larger in females than 

in males (F1,202 = 13.14, P < 0.001) and differed with 

year (F2,202 = 4.82, P = 0.009). The growth bar width 

also correlated negatively with the carotenoid chroma of 

yellow breast feathers (F1,202 = 5.82, P = 0.017; Fig. 1a) 

and positively with the total length of the feather 

(F1,202 = 57.91, P < 0.001; whole model R 2 = 0.56). 

There was no significant relationship of growth bar 

width to the size of the black breast stripe 

(F1,199 < 0.01, P = 0.971; Fig. 1b), to condition

(a) (b) 

(F1,200 = 0.83, P = 0.362) or to age (F1,201 = 1.06, 

P = 0.305). 

Individual identity as a random repeated factor was 

significant in the analysis of feather growth in both the 

full model (estimate = 332.8 ± 33.88 se, z = 9.82, 

P < 0.001) and the final model after non-significant 

fixed-factors were removed (estimate = 322.6 ± 32.10 

se, z = 10.05, P < 0.001). 

DISCUSSION 

We found no relationship of growth bar width to the 

size of the melanin ornament but, unexpectedly, a negative 

relationship to the chroma of the carotenoid ornament. 

Thus, the growth rate of tail feathers declined as 

carotenoid levels in breast feathers increased. 

The available evidence suggests that carotenoid-rich 

feather ornaments are a reflection of good body condition 

during feather growth (von Schantz et al. 1999, Hill 

2002, McGraw 2006a). This is also true for the Great 

Tit (Hõrak et al. 2000, Tschierren et al. 2003, but see 

Fitze & Richner 2002). Given comparatively well-established 

condition-dependence of carotenoid-based feather 

ornaments, we expected positive relationships between 

their expression and the growth rate of tail feathers. 

However, contrary to our expectations, there was a significant 

negative relationship between the carotenoid 

chroma of yellow breast feathers and growth rate of tail 

feathers. This runs contrary to previous studies, where 

the correlation between the intensity of carotenoidbased 

ornaments and feather growth rate was either 

positive (Hill & Montgomerie 1994, Senar et al. 2003) 

or absent (Eeva et al. 1998, van Oort & Dawson 2005, 

Hegyi et al. 2007). However, the significance of our 

results should not be overstated, because the relationship 

between carotenoid content and feather growth was not 

very strong (r = )0.16; see also Fig. 1). 

Melanin-based ornaments were thought not to be 

condition-dependent (McGraw 2006b). However, recent 

Ptilochronology and ornaments 399 

Figure 1. Relationship between the width of growth bars of tail feathers (mm) and (a) carotenoid chroma of yellow breast feathers 

(for definition see Methods; n = 210), and (b) black breast stripe area (cm 2 , n = 210). For the sake of convenience, untransformed 

data not adjusted for other covariates are presented. However, note that all analyses were conducted on transformed data. 

evidence suggests that they might be as conditiondependent 

as carotenoid-based ornaments (Griffith et al. 

2006). Potential proximate mechanisms of conditiondependence 

might include corticosterone-mediated 

stress (Roulin et al. 2008), oxidative stress (Galván & 

Alonso-Alvarez 2008), or the allocation of calcium 

among competing physiological functions (Roulin et al. 

2006). However, we found no relationship of growth bar 

width to the size of the melanin-based black breast 

stripe, commensurate with the findings of previous studies 

(Senar et al. 2003, Hegyi et al. 2007, Kimball 2009). 

Thus, our study adds to a growing body of evidence that 

feather growth does not correlate with the expression of 

melanin ornaments, at least in small songbirds. 

The usefulness of ptilochronology has been challenged 

(Murphy & King 1991, Takaki et al. 2001, Kern 

& Cowie 2002, van Oort & Otter 2005, Bostrom & 

Ritchison 2006). Here, we did not test methods of conducting 

ptilochronology but used standard methods to 

compare the relationship between feather ornaments 

and feather growth from a large sample of birds. Results 

of studies conducted to date are highly inconsistent, even 

when conducted on the same species. For instance, in 

the Great Tit, feather growth has been shown to correlate 

positively with hue of yellow breast feathers (Senar 

et al. 2003), negatively with chroma (this study), or not 

at all with either brightness (Eeva et al. 1998, Senar 

et al. 2003, Hegyi et al. 2007) or chroma (Senar et al. 

2003, Hegyi et al. 2007). Similar inconsistencies can be 

found in studies of other bird species (Hill & Montgomerie 

1994, van Oort & Dawson 2005). At least two possible 

conclusions can be drawn from these studies. First, 

ptilochronology may be an unreliable approach for 

assessing condition in wild-ranging birds, at least until 

rigorous methodological studies demonstrate otherwise. 

Secondly, ptilochronology may be reliable in certain 

species or for application to certain types of ornaments, 

but to reveal interspecific patterns would require many 

more studies to be conducted on a broader spectrum of 


Journal compilation ª 2010 British Ornithologists’ Union

400 B. Matysioková & V. Remesˇ 

species. Moreover, differences in results within a species 

are known to occur due to population differences in the 

information content of the ornamental traits (Dunn et al. 

2008, Galván & Moreno 2009) and different expression 

of ornaments in different populations and subspecies 

(Hill 2002). Again, studies conducted on populations 

differing in resource limitation (e.g. carotenoids, see Hill 

2002) or expression and information content of feather 

ornaments could reveal interesting patterns. The usefulness 

of ptilochronology as a simple field method to estimate 

a bird’s condition during moult could still emerge 

from future studies, especially if these are done in an 

explicitly inter- or intraspecific comparative framework. 

We are grateful to Kristyńa Bártlová and Jana Šuterová for assistance 

in the field, and Peter Adamík for helpful comments on 

the manuscript. This study was supported by the Czech Ministry 

of Education (MSM6198959212). This study complies with 

the current law of the Czech Republic. 

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Received 23 July 2009; 

revision accepted 8 December 2009. 


Journal compilation ª 2010 British Ornithologists’ Union

Incubation Feeding and Nest Attentiveness in a Socially 

Monogamous Songbird: Role of Feather Colouration, Territory 

Quality and Ambient Environment 

Beata Matysioková & Vladimír Remesˇ 

Department of Zoology and Laboratory of Ornithology, Palacky´ University, Olomouc, Czech Republic 

Correspondence 

Beata Matysioková, Department of Zoology 

and Laboratory of Ornithology, Palacky´ 

University, Trˇ. Svobody 26, 77146 Olomouc, 

Czech Republic. 

E-mail: betynec@centrum.cz 

Received: December 4, 2009 

Initial acceptance: January 30, 2010 

Final acceptance: March 5, 2010 

(S. Foster) 

doi: 10.1111/j.1439-0310.2010.01776.x 

Introduction 

Abstract 

Parental investment and environmental conditions 

during reproduction are key determinants of reproductive 

output in free-ranging animals. Incubation is 

one of the key processes in avian reproduction 

(White & Kinney 1974; Deeming 2002a). Some form 

of incubation behaviour is present in 99% of all bird 

species. In species with uniparental incubation, 

which is usually done by females (Skutch 1957), the 

incubating individual has reduced time for foraging 

and self-maintenance (Drent 1975). As incubation is 

energetically demanding (Williams 1996; Thomson 

et al. 1998; Tinbergen & Williams 2002), this can 

ethology 

international journal of behavioural biology 

Ethology 

Parental investment and environmental conditions determine reproductive 

success in wild-ranging animals. Parental effort during incubation, 

and consequently factors driving it, has profound consequences for 

reproductive success in birds. The female nutrition hypothesis states that 

high male feeding enables the incubating female to spend more time on 

eggs, which can lead to higher hatching success. Moreover, both male 

and female parental investment during incubation might be signalled by 

plumage colouration. To test these hypotheses, we investigated relationships 

between male and female incubation behaviour and carotenoid 

and melanin-based plumage colouration, territory quality and ambient 

temperature in the Great Tit Parus major. We also studied the effect of 

female incubation behaviour on hatching success. Intensity of male 

incubation feeding increased with lower temperatures and was higher in 

territories with more food supply, but only in poor years with low overall 

food supply. Female nest attentiveness increased with lower temperatures. 

Plumage colouration did not predict incubation behaviour of 

either parent. Thus, incubation behaviour of both parents was related 

mainly to environmental conditions. Moreover, there was no relationship 

between male incubation feeding, female nest attentiveness and 

hatching success. Consequently, our data were not consistent with the 

female nutrition hypothesis. 

have negative effects on body condition of the incubating 

parent, subsequent care during the same or 

next breeding attempt, or survival to the next breeding 

season (Heinsohn & Cockburn 1994; Heaney & 

Monaghan 1996; Reid et al. 2000; Visser & Lessells 

2001; de Heij et al. 2006). 

In many species in which the male does not 

participate directly in warming the eggs, he feeds the 

incubating female. This behaviour is called incubation 

feeding (Lack 1940; Kendeigh 1952). In hornbills, 

some raptors and some songbirds, the 

incubating female is completely dependent on incubation 

feeding (Kendeigh 1952; Poulsen 1970; 

Verbeek 1972). However, in the majority of species 

596 Ethology 116 (2010) 596–607 ª 2010 Blackwell Verlag GmbH

B. Matysioková & V. Remesˇ Incubation Behaviour in Great Tit 

males provide only a certain part of the daily food 

intake of incubating females (Davies 1977). The 

intensity of incubation feeding differs both within 

and among species (Kendeigh 1952; Conway & Martin 

2000), and can be influenced by various factors. 

Its intensity was found to increase with decreasing 

ambient temperature (Nilsson & Smith 1988; Smith 

et al. 1989; Pearse et al. 2004), higher male quality 

(Lifjeld et al. 1987; Siefferman & Hill 2005), or 

higher food supply on territory (Zanette et al. 2000). 

Currently, the most popular hypothesis to explain 

the occurrence and patterns of incubation feeding is 

the female nutrition hypothesis (von Haartman 

1958; Royama 1966). It claims that incubation feeding 

is an important source of energy for the incubating 

female. Consequently, male provides female 

with an important part of her daily energy intake, 

that allows her to spend more time on eggs, i.e. to 

increase her nest attentiveness (Martin & Ghalambor 

1999; Tewksbury et al. 2002). 

As higher nest attentiveness can lead to higher 

hatching success (Lyon & Montgomerie 1985; Webb 

1987), incubation feeding can significantly affect 

reproductive performance of birds. However, nest 

attentiveness can be influenced by other factors in 

addition to incubation feeding. Females increase nest 

attentiveness in cold temperatures to keep eggs 

within temperature limits necessary for successful 

development of the embryo (Yom-Tov et al. 1978; 

Webb 1987; Sanz 1997). High-quality territories with 

superior food supply enable females to spend more 

time on eggs (Rauter & Reyer 1997; Zanette et al. 

2000; Zimmerling & Ankney 2005). Apart from 

environmental conditions, higher quality females 

also spend more time on eggs (Ardia & Clotfelter 

2007), which is also evidenced by a positive relationship 

between clutch size and nest attentiveness 

found in some species of birds (Blagosklonov 1977; 

Jones 1987; Deeming 2002b). 

Individual quality and ability to provide parental 

care and invest in a given breeding attempt can be 

signalled by plumage colouration (Hill & McGraw 

2006). Carotenoid-based feather colouration is widespread 

in birds (Olson & Owens 2005). Carotenoids 

cannot be synthesized by birds, and thus their concentration 

in feathers is dependent on both food 

availability and foraging efficiency (McGraw 2006a). 

Moreover, when deposited into feathers they cannot 

be used for important physiological functions, 

including immunological defence or mitigating 

oxidative stress. Consequently, carotenoids allocated 

to feathers should indicate individual quality and ⁄ or 

condition (Møller et al. 2000). Although melanin 

ornaments are often claimed not to reflect condition 

(McGraw 2006b), recent evidence suggests that in 

certain conditions they might be as condition-dependent 

as carotenoid-based ornaments (Griffith et al. 

2006). Potential proximate mechanisms of condition-dependence 

might include corticosterone-mediated 

stress (Roulin et al. 2008), oxidative stress 

(Galván & Alonso-Alvarez 2008, 2009), allocation of 

calcium among competing physiological functions 

(Roulin et al. 2006) or hormonal control of melanin 

deposition (McGraw 2008). Thus, both types of 

plumage colouration can be important in signalling 

capability of parental investment. 

In our study, we examined male and female incubation 

behaviour in the Great Tit Parus major, a 

typical socially monogamous passerine with femaleonly 

incubation. Our aims were to find out: (1) 

which factors affect the intensity of male incubation 

behaviour, i.e. incubation feeding, (2) what is the 

effect of incubation feeding and other factors on 

female incubation behaviour, i.e. nest attentiveness, 

(3) what is the effect of nest attentiveness on 

hatching success, and (4) whether carotenoid- and 

melanin-based plumage colouration predicts parental 

effort during incubation in both males and females. 

Based on the predictions of the female nutrition 

hypothesis, we expected to find a positive relationship 

between incubation feeding and nest attentiveness; 

we also expected a positive relationship 

between nest attentiveness and hatching success. We 

expected that parental effort will be positively 

related to the intensity of both carotenoid- and 

melanin-based plumage colourations. 

Methods 

General Fieldwork 

This work was conducted on three adjacent nest-box 

plots (188 nest-boxes in total), in a deciduous forest 

near Grygov (49°31¢N, 17°19¢E) in eastern Czech 

Republic. The forest is dominated by lime Tilia spp. 

and oak Quercus spp. with interspersed ash Fraxinus 

excelsior, hornbeam Carpinus betulus, and alder Alnus 

glutinosa. Nest boxes are placed about 1.5 m above 

ground and besides Great Tit are inhabited by Blue 

Tit Cyanistes caeruleus, Collared Flycatcher Ficedula 

albicollis, and Nuthatch Sitta europea. Fieldwork was 

carried out between 2005 and 2007 from early Apr. 

until mid-Jun. We checked nest-boxes daily to 

record the laying of the first egg and final clutch 

size. Later, we checked which eggs hatched to determine 

the hatching success. We defined hatching 

Ethology 116 (2010) 596–607 ª 2010 Blackwell Verlag GmbH 597

Incubation Behaviour in Great Tit B. Matysioková & V. Remesˇ 

success as the percentage of eggs that hatched. We 

used only nests where we knew the exact fate of all 

the eggs. 

Incubation Behaviour 

During incubation, we monitored nest attentiveness 

of females and incubation feeding by males. We 

obtained one sample from each nest. To determine 

nest attentiveness, we deployed temperature data 

loggers (Hobo H8 Temp ⁄ External; Onset Computer 

Corp., Pocasset, MA, USA), by putting their first 

probe (1.8 m cable) through the nest wall into the 

bottom of the nest cup. The data logger itself with 

the second, inner probe was mounted under the 

nest-box. These probes measured inner and outer 

temperature at every nest from 06.00 until 

12.00 hours (i.e. for 6 h) in 9-s intervals. This interval 

is the shortest possible to enable the coverage of 

6 h of recording with respect to the memory capacity 

of our data loggers. To determine incubation feeding, 

we placed video cameras about 5 m in front of the 

nest-box on the ground and recorded bird activity for 

90 min. This is a standard recording period in studies 

of incubation feeding in songbirds (e.g. Zanette et al. 

2000; Badyaev & Hill 2002; Doerr & Doerr 2007). We 

deployed cameras in the morning, between 07.30 

and 12.00 hours. We took ambient temperature during 

incubation feeding from a local meteorological 

station. Both data loggers and cameras were deployed 

early in the incubation period. Median was day 4 

(range 1–8) for data loggers and day 3 (1–9) for cameras, 

where day 0 means the day when the last egg 

was laid. Female Great Tits were in full incubation 

already on day 1, as evidenced by no significant 

effect of the day of incubation on nest attentiveness 

(see Results). In 80 nests, cameras were deployed on 

the same morning when data loggers were recording 

temperature. Setting cameras took very short time 

(


Territory Quality 

We characterized territory quality by assessing food 

supply during incubation of every pair in the vicinity 

of its nest-box. As the main food consumed by Great 

Tit during incubation, which takes place in our population 

mostly in early May, is caterpillars (Betts 

1955), we characterized food supply as the amount 

of caterpillars on trees within the territory. We 

determined (1) relative food supply on each of the 

five most numerous tree species by the frass fall 

method (Zandt 1994) and (2) counted all trees with 

a diameter above 10 cm at breast height in a circle 

with 20-m radius around every occupied nest-box. 

We put three plates (0.15 m 2 ) around an occupied 

box during incubation under randomly chosen trees, 

always ca. 5 m from the box in three equidistant 

directions. We collected fallen frass after 48 h into 

small plastic bags that were sealed and stored in a 

cold place. After the field season, we let the contents 

dry overnight under room temperature and humidity, 

removed large debris and weighed the rest to the 

nearest 0.0001 g. 

We analysed the amount of frass fallen on the 

plate in relation to tree species and controlled for 

canopy height (three categories: low, medium, high), 

year and date. There was a significant effect of tree 

species (F4,170 = 11.7, p < 0.001, n = 179). Least 

squares means for the five tree species were 0.075 

for oak, 0.060 for hornbeam, 0.031 for lime, 0.026 

for alder, and 0.016 for ash (in g ⁄ 48 h ⁄ 0.15 m 2 ). We 

recalculated these means so that ash, species with 

the least frass, had coefficient of 1. Other species had 

accordingly higher coefficients: 4.6 for oak, 3.7 for 

hornbeam, 1.9 for lime and 1.6 for alder. Thus, for 

example one oak was equivalent to 4.6 individuals 

of ash, because our data indicated that it had 4.6 

times more caterpillars as compared to ash, when 

controlled for possible confounding effects of sampling. 

Our results concerning relative food supply on 

different species of trees agree with previous analyses 

(Keller & van Noordwijk 1994; Naef-Daenzer 

2000). To determine territory quality, we summed 

the number of trees within 20-m radius around the 

nest multiplied by their respective coefficients. Tit 

parents do not limit their foraging exclusively to 

20 m around their nest. The distance where the 

great majority of their foraging takes place is given 

in literature, variously as within 25 m (Naef-Daenzer 

2000), 30 m (Smith & Sweatman 1974) or 45 m 

from the nest (Naef–Daenzer & Keller 1999) in similar 

habitats to ours. These figures come from studies 

done during feeding of the young; comparable 

figures for the incubation period are currently lacking. 

The radius of 20 m was chosen as a compromise 

between biological plausibility and the workload of 

counting trees. 

Analyses of Samples 

We measured the area of the black breast stripe from 

photos in Adobe Photoshop CS3 Extended. We used 

the quick selection tool to roughly delimit the breast 

stripe. Then, we manually finished the selection so 

that it was as precise as possible and measured its 

area. We used a ruler photographed together with 

every bird to adjust the scale of each photo and to 

obtain absolute area (in cm 2 ). We defined stripe area 

as the area of the black band between the point of 

inflexion, where the ventral stripe widens to a throat 

patch, and the posterior end of the stripe (Figuerola 

& Senar 2000). All measurements were taken by BM. 

To assess repeatability, a different observer measured 

a subsample of photos. Repeatability, calculated as an 

intraclass correlation coefficient (Lessels & Boag 

1987), was high (r i = 0.87, p < 0.001, n = 75). 

According to standard procedures (Andersson & 

Prager 2006), we quantified reflectance spectra of 

yellow feathers sampled from the breast. We used 

10–15 feathers from each bird, which is enough to 

obtain reliable values in Great Tit (Quesada & Senar 

2006). We used Avantes AvaSpec-2048 fiber optic 

spectrometer (Avantes BV, Eerbeek, The Netherlands) 

together with AvaLight-XE xenon pulsed light 

source and WS-2 white reference tile. The probe was 

used both to provide light and to sample reflected 

light stream, and was held perpendicular to feather 

surface. We took five readings, each from different 

part of each set of feathers. Feathers were arranged 

on black, non-reflective surface so that they overlapped 

extensively. 

We needed a spectrophotometric measure of the 

amount of carotenoids in the breast feathers. Senar 

et al. (2008) showed that hue, measured by a 

Minolta colorimeter (Minolta CR200 colorimeter; 

Konica Minolta, Tokyo, Japan), correlated with 

lutein content of breast feathers in the Great Tit. 

Similarly, Isaksson & Andersson (2008) and Isaksson 

et al. (2008) showed that so called carotenoid 

chroma correlated positively with feather carotenoids 

in the Great Tit. Carotenoids present in Great 

Tit breast feathers (lutein, zeaxanthin) absorb the 

most at around 450 nm, and theoretical modelling 

also showed that carotenoid chroma directly reflects 

the amount of carotenoids in feathers (Andersson 

& Prager 2006). As we used a spectrophotometric 



approach equal to that of Isaksson & Andersson 

(2008) and Isaksson et al. (2008), and as the hue 

calculated from spectrophotometric measurement of 

feathers does not correlate with the amount of 

carotenoids in feathers (Isaksson et al. 2008), we use 

here carotenoid chroma. We obtained reflectance 

(in %) from the wavelength of 320–700 nm in 

1-nm increments. We calculated carotenoid chroma 

as (R 700–R 450) ⁄ R 700, where R 700 is reflectance at 

700 nm and R 450 reflectance at 450 nm. In statistical 

analyses, we used the average carotenoid chroma 

calculated from the five readings from each set of 

feathers. To assess the repeatability of our measurements, 

in a subsample of feathers, we arranged 

feathers anew and took another five readings and 

again averaged the carotenoid chroma calculated 

from them. We calculated repeatability of these two 

average carotenoid chroma estimates, calculated as 

intraclass correlation coefficient (Lessels & Boag 

1987), which was high (ri = 0.85, p < 0.001, 

n = 55). 

Statistical Analyses 

Due to the fidelity of birds to their breeding grounds, 

some females were sampled in more than 1 yr (22 

females two times, 4 females three times, n = 143). 

Thus, we adjusted for this by fitting female identity 

as a random effect in mixed models for repeated 

measurements. In contrast, only three males were 

Factor 

Incubation feeding 

v 2 

df p Estimate (SE) 

Intercept 4.44 (0.941) 

Year 11.06 2, 81 0.004 0.38 (0.688): 2005 

2.42 (0.744): 2006 

Clutch size 2.03 1, 80 0.154 + 

Laying date 1.63 1, 79 0.202 + 

Age of clutch 4.1 1, 81 0.043 )0.22 (0.115) 

Time of day 20.78 1, 81


Table 2: Results of a general linear mixed 

model explaining female nest attentiveness 

(n = 143) 

Factor 

male incubation feeding, we fitted interactions of 

territory quality with year, male breast stripe area 

and male breast carotenoid chroma (Table 1). In the 

analyses of female nest attentiveness, we also fitted 

interactions of territory quality with year, female 

breast stripe area, female breast carotenoid chroma 

and male incubation feeding, and the interaction of 

male incubation feeding with year (Table 2). We 

included these interactions because we wanted to 

know whether effects of territory quality and male 

incubation feeding differ with year, and whether the 

effects of male and female colouration and male 

incubation feeding depend on territory quality. Variables 

included into models differed according to the 

dependent variable and are apparent from Tables 1– 

3. We did not include male incubation feeding as a 

predictor in the analysis of hatching success, because 

the only way incubation feeding could affect hatching 

success is through nest attentiveness. We also 

did not include male plumage colouration as a 

predictor in the analysis of female nest attentiveness, 

because we did not want to test hypotheses on 

differential allocation or compensation. Male plumage 

colouration was not likely to bias the results 

obtained on the effects of female colouration on 

nest attentiveness, because there was no assortative 

pairing in relation to colouration in our population 

(correlation between mates: carotenoid chroma 

Nest attentiveness 

F df p Estimate (SE) 

Intercept 0.76 (0.034) 

Year 2.20 2, 137 0.114 2007 > 2005 > 2006 

Clutch size 6.76 1, 140 0.010 0.01 (0.003) 

Laying date 1.37 1, 139 0.244 + 

Age of clutch 0.04 1, 131 0.852 + 

Ambient temperature 38.04 1, 140 older 

Female stripe area 2.07 1, 136 0.153 ) 

Female carotenoid chroma 0.13 1, 132 0.720 ) 

Male incubation feeding 0.14 1, 133 0.710 ) 

Territory quality · year 0.18 2, 125 0.832 

Territory quality · female 

stripe area 

1.00 1, 130 0.320 

Territory quality · female 

carotenoid chroma 

0.02 1, 124 0.900 

Territory quality · male 

incubation feeding 

0.12 1, 129 0.730 

Male incubation feeding · year 0.48 2, 127 0.618 

p-values of the final model are in bold. Sign (+ or )) or text in Estimate show the direction of 

the non-significant effects; exact parameter estimates are listed only for variables retained in the 

final model. 

Table 3: Results of a generalised linear mixed model explaining 

hatching success (n = 119) 

Factor 

Hatching success 

F df p Estimate (SE) 

Intercept 0.21 (1.167) 

Year 0.15 2, 110 0.860 2005 > 2006 > 2007 

Clutch size 4.98 1, 117 0.028 0.27 (0.121) 

Laying date 0.01 1, 112 0.927 + 

Female age 0.58 1, 116 0.446 1 yr old > older 

Female stripe area 0.10 1, 114 0.751 + 

Female carotenoid 

chroma 

0.12 1, 113 0.729 ) 

Nest attentiveness 0.25 1, 115 0.619 + 

p-values of the final model are in bold. Sign (+ or )) or text in Estimate 

show the direction of the non-significant effects; exact parameter 

estimates are listed only for variables retained in the final model. 

r = )0.15, p = 0.121, n = 104; breast stripe size 

r = 0.07, p = 0.487, n = 104). We subsequently 

removed one by one the least significant factors until 

we ended with only statistically significant variables 

of the final model (Grafen & Hails 2002). In tables, 

we give F, v 2 , df and p-values of non-significant predictors, 

immediately before they were removed from 

the model. Residuals were always checked to conform 

to the requirements of a particular model. 

Denominator df were estimated by Satterthwaite 

method. 



Ethical Note 

We used standard methods in capturing and handling 

birds used in the research of cavity-nesting 

passerines. We captured adults in the nest-box. We 

handled them for as short time as possible to minimize 

any distress. We plucked the smallest number 

of feathers possible to obtain reliable results based 

on a previous methodological study (Quesada & 

Senar 2006). Our temperature probes had no 

adverse effects on birds. 

This study complies with the current law of the 

Czech Republic. We had all necessary permits for 

this study and it was overseen by the Ethical 

Committee of Palacky University. 

Results 

Altogether we had 176 active nests over the 3 yr. 

However, we did not obtain all measurements for all 

the nests and thus, sample sizes for individual 

analyses differ. Clutch size was 10.6 1.34 eggs 

(mean SD, range 7–16, n = 174), nest attentiveness 

75 6% (range 61–89, n = 161), incubation 

feeding 0.86 1.19 per hour (range 0–5.4, n = 166), 

and hatching success was 94 9.5% (range = 

44–100, n = 136). 

Incubation Feeding 

Male incubation feeding was negatively associated 

with ambient temperature (Fig. 2a), time of day 

and age of the clutch. Thus, the intensity of incubation 

feeding decreased with higher temperatures, 

later time of the day and advancing age of the 

clutch. The frequency of incubation feeding 

increased with territory quality, but only in years 

with low overall food supply (2005 and 2007; 

Fig 3). Frass fall amounted to 0.14 (g ⁄ 48 h ⁄ 1m 2 )in 

2005, 0.11 in 2007 and 0.51 in 2006. Thus, year 

2006 had about five times more frass compared 

with years 2005 and 2007. Other effects were not 

significant (Table 1). 

Nest Attentiveness 

Nest attentiveness was negatively related to ambient 

temperature (Fig. 2b) and positively to clutch size. 

Thus, percentage of time females spent on eggs 

increased with lower temperatures and a higher 

number of eggs in the nest. Other factors were not 

significant (Table 2; Fig. 4a). 

(a) 

(b) 

Fig. 2: Relationships of (a) incubation feeding (n = 90) and (b) nest 

attentiveness (n = 143) to ambient temperature. Lines are least 

squares regression fits to the data. 

Fig. 3: Relationship between incubation feeding and territory quality 

(for units see Methods) separately for years 2005 (dashed line), 2006 

(full line) and 2007 (dotted line; n = 90). 



(a) 

(b) 

Fig. 4: Relationships between (a) female nest attentiveness and male 

incubation feeding (n = 143) and (b) hatching success and nest attentiveness 

(n = 114). These relationships are predicted to be positive 

under the female nutrition hypothesis. 

Hatching Success 

Hatching success was positively related to clutch size; 

other factors were not significant (Table 3; Fig. 4b). 

Discussion 

Incubation feeding rate in our population was similar 

to that observed in other secondary cavity nesters 

(e.g. Moreno & Carlson 1989; Siefferman & Hill 

2005) and varied substantially among males (0–5.4 

per hour). Neither of male characteristics (plumage 

colouration, age) predicted the rate of incubation 

feeding, whereas environmental conditions and 

characteristics of the nesting attempt did (ambient 

temperature, territory quality, time of day and age 

of the clutch). Incubation feeding did not predict 

nest attentiveness (Fig. 4a). Nest attentiveness was 

associated neither with any of the female characteristics 

(plumage colouration, age), nor territory quality, 

but it was associated with ambient temperature 

and clutch size. Nest attentiveness did not predict 

hatching success (Fig. 4b). 

Predictors of Incubation Behaviour 

Carotenoid and melanin-based ornaments can signal 

male quality and his capacity to invest in a given 

breeding attempt (Griffith & Pryke 2006). However, 

in our population of Great Tit neither of the examined 

colouration traits predicted male incubation 

behaviour. This agrees with results obtained for the 

same type of feather colouration in Northern Cardinal 

Cardinalis cardinalis (Jawor & Breitwisch 2006) 

and Eastern Bluebird Sialia sialis (Siefferman & Hill 

2003, 2005), although structural feather colour 

predicted incubation feeding in Eastern Bluebird. 

The only species where a positive relationship 

between carotenoid-based feather colouration and 

incubation feeding was observed is House Finch 

Carpodacus mexicanus (Hill 1991; Badyaev & Hill 

2002). Male age did not correlate with incubation 

feeding in our population, whereas it did so in some 

other species (Røskaft et al. 1983; Lifjeld & Slagsvold 

1986). Statistically significant predictors of incubation 

feeding in our population of Great Tit were 

ambient temperature, territory quality, time of day 

and age of the clutch. Thus, male incubation feeding 

in our population was related to environmental 

conditions rather than to male characteristics. 

All studies examining female parental care and ⁄ or 

breeding success in relation to female plumage colouration 

have focused on nestling period (Amundsen 

& Pärn 2006). Our study is the first that focused 

on the relationship between female plumage colouration 

and female behaviour during incubation. 

Neither carotenoid-based nor melanin-based female 

plumage colouration predicted female nest attentiveness 

in our population of Great Tit. In species where 

male does not participate directly in incubating the 

eggs, he can increase female nest attentiveness, and 

hence probably hatching success, by higher intensity 

of incubation feeding (Sedgwick 1993; Tewksbury 

et al. 2002; Fontaine et al. 2007). However, we did 

not find such a relationship. Female incubation 

behaviour in our population was related only to 

ambient temperature and clutch size and not to male 

behaviour, female characteristics, or quality of the 

breeding territory. 

Environmental conditions were the main correlates 

of incubation behaviour in both male and 



female Great Tit. In particular, females increased 

nest attentiveness with colder temperatures (see also 

Hinde 1952; White & Kinney 1974; Sanz 1997), 

which was mirrored by increased male incubation 

feeding. Attentiveness increased on average from 

68% at 20°C to 81% at 4°C. Variation between 

females at the same temperature was about 20% 

(Fig. 2b). Similarly, male incubation feeding 

increased from 0.2 to 1.8 per hour with temperature 

drop from 24 to 6°C, whereas variation among males 

at the same temperature might have been as high as 

from 0 to 5 per hour (Fig. 2a). 

Male incubation feeding increased with territory 

quality, but only in years with low overall food supply. 

Zanette et al. (2000) found out that male Eastern 

Yellow Robins Eopsaltria australis fed their 

incubating females more in a habitat with higher 

food supply. The effect of territory food supply on 

male incubation feeding agrees with previous observations 

that Great Tit parents on territories with 

higher food supply have lower energy expenditure 

during nestling feeding (Tinbergen & Dietz 1994), 

breed early (Wilkin et al. 2007) or have better growing 

nestlings that fledge in higher body weight 

(Naef–Daenzer & Keller 1999). Thus, territories with 

higher food supply enable parents to invest more in 

current breeding attempt and rear higher quality 

young or to save energy. It is remarkable that this 

effect was apparent only in years with low overall 

food supply. It seems that in a good year, all males 

had territories with enough food to supply their 

incubating mate and male provisioning capacity 

played a role only in poor years. On the other hand, 

nest attentiveness was not related to territory quality 

in our study, whereas in some other species it was 

(Rauter & Reyer 1997; Zanette et al. 2000). Extra 

resources obtained by females on good territories 

could be allocated to self-maintenance rather than to 

incubation effort. Thus, females foraging in good territories 

during incubation off-bouts and receiving 

more food from males during on-bouts might have 

lost less weight during incubation, suffered less costs 

to other functions (physiological, behavioural) or 

increased their parental effort in other parts of the 

breeding cycle or in future breeding bouts. 

Female Nutrition Hypothesis 

Incubation feeding did not predict nest attentiveness, 

which in turn was not related to hatching success 

(Fig. 4). Thus, our data were not consistent with the 

female nutrition hypothesis. However, three alternatives 

should be mentioned here. First, as mentioned 

above, females might have allocated extra food 

obtained from males into self-maintenance instead 

of incubation effort. Second, female nest attentiveness 

might respond to the amount or quality of food 

brought by the male on the nest. Any patterns that 

could arise from the effects of food load or quality 

would be missed by our approach. Third, Great Tit 

male feeds his female also while she is off the nest 

(Hinde 1952; Royama 1966; de Heij 2006). Incubation 

feeding off the nest is very difficult to record 

(Hinde 1952; Nilsson & Smith 1988; Pearse et al. 

2004). We were not able to follow females during 

off-bouts in the dense canopy of our flood-plain 

forest (B. Matysioková & V. Remesˇ, unpubl. data). 

Similarly, most of the previous studies focused 

exclusively on feeding on the nest, even in species 

with off-nest incubation feeding occurring (e.g. Hinde 

1952; Lifjeld et al. 1987; Nilsson & Smith 1988; 

Smith et al. 1989; Sanz 1997; Pearse et al. 2004; de 

Heij 2006; Lloyd et al. 2009; but see Klatt et al. 

2008). However, rate of incubation feeding on the 

nest need not fully correspond to overall incubation 

feeding rate. In the only study that quantified incubation 

feeding both on and off the nest, which was 

done on Scarlet Tanager Piranga olivacea, no significant 

relationship between incubation feeding on and 

off the nest was found (Klatt et al. 2008; B. Stutchbury, 

pers. comm.). Thus, if incubation feeding off 

the nest were more important for the female, we 

would have missed some important patterns in our 

population. On the other hand, food load delivered 

to female when on the nest might be larger, i.e. 

more important, than that delivered to female outside 

of nest (Nilsson & Smith 1988). However, these 

questions remain unresolved. 

Conclusions 

In summary, our work is one of the first studies that 

deal with relationships between feather colouration 

and incubation behaviour in birds. Our data suggest 

that neither carotenoid-based nor melanin-based colouration 

predict bird behaviour during incubation, 

which agrees with previous findings in other species 

(Siefferman & Hill 2003, 2005; Jawor & Breitwisch 

2006; but see Hill 1991). However, it is possible that 

they play a role in other parts of the breeding cycle, 

e.g. in nestling period (Senar et al. 2002; Doutrelant 

et al. 2008; Quesada & Senar 2007; review in Griffith 

& Pryke 2006), or during non-breeding season 

(review in Senar 2006). On the contrary, we revealed 

significant relationships with environmental conditions, 

including ambient temperature and territory 



quality. There was no relationship between incubation 

feeding, nest attentiveness and hatching success. 

Consequently, although alternative explanations are 

possible, our data are not consistent with the female 

nutrition hypothesis conceived to explain the occurrence 

and rate of male incubation feeding in birds. 


We are grateful to Kristyńa Bártlová and Jana Sˇ uterová 

for assistance in the field, Milosˇ Krist, Keith 

Tarvin and an anonymous referee for helpful comments 

on the manuscript, and Sharon Kennedy for 

improving the English. The study was supported by 

the Ministry of Education of the Czech Republic 

(MSM6198959212). 

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J Ornithol 

DOI 10.1007/s10336-010-0594-9 

ORIGINAL ARTICLE 

Responses to increased costs of activity during incubation 

in a songbird with female-only incubation: does feather colour 

signal coping ability? 

Beata Matysioková • Vladimír Remesˇ 

Received: 9 January 2010 / Revised: 2 September 2010 / Accepted: 28 September 2010 

Ó Dt. Ornithologen-Gesellschaft e.V. 2010 

Abstract Individuals differ in their ability to cope with 

energetically demanding situations while caring for the 

current brood, and they can signal this ability by their 

colouration. We examined the impact of handicapping 

(clipping of wing and tail feathers) on an energetically 

demanding care behaviour (incubation) in female Great 

Tits (Parus major). We hypothesised that the intensity of 

carotenoid-based breast feather colouration signals the 

ability to cope with impaired flight ability and the consequent 

increased energetic demands. If this is the case, 

females with more intensely coloured feathers should cope 

better with the handicap compared with less intensely 

coloured females, i.e. the impact of handicapping on mass 

loss and nest attentiveness should be negatively correlated 

with colouration. Handicapped females lost more weight 

than control females but did not decrease nest attentiveness 

to a greater extent, suggesting that females take the costs of 

handicapping on themselves. Females in poor condition 

were more severely influenced by handicapping. Intensity 

of female breast feather colouration did not correlate with 

either change in nest attentiveness or body mass loss during 

incubation. Intensity of breast feather colouration therefore 

does not appear to signal female ability to cope with this 

energetically demanding situation during incubation. 

Keywords Feather colouration Female ornaments 

Great Tit Handicapping Incubation behaviour 


Communicated by T. Friedl. 

B. Matysioková (&) V. Remesˇ 

Department of Zoology and Laboratory of Ornithology, Palacky´ 

University, Trˇ. Svobody 26, 77146 Olomouc, Czech Republic 

e-mail: betynec@centrum.cz 

Zusammenfassung Reaktionen auf erhöhte Kosten bei der 

Bebrütung von Singvögeln mit Inkubation ausschließlich 

durch das Weibchen: Ist die Gefiederfarbe ein Anzeichen für 

bessere Stressbewältigung? Individuen unterscheiden sich in 

ihrer Fähigkeit mit energetisch ungünstigen Situationen 

während der Brutpflege umzugehen und sie zeigen dies anhand 

ihrer Gefiederfarbe. Wir untersuchten die Auswirkung 

einer zusätzlichen Belastung (dem Stutzen von Flügel- und 

Schwanzfedern) auf die Energie aufwändige Inkubation bei 

Weibchen der Kohlmeise (Parus major). Wir nahmen dabei 

an, dass die Intensität der auf Karotinoiden basierenden Färbung 

der Brustfedern die Fähigkeit anzeigt, mit der energetisch 

kostspieligen Einschränkung der Flugfähigkeiten 

umzugehen. Sollte dies der Fall sein, sollten intensiver 

gefärbte Weibchen besser mit der zusätzlichen Belastung 

umgehen können, als weniger stark gefärbte Weibchen. 

Dementsprechend sollten der Masseverlust und die Nestattraktivität 

negativ mit der Gefiederfärbung korreliert sein. 

Weibchen mit gestutzten Federn nahmen stärker ab als die 

Weibchen der Kontrollgruppe, hatten aber nicht deutlich unattraktivere 

Nester, was darauf hindeutet, dass beeinträchtigte 

Weibchen die Mehrkosten durch die zusätzliche Belastung 

auf sich nehmen. Bereits schwache Weibchen wurden durch 

die zusätzliche Belastung stärker beeinträchtigt. Die Intensität 

der Färbung des Brustgefieders korrelierte weder mit 

Nestattraktivität noch mit Gewichtsverlust während der 

Inkubation. Das deutet darauf hin, dass die Färbung des 

Brustgefieders nicht auf die Fähigkeit der Stressbewältigung 

eines Weibchens während der Inkubation schließen lässt. 

Introduction 

One of the basic tenets of evolutionary biology is that 

individuals differ in their ability to survive and cope with 

123

challenging environmental conditions. This ability can be 

influenced by the quality, age and condition of the individual 

(Fox et al. 2001). Individual quality and condition 

can be signalled to potential mates or rivals by various 

types of ornaments, including those based on carotenoids 

(Searcy and Nowicki 2005). Carotenoid-based colouration 

is widespread in animals, including feathers and bare parts 

in birds (Olson and Owens 2005). Carotenoids cannot be 

synthesised by animals, must be obtained from food, and 

thus are potentially in short supply (Olson and Owens 

1998). Full expression of carotenoid-based colouration is 

costly, and carotenoids are involved in a number of tradeoffs 

with important physiological functions, including 

immune function and the level of oxidative stress (von 

Schantz et al. 1999; McGraw 2006). Consequently, intensity 

of carotenoid-based colouration is expected to indicate 

individual quality, condition, and/or capability of parental 

effort (Møller et al. 2000; Griffith et al. 2006). 

The role of feather ornaments as indicators of quality, 

condition, and parental effort has been traditionally studied 

in males (reviewed in Griffith and Pryke 2006). However, 

there has been a recent surge of interest in the function and 

evolution of female ornaments (reviewed in Amundsen 

2000; Amundsen and Pärn 2006; Kraaijeveld et al. 2007; 

Clutton-Brock 2009). Recent studies have demonstrated 

that female ornaments might work as badges of status 

enabling better access to resources (Murphy et al. 2009; 

Griggio et al. 2010) or as signals of good parenting abilities 

(Linville et al. 1998; Siefferman and Hill 2005; but see 

Smiseth and Amundsen 2000; Griggio et al. 2010). It has 

been even demonstrated that breeding success might be 

correlated with female ornament expression (Morales et al. 

2007; Bitton et al. 2008), and males might base their mate 

choice at least partly on the degree of female ornamentation 

(Griggio et al. 2009; but see Murphy et al. 2009). 

However, studies examining female ornaments during 

reproduction in birds have been carried out during the 

nestling period, while incubation was almost completely 

neglected (Amundsen 2000; Amundsen and Pärn 2006; but 

see Hanssen et al. 2006). 

Incubation is a very important part of the breeding cycle 

in birds, and parental effort during incubation can have 

strong consequences for the reproductive success of the 

pair (Deeming 2002). Normal embryo development 

requires eggs to be kept within a narrow range of temperatures 

(Webb 1987). Nonoptimal temperatures can lead 

to reduced hatchability and longer incubation periods 

(Lyon and Montgomerie 1985; Webb 1987; Martin 2008). 

At the same time, incubation is energetically demanding 

for the incubating individual (Williams 1996; Thomson 

et al. 1998; Tinbergen and Williams 2002), who has to split 

its time between warming the eggs and foraging for itself. 

Hence, the ability to cope with energetically challenging 

123 

situations during incubation can be very important for the 

reproductive success of the pair. In species with femaleonly 

incubation, females can signal this ability by their 

carotenoid-based feather colouration, and males might 

accordingly base their mate choice on the intensity of 

the female’s colouration (Amundsen and Pärn 2006; 

Kraaijeveld et al. 2007). 

Handicapping is a useful and widely employed method 

to study the effects of energetically challenging situations 

on bird behaviour (Harrison et al. 2009). Birds can be 

handicapped by adding weights (Wright and Cuthill 1989; 

Griggio et al. 2005), taping their feathers (Senar et al. 

2002a) or feather clipping (Slagsvold and Lifjeld 1988; 

Sanz et al. 2000). The last method is particularly suitable 

because it simulates events that can happen in the wild due 

to attacks by predators, and hence represents a risk to 

which birds might have become adapted (Slagsvold and 

Lifjeld 1990). Broken or missing feathers are among the 

most commonly encountered natural handicaps in freeranging 

birds (Dawson et al. 2001). The ability to cope 

with such a handicap might therefore reveal an important 

component of individual quality (Harding et al. 2009). 

In our study, we examined the effects of handicapping 

(feather clipping) on incubation behaviour in the Great Tit 

(Parus major), a small, short-lived songbird with femaleonly 

incubation. In particular, we determined whether 

females differed in their responses to this energetic constraint 

in relation to the intensity of their yellow, carotenoidbased 

feather colouration. We predicted that impaired flight 

ability caused by handicapping would (1) extend the time 

females spent foraging off the nest, and hence decrease the 

time they spent on the nest, and/or (2) lead to higher body 

mass loss during incubation compared with controls. 

Moreover, if carotenoid-based feather colouration of the 

Great Tit females indicates ability to cope with such an 

energetic constraint, females with more intense feather colouration 

can be expected to be less affected by the challenge 

(Smiseth and Amundsen 2000; Doutrelant et al. 2008). 

Methods 

General field work 

J Ornithol 

This work was conducted on three adjacent nest-box plots 

which are ca. 1 km apart in a broad-leaved forest dominated 

by oak (Quercus petraea) on Velky´ Kosírˇ in the east 

of the Czech Republic (49°32 0 N, 17°04 0 E). There are 300 

nest-boxes in total placed about 1.5 m above the ground. 

Besides Great Tits, these nest-boxes are inhabited by 

Collared Flycatchers (Ficedula albicollis), Blue Tits 

(Cyanistes caeruleus), Nuthatches (Sitta europea) and Coal 

Tits (Periparus ater). Fieldwork was carried out in 2008

J Ornithol 

from early April until May. We checked nest-boxes daily 

to record the laying of the first egg and the final clutch size. 

Day 0 was the day when the last egg was laid. Eggs in our 

population usually start to hatch on days 11–13, and 

hatching lasts for 2–3 days. 

Cross-fostering 

We wanted to isolate the direct effects of female incubation 

behaviour (i.e. egg warming) on hatching success and 

incubation period length, excluding any genetic or maternal 

effects. Therefore, we matched pairs of nests by their 

age and clutch size and exchanged clutches between pairs 

of nests. Clutches were exchanged as soon as, or immediately 

after, egg laying ended. We took the whole clutch 

from a nest, weighed it on a digital balance to the nearest 

0.01 g, and swapped it within the dyad (in 67 out of 82 

nests on day 1, range 0–3). Nests were always exactly 

matched by the date when the last egg was laid. There was 

no difference in clutch size in 52 nests, a difference of one 

egg in 28 nests, and of two eggs in two nests. The transfer 

of eggs took on average 8 min (range = 3–14 min). 


During incubation, we monitored the percentage of time 

incubating females spent on eggs, i.e. nest attentiveness. 

We deployed temperature data loggers by inserting a probe 

through the nest wall into the bottom of the nest cup. A 

second probe was mounted under the nest-box. We measured 

inner and outer temperatures from 5 a.m. until 

10:40 p.m. in 16-s intervals. In the nest temperature 

recordings, the time when the incubating female is away 

from the nest is recognizable by downward spikes. The 

temperature drops quickly when the female leaves the 

clutch (off-bout) and then starts to increase sharply when 

she returns (on-bout; Fig. 1). Consequently, it is easy to tell 

the difference between an attended and an empty nest (e.g. 

Zimmerling and Ankney 2005). From the pattern of nest 

temperatures, we calculated nest attentiveness throughout 

the day. To get ambient temperature for every nest, we took 

the outer temperature for the start of each on-bout and offbout 

and averaged it across the day. The data loggers were 

deployed on day 3 or 4 of incubation, and the nest attentiveness 

was measured on the subsequent day (i.e. on days 

4–5). Four days after experimental treatment (see below), 

we measured nest attentiveness again in the same way 

(i.e. on days 9–10). 

Experimental treatment 

The day after nest attentiveness was measured for the first 

time, we captured females in the nest-box (i.e., on days 

Temperature (°C) 

45 

40 

35 

30 

25 

20 

15 

10 

Nest temperature 

Ambient temperature 

5 

05:00:00 10:00:00 15:00:00 20:00:00 

Time of day 

Fig. 1 Graph of a typical incubation profile for the Great Tit 

5–6) and weighed them on a spring Pesola balance (to the 

nearest 0.25 g). We handicapped every first and second 

female and left every third female as a control. In experimental 

females, we clipped primaries 5, 6 and 8 (out of the 

total of 10 primaries, counted from the outside) on both 

wings, together with the four central tail feathers (out of the 

total of 12 tail feathers). We clipped the feathers as close to 

their bases as possible. This methodology was modified 

from Slagsvold and Lifjeld (1990). We handled control 

females in the same way as experimental females, except 

that we did not clip the feathers. We returned all of the 

females back to the nest-box through the entrance. Then we 

covered the entrance and waited for about one minute 

before leaving. The effect of handicapping was temporary 

and lasted until the post-breeding moult. Experimental and 

control females did not differ significantly in their initial 

body weight (F1,75 = 1.2, P = 0.283). 

Females and clutches 

The day after nest attentiveness was measured for the second 

time, we captured females in the nest-box again (i.e., on 

days 10–11). We aged them (one year old or older, 

Svensson 1992), weighed them on a spring Pesola balance 

(nearest 0.25 g), and measured their tarsus by a digital 

calliper (nearest 0.01 mm). We took 10–15 yellow feathers 

from the upper right part of the breast for later spectrophotometric 

analysis. Experimental and control females did 

not differ significantly in their tarsus length (F1,54 = 0.14, 

P = 0.706). After this day we checked nest-boxes daily to 

determine hatching success. We removed eggs that did not 

hatch and dissected them to determine the cause of hatching 

failure, i.e. eggs with no sign of embryo development or an 

apparent dead embryo. We defined hatching success as 

percentage of fertilised eggs that hatched. Since we were 

interested in the effects of incubation behaviour on hatching 

success, we excluded eggs with no sign of embryo 

123

development from the analyses. Some unhatched eggs disappeared 

from the nest before we were able to dissect them. 

We removed these nests from the analyses of hatching 

success and thus the sample size was reduced. We calculated 

incubation period as the time from laying the last egg 

to hatching the first egg (Lyon and Montgomerie 1985). 

Laboratory analyses 

We quantified reflectance spectra of yellow feathers sampled 

from the breast using standard procedures (Andersson 

and Prager 2006). We used 10–15 feathers from each bird, 

which is sufficient to obtain reliable values from our species 

(Quesada and Senar 2006). We used an Avantes 

AvaSpec-2048 fibre optic spectrometer together with an 

AvaLight-XE xenon pulsed light source and a WS-2 white 

reference tile. The probe was used both to provide light and 

to sample the reflected light, and was held perpendicular to 

the feather surface. We took five readings, each from a 

different part of each set of feathers. Feathers were arranged 

on a black, nonreflective surface so that they overlapped 

extensively. 

We obtained the reflectance (%) in the wavelength region 

of 320–700 nm in 1-nm increments (Fig. 2). We calculated 

so-called carotenoid chroma because it has been demonstrated 

that it is positively correlated with the amount of 

carotenoids deposited in feathers in the Great Tit (Isaksson 

et al. 2008; Isaksson and Andersson 2008; see also Andersson 

and Prager 2006). Carotenoid chroma is a preferable 

index of the concentration of carotenoids in feathers in 

unsaturated carotenoid-based colours (Andersson and Prager 

2006). Carotenoids present in Great Tit breast feathers 

(lutein, zeaxanthin) absorb maximally at around 450 nm 

(Andersson and Prager 2006), and the colour of our Great 

Reflectance (%) 

50 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 

320 360 400 440 480 520 560 600 640 680 

Wavelength (nm) 

Fig. 2 Average (± SD) reflectance spectrum of yellow breast 

feathers of female Great Tits, as measured in 1-nm increments 

(n = 56) 

123 

Tits was unsaturated, because we still had reasonable 

reflectance around 450 nm (see Fig. 2). We calculated 

carotenoid chroma as (R700 minus R450) divided by R700, 

where R700 is the reflectance at 700 nm and R450 is the 

reflectance at 450 nm. In statistical analyses, we used the 

average carotenoid chroma calculated from the five readings 

from each set of feathers. To assess the repeatability of our 

measurements, in a subsample of feathers, we arranged 

feathers anew, took another five readings and again averaged 

the carotenoid chroma calculated from them. We calculated 

the repeatability of these two average carotenoid chroma 

estimates using the intraclass correlation coefficient (Lessels 

and Boag 1987), which was high (ri = 0.85, P \ 0.001, 

n = 55). As previous studies also used other characteristics 

derived from reflectance spectra, we also calculated brightness 

(Ravg), hue (kR50), and UV chroma (see Montgomerie 

2006). We calculated brightness (R avg) and hue (k R50) 

according to Andersson and Prager (2006, p. 78). Ravg is the 

reflectance averaged over the interval from 320 to 700 nm. 

k R50 is the wavelength halfway between R max and R min, 

where Rmax is the maximum reflectance and Rmin is the 

minimum reflectance between 320 and 700 nm. We also 

calculated UV chroma as the reflectance between 320 and 

400 nm divided by the reflectance between 320 and 700 nm. 

Experimental and control females did not differ significantly 

in either of the four colour characteristics: carotenoid chroma 

(F1,54 = 0.02, P = 0.883), brightness (F1,54 = 0.78, 

P = 0.381), hue (F1,54 = 1.49, P = 0.227), and UV chroma 

(F 1,54 = 1.43, P = 0.237). 


J Ornithol 

We analysed the effects of experimental treatment on 

desertion rate (likelihood-ratio test), change in nest attentiveness 

and female mass, incubation period length (general 

linear models), and hatching success (generalised linear 

models with a binomial error distribution and a logit link). 

We analysed all data using JMP software, with the exception 

of hatching success, where we used SAS. Binomial 

models were fitted as the number of eggs that hatched/ 

clutch size. We confirmed that the data met the assumptions 

of general linear models where these were used (Grafen and 

Hails 2002). We also checked that data in the binomial 

model were not overdispersed (deviance/df = 1.30). 

Initial models included treatment and relevant other 

factors as predictors, which are apparent from Tables 1 and 

2. In the analyses of the change in nest attentiveness 

(attentiveness before treatment minus after treatment) and 

body mass (mass after treatment minus before treatment), 

we also fitted interactions of treatment with female initial 

condition and breast carotenoid chroma (see Table 1). We 

did this because we wanted to know whether females differed 

in their response to handicapping based on their

J Ornithol 

initial condition and yellow colouration. In the analyses of 

incubation period length and hatching success, we fitted 

only the interaction of treatment with female breast 

carotenoid chroma (see Table 2). We also re-ran all the 

models with other colour characteristics (hue, brightness, 

UV chroma) instead of carotenoid chroma (see Table 3). 

Table 1 Models explaining the changes in nest attentiveness and female body mass during incubation 

Factor Change in nest attentiveness (%) a 

Change in body mass (g) b 

F df P c 

Estimate (SE) d 

F df P c 


Intercept -4.71 (2.370) -0.36 (0.116) 

Treatment 0.1 1, 49 0.818 0.35 (1.531) (handic.) 10.0 1, 52 0.003 -0.46 (0.144) (handic.) 

Date of experiment 7.4 1, 49 0.009 0.84 (0.308) 0.3 1, 49 0.601 ? 

Female carotenoid chroma \0.1 1, 46 0.952 ? 1.6 1, 50 0.213 - 

Female age 3.6 1, 48 0.064 Older [ 1 year old 3.1 1, 51 0.086 Older [ 1 year old 

Female condition 2.2 1, 47 0.144 ? 5.8 1, 52 0.020 -0.41 (0.140) 

Female condition 9 treatment 0.1 1, 44 0.719 5.2 1, 52 0.026 0.40 (0.175) (handic.) 

Female carotenoid chroma 9 treatment 0.6 1, 45 0.461 0.6 1, 48 0.425 

Final models: a F 2,49 = 3.7, P = 0.031, R 2 = 0.13, n = 52 

b F3,52 = 5.9, P = 0.002, R 2 = 0.25, n = 56 

c P values of the final models are shown in italics 

d Sign (? or -) or text in the estimate shows the direction of the nonsignificant effect; exact parameter estimates are listed only for variables 

retained in final models, including treatment, whatever its significance 

Table 2 Models explaining incubation period length and hatching success 

Factor Incubation period (day) a 

F df P c 


Hatching success (logit scale) b 

v 2 

df P c 


Intercept 13.43 (0.358) 3.74 (0.584) 

Treatment 0.8 1, 44 0.372 0.21 (0.236) (handic.) 0.3 1, 39 0.570 0.44 (0.771) (handic.) 

Date of experiment 24.1 1, 44 \0.001 -0.23 (0.047) 0.1 1, 35 0.925 - 

Temperature-indep. nest attentiveness 4.6 1, 44 0.037 -7.14 (3.325) 0.4 1, 37 0.515 ? 

Clutch size 1.5 1, 43 0.232 ? 1.8 1, 38 0.186 ? 

Female carotenoid chroma 0.3 1, 42 0.567 ? 0.3 1, 36 0.592 - 

Female carotenoid chroma 9 treatment 0.1 1, 41 0.730 3.5 1, 34 0.063 

Final models: a F 3,44 = 10.3, P \ 0.001, R 2 = 0.41, n = 48 

b n = 41 

c P values of the final models are shown in italics 

d 

Sign (? or -) in the estimate shows the direction of nonsignificant effects; exact parameter estimates are listed only for variables retained in 

final models, including treatment, whatever its significance 

Table 3 Tests of the effects of brightness (R avg), hue (k R50), and UV chroma, together with their interaction with handicapping, on changes in 

nest attentiveness and body mass, incubation period length, and hatching success 

Attentiveness (%) Body mass (g) Incubation period (day) Hatching success (logit scale) 

F df P F df P F df P v 2 

df P 

Ravg \0.1 1, 45 0.838 0.3 1, 49 0.59 0.4 1, 42 0.543 0.7 1, 35 0.407 

Ravg 9 treatment 0.3 1, 44 0.564 0.1 1, 48 0.753 0.7 1, 41 0.406 \0.1 1, 34 0.879 

kR50 \0.1 1, 45 0.874 1.2 1, 49 0.286 0.1 1, 42 0.778 0.1 1, 35 0.769 

kR50 9 treatment 0.1 1, 44 0.795 0.2 1, 48 0.701 0.2 1, 41 0.621 0.5 1, 34 0.472 

UV chroma 0.8 1, 45 0.386 0.9 1, 49 0.336 \0.1 1, 42 0.870 \0.1 1, 35 0.930 

UV chroma 9 treatment 0.1 1, 44 0.733 1.1 1, 48 0.299 0.9 1, 41 0.353 2.5 1, 34 0.115 

Colour characteristics were tested while added in turn to the full models presented in Tables 1 and 2 (without carotenoid chroma) 

123

Date of experiment was set so that the day of first experiment 

= 1. Body condition for each female was calculated 

as the residual from the linear regression of initial body 

mass on tarsus length. We always retained treatment in the 

final model as our main factor of interest whatever its 

statistical significance (see Grafen and Hails 2002). Other 

predictors were removed from the models starting with 

interactions. We removed nonsignificant predictors until 

we ended up only with factors significant at a = 0.05. In 

the tables, we give F, df, and P values of nonsignificant 

predictors immediately before they were removed from the 

model. Data are presented as mean ± SD. 

Nest attentiveness is strongly affected by ambient temperature 

in the Great Tit (Kluijver 1950). Thus, when using 

attentiveness as a predictor in the analyses of incubation 

period length and hatching success, we adjusted for variation 

in ambient temperature among nests during sampling 

as follows. We fitted a regression of nest attentiveness on 

ambient temperature separately for both measurements 

(i.e., before and after treatment). In both cases there was a 

significant negative relationship (linear regression: before 

treatment F1,75 = 13.3, P = 0.001, R 2 = 0.15; after treatment 

F 1,50 = 18.6, P \ 0.001, R 2 = 0.27). We calculated 

the residual nest attentiveness and averaged the residuals 

from these two regressions. In this way, we obtained 

temperature-independent attentiveness for each female as a 

predictor variable. 

When analysing observational data on mass decrease, it is 

necessary to take into account the problem of the regression 

toward the mean. Regression toward the mean occurs in 

repeated-measures analyses where subsets of population are 

compared based on their initial measurements. Thus, for 

instance, it follows from this effect that initially heavy 

individuals will lose more mass than initially light individuals. 

However, since regression to the mean will affect both 

experimental and control groups, experimental studies are 

not subject to this problem (Kelly and Price 2005). 

Accordingly, in our study, we interpret only the difference in 

mass loss between handicapped and control females, not the 

pattern in control females itself, which may be subject to the 

problem of the regression to the mean. However, this does 

not seem to be the case, because our results are the same 

even when the data is mathematically adjusted according to 

Kelly and Price (2005: Equation 6; results not shown). 

Results 

Altogether we performed cross-fostering on 82 nests. Five 

females deserted their nests after cross-fostering, leaving 

77 females for our experiment (54 experimental and 23 

control). There was a strong tendency for experimental 

females to desert their nests more often after treatment 

123 

compared to control females (18 experimental and three 

control, v 2 = 3.68, P = 0.055, n = 77). Clutch size in our 

population was 10.4 ± 1.20 eggs (n = 82). Carotenoid 

chroma of yellow breast feathers was 0.64 ± 0.06 (range: 

0.44–0.75), brightness was 0.24 ± 0.038 (0.17–0.33), hue 

was 501.3 ± 3.95 (495.0–505.6), and UV chroma was 

0.14 ± 0.008 (0.12–0.16, n = 56 in all four cases). 


Nest attentiveness before the treatment was 76.6 ± 4.77% 

(n = 77), and did not differ between experimental and 

control females (F1,75 \ 0.1, P = 0.875). On average, nest 

attentiveness decreased between the first and second measurements 

by 1.4 ± 5.52% (n = 52). Treatment had no 

influence on the amount of change in nest attentiveness. 

However, although our nests were highly synchronised and 

differed by less than 14 days, there was a significant effect 

of date. In the first nests of the breeding season, nest 

attentiveness increased by about 5%, whereas in the last 

nests, it decreased by about 5% (Table 1). No other factor 

had any influence on the change in nest attentiveness 

(Table 1, Fig. 3a). 

Female mass (g) 

(a) 

Nest attentiveness (%) 

(b) 

80 

78 

76 

74 

72 

20.5 

20.0 

19.5 

19.0 

Before After 

J Ornithol 

Control 

Experimental 

Control 

Experimental 

Fig. 3a–b Nest attentiveness (a; mean ± SE) and body mass of 

incubating females (b) in control and experimental nests before and 

after handicapping (feather clipping)

J Ornithol 

Mass change (g) 

1.5 

1.0 

0.5 

0.0 

-0.5 

-1.0 

-1.5 

-2.0 

-2.5 

Body mass loss 

Female body mass before the experiment was 

20.37 ± 0.88 g (n = 77). Body mass loss between the first 

and the second weighings was 0.66 ± 0.58 g (n = 56). 

Mass loss was significantly higher in experimental females 

(0.81 ± 0.52 g, n = 36) than in control females 

(0.38 ± 0.59 g, n = 20; simple effect of treatment: 

F1,54 = 8.0, P = 0.007, Fig. 3b). Mass loss was, however, 

also related to the initial condition of the female, and the 

relationship differed between experimental and control 

females, as evidenced by the significant interaction 

between treatment and initial female condition (Table 1, 

Fig. 4). No other factors were significant (Table 1). 

Incubation period 

Length of the incubation period was 11.8 ± 0.97 days 

(n = 48). Treatment had no effect on the length of the 

incubation period. It was negatively related to season and 

temperature-independent nest attentiveness; other factors 

were not significant (Table 2). 

Hatching success 

Control 

Experimental 

-2 -1 0 1 2 

Female condition before treatment 

Fig. 4 Body mass change of incubating Great Tit females in relation 

to female condition before the experiment (mass residuals in relation 

to tarsus), shown separately for control and experimental (clipped 

feathers) nests. More negative values of mass change mean higher 

mass loss over incubation 

Overall hatching success was 91.1 ± 11.52% (n = 51). 

There was no effect of treatment on hatching success; 

similarly, no other factor was significant (Table 2). 

It follows from the above results that female carotenoidbased 

feather colouration expressed as carotenoid chroma 

was not correlated with her ability to cope with energetic 

handicap during incubation (see also Table 1). Similarly, 

no other colour characteristic (hue, brightness or UV 

chroma) was correlated with female coping ability, incubation 

period length or hatching success (Table 3). 

Discussion 

Handicapping had no influence on female incubation 

behaviour, length of incubation period or hatching success. 

However, during incubation, handicapped females lost 

more body weight overall than control females. Females in 

poor condition were more severely influenced by handicapping. 

Intensity of female breast feather colouration did 

not correlate with either female incubation behaviour, body 

mass loss during incubation, incubation period length or 

hatching success. 

It seems that most of the costs of handicapping were 

channelled to female mass loss. This agrees with a previous 

study of the Great Tit, where handicapped females kept 

feeding rates to the nestlings unchanged at the cost of 

deteriorating their own body conditions (Sanz et al. 2000). 

Similar results were obtained in a study of the Tree 

Swallow Tachycineta bicolor, where the costs of handicapping 

were paid through the loss of female body mass, 

while nestling condition was unaffected (Winkler and 

Allen 1995). However, in incubating Tree Swallows, 

handicapped females both lost more mass than control 

females and also slightly decreased nest attentiveness 

(Ardia and Clotfelter 2007). In some other species, handicapping 

did not influence female body mass or body 

condition, but it did influence feeding rate and consequently 

nestling condition and growth, e.g. in Antarctic 

Petrels Thalassoica antarctica, Leach’s Storm-petrels 

Oceanodroma leucorhoa, Cory’s Shearwaters Calonectris 

diomedea, and tropical House Wrens Troglodytes aedon 

(Sæther et al. 1993; Mauck and Grubb 1995; Navarro and 

Gonzáles-Solís 2007; Tieleman et al. 2008). The two species 

where females invested in the current brood at the 

expense of their own conditions (Great Tits and Tree 

Swallows) are both short-lived, with low probabilities of 

future reproduction, which selects for increased investment 

into the current breeding attempt. On the contrary, longlived 

species with a high probability of future reproduction, 

including Antarctic Petrels, Leach’s Storm-petrels, Cory’s 

Shearwaters, and tropical House Wrens, are expected to 

reduce any increases of investment into the current brood 

in order to maximise their own survival (Roff 1992; 

Ghalambor and Martin 2001). 

Our experimental treatment affected females that were 

in poor condition disproportionately more than those in 

good condition (see Fig. 4). The importance of a good 

overall female state for successful incubation in the Great 

Tit is further supported by our finding that handicapped 

females deserted their clutches more often than control 

123

females. Similar relationships between female condition 

and nest desertion have been also found in other species 

(Wiggins et al. 1994; Yorio and Boersma 1994; Merilä and 

Wiggins 1997; but see Bleeker et al. 2005). An obvious 

explanation for this pattern is that incubation is energetically 

demanding and females in poor condition, caused by 

low body mass or impaired flight abilities, are not able to 

withstand the energetic stress (Williams 1996; Thomson 

et al. 1998; Tinbergen and Williams 2002). 

Intensity of yellow breast feather colouration was not 

related to the ability of females to cope with the handicap. 

One might ask how female colouration could help prevent 

a change in body mass. Handicapping is a standard way of 

testing whether an individual is of higher quality, i.e. is 

better able to cope with a challenging situation. Our 

experimental approach was motivated by a widespread 

finding that individual quality often shows up only under 

unfavourable conditions (e.g. brood size manipulations, 

various forms of handicapping, food restrictions; e.g. Ardia 

and Clotfelter 2007; Doutrelant et al. 2008). We conjecture 

that handicapped females could overcome the handicap by 

working harder. On a mechanistic basis, this means putting 

more energy into flight to get resources (self-maintenance) 

and simultaneously caring for the clutch (incubating) 

without these functions being compromised. Of course, this 

higher effort is expected to bear costs, e.g. a higher metabolic 

rate and higher oxidative stress generated by heavy 

work. This can presumably be achieved only by higherquality 

individuals. There certainly was a variation among 

females in the degree of their body mass loss (see Figs. 3, 

4), i.e. in their ability to cope. We were interested in 

whether this variation could be ascribed to female colouration, 

and found out that this was not the case. 

We would like to mention three potential problems when 

generalising our results. First, costs of the manipulation 

could also have been observed after hatching. This might 

have been particularly true during nestling feeding, when 

females have to fly more. Previously, all studies examining 

female feather colouration during reproduction in birds 

have been carried out during the nestling period (Amundsen 

and Pärn 2006; but see Hanssen et al. 2006). Several of them 

investigated the function of yellow breast feather colouration 

in Great and Blue Tits, but generated mixed results. 

Some found a positive relationship between the intensity of 

female yellow colouration and breeding success, whereas 

others found no or even a negative relationship (correlative 

studies: Senar et al. 2002b; Mänd et al. 2005; Hidalgo- 

Garcia 2006; experiment: Doutrelant et al. 2008). Hence, 

the information content of female yellow colouration might 

differ between parts of the breeding cycle, i.e. incubation 

versus feeding of young. Second, the colouration of the 

females that deserted just after the manipulation is missing. 

It is possible that these deserting females had low 

123 

carotenoid chroma values and were of inferior quality. 

Consequently, if we were left with only higher-quality 

individuals, our test of the indicator potential of the carotenoid-based 

colouration in females would have been 

weakened. Third, males feed females during incubation in 

the Great Tit. If we found better coping ability in more 

colourful females, we would not be sure whether they cope 

better because they are able to work harder, or because they 

are more helped by their males. However, male incubation 

feeding is not a source of potential bias in our study, 

because we found no effect of female colour on the ability 

to cope with energetic stress. Moreover, we studied this 

problem for three years in a nearby population, and there 

was no effect of female colour on male incubation feeding 

(Matysioková and Remesˇ 2010). 

While bearing the abovementioned reservations in mind, 

our results are not consistent with a role for feather 

carotenoids as indicators of female quality or capacity for 

extra parental effort, as has been demonstrated by several 

other studies (see above). Differences in the results of 

multiple studies investigating feather ornaments in the 

same species are known to occur due to population differences 

in the information content of the ornamental traits 

(Dunn et al. 2008; Galván and Moreno 2009) and different 

expressions of ornaments in different populations and 

subspecies (Hill 2002). Great Tit subspecies differ strongly 

in the intensity of yellow breast colouration (Harrap and 

Quinn 1996). However, there is no work quantifying differences 

in yellow colouration and in the functional ecology 

of feather ornaments among populations of the Great 

Tit. Nevertheless, it is at least possible that different populations 

are subject to different constraints on the expression 

of yellow colouration, and that the information content 

of feather ornaments varies in space. Only rigorous studies 

conducted in an explicitly comparative framework may 

provide answers to the heterogeneity of studies conducted 

so far (Senar et al. 2002b; Mänd et al. 2005; Hidalgo- 

Garcia 2006; Doutrelant et al. 2008; this study). 

Acknowledgments We are grateful to Freya Harrison and Milosˇ 

Krist for helpful comments on the manuscript. This study was supported 

by the Czech Ministry of Education (MSM6198959212). It 

complies with the current laws of the Czech Republic. 

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Behav Ecol Sociobiol 

DOI 10.1007/s00265-011-1139-9 

ORIGINAL PAPER 

Yolk androgens in great tit eggs are related to male 

attractiveness, breeding density and territory quality 


Received: 15 July 2010 /Revised: 21 November 2010 /Accepted: 4 January 2011 


Abstract Females can adaptively adjust phenotype of their 

offspring via deposition of various compounds into eggs, 

including androgens and other hormones. Here, I investigated 

how egg yolk androgens (testosterone and androstenedione) 

related to environmental conditions and parental 

traits in the great tit (Parus major) across three breeding 

seasons. Male and female traits studied included age, 

condition and multiple feather ornaments, both carotenoidand 

melanin-based (carotenoid and UV chroma of yellow 

breast feathers, area of black breast band and white cheek 

immaculateness). Yolk mass increased with laying temperature, 

laying date and area of male black breast band. 

Concentration of androgens increased with breeding density, 

territory quality and carotenoid chroma of male yellow 

breast feathers and was higher in mates of 1 year old as 

compared to older males. Yolk androgens were not related 

to any of the female traits analysed. These patterns were 

thus consistent with (1) social and environmental effects on 

yolk mass and composition and (2) both positive and 

negative differential allocation strategies of resource allocation 

in females. Overall, male traits were the most 

important predictors of egg yolk characteristics in this 

socially monogamous songbird. 

Communicated by J. Graves 

Electronic supplementary material The online version of this article 

(doi:10.1007/s00265-011-1139-9) contains supplementary material, 

which is available to authorized users. 

V. Remeš (*) 

Laboratory of Ornithology, Palacký University, 

Tr. Svobody 26, 



Keywords Feather colouration . Maternal effects . 

Offspring engineering . Paternal effects . Sexual selection 

Introduction 

Parents can significantly affect phenotype and performance 

of their offspring through non-genetic pathways by modifying 

prenatal and rearing environments of their young 

(Badyaev and Uller 2009). Birds are particularly interesting 

animals in this context because embryo development takes 

place within a sealed system, the egg, whose contents are 

fixed by the mother at laying, and no further adjustments of 

the egg components are possible once the egg is laid. 

Besides altering egg size (Krist 2010), avian mothers transfer 

to their eggs many valuable compounds, including antioxidants 

(Surai 2002), hormones (Groothuis et al. 2005a) and 

antibodies (Grindstaff et al. 2003). In this way, mothers 

modify the quality of their eggs and, indirectly, morphology, 

physiology and behaviour of the offspring. 

Androgens in avian eggs can have multiple effects on 

embryo and the young (Groothuis et al. 2005a) mediated 

by various potential proximate pathways (Navara and 

Mendonça 2008). So far, the range of these hormonal 

effects identified has involved benefits in terms of enhanced 

growth and competitive ability (Eising et al. 2001; Eising 

and Groothuis 2003; Groothuis et al. 2005b; Navara et al. 

2005; Müller et al. 2009; but see Andersson et al. 2004; 

Pilz et al. 2004) as well as costs in terms of immunosuppression 

(Andersson et al. 2004; Groothuis et al. 2005b; 

Müller et al. 2005; Navara et al. 2005; but see Navara et al. 

2006a; Pitala et al. 2009), poor survival (Müller et al. 2009) 

and elevated metabolic rate (Tobler et al. 2007). These 

effects are, moreover, often sex-specific (Saino et al. 2006; 

von Engelhardt et al. 2006; Sockman et al. 2008; Pitala et

al. 2009), environment-dependent (Pilz et al. 2004; Müller 

et al. 2010) and might not be demonstrated until well later 

in life (Strasser and Schwabl 2004; Eising et al. 2006; 

Rubolini et al. 2007; Müller et al. 2009). Thus, there is no 

simple relationship between yolk androgens and offspring 

performance, but the outcome of embryonic androgen 

exposure likely depends on the post-hatching circumstances 

for the developing offspring such as parasite exposure and 

the degree of sibling competition. 

Deposition of androgens in eggs can be affected by 

various characteristics of environmental conditions, females 

and males. Egg yolk androgens were found to be related to 

food supply (Sandell et al. 2007; Dentressangle et al. 2008), 

timing of breeding (Gil et al. 2006) and breeding density 

(Pilz and Smith 2004; Eising et al. 2008; Safran et al. 2010; 

but see Gil et al. 2006). They were also related to female 

age (Pilz et al. 2003) and condition (Tobler et al. 2007), 

female aggressive behaviour (Whittingham and Schwabl 

2002), female social status (Müller et al. 2002; Tanvez et al. 

2008) and social stress (Mazuc et al. 2003), female immune 

status (Gil et al. 2006) and exposure to parasites (Tschirren 

et al. 2004). Females might also positively or negatively 

differentially allocate yolk androgens in relation to social 

mate quality and attractiveness (Gil et al. 1999, 2006; 

Tanvez et al. 2004; Michl et al. 2005; Navara et al. 2006b; 

Loyau et al. 2007; Dentressangle et al. 2008; Safran et al. 

2008; Kingma et al. 2009; Garcia-Fernandez et al. 2010; 

Ratikainen and Kokko 2010). 

Identifying robust correlates of egg yolk androgen 

deposition is a prerequisite for better understanding of 

parental investment strategies, potential role of these substances 

in context-dependent adaptive offspring engineering 

and the role of environment in modulating patterns of 

deposition (Gilbert et al. 2005). Here, I investigated 

correlates of androgen (testosterone and androstenedione) 

deposition in egg yolk in a wild-ranging population of the 

great tit Parus major in terms of both environmental 

conditions and female and male quality. Environmental 

conditions often strongly modulate deposition of egg 

androgens, either facilitating or constraining it (see above). 

Thus, (1) I studied how timing of breeding, temperature, 

breeding density and territory quality predicted yolk mass 

and deposition of androgens. Females of high quality should 

allocate more yolk androgens to their eggs (investment 

hypothesis; Safran et al. 2008). Thus, (2) I studied how 

female characteristics (feather colouration, condition and 

age) predicted yolk mass and deposition of yolk androgens. 

Further, females paired to high quality males might allocate 

more (positive differential allocation) or less (negative 

differential allocation, Ratikainen and Kokko 2010) resources 

to their eggs. Thus, (3) I studied how social male 

characteristics (feather colouration, condition and age) 

predicted yolk mass and deposition of yolk androgens. 

Methods 

General fieldwork 


This work was conducted on three adjacent nest-box plots 

(188 nest-boxes in total) in a deciduous forest near 

Grygov (49°31′N, 17°19′E, 205 ma.s.l.) in eastern Czech 

Republic. The forest was dominated by lime Tilia and oak 

Quercus with interspersed ash Fraxinus excelsior, hornbeam 

Carpinus betulus and alder Alnus glutinosa. Nestboxes 

were placed about 1.6 m above ground, and besides 

the great tit were inhabited by collared flycatchers 

Ficedula albicollis, blue tits Cyanistes caeruleus and 

nuthatches Sitta europea. 

Fieldwork was carried out between 2005 and 2007 from 

early April until mid-June. Nest-boxes were checked daily 

to record laying of the first egg and final clutch size. When 

there were between six and seven eggs laid (i.e. before the 

incubation started, V. Remeš, unpublished data), the fourth 

egg was collected. This egg was weighed to the nearest 

0.01 g and placed into the freezer under −20°C. Within 

1 month after the field season had ended, the eggs were let 

thaw under room temperature, yolk was separated, weighed 

and again frozen under −20°C. Yolk androgens were 

analysed during autumn but no later than in October. I 

collected only one egg per clutch because of ethical reasons 

and also because this population is subject to detailed 

investigation taking place during chick rearing (including 

capturing the adults). One egg should reasonably represent 

the whole clutch, because in the great tit, variation in 

steroid hormone concentrations between clutches is much 

higher than within clutches (Tschirren et al. 2004). 

During feeding of nestlings (median age of young for 

females=7 days, for males=9 days), parents were captured 

in the nest-box. Females were captured on all the nests (n= 

163). However, because of time constraints, males were 

captured on a subset of nests only (n=101). Tarsus length 

was measured with a digital calliper (nearest 0.01 mm), and 

birds were weighed on a spring Pesola balance (nearest 

0.125 g). Body condition was calculated as residuals from 

the regression of body mass on tarsus length. From each 

bird, 10 to 15 yellow feathers were taken from the upper 

right part of the breast for later spectrophotometric analysis. 

The bird's white cheek (right side of the head) and breast 

were photographed with a digital camera (Panasonic DMC- 

FZ5). While the picture of the cheek was taken, the bird 

was held in a standardised position on its left side. While 

the picture of the breast was taken, the bird was held 

outstretched by its tarsi and beak and photographed 

together with a ruler from a standard distance following 

the protocol of Figuerola and Senar (2000). The age of the 

birds was determined based on their plumage as 1 year old 

or older (Svensson 1992).


Breeding density was defined as the number of adjacent 

nest-boxes (within 2.5 ha centred on the breeding nest-box) 

occupied by great tit pairs during the formation of the yolk 

of the sampled egg, i.e. during 7 days preceding egg laying 

(Perrins 1979). Breeding density varied from zero to nine. 

The area of 2.5 ha was chosen because this figure lies at the 

upper end of territory sizes reported for great tit (Wilkin et 

al. 2006 and references therein). Territory quality was 

defined as the number of years in which the particular nestbox 

was occupied by great tit between 2005 and 2009. 

Territory quality varied between one and five. Laying 

temperature was defined as the mean of average daytime 

temperatures (from 0600 to 2000 hours) during 7 days 

preceding the laying of the sampled egg. 

Quantification of feather colouration 

The following characteristics of feather colouration were 

chosen for the analysis: area of the black breast stripe 

(Norris 1990), carotenoid and UV chroma of yellow breast 

feathers (Isaksson et al. 2008), and immaculateness of the 

white cheek (Ferns and Hinsley 2004). Photos of breast and 

cheek were analysed in Adobe Photoshop CS3 Extended. 

Quick selection tool was used to roughly delimit the black 

stripe or the white cheek. Selection was then finished 

manually so that it was as precise as possible and measured 

the surface area of the stripe or cheek. A ruler photographed 

with every bird was used to adjust the scale of each photo 

and to obtain absolute surface area (in square centimetre) 

and in the case of the cheek also perimeter (in centimetre). 

Stripe surface was defined as the area of the black feathers 

between the point of inflexion, where the ventral stripe 

widens to a throat patch, and the posterior end of the stripe 

(Figuerola and Senar 2000). Immaculateness of the white 

cheek was calculated as 4*π*(area per square perimeter), 

which served as an index to measure regularity of the 

cheek's borders. It is equivalent to the index used by Ferns 

and Hinsley (2004), and the value of 1 indicates a perfect 

circle, whereas lower values (approaching zero) indicate 

shapes with lower area for a given perimeter, i.e. shapes 

with irregular borders and thus having lower immaculateness 

(Ferns and Hinsley 2004). To assess repeatability, a 

different observer measured a subsample of photos. 

Repeatability, calculated as the intraclass correlation coefficient 

(Lessells and Boag 1987), was high for both stripe 

area (r i=0.87, p

as described previously. The methanol phase was then 

evaporated to dryness. The dry residue was dissolved in 

20 mM sodium phosphate-buffered saline pH 7.0 containing 

1 mg/ml BSA (T, 600 μl; A4, 300 μl) and used for 

radioimmunoanalysis. 

Radioimmunoassay for T was prepared as follows: [ 125 I] 

iodo testosterone-3-carboxymethyl-tyrosine methyl ester 

(8.5 kBq/ml) in 20 mM PBS pH 7.0 as a tracer, polyclonal 

antibody raised against testosterone-3-O-carboxymethyl- 

BSA conjugate (dilution 1:40,000 in 20 mM PBS pH 7.0) 

and testosterone (from 0 to 8.0 nM) in 20 mM PBS pH 7.0 

as a standard. Analogously, for A4: [1,2,6,7-3 H]-Δ4androstenedione 

(6.5 kBq/ml) in 20 mM PBS pH 7.0 as a 

tracer, polyclonal antibody raised against 6β-hydroxy-Δ4androstenedione-6β-hemisuccinate-BSA 

conjugate (dilution 

1:20,000 in 20 mM PBS pH 7.0) and Δ 4 -androstenedione 

(from 0 to 34.915 nM) in 20 mM PBS pH 7.0 as a standard. 

The content of tubes was mixed and incubated (12 h, 

4°C). Dextran-coated charcoal (500 μl)wasaddedtoeach 

sample except those for total activity determination. The 

mixture was mixed briefly, incubated (10 min, 4°C) and 

centrifuged (3,000×g, 10 min, 4°C). The supernatant was 

decanted and taken for assessment of [ 125 I] radioactivity 

(T)or[ 3 H] radioactivity (A4). Concentrations of T and A4 

were calculated from the log-logit plot and corrected 

according to previously determined losses. 

All samples were assayed in duplicates, and intra-assay 

coefficients of variation were 8.2% for T and 5.8% for A4. 

Inter-assay coefficients of variation were 10.7% for T and 

11.6% for A4. Recovery rates ranged between 61% and 75%. 

The cross-reactivity between T and A4 was less than 2%. 


I was not a priori sure whether the concentration or the 

amount of yolk androgens is biologically more relevant 

(Safran et al. 2008). Thus, as dependent variables, I 

modelled (1) total summed concentration of androgens 

and (2) total amount of androgens per yolk, obtained by 

multiplying concentrations by yolk mass. To provide more 

detailed insight and statistical estimates for potential future 

meta-analyses, I also modelled testosterone and androstenedione 

concentrations separately. Besides yolk androgens, 

I also modelled yolk mass. To use as much data as possible 

and to avoid overly complex models, three sets of variables 

were used as predictors: (1) environmental factors (laying 

date, laying temperature, breeding density and territory 

quality), (2) female phenotypic traits (UV and carotenoid 

chroma of yellow breast feathers, area of the black breast 

band, cheek immaculateness, condition, age and clutch 

size) and (3) male phenotypic traits (the same as in females 

except clutch size). To compare relative importance of these 

three sets of predictors, I compared the models when using 

the exact same data for which I had all the predictors. I 

compared the strength of evidence for each of the three 

models by means of Akaike information criterion (AIC) in 

Proc Mixed of SAS. 

Some females were sampled in more than one season. 

Seven females were sampled in three seasons, 21 females in 

two seasons and 100 females in one season only. Female 

identity was used as a random factor with random intercepts 

only; general linear mixed models (Proc Mixed of SAS) 

were always used. No male was sampled in more than one 

season, which was certainly caused by much lower number 

of males captured. Denominator degrees of freedom were 

calculated by the Satterthwaite method. 

Concentrations of yolk androgens significantly differed 

among years (testosterone: F2,160=134.8, p


(SD=0.03, median=0.34, n=128) and it made on average 

20.48% of egg mass (relative yolk mass hereafter, SD= 

1.58, median=20.42, n=126). Average testosterone concentration 

for individual females was 55.89 pg/mg (SD= 

29.04, median=50.12, n=128), androstenedione concentration 

was 47.87 pg/mg (SD=15.25, median=47.43, n=128) 

and total androgens per yolk averaged 35.09 pg (SD= 

13.95, median=33.58, n=128). 

Egg and yolk mass were highly positively correlated, 

whereas egg mass and relative yolk mass were correlated 

negatively (Table S1 in the Online Resource). Correlations 

between yolk mass and relative yolk mass, and androgen 

concentrations were weak, but consistently negative. This 

resulted in no overall correlation between yolk mass and the 

amount of androgens per yolk. However, correlations 

between androgen concentrations and the amount of 

androgens per yolk were highly positive (Table S1 in the 

Online Resource). These results suggest that the amount of 

androgens in yolks was driven more by androgen concentration 

than by yolk mass. Concentrations of testosterone 

and androstenedione were highly positively correlated 

(Table S1 in the Online Resource). 

Standardized regression coefficients for the relationships 

between yolk characteristics and the three sets of predictors 

(environment and female and male traits) are presented in 

Figs. 1 and 2. 

Standardized regression 

coefficient (SE) 

0.4 

0.2 

0.0 

-0.2 

-0.4 

* 

Yolk mass 

Total androgens 

Androgens per yolk 

** * 

Laying date 

Laying temperature 

Breeding density 

Territory quality 

Predictor variable 

Fig. 1 Summary of the linear mixed models relating yolk mass and 

composition to the factors of the environment. Full results of 

modelling are available in Table S2 in the Online Resource. Asterisks 

denote significance at **p

chroma of yellow breast feathers of males, which was driven 

by testosterone. However, the amount of androgens per yolk 

was not related to carotenoid chroma of males. Androgen 

concentration was higher in 1-year-old males as compared to 

older males, which was driven by androstenedione. However, 

the amount of androgens per yolk was not related to male 

age. Although yolk mass increased with the area of male 

black breast band, the amount of androgens per yolk did not 

increase significantly (Fig. 2b; Table S2 in the Online 

Resource). 

As environmental factors, female traits and male traits 

were generally not inter-correlated, the results of independent 

modelling of the effects of these three sets of 

predictors were genuine and not confounded (Table S3 in 

the Online Resource). Comparison of AIC values for the 

three sets of predictors suggested that male traits were the 

most important set of predictors in explaining variation in 

egg composition (Table S2 in the Online Resource). 

Discussion 

Androgen concentrations 

Yolk composition in terms of androgen concentrations was 

similar to values reported by other studies of the great tit. 

Yolk testosterone concentration in a Swiss population of the 

great tit was 25.3 pg/mg, whereas concentration of 

androstenedione was 52.8 pg/mg (Tschirren et al. 2004), 

values broadly comparable to mine. Concentration of 

testosterone in my population of the great tit was higher 

than any species-specific value reported for 36 songbird 

species (Garamszegi et al. 2007) and for 101 other bird 

species (Gil et al. 2007). Androstenedione concentration 

fell within the values reported by Gil et al. (2007) for the 

same sample of 101 species of bird. 

Environment 

Yolk mass increased with laying date and temperature, 

which has been observed previously in another population 

of the great tit (Lessells et al. 2002). It has been suggested 

that when temperatures are low, females are either constrained 

by low activity of insect food during egg formation 

or they must devote more energy to maintenance metabolism 

and less is spared to form the eggs. Alternatively, 

increasing yolk mass with laying date might be an adaptive 

strategy to provide extra prenatal resources for offspring. 

Food supply, foraging success (Naef-Daenzer and Keller 

1999) and survival prospects of fledging great tits decline 

with season (Perrins 1979; Naef-Daenzer et al. 2001), and 

thus, it might be adaptive to boost offspring performance 

with extra resources (Krist 2010). However, increases of 

yolk mass with laying date and temperature did not 

translate into more androgens per yolk due to consistently 

negative correlations between yolk mass and androgen 

concentrations (see Table S1 in the Online Resource). Their 

consistently negative direction might have been enough to 

weaken the relationship between laying date and temperature 

and the amount of androgens per yolk. 

The concentration of androstenedione increased with 

breeding density and territory quality. Higher yolk androgen 

concentrations in denser populations were reported in 

the European starling Sturnus vulgaris (Pilz and Smith 

2004; Eising et al. 2008) and the house sparrow Passer 

domesticus (Mazuc et al. 2003), but not in the barn swallow 

Hirundo rustica (Gil et al. 2006; Safran et al. 2010). 

Females may actively allocate androgens to eggs laid under 

high density, thus preparing offspring for a difficult 

breeding situation. Alternatively, high frequency or intensity 

of aggressive interactions can lead to high yolk 

androgen concentrations (Whittingham and Schwabl 2002; 

Hargitai et al. 2009), thus possibly providing an example of 

physiological constraint which females cannot avoid (Pilz 

and Smith 2004). Recent evidence suggests that food 

supply may modify deposition of yolk androgens in relation 

to laying order (Sandell et al. 2007) or male attractiveness 

(Dentressangle et al. 2008). If my measure of territory 

quality (frequency of occupation over 5 years) reflected 

food supply, more resources would enable females to 

deposit more androstenedione into eggs. Alternatively, 

attractive territories might be pre-empted by high-quality 

females with an intrinsic ability of depositing more 

androgens into eggs without a direct effect of food supply. 

Female traits 


Neither yolk mass nor composition was related to any of 

female phenotypic characteristics. This is quite surprising 

given the evidence from other great tit populations that 

feather ornaments are condition-dependent (Senar et al. 

2003) and signal individual condition or quality (Hõrak et 

al. 2001; Ferns and Hinsley 2004, 2008). However, the 

signal content of individual ornaments might differ between 

populations. For example, there was a negative relationship 

between the intensity of yellow feather colour and fledging 

success in great tits in Estonia (Mänd et al. 2005). In a 

similar vein, in my population and in a population in 

Hungary, neither yellow breast feather colour nor the size of 

the black breast stripe was correlated with and index of 

condition (Hegyi et al. 2007; Matysioková and Remeš 

2010a). This contrasts with a Spanish population where 

carotenoid-based yellow colour was condition-dependent, 

whereas the size of the black stripe was not (Senar et al. 

2003). Accordingly, in my population, there was no 

correlation between carotenoid chroma of yellow breast


feathers or the size of their black breast stripe in females 

and female incubation effort (Matysioková and Remeš 

2010b). Similarly, carotenoid chroma of breast feathers did 

not indicate an ability of females to cope with energetic 

stress (assessed by a handicapping experiment, Matysioková 

and Remeš 2010c). All this evidence indicates that feather 

colouration is not an indicator of female quality in my 

population of the great tit, and thus the absence of any 

relationships between yolk mass and composition and female 

phenotype agrees with other findings from the same 

population. 

Male traits 

It has been suggested that females should allocate more 

resources into offspring when paired with higher-quality 

males, indicated for instance by their ornaments, a strategy 

known as positive differential allocation. On the contrary, 

females might instead boost performance of offspring of 

lower-quality males, a strategy called negative differential 

allocation (Harris and Uller 2009; Ratikainen and Kokko 

2010). Several studies of yolk androgens in wild birds 

reported patterns consistent with both positive and negative 

differential allocation. First, females of the blue tit 

deposited more yolk androgens for more attractive social 

partners (Kingma et al. 2009), and females of the grey 

partridge Perdix perdix deposited more androgens for 

preferred males (Garcia-Fernandez et al. 2010). Second, 

females in the collared flycatcher laid eggs with higher 

concentration of yolk testosterone for young as opposed to 

older males (Michl et al. 2005). Similarly, female house 

finches Carpodacus mexicanus deposited significantly 

more androgens into eggs sired by less attractive males 

(Navara et al. 2006b). Finally, there was no relationship of 

yolk volume or testosterone concentration to the area of a 

white forehead patch of males in the collared flycatcher 

(Michl et al. 2005; Török et al. 2007). The complexity of 

situation is demonstrated by studies on the barn swallow. In 

a Spanish population, females increased the concentration 

of androgens in their eggs when mated to males with 

experimentally elongated tails (Gil et al. 2006). In a US 

population, androgen concentration increased with male 

throat colour but did not change with his tale length (Safran 

et al. 2008). These results hint to possible differences 

between populations and different male ornaments in 

female allocation strategies. 

Females in my population deposited yolk mass and 

androgens consistent with both positive and negative 

differential allocation strategies. First, females laid larger 

yolks for males with larger black band area, although this 

did not translate into more androgens per yolk due to 

negative correlations between yolk mass and androgen 

concentrations (see Table S1 in the Online Resource). They 

deposited testosterone in higher concentrations when mated 

to males with more intense carotenoid chroma of yellow 

breast feathers, but at the same time laid lighter yolks with 

the result that the total amount of androgens per yolk did 

not change. All these patterns are consistent with the 

positive differential allocation, provided carotenoid- and 

melanin-based male ornaments in the great tit indicate male 

quality. Size of the black breast band was positively related 

to the social status of the bird (Lemel and Wallin 1993) and 

to the frequency (Norris 1990) and intensity of nest defence 

(Quesada and Senar 2007). Thus, black stripe signals the 

ability of the male to win agonistic intraspecific encounters 

and defend offspring. It may also indicate the ability of the 

male to provide superior parental care (Norris 1990). 

Saturation of carotenoid-based colouration might signal 

the superior foraging ability of individuals in terms of food 

quality or quantity (foraging performance hypothesis, 

Møller et al. 2000). In a Swedish population of the great 

tit, nestling plumage carotenoid chroma was predicted by 

the chroma of the rearing father (after cross-fostering of the 

young), indicating that foraging ability of the social mate 

might have significant effects on offspring phenotype 

(Isaksson et al. 2006). However, although it has been 

demonstrated that great tits prefer carotenoid-rich food 

items in general (Senar et al. 2010), specific tests of the 

foraging performance hypothesis remain to be carried out. 

Second, females laid eggs with higher concentration of 

yolk androstenedione for 1 year old as opposed to older 

males, similarly to the situation in the collared flycatcher 

(Michl et al. 2005). This pattern is consistent with the negative 

differential allocation and might reflect an attempt by the 

female to boost the growth of offspring of an inferior father. 

An alternative explanation is that females try to manipulate 

their partner's contribution to parental care by allocating yolk 

androgens that boost offspring solicitation behaviour (Michl 

et al. 2005). However, this explanation seems unlikely for the 

great tit because it has been demonstrated that males are not 

responsive to androgen-mediated solicitation signals in this 

species (Tschirren and Richner 2008), which is also true in 

the collared flycatcher (Ruuskanen et al. 2009) and canary 

Serinus canaria (Müller et al. 2010). 

Besides direct benefits (offspring feeding and defence), 

females might gain indirect, genetic benefits from more 

ornamented males and allocate yolk compounds accordingly. 

For example, great tit offspring of males with large black 

band area survived better (Norris 1993). However, revealing 

allocation based on indirect benefits might be undermined 

by the occurrence of extra-pair paternity. Then, if 

most offspring were sired by male(s) other that the social 

mate, or if social and genetic mates differed systematically 

in their ornaments, patterns revealed without paying 

attention to extra-pair paternity could be misleading. 

However, both of these potential sources of complications

seem to be minimal in the great tit. First, rates of extra-pair 

paternity are comparatively low in this species, accounting 

for less than 10% of the young (Verboven and Mateman 

1997; Krokene et al. 1998; Strohbach et al. 1998; Lubjuhn 

et al. 1999, 2001; Otter et al. 2001; Johannessen et al. 

2005). Second, social and genetic mates do not differ in 

feather ornaments (Krokene et al. 1998; Strohbach et al. 

1998), and effects of male quality on paternity loss are 

generally low in this species (Lubjuhn et al. 1999; Otter et 

al. 2001; Johannessen et al. 2005). The only exception was 

a Japanese population (a different subspecies, P. major 

minor), where 16.6% of offspring were sired by extra-pair 

males, and extra-pair sires had larger black breast band than 

social mates (Kawano et al. 2009). 

Conclusions 

It is not clear whether the concentration or the total amount 

of a compound per yolk is more important for developing 

offspring (Safran et al. 2008). In this study, correlations 

between yolk mass and androgen concentrations were not 

significant but overall were slightly negative. This was the 

reason for the observation that although yolk mass 

increased with certain factors (laying temperature and male 

breast band area), total androgens per yolk did not (Figs. 1 

and 2). Similarly, although the total androgen concentration 

increased with male carotenoid chroma, androgen amount 

per yolk did not because yolk mass correlated slightly 

negatively with male carotenoid chroma (Fig. 2). These 

results suggest that offspring may originate from eggs with 

different combinations of yolk mass, androgen concentrations 

and total amounts of androgens with possibly different 

consequences for their performance. 

The main findings of this study of yolk androgens in the 

great tit were as follows. (1) Correlations of androgens with 

yolk mass were weak. (2) Comparison of Figs. 1 and 2 and 

AIC values of comparable models (Table S2 in the Online 

Resource) suggest that male traits were the best predictors 

of egg characteristics. Further, I conclude that large 

variation among females in egg yolk androgens suggests 

that there is a potential for adaptive offspring engineering in 

relation to environmental and social factors. However, it is 

important to acknowledge that androgens can have positive 

as well as negative effects on offspring (see “Introduction” 

section). Thus, understanding adaptiveness or otherwise of 

the patterns indentified in this study will require an 

experimental approach. 

Acknowledgements I am grateful to Kristýna Bártlová, Beata 

Matysioková and Jana Šuterová for assistance in the field, Lubomír Kříž 

for running androgen assays, and Beata Matysioková and two anonymous 

reviewers for helpful comments on the manuscript. This study was 

supported by Czech Ministry of Education (MSM6198959212). 

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