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Habilitation à Diriger des Recherches 

Université de Bretagne Occidentale 

Document de synthèse 

'Dispersion, modes reproducteurs et 

réseaux en environnement marin: 

là où il y a des gènes’ 

Sophie ARNAUD-HAOND 

IFREMER, Centre de Brest 

Département Etude des Ecosystèmes Profonds 

Laboratoire Environnement Profond 

Document provisoire- Soutenance prévue le 7 mars 2007 

Devant le jury composé de: 

Myriam Valéro rapporteur 

Directeur de Recherche, CNRS 

François Lallier rapporteur 

Professeur, Université Paris VI 

Pierre Boudry rapporteur 

Cadre de Recherche, IFREMER, Brest 

Jean Laroche examinateur 

Professeur, Université de Bretagne Occidentale 

Jacques Clavier examinateur 

Professeur, Université de Bretagne Occidentale 

Daniel Desbruyères examinateur 

Directeur de Recherche, IFREMER, Brest 

François Bonhomme examinateur 

Directeur de Recherche, CNRS

Caminante no hay camino……. 

Manu San Felix 

...se hace el camino a andar… 

- Antonio Machado – 

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Sophie ARNAUD-HAOND - Candidature à une Habilitation à Diriger des Recherches, Février 2008 

Sommaire 

SOMMAIRE 

Sommaire ...........................................................................................................................3 

Avant-Propos .....................................................................................................................4 

Curiculum Vitae .................................................................................................................8 

Résumé des travaux de Recherche ...............................................................................22 

I. Le milieu marin : un milieu hétérogène et instable ..................................................23 

I.1 Migration-Dérive : hétérogénéité, barrières au flux génique et taille efficace....................... 23 

I.2 Sélection ....................................................................................................................................... 28 

I.3 Impact des activités anthropiques ............................................................................................. 30 

II. Clonalite, ecologie et evolution ...............................................................................33 

II.1 Assessing genetic diversity in clonal organisms: Low diversity or low resolution? 

Combining power and cost efficiency in selecting markers. Journal of Heredity, 2005............... 34 

II.2 Within-population spatial genetic structure, neighbourhood size and clonal subrange in 

the seagrass Cymodocea nodosa. Molecular Ecology, 2005........................................................... 42 

II.3 Standardizing methods to describe population structure of clonal organisms. Molecular 

Ecology, Invited Review, 2007............................................................................................................. 55 

II.4 Feed-backs between genetic structure and perturbation-driven decline in seagrass 

(Posidonia oceanica) meadows. Conservation Genetics, 2007....................................................... 82 

II.5 GenClone 1.0: a new program to analyse genetics data on clonal organisms. Molecular 

Ecology Notes, 2007. ............................................................................................................................ 98 

II.6 Conclusions et Perspectives................................................................................................ 102 

III. Les métapopulations considérées comme des systèmes complexes : networks 

génétiques ....................................................................................................................103 

III.1 Spectrum of genetic diversity and networks of clonal populations. Journal of the Royal 

Society Interface, 2007. ...................................................................................................................... 108 

III.2 Population genetics networks: identifying weak and strong links in a metapopulation 

system. Soumis................................................................................................................................... 119 

IV. Perspective : Biodiversité et évolution des écosystèmes profonds .......................143 

IV.1 Du milieu terrestre au milieu profond : un abysse de recherche et d’efforts.................. 143 

IV.2 Quelques un des écosystèmes profonds principaux ........................................................ 146 

IV.3 Axes de Recherche................................................................................................................ 149 

V. Références............................................................................................................155 

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Avant-Propos 

AVANT-PROPOS 

La démarche scientifique est un terme qui évoque la rigueur, la planification à long 

terme, le respect d’un parcours millimétré pour atteindre les buts, tester les hypothèses, 

dévoiler les processus, faire la lumière sur les mystérieux mécanismes dont nous 

cherchons les clés, comme des enfants devant un fascinant jeu de mécano … Le parcours 

du chercheur, lui, est tout autre parce qu’il s’effectue dans « la vraie vie ». A la différence 

de la théorie, ou de la réalité in silico (n’en déplaise aux modèles de mes collègues 

physiciens) « la vraie vie » est hétérogène, chaotique, pleine d’imprévus, de rencontres, 

de trains ratés, d’incidents, de surprises. Un examen mieux ou moins bien réussi que 

prévu, une lettre de motivation écrite un jour de blues ou d’euphorie, un entretien 

pathétique ou empreint d’un miraculeux et incompréhensible éclair de génie…et bien que 

nous ayons tracé nos plans sur la comète pour faire ci, nous nous retrouvons à faire ça, 

ou encore ci^10 . Quelques portes qui semblent se fermer irrémédiablement pour en laisser 

d’autres, parfois inespérées, s‘ouvrir bientôt, parfois mieux… Il en est de même pour le 

parcours scientifique, pour les mêmes raisons et bien d’autres encore. Malgré nos 

analyses méticuleuses, nos théories à l’épreuve des équations, nos plans expérimentaux 

irréprochables et nos échantillonnages hiérarchiques improbables, ce que nous avons 

trouvé de mieux est parfois (souvent ?) ce que nous n’avions pas pensé à chercher. Le 

défi quotidien est de garder notre regard d’enfant pour être capable de considérer un 

imprévu comme une curiosité plutôt qu’un obstacle ou un contretemps irritant. De se 

défaire de nos œillères de temps en temps pour changer de cap si un autre semble plus 

prometteur, malgré le rapport de projet qu’il faudra peut-être rendre dans 2 ans sur la base 

des objectifs qu’on avait mis sur le papier l’année dernière… par exemple. Ce n’est pas 

un mince défi… 

Ainsi va, souvent, le parcours du chercheur : souvent chaotique, parfois 

désespérant (exaspérant ?), toujours imprévisible, et de temps à autre exaltant. A l’heure 

de rédiger un mémoire de synthèse sur les recherches passées, et de se projeter dans le 

futur, j’ai tendance à chercher une ligne directrice qui me permettrai de tracer un chemin 

bien droit avec une ligne de fuite bien comme il faut, bref qui homogénéiserait tout cela 

pour le confort du lecteur. Et pour toutes ces raisons je m’aperçois que c’est plutôt une 

mosaïque de type habitat fragmenté, de pièces qui, certes, appartiennent pour la plupart 

au même puzzle (enfin, j’espère…), mais bien distantes parfois les unes des autres. Parce 

que dans le miroir de la migration, le spectre de la sélection est apparu, qu’en cherchant 

dérive et conservation je me suis heurtée au système reproducteur, qu’en essayant de me 

débrouiller des problèmes d’équilibre, je me suis pris les pieds dans les réseaux…je 

présente mes humbles excuses à ceux qui, par devoir ou par masochisme, lirons ce 

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Avant-Propos 

document et auront donc peut-être du mal à y trouver un fil conducteur. J’ai envie de leur 

dire qu’ils se rassurent : il n’y en a pas. Disons alors que c’est un puzzle qui parle de vie, 

d’évolution, de spéciation et d’extinction, et que je me suis concentrée sur la partie 

immergée du tableau. Parmi les milliards de pièces probablement nécessaires à sa 

réalisation, on en retrouvera ici quelques unes (si peu) qui appartiennent à des scènes 

illustrant (avec un suite chronologique un peu personnelle) la migration et ses limites, la 

dynamique de la distribution, la sélection, le sexe –et son absence-, la diversité ; et 

quelques efforts pour relier ce tableau à d’autres, principalement à l’un d’entre eux, qui 

parle de la matière et de son mouvement. 

J’ai choisi de ne pas donner le même poids à ces différents thèmes. Certains 

seront évoqués brièvement, principalement les plus anciens, tandis que d’autres, ceux 

probablement pour lesquels je me sens encore ‘dans le feu de l’action’ et donc plus 

enthousiaste, seront plus approfondis : l’étude de la clonalité, l’approche des 

métapopulations sous forme de réseaux. Ce sont ceux qui constituent les « steppingstones 

» qui me permettent d’amorcer en douceur ma migration récente depuis les côtes 

vers le milieu profond. 

J’ai commencé mon parcours à Montpellier, lorsque l’opportunité m’a été offerte en 

DEA de concilier ma passion pour la biologie marine et mon intérêt pour l’écologie avec 

ma plus récente curiosité pour la théorie de l’évolution ; intérêt éveillé par les cours 

inoubliables de Jean-Jacques Jaeger, Louis Thaler, Isabelle Olivieri et Patrice David. 

François Bonhomme a démarré dans son laboratoire la génétique des populations des 

organismes marins : c’est ce que je cherchais sans le savoir. Et c’était parti pour défier le 

paradigme de la panmixie en milieu dispersif, et rechercher des barrières aux flux génique 

en milieu marin en travaillant sur les chinchards, poissons pélagiques exploités par les 

pêcheries Indonésiennes. Ce projet était réalisé en collaboration avec l’IRD a1 et 

s’inscrivait également dans le cadre de la gestion des ressources. C’est durant ce stage 

que j’ai encadré pour la première fois un étudiant, il s’agissait de Céline Viray qui à l’issu 

de sa maîtrise, souhaitait faire un stage d’été. Je suis donc passée de la théorie à 

l’empirique au sujet de « …ce qui se conçoit bien s’énonce clairement… » 1 . J’ai découvert 

qu’en apprenant, on touche du doigt des failles dont on ignorait l’existence dans notre 

compréhension des concepts clés, et que souvent, tel est appris qui croyait apprendre. Je 

n’ai pas pu poursuivre cette aventure dans l’Indo Pacifique et le mystère de la 

concentration de biodiversité marine qu’on y trouve. Mais toujours au sein du même 

laboratoire avec François Bonhomme, et co-encadrée par Françoise Blanc (Université 

Montpellier III) et Mario Monteforte (CIBNOR de La Paz, Mexique), j’ai continué à étudier 

la structure génétique et les barrières au flux géniques en milieu marin. Il s’agissait de 

définir, avec des marqueurs moléculaires a2,3 les stocks génétiquement différenciés des 

1 Boileau, l’Art Poétique, Chant I 

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Avant-Propos 

nacres perlières du Mexique a2,6 , et de Polynésie a5 , aidée par Jérôme du Barry et Lionel 

Valera qui souhaitaient dans leurs cursus respectifs s’initier aux joies de la biologie 

moléculaire. 

Mon premier post-doctorat a été en quelque sorte la continuité de ma thèse. Il 

s’agissait d’approfondir et de réactualiser nos connaissances des stocks de nacres 

perlière en Polynésie, entre l’IFREMER de la Tremblade et celui de Vairao, et de tenter 

d’évaluer l’impact des pratiques culturales sur les ressources génétique et l’évolution 

« naturelle » des populations a4,10, s27 . Les applications de la génétique des populations à 

l’aquaculture et à la gestion des stocks exploités sont passionnantes, en particulier grâce 

au travail d’équipe réalisé avec Emmanuel Goyard, qui m’a permis une incursion dans le 

monde des écloseries et de la valorisation des ressources a9,a18 et Vincent Vonau qui m’a 

toujours rappelé que le bon sens peut (et doit !) primer sur la théorie. Toutefois, en 

l’absence de données démographiques et écologiques pour essayer d’élaguer un peu la 

forêt de scénarios explicatifs qui s’offraient à moi je me sentais fortement limitée dans 

l’interprétation des données génétiques. 

J’ai donc rejoint un projet Européen portant sur phanérogames marines, entre le 

laboratoire d’Ester Serrão au CCMar (Faro, Portugal) où je devais assumer la partie 

« génétique des populations », et celui de Carlos Duarte à l’IMEDEA (Mallorque, Espagne) 

où je devais assurer l’interface génétique / démographie, écologie. Durant ces cinq 

années certains objectifs initiaux ont du être drastiquement révisés, voire abandonnés 

(comment comprendre les interactions entre la démographie, phénomène éminemment 

contemporain, et la composition génétique des populations d’une plante quand ses genets 

peuvent atteindre plusieurs millénaires…). Ces révisions ont été le fruit d’interactions 

scientifiques passionnantes avec des scientifiques de culture différentes. J’étais venue 

étudier la biogéographie et la conservation des prairies de phanérogames a15,17,s26 avec 

Filipe Alberto a7,14,24 et Elena Diaz-Almela a20 dans le cadre de leurs thèses respectives. 

Nous nous avons finalement passé au moins autant de temps à nous interroger sur 

l’influence de la clonalité sur nos analyses et conclusions que sur la dynamique et l’histoire 

des populations a12,13,17,22 . 

Finalement, toutes ces questions ont fini par en rejoindre d’autres, sur 

l’interprétation des données quand on étudie les espèces qui sont les plus fréquemment 

étudiées à l’aide de marqueurs moléculaires. Il s’agit pour ne citer qu’elles des espèces 

invasives, en déclin, ou même encore les espèces pathogènes, qui sont étudiées 

précisément pour des raisons qui font que nous savons d’emblée que les hypothèses 

sous-jacentes aux modèles classiques de génétique des populations ne sont pas 

respectées (équilibre migration-dérive, égalité des tailles de populations, de migration 

symétrique, etc…). Par ailleurs les avancées des techniques moléculaires nous 

permettent de réunir des jeux de données de plus en plus fourni, qu’il est souvent frustrant 

de réduire à des « statistiques résumées » telles que les indices F ou H. Au détour d’une 

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Avant-Propos 

rencontre avec des physiciens passionnés de réseaux et de systèmes complexes a germé 

l’idée d’essayer de prendre le problème par l’autre bout. C'est-à-dire d’oublier 

temporairement ce que nous savions ou croyions savoir, théoriquement, de la dynamique 

de la transmission des gènes dans l’espace et dans le temps, pour analyser en aveugle 

nos données moléculaires en considérant, tout simplement, nos groupes d’individus ou 

nos systèmes de métapopulations et leurs génotypes comme des systèmes complexes. A 

ce titre nous avons donc tenté de leur appliquer une des méthodes d’étude adaptées : la 

théorie des réseaux. Ce travail a été pour moi, et est encore (j’espère qu’il le restera 

longtemps), l’un des plus passionnants auquel il m’ait été donné de participer. 

L’universalité des systèmes complexes rends la théorie des réseaux fascinante, et le fait 

de se retrouver en groupe mélangé de physiciens et des biologistes qui doivent d’abord 

s’entraîner à parler le même langage (un pré-requis qui s’est avéré ne pas être trivial !), 

puis essayer de se faire passer les uns aux autres les clés qui donnent accès aux bases 

de nos disciplines respectives a été une expérience extraordinairement drôle, humaine, et 

enrichissante. Cet amusement collectif de chaque instant, malgré les points de blocages 

et les tensions parfois, ne nous a pas déçus. Les premiers résultats scientifiques, pas 

seulement les publications qui commencent à émerger après bientôt trois ans d’efforts 

mais ce que nous apprenons les uns des autres et les perspectives que nous pensons voir 

dans ces résultats nous encouragent à poursuivre dans cette voie a21,s25 . A l’heure ou un 

projet national (NETWORK) qui nous a permis de démarrer se termine, nous commençons 

un projet Européen (EDEN) : l’aventure ne fait, nous l’espérons, que commencer. 

Mais je parlais de migration, en profondeur. Les derniers axes que je viens 

d’évoquer et qui sont suffisamment méthodologiques et/ou théorique pour offrir une 

certaine plasticité, ils m’ont donc permis d’intégrer l’Ifremer au Département d’Etudes des 

Ecosystème Profonds de Brest pour continuer à les développer et les mettre en 

application sur des invertébrés, et éventuellement sur leurs symbiontes. La seconde partie 

de ce manuscrit sera dédiée aux perspectives de Recherches et aux quelques projets déjà 

ébauchés avec pour thème/modèles spécifiques les populations et assemblages 

d’espèces d’environnement profond. 

7


1. Curriculum vitae 

CURICULUM VITAE 

Date de naissance: 25 Mai 1973 

Nationalité : Française 

Statut familial : mariée, deux enfants 

Adresse : 20 rue des Camélias. 292170 Plougonvelin; 

Téléphone : +33 (0)6 50 00 87 14 

Email: 

sarnaud@ifremer.fr; sarnaud@ualg.pt 

FORMATION UNIVERSITAIRE ET PARCOURS PROFESSIONNEL 

2007- Cadre de Recherche, « Ecologie évolutive des écosystèmes 

chimiosynthétiques : dynamique du flux génique et de la sélection 

dans des habitats extrêmes et chroniquement instables » 

Au Centre Ifremer de Brest, Département Etude des Ecosystèmes 

Profonds, Laboratoire Environnement Profond dirigé par Daniel 

DESBRUYERES. 

2005-2007 Contrat post-doctoral: « Diversité génétique neutre et 

sélectionnée, et stabilité démographique » et « Propriétés 

émergentes des networks, et analyses de données génétiques » 

Au Centre des Sciences Marines (Université de Faro, Portugal), 

superviseurs scientifiques : Ester A. SERRÃO (CCMar, Faro, 

Portugal) et Carlos M. DUARTE (Instituto Mediterraneo de 

Estudios Avanzados, Baléares, Espagne). 

2002-2005 Contrat post-doctoral: « Démographie et stratégies de 

reproduction, dispersion et biogéographie d’une phanérogame 

marine clonale, Posidonia oceanica. » et « Impact des activités 

anthropiques et de la fragmentation de l'habitat sur la variabilité 

génétique des populations de mangroves du genre Avicennia sp. 

en Asie » 

Au Centre des Sciences Marines (Université de Faro, Portugal), 

superviseurs scientifiques : Ester A. SERRÃO (CCMar, Faro, 

Portugal) et Carlos M. DUARTE (Instituto Mediterraneo de 

Estudios Avanzados, Baléares, Espagne). 

2000-2002 Contrat post-doctoral : « Structure génétique spatio-temporelle 

de populations sauvages et d’élevages de la nacre perlière de 

Polynésie, Pinctada margaritifera». Evaluation des ressources 

génétiques sauvages et de l’impact des pratiques culturales sur 

les populations naturelles. 

Au Laboratoire d'Aquaculture Tropicale (Ifremer Tahiti), en 

collaboration avec Emmanuel GOYARD (Ifremer-Cop, Tahiti) et 

Pierre BOUDRY (Ifremer La Tremblade). 

8



1997-2000 Thèse de doctorat de l’Université Montpellier II (Mention très 

honorable) : « Flux génique et phylogéographie comparés de 

deux espèces de bivalves du Pacifique Pinctada mazatlanica et 

Pinctada margaritifera, marqueurs mitochondriaux et nucléaires ». 

Thèse réalisée au CIBNOR de La Paz (Mexique) et au Laboratoire 

"Génome, Populations, Interactions" (Université Montpellier II), 

sous la direction de : François BONHOMME et Françoise BLANC. 

1995-1996 DEA « Biologie de l’Evolution et Ecologie » de l’Université 

Montpellier II, spécialisation en génétique des populations et 

génétique évolutive (Mention AB). 

Stage : 

"Le chinchard Decapterus macrosoma, poisson pélagique 

d’importance halieutique en mer de Java, un exemple d’espèce 

marine génétiquement structurée". 

Stage effectué au laboratoire "Génome et Populations" sous la 

direction de François BONHOMME. 

1994-1995 Maîtrise de Biologie des Organismes et des Populations de 

l’Université Montpellier II, spécialisation écologie et évolution 

(Mention B). 

EXPERIENCE PROFESSIONNELLE 

Participation à la réalisation ou à la gestion de projets de recherche (travail 

réalisé dans le cadre de la gestion du projet, montant obtenu pour les projets 

dont l’obtention de financement et la coordination sont ou ont été à ma charge) : 

Post-doctorat Ifremer (2000-2002) 

1) Acquisition et utilisation de marqueurs moléculaires comme outils de 

caractérisation génétique de la nacre perlière, Pinctada margaritifera : Aide 

à la conservation des ressources génétiques et à l’amélioration des 

cheptels. FIDES, 2000-2001. (Réalisation du travail, rédaction du rapport 

final et de publications). 

2) Ressources génétiques de l'huître perlière de Polynésie française, 

Pinctada margaritifera : recherche de populations locales originales et aide 

à la définition d'une stratégie de conservation. Contrat de 

Développement État-Territoire de Polynésie Française, 2000-2002 

(Réalisation du travail, rédaction du rapport final et de publications) 

9



3) Gestion des ressources génétiques de l’huître perlière Pinctada 

margaritifera de Polynésie française : Caractérisation génétique des 

populations et optimisation du recrutement pour l'exploitation perlière. 

BRG (Bureau des Ressources Génétiques), 2002 (26 Keuros ; réalisation 

du travail et rédaction du rapport final). 

Post-doctorat CCMar (I : 2002-2005) 

4) PREDICT. Prediction of the resilience and recovery of disturbed coastal 

communities in the tropics. Projet INCO-DC: International Cooperation with 

developing countries, 1998-2002 (Finalisation de travaux et rédaction 

d’articles) 

5) Monitoring and Management of seagrasses. Projet Européen 5 ème 

PCRD, 2001-2004 (réalisation de la partie du travail concernant la diversité 

génétique, des herbiers de Posidonie, rédaction de rapports et de 

publications, participation à la rédaction d’un chapitre de livre à destination 

des gestionnaires de réserves). 

Projets en cours 

6) Networks Génétiques et Evolution : des individus aux populations. 

Fondation des Sciences et Technologies, 2005-2007 (45 Keuros; 

coordination du projet). 

7) Gènes neutres et non neutres : Diversité et stabilité des populations. 

Fondation des Sciences et Technologies, 2006-2009 (86 Keuros; 

coordination du projet). 

8) Conservation des prairies marines: Causes et effets de la régression sur 

le fonctionnement des écosystèmes. Fondation FBBVA., 2006-2010 

(coordination d’un workpackage). 

9) Divergence adaptative des populations, et structure comparée des 

populations d’algues brunes du genre Fucus. Fondation des Sciences et 

Technologies, 2006-2009 (chercheur associé). 

0 

10



10) Diversité génétique et différentiation chez les phanérogames Zostera 

noltii et Cymodocea nodosa à la jonction Atlantique - Méditerranée. 

Fondation des Sciences et Technologies, 2006-2008 (chercheur associé). 

11) Écologie et évolution du système reproducteur chez une algue fucoide. 

Fondation des Sciences et Technologies, 2006-2008 (chercheur associé). 

12) EDEN : Ecological Diversity and Evolutionary Networks . Projet 

Européen. 6 ème PCRD, 2006-2010 (coordination d’un workpackage). 

13) DeepOases : Biodiversité des écosystème chimiosynthétiques dans 

l’environnement profond. Projet de l’Agence Nationale pour la Recherche, 

2007-2010 (chercheur associé). 

14) CORALFISH : Assessment of the interaction between corals, fish and 

fisheries, in order to develop monitoring and predictive modelling tools for 

ecosystem based management in the deep waters of Europe and beyond. 

Projet Européen 7ème PCRD, 2008-2012 (coordination d’un 

workpackage). 

Participation aux networks d’excellence Européens Marine Biodiversity and 

Ecosystem Functioning (implication dans les Actions concertées « Diversité 

Génétique » du Thème 1 et « Stabilité des écosystèmes » du Thème 2) dans 

les et Marine Genomics. (participation au nœud « algue- génomique 

environnementale »). 

11



ENCADREMENT, ENSEIGNEMENT ET TRAVAIL D’EQUIPE 

Expérience d’encadrement 

Supervision de post-doctorants 

Yann Moalic (2007-) ‘Network of gene flow’. Post-doctorat supervisé avec 

Carlos M. Duarte. 

Co-encadrement de thèses. 

Filipe Alberto (2001-2005) : thèse soutenue en septembre 2005 ‘Sex and 

dispersal in the sea’ (articles a12-a14, a17, a22, a23). Thèse co-encadrée 

avec Ester Serrão et Carlos M. Duarte. Statut actuel : chercheur associé au 

CCMar, Faro, Portugal. 

Sara Mira (2001-2006): thèse soutenue en novembre 2006 ‘Population 

genetics of an endangered species : the Bonelli’s eagle’ (Hieraaetus 

fasciatus)’. Thèse encadrée par Leonor Cancela. Statut actuel : postdoctorante 

au CCMar Faro, Portugal. 

Elena Diaz-Almela (2002- soutenance de thèse prévue en 2008) : (articles 

a19, a20, s26). Thèse encadrée par Carlos M. Duarte et Nuria Marba. 

Sonia Massa (2006-) : Genetic diversity of basal species and the stability of 

population and ecosystems. Thèse co-encadrée avec Gareth Pearson. 

Tania Aires (2006-) : Biotic interactions and the success of invasive 

species: the case of the bacterial flora of Caulerpa taxifolia. Thèse coencadrée 

avec Ester Serrão et Carlos M. Duarte. 

Encadrement d'étudiants en stages de Master II ou équivalent (Stage ‘prédoctoraux’) 

Sara Teixeira (2002-2003, 18 mois): Finalisation du stage de Master II: 

‘Study of the population genetics of South-Eastern Asian mangroves’, et 

initiation à la génétique des populations (articles a13, a15, a19, s24). Statut 

actuel : thèse 2003-2007 dans le département d’écologie et évolution de 

l’Université de Lausanne, en contrat au CCMar (Université d’Algarve). 

12



Sonia Massa (2005, 6 mois) : Stage de fin d’étude, initiation à la génétique 

des populations appliquée à la conservation (articles a19, a23, s24). Statut 

actuel : doctorante. 

Tania Aires (2005-2006, 18 mois) : Stage de fin d’étude, initiation à la 

génétique des populations et à la génomique. Statut actuel : doctorante. 

Encadrement d’étudiants de Master I ou équivalent (4). 

Mélanie Veyret (2001, 2 mois) stage de fin de Master I, Option Génétique 

des Populations. Mémoire : "Migration et différentiation génétique entre les 

stocks de nacre perlière de Polynésie", (article a4). Statut actuel : 

institutrice. 

Jérôme De Barry (1999, 3 mois) stage de Maîtrise De Biochimie, Option 

Génétique (Université de Montpellier II), 3 mois. Mémoire : ‘Utilisation de 

marqueurs moléculaires pour l’étude de la dynamique des populations de 

deux espèces de nacres perlières’. ?. 

Lionel Valera (1999, 2 mois) stage de fin de première année d’école 

d’ingénieur (INSA, Lyon), 2 mois. Mémoire ‘Étude phylogénétique de deux 

espèces de nacres perlières à partir de séquences d’haplotypes 

mitochondriaux‘. Statut actuel : Cadre chez ‘BioRad’. 

Céline Viray (1997, 2 mois) Céline Viray (1996, 2 mois). Licence de 

Biologie des Organismes. Mémoire " Utilisation des techniques de biologie 

moléculaire pour résoudre des Problématiques Ecologiques: Etude de 

structure de populations." ?. 

Responsable de techniciens : 

Vincent Vonau, Ifremer 18 mois (articles a4, a9, a10, a18, s27). 

Catherine Rouxel, Ifremer, 6 mois (article s27). 

Vincent Bishoff, VAT Ifremer, 10 mois (article a9). 

Carla Monteiro, CCmar 4 mois. 

Vacations d’enseignement 

Les Organismes Clonaux : un défi en termes de théorie et d’analyse de 

13



données en écologie et en évolution. Cours de DEA ‘Biologie de 

l’Evolution et Ecologie’, Module ‘Génétique des Populations Marines’. 

Université Montpellier II 

Ecosystèmes côtiers en Méditerranée : états des lieux et enjeux. Cours 

de Master de Biologie Marine, Université de Faro, Portugal. 

Bases théoriques et travaux pratiques d’écologie moléculaire 

appliquée à la gestion des ressources exploitées (Ecole thématique 

“ Concepts and methods for studying marine biodiversity, from gene to 

ecosystem “ à l’Observatoire océanologique de Banyuls-sur-mer, réalisée 

dans le cadre du Programme Européen TMR -Training Mobility and 

Research- en Mars 1998). 

Animation scientifique 

Organisation des séminaires internes du laboratoire Ecologie et Evolution 

des Organismes Marins (MAREE) pendant 3 ans à l’Université d’Algarve. 

Organisation d’un workshop dans le cadre du réseau Européen d’Excellence 

Marbef (Responsive Mode Program GBIRM : Genetic Biodiversity) : 2-5 Mai 

2006 à Tavira (Portugal). 

Participation à des jurys de thèse : 

Filipe Alberto (septembre 2005) ‘Sex and dispersal in the Sea’ 

Sara Mira (novembre 2006) ‘Population genetics of an endangered 

species : the Bonelli’s eagle’ (Hieraaetus fasciatus)’. 

AUTRES 

-Outils informatiques : programmation (Delphi, R, Mathematica). 

-Langues : Français, Anglais, Espagnol, Portugais. 

-Permis côtier, Plongée -CMAS 3- 

14


2. Liste de publications et communications 

LISTE DE PUBLICATIONS 

Revues scientifiques internationales à comité de lecture: 

a23. Alberto, F., Massa, S.I., Diaz-Almela, E., Arnaud-Haond, S., Duarte, C.M., Serrão, 

E.A.,Genetic differentiation in the seagrass Cymodocea nodosa across the 

Mediterranean-Atlantic transition region. Journal of Biogeography, sous-presse. 

a22. Arnaud-Haond, S., Duarte, C.M., Alberto, F., Serrão, E.A. (2007). Standardizing 

methods to describe population structure of clonal organisms. Molecular Ecology 

16: 5115-5139 . 

a21. Rozenfeld AF, Arnaud-Haond S, Hernández-García E, Eguíluz VM, Matías MA, 

Serrão EA and CM Duarte (2007) Spectrum of genetic diversity and networks of 

clonal populations Journal of the Royal Society Interface 4: 1093-1102. 

a20. Diaz-Almela E, Arnaud-Haond S, van de Vliet MS, Alvarez E, Marba N, Duarte CM 

and EA Serrão (2007) Feed-backs between genetic structure and perturbationdriven 

decline in seagrass (Posidonia oceanica) meadows Conservation Genetics 

8: 1377-1391. 

a19. Arnaud-Haond, S., Miggliaccio M., Diaz-Almela, E., Teixeira, S., Alberto, F., 

Procaccini, G., Duarte, C.M. and E.A. Serrão (2007). Vicariance patterns in the 

Mediterranean Sea : East-West cleavage and low dispersal in the endemic 

seagrass Posidonia oceanica. Journal of Biogeography 34: 963-976. 

a18. Arnaud-Haond S., Goyard E., Vonau V., Herbaut C., Prou J. and D. Saulnier (2007) 

Pearl formation: Persistence of the graft during the entire process of biomineralization. 

Marine Biotechnology 9: 113-116. 

a17. Arnaud-Haond S, Belkhir K (2007) GenClone 1.0: a new program to analyse genetics 

data on clonal organisms. Molecular Ecology Notes 7, 15-17. 

a16. Hernandez-Garcia E, Rozenfeld AF, Eguiluz VM, Arnaud-Haond S and CM Duarte 

(2006) Clone size distributions in networks of genetic similarity. Physica D 224: 166- 

173 

a15. Arnaud-Haond, S., Teixeira, S., Massa, S.I., Billot, C.P., Saenger, P., Coupland, G., 

Duarte, C.M. and E.A. Serrão (2006) Genetic structure at range-edge: low diversity and 

high inbreeding in SE Asia mangrove (Avicennia marina) populations. Molecular Ecology, 

15: 3515-3525 

15



a14. Alberto, F., Arnaud-Haond, S., Duarte, C.M. and E. A. Serrão (2006) Genetic diversity of 

a clonal angiosperm near its range limit: the case of Cymodocea nodosa in the Canary 

Islands. Marine Ecology Progress Series, 309: 117-129. 

a13. Alberto, F., L. Gouveia, S. Arnaud-Haond, J. L. Pérens-Lloréns, C. M. Duarte, and E. A. 

Serrão (2005) Spatial genetic structure, neighbourhood size and clonal subrange in 

seagrass (Cymodocea nodosa) populations. Molecular Ecology, 14: 2669-2681. 

a12. Arnaud-Haond, S., Alberto, F., Teixeira, S., Procaccini, G., Serrão, E.A., and C.M. 

Duarte (2005) Assessing molecular markers of genetic diversity in clonal organisms: 

combining power and cost-efficiency in selecting markers. Journal of Heredity, 96: 434- 

440. 

a11. Arnaud-Haond, S., F. Blanc, F. Bonhomme, and M. Monteforte (2005) Recent 

foundation of Mexican populations of pearl oysters (Pteria sterna) revealed by lack of 

genetic variation on two mitochondrial genes. Journal of the Marine Biological Association 

of the United Kingdom, 85:363-366. 

a10. Arnaud-Haond, S., Vonau, V., Bonhomme, F., Blanc, F., Boudry, P. , Prou, J., 

Seaman, T., and E. Goyard (2004) On the impact of cultural practices on genetic 

resources: evolution of the genetic composition of wild stocks of pearl oyster (Pinctada 

margaritifera cumingii) in French Polynesia after ten years of spat translocation. 

Molecular Ecology 13: 2001-2007. 

a9. Goyard E., Arnaud-Haond, S., Vonau, V., Bishoff, V., Mouchel O., Guogenheim J., 

Pham D., and Aquacop. (2003) Residual genetic variability in domesticated populations 

of the Pacific blue shrimp (Litopenaeus stylirostris) of New Caledonia, French Polynesia 

and Hawaii and some management recommendations. Aquatic Living Ressources. 16: 

501-508. 

a8. Teixeira, S., Arnaud-Haond, S., Duarte, C.M., and E. A. Serrão (2003) Polymorphic 

microsatellite DNA markers in the mangrove tree Avicennia alba. Molecular Ecology 

Notes, 3: 544-546. 

a7. Alberto, F., Correia, L., Arnaud-Haond, S., Billot, C., Duarte, C.M., and E. A. Serrão 

(2003) New microsatellite markers for the endemic Mediterranean seagrass Posidonia 

oceanica. Molecular Ecology Notes, 3: 353-355. 

a6. Arnaud-Haond, S., Monteforte, M., Blanc, F., and F. Bonhomme (2003) Evidence for 

male-biased effective sex ratio and recent colonisation in the bivalve Pinctada 

mazatlanica. Journal of Evolutionary Biology,16: 790-796. 

16



a5. Arnaud-Haond, S., Bonhomme, F., and F. Blanc (2003) Large discrepancies in 

differentiation of allozymes, nuclear and mitochondrial DNA loci in recently founded 

Pacific populations of the pearl oyster Pinctada margaritifera. Journal of Evolutionary 

Biology,16:388-398. 

a4. Arnaud-Haond, S., Vonau, V., Bonhomme, F., Boudry, P., Prou, J., Seaman, T., 

Veyret, M., and E. Goyard (2003) Spat collection of the pearl oyster (Pinctada 

margaritifera cumingii) in French Polynesia: an evaluation of the potential impact on 

genetic variability of wild and farmed populations after 20 years of commercial 

exploitation. Aquaculture, 219 : 181-192. 

a3. Arnaud-Haond, S., Boudry, P. , Saulnier, D. , Seaman, T., Vonau, V., Bonhomme, F., 

E. Goyard (2002) New anonymous nuclear DNA markers for the pearl oyster Pinctada 

margaritifera and other Pinctada species. Molecular Ecology Notes, 2:220-222. 

a2. Arnaud, S., Galtier, N., Monteforte, M., Blanc, F., and F. Bonhomme (2000) Genetic 

structure and variability of protected populations of pearl oysters (Pinctada 

mazatlanica) from American Pacific coasts. Conservation Genetics, 1: 299-308. 

a1. Arnaud, S., Bonhomme, F., and P. Borsa (1999) Mitochondrial DNA analysis of the 

genetic relationships among South-East Asian scad mackerel Decapterus macarellus, 

D. macrosoma and D. russelli populations. Marine Biology, 135: 699-707. 

Soumis 

s24. Arnaud-Haond, S., Teixeira, S., Massa, S.I., Terrados, J., Tri, N.H., Hong, P.N., 

Duarte, C., Serrao, E.A., submitted. Mangrove Genetic Diversity Three Decades 

after Agent Orange. 

s25. Rozenfeld, A.F., Arnaud-Haond, S., Hernández-García, E., Eguíluz, V.M., Serrão, 

E.A., Duarte, C.M. Population genetics networks: identifying weak and strong links 

in a metapopulation system. 

s26. Arnaud-Haond, S., Duarte, C.M., Diaz-Almela, E., Marbà, N., Serrao, E.A., Extant 

Pleistocene Clones Detected in a Threatened Seagrass 

s27. Arnaud-Haond, S., Vonau, V., Rouxel, C., Bonhomme, F., Prou, J., Goyard, E., 

Boudry, P. Contrasted patterns of genetic structure at different spatial scales in the 

pearl oyster (Pinctada margaritifera cumingii) in French Polynesian lagoons. 

17



Autres : vulgarisation, diffusion: 

Arnaud-Haond, S., E. Goyard, J. Prou, V. Vonau, F. Bonhomme, and P. Boudry. 2005. 

Gestion des Ressources génétiques de l’huitre perlière Pinctada margaritifera de 

Polynésie Française : caractérisation génétiques des populations et optimisation du 

recrutement pour l’exploitation perlière. Les Actes du BRG (2005), 215-229. 

Arnaud-Haond, S., Goyard, E., Blanc, F., Saulnier, D., Prou, J., Vonau, V., Dao, T., 

Seaman, T., Bonhomme, F. 2003 Gestion durable des ressources de Polynésie : 

Premiers apports de la génétique à la perliculture polynésienne. Te Reko Parau (Revue 

Polynésienne destinée aux Perliculteurs). 

Arnaud, S., Borsa, P., Bonhomme, F. 1999. Mitochondrial DNA Variation in the South- 

East Asian Scad Mackerel Decapterus cf. macrosoma. Proc. 5 th Indo-Pac. Fish Conf. 

Nouméa 1997. Séret B; & J. Y. Sire eds Paris: Soc. Fr. Ictyol., 1999: 407-411. 

Arnaud S., Bonhomme, F., 1998. Genetic structure and biogeography of South east Asian 

scad mackerels Decapterus macarellus, D. macrosoma and D. russelli. Oceanis, 24(4): 

1-7. 

Chapitre de livre : 

l1. Kennedy, H., Papadimitriou, S., Marba, N., Duarte, C.M., Serrão, E.A., Arnaud-Haond, 

S. 2004. How are seagrass processes, genetics and chemical composition monitored? 

in: European seagrasses: an introduction to ecology, monitoring and management. 

Eds: Jens Borum, Carlos M. Duarte and Dorte Krause-Jensen, pp54-62. 

18



PARTICIPATION A DES CONGRES 

Communications orales réalisées: 

Rozenfeld AF, Arnaud-Haond S, Hernández-García E, Eguilúz, VM, Serrao E.A., Duarte 

C.M. Population genetics networks: gene flow, source and sinks in the metapopulation 

system of the seagrass Posidonia oceanica. ASLO aquatic science meeting, Santa Fe 

(New Mexico, USA), 4-9 February 2007. 

Rozenfeld A.F., Arnaud-Haond S., Hernández-García E., Eguilúz, V.M., Serrao E.A., 

Duarte CM. Population genetics networks: identifying weak and strong links in a 

metapopulation system. Marbef General Assembly, Lecce 8-12 May 2006. 

Arnaud-Haond, S., S. Teixeira, Massa SI, C. Billot, P. Saenger, C. M. Duarte, and E. A. 

Serrao. Genetic structure and mating system at range-edge: low diversity and high 

inbreeding in SE Asia mangrove (Avicennia marina) populations. First DIVERSITAS open 

science conference: integrating biodiversity science for human well being, Oaxaca, 

Mexico, 9-12 Novembre 2005. 

Arnaud-Haond, S., Vonau, V., Bonhomme, F., Boudry, P., Prou, J., Seaman, T., Goyard, 

E. Gestion des ressources génétiques de l'huître perlière Pinctada margaritifera de 

Polynésie française : caractérisation génétique des populations et optimisation du 

recrutement pour l'exploitation perlière. 5 ème Colloque national du Bureau des 

Ressources Génétiques, Lyon, France, 3-5 Novembre 2004. 

Arnaud-Haond, S., Vonau, V., Bonhomme, F., Boudry, P., Prou, J., Seaman, T., Goyard, 

E. On the impact of cultural practices on marine genetic resources: evolution of the 

genetic composition of wild stocks of pearl oyster (Pinctada margaritifera cumingii) in 

French Polynesia after ten years of spat translocation. Biodiversity of Coastal Marine 

Ecosystems, Renesee, Hollande, 11-15 Mai 2003. 

Arnaud, S., Blanc, F., Bonhomme, F. Comparative analysis of Polynesian stocks of the 

pearl oyster Pinctada margaritifera using nuclear and mitochondrial DNA markers. 

International meeting of the World Aquaculture Society, Nice, Mai 2000. 

Arnaud, S., Bonhomme, F., Blanc, F. Phylogeography of two pearl oysters Pinctada 

margaritifera and Pinctada mazatlanica using mitochondrial markers. International 

meeting on Biology and Evolution of the Bivalvia, Cambridge Septembre 1999. 

19



Arnaud, S., Bonhomme, F., Borsa, P. Mitochondrial DNA analysis of the genetic structure 

among population of South-East Asian scad mackerel D. macrosoma. Petit Pois 

Déridé, Université de Perpignan, Septembre1997. 

Communications orales réalisées par des collaborateurs: 

Alberto, F., Massa, S.I., Manent, P., Diaz-Almela, E., Arnaud-Haond, S., Duarte, C.M. & 

Serrão, E.A. Genetic differentiation in the Seagrass Cymodocea nodosa across the 

Mediterranean-Atlantic transition region. Third International Biogeography Society 

Conference, Tenerife, January 9-13, 2007. 

Procaccini G., Arnaud-Haond, S., Migliaccio, M., Diaz-Almela, E., Teixeira, S., Alberto, F., 

Duarte, C.M., Serrão, E. A.. Vicariance patterns in the Mediterranean Sea: East-West 

cleavage and low dispersal in the endemic seagrass Posidonia oceanica. 

Mediterranean Seagrass Workshop, Malta May 29- June 3 2006. 

Posters: 

Hernández-García, E. Rozenfeld, A. F. Arnaud-Haond, S. Eguíluz, V.M., Serrão, E.A. and 

Duarte, C. M.. Genetic similarity networks: Weak and strong links in populations and in 

metapopulations. European Conference on Complex Systems (ECCS07). Dresden, 

Germany, 30 September - 6 October 2007. 

Rozenfeld, A., Eguíluz, V., Hernández-García, E., Matías, M. A., Duarte, C.M., Arnaud- 

Haond, S. Network Approach to the Genetic Structure of Clonal Plants. IV Jornades de 

la Xarxa Temàtica Nonlinear Dynamics of Spatio-Temporal Self organization. 

Barcelona, 1-3 febrero 2006. 

Rozenfeld, A, F., Eguíluz, V. M., Hernóndez-García, E., Matías M. A., Duarte, C.M., 

Arnaud-Haond, S. Network approach to the genetic relationship between clonal plants. 

Dynamics Days 2005, Berlin, 25-28 Julio 2005. 

Arnaud-Haond, S., Diaz-Almela, E., Teixeira, S., Alberto, F., Procaccini, G., Duarte, C., 

Serrão, E.A. Vicariance patterns in the Mediterranean Sea : East-West cleavage and 

low dispersal in the endemic seagrass Posidonia oceanica. 8 th Evolutionary Biology 

Meeting at Marseilles, Marseilles, France, 22-14 Septembre 2004. 

20



Arnaud-Haond, S., Alberto, F., Teixeira, S., Diaz-Almela, E., Procaccini, G., Duarte, C., 

Serrão, E.A. Genetic variability and population stability in Posidonia oceanica (Delile) 

meadows. Biodiversity of Coastal Marine Ecosystems, Renesee, Hollande, 11-15 Mai 

2003. 

Arnaud-Haond, S., Boudry, P., Blanc, F., Saulnier, D., Prou, J., Vonau, V., Seaman, T., 

Bonhomme, F., Goyard, E.. Perliculture et gestion durable des ressources génétiques 

de l'huître perlière, Pinctada margaritifera de Polynésie française : constat et 

recommandations. 4 ème Colloque national du Bureau des Ressources Génétiques, La 

Châtre, France, 14-16 Octobre 2002. 

Goyard, E., Arnaud-Haond, S., Vonau, V., Pham, D., Boudry, P., Aquacop. Ressources 

génétiques de la population de crevettes Litopenaeus stylirostris domestiquée en 

Nouvelle-Calédonie: définition d'une stratégie de ré-introduction de la variabilité. 4 ème 

Colloque national du Bureau des Ressources Génétiques, La Châtre, France, 14-16 

Octobre 2002. 

Arnaud, S., Goyard, E., Blanc, F., Saulnier, D., Prou, J., Vonau, V., Seaman, T., 

Bonhomme, F. What has to be conserved in the genetic ressources of the Pearl Oyster 

Pinctada margaritifera of French Polynesia? Marine Conservation Biology Institute’s 

Second Symposium, San Francisco, Juin 2001. 

Arnaud, S., Bonhomme, F., Blanc, F. Pattern of genetic variation of two species of pearl 

oyster of genus Pinctada : on the impact of past and present ecological factors on 

restriction to gene flow. VIITH Congress of the European Society for Evolutionary 

Biology, Universitat Autonoma de Barcelona, Août 1999. 

21


3. Résumé des travaux de Recherche 

RESUME DES TRAVAUX DE RECHERCHE ET PERSPECTIVES 

La génétique des populations, apparue au début du vingtième siècle, visait à l’origine à 

concilier les lois de Mendel récemment découvertes avec la théorie de l'évolution 

proposée par Darwin quelques années plus tôt. Les développements ont dans un 

premier temps été uniquement théoriques (Fisher 1930, Wright 1931, Haldane 1932). Si 

ces théories ont pu être mise en pratique, de façon limitée, sur la base de caractères 

phénotypiques dont l’hérédité était supposée, elles ont pu être réellement confrontées 

aux données empiriques vers la fin des années 50, lorsque les premiers systèmes 

allozymiques ont été isolés et étudiés par électrophorèse (Markert & Moller 1959). 

Depuis lors, l’utilisation des marqueurs moléculaires dans des études empiriques de 

génétique de populations n’a cessé de s’accroître, particulièrement avec le 

développement de marqueurs basés sur le principe de Polymérisation en Chaîne (PCR) 

qui a connu son essor au début des années 1990 (Cavalli-Sforza 1965, Levin et al. 

1972, Jarne & Lagoda 1996, Morin et al. 2004). Il a permis des avances considérables 

dans la compréhension des forces évolutives agissant à des échelles spatio-temporelles 

différentes sur les populations et les espèces (Avise 1989, Parker et al. 1998, Luikart et 

al. 2003). 

En écologie comme en biologie évolutive, l’interprétation des données 

moléculaires sur la base des modèles théoriques de génétique des populations a permis 

l’étude de phénomènes qui ne pouvaient être observés directement, à l’échelle 

humaine. Il s’agit bien entendu de révéler des évènements passés ayant influencé 

l’évolution des espèces ou des populations (phylogénie, phylogéographie, évolution 

moléculaire), mais également des phénomènes contemporains que les moyens 

techniques ne nous permettent pas, ou très difficilement, d’étudier de façon directe. 

Dans cette catégorie on peut citer l’étude des mouvements d’individus dans l’espace 

(migration), des variations démographiques (dérive), des variations environnementales 

ou des comportements reproducteurs (processus sélectifs, modes de reproduction), ou 

encore de l’impact des activités anthropiques sur la dynamique et l’évolution des 

populations et des espèces. Les obstacles à l’observation ou au suivi direct sont 

considérablement accentués en milieu marin, dont la difficulté d’accès a longtemps 

limité nos connaissances dans ces domaines. La combinaison des approches 

moléculaires et du cadre théorique de la génétique des populations ont apporté les 

outils et le cadre conceptuel qui ont permis de remplacer l’approche directe consistant 

dans le suivi des individus et des populations dans l’espace, là ou elle est 

techniquement impossible, par une approche indirecte visant à retracer les mouvements 

des gènes dans l’espace et dans le temps. 

C’est dans ce contexte que s’inscrit ma démarche de recherche. Elle consiste à 

étudier les déterminants de l’évolution, en particulier des populations marines, en 

utilisant des données moléculaires analysées dans le cadre théorique de la génétique 

des populations. Initié au milieu des années 1990, mon parcours scientifique m’a permis 

22



d’assister à l’essor de ce type d’approche empirique, d’apprécier les connaissances 

qu’elles ont permis d’acquérir en une décennie sur l’écologie et l’évolution des 

populations et des espèces, mais également de voir leurs limites se dessiner, et de 

tenter de les repousser en améliorant l’analyse et l’interprétation des données 

auxquelles nous avons accès. Cette synthèse de mes expériences de recherche, sera 

donc construite de façon à résumer d’une part les résultats obtenus et les 

connaissances qu’ils ont permis de contribuer à acquérir, et d’autre part les problèmes 

d’analyse et d’interprétation communément rencontrés et les méthodes que nous avons 

tenté de développer pour les résoudre. La transition avec les Projets de Recherche se 

fera à la partie 3, qui résume les résultats et ouvre également sur les perspectives de 

développement de ces axes de recherches que je compte poursuivre durant les 4 

prochaines années en les appliquant davantage aux populations d’organismes vivants 

dans les écosystèmes profonds. 

I. LE MILIEU MARIN : UN MILIEU HETEROGENE ET INSTABLE 

Le milieu marin a longtemps été considéré comme un milieu extrêmement 

homogène et relativement dépourvu de barrières physiques au passage des individus, 

et donc des gènes. Ainsi, la panmixie a souvent été attendue comme une règle dans 

les populations des organismes marins (Vermeij 1987), accompagnée au delà d’une 

certaine distance dépendant de l’organisme étudié, d’une différentiation génétique 

progressive (de type pas japonais, ou isolement par la distance ; voir Encadré 1) en 

relation étroite avec le potentiel de dispersion de l’espèce (Palumbi 1992, 1994). Les 

différentes études de génétique des populations marines ont rapidement poussé à 

réviser ce paradigme de panmixie, probablement lié à notre difficulté à formaliser le 

concept de barrière au flux génique et à notre méconnaissance des preferenda 

écologiques en milieu marin (Avise 1994, Hedgecock 1994, Palumbi 1994). 

Mon objectif au cours de mes premières expériences de recherche a été d’utiliser 

différents types de marqueurs moléculaires pour identifier les principaux déterminants 

présents et passés de la divergence génétique en milieu marin. En parallèle, il s’agissait 

également de tester de façon comparative les différents marqueurs disponibles, et 

d’inférer leurs propriétés comparées et leur adéquation en fonction des questions 

posées et des échelles de temps concernées. 

I.1 Migration-Dérive : hétérogénéité, barrières au flux génique et taille efficace 

La fragmentation de l’habitat 

L’étude de la structure génétique des populations permet de révéler les zones 

géographiques de restriction du flux génique, de part et d’autre desquelles les 

populations évoluent de façon relativement indépendante, conduisant à une divergence 

23



. 

Encadré 1: Modèles classiques de structure de populations: 

basés sur des dèmes échangeant des taux uniformes m de migrants avec les autres dèmes 

du système 

I. Modèle en îles de Wright (1931) 

Chaque dème échange m/(n-1) avec les 

autres n dèmes du système: aucune 

relation n’est attendue entre 

distance génétique et distance 

géographique 

II. 

Modèle en pas japonais de Kimura 

a) 

b) 

c) 

a) une dimension (chaque dème échange 

m/2 avec les dèmes voisins). 

b) une dimension, modifié pour éviter les 

“effets bords” (Maruyama, 1971). 

c) deux dimensions (chaque dème échange 

m/4 avec ses quatre plus proches voisins; 

un tore devrait représenter sa 

modification pour éviter les” effets bords”). 

III. Ceci est la « version discrète » du modèle 

d’isolement par la distance proposé initialement par 

Malécot (1950). 

Le taux de migration est une fonction décroissante et continue de la 

Dans les deux cas (pas japonais discret ou isolement par la distance continu) on attends une 

décroissance –exponentielle dans un système à deux dimensions) de la corrélation entre 

gènes lorsque la distance augmente. 

génétique. En fonction de la durée d’isolement, cette divergence peut se traduire par 

une différence des fréquences alléliques, ou si le temps d’isolement a été plus long, par 

l’accumulation de mutations propres à chaque entité, l’apparition d’allèles propres et la 

divergence des séquences. L’analyse de populations réparties de part et d’autre de ces 

zones permet de mettre en évidence les caractéristiques topographiques ou 

écologiques du milieu qui sont (Edmands et al. 1996, Planes et al. 1996) ou ont été 

dans le passé (Saunders et al. 1986, Bowen & Avise 1990, Benzie & Williams 1997) 

24



susceptibles de jouer le rôle de barrière au flux génique. Une approche idéale pour 

mettre en relation la limitation du flux génique et les barrières environnementales 

potentielles, en minimisant les risques d’interférence avec des caractéristiques 

biologiques distinctes des espèces étudiées, est la comparaison d’espèces très proches 

dans des habitats présentant des caractéristiques spécifiques différentes. 

Cette approche a été réalisée durant ma thèse, dont l’un des objectifs était 

d’évaluer la possibilité d’effectuer des prédictions sur le modèle théorique de 

métapopulations résumant au mieux le patron de différentiation génétique des 

populations d’une espèce en fonction des caractéristiques de son habitat. Nous avons 

comparé les patrons de structure génétique chez deux espèces proches de bivalves, 

Pinctada mazatlanica et P. margaritifera, présentant des exigences écologiques et des 

caractéristiques biologiques (notamment la durée de la phase larvaire dispersante) 

extrêmement similaires mais ayant une répartition géographique très différente : 

continue le long des côtes pour P. mazatlanica et fragmentée autour des îles du 

Pacifique pour P. margaritifera. 

Chez la nacre mexicaine P. mazatlanica le long des côtes américaines, bien que trois 

groupes de populations qui n’étaient pas significativement différenciées entre elle ait été 

détecté, un patron d’isolement par la distance a été observé a2 (Figure 1). A l’inverse, en 

Polynésie, une structuration génétique indépendante de la distance géographique a été 

mise en évidence a5 (Figure 2) correspondant davantage à un modèle en île. Ces 

résultats ont contribué à démontrer l’importance de la distribution de l’habitat (continu ou 

fragmenté) dans les patrons de différentiation génétique. 

Les barrières au flux géniques 

Le patron de structure génétique en Polynésie comme au Mexique, correspond 

en plusieurs points à la mosaïque délimitée par les principaux courants de surface a2,a5 , 

comme cela avait été suggéré sur des espèces de poissons et d’invertébrés (Edmands 

et al. 1996, Planes et al. 1996). 

25



Manuae 

Manuae 

Raïatea 

Maupihaa 

Mangareva 

Mangareva 

Takaroa 

Takaroa 

Suwarrow 

Tahuata 

Perhyn 

0.1 

0.1 

Figure 2: patron de différentiation génétique entre les populations des îles d’archipels Polynésiens, et courants 

principaux dans la zone. 

Dans une autre étude portant cette fois sur des populations du phanérogame 

marin Posidonia oceanica les résultats que nous avions obtenus suggéraient également 

l’influence des courants marins en tant que barrière au flux génique a19,s25 . Dans cette 

même étude l’existence de terres émergées s’est également révélée être un facteur 

limitant du flux génique, comme j’avais pu l’observer sur des poissons pélagiques de 

l’Indo-Pacifique a1 . Cela peut sembler évident quand ces barrières cloisonnent l’aire de 

distribution actuelle de l’espèce étudiée comme cela s’est produit lors de la fermeture 

de l’isthme de Panama (Knowlton et al. 1993), mais dans le cas des Posidonies ces 

barrières ont agit lors d’ères géologiques antérieures et ont depuis disparu. Les traces 

d’un événement de vicariance induit par la séparation quasi totale des bassins Est et 

Ouest durant le Pléistocène ont cependant été retrouvées sur le patron de distribution 

a19, s25 

de la variabilité génétique de Posidonia oceanica à l’échelle de la Méditerranée 

(Figure 3). 

Ce patron s'est apparemment maintenu jusqu'à aujourd'hui du fait de deux 

facteurs : i) d’une part d'une dispersion très limitée à l’échelle de seulement quelques 

dizaines de mètres comme le montre les analyses d'autocorrélation spatiale au sein des 

herbiers ii) d’autre part un recrutement par voie sexuée pouvant être très faible. Nous 

avons en effet obtenu par ailleurs des résultats indiquant que certains clones de 

Posidonie peuvent s’étendre sur plusieurs dizaines de kilomètres. Ce résultat interprété 

à la lumière des modèles de croissances les plus conservatifs (Sintes et al. 2006) 

permet d’estimer la date de l’évènement de reproduction sexuée au moins au 

Pleistocène s26 , suggérant un turn-over très limité au moins dans les prairies où ces 

genets ‘géants’ prédominent. 

26



Figure 3: Distribution de la richesse allélique et des allèles communs à plusieurs zones (bleu), privés 

au bassin Ouest (orange), Est (vert), et à la zone Siculo Tunisienne (rouge) interprétée à l’issue de ce 

travail comme une zone de contact entre les populations de l’Est et de l’Ouest de la Méditerranée 

demeurées isolées pendant les dernières glaciations du Pléistocène. 

Certaines études phénologiques et caryotypiques avaient suggéré l’existence de 

sous-espèces présentes dans chacun des bassins Est et Ouest. Cependant, aucun 

déséquilibre de liaison ni déficit en hétérozygotes n’a été observé dans les populations 

mixtes de la zone de contact, suggérant l’absence d’isolement reproducteur entre les 

populations des bassins Est et Ouest, malgré une divergence ancienne a19 . Toutefois, 

ces observations reposent sur l’analyse de fréquences alléliques avec des données 

microsatellites. L’étude des populations à l’aide de séquences nucléaire de type ITS est 

en court afin de vérifier l’absence de divergence génétique des lignées présentes dans 

chacun des deux bassins. 

La taille efficace 

On retrouve également l’importance et la trace d’événement historiques sur les 

données de séquences mitochondriales des deux espèces de Pinctada, non plus sous 

la forme de barrières aux flux géniques, mais sous la forme d’effets fondateurs passés 

qui semblent influencer encore aujourd’hui le patron de structure génétique a5, a6 . 

L’étude des mangroves du delta du Mekong, éradiquées pendant la guerre du 

Vietnam par l’agent orange, nous a également permis de montrer que la recolonisation 

génétique est un processus lent nécessitant plus de temps que celui nécessaire à la 

récupération démographique s23 . En effet, le couvert de mangrove a été recouvré 

seulement quelques années après la fin de la guerre. L’utilisation des tries de 

croissances pour dater les arbres échantillonnés pour être analysés avec des 

marqueurs microsatellites montre qu’au contraire la richesses allélique n’a cessé 

d’augmenter dans les 20 années qui sont suivi. Malgré une augmentation marginale les 

27



cinq dernières années, cette augmentation 

semble indiquer que l’état d’équilibre n’est 

pas encore atteint et que la recolonisation 

génétique n’est pas achevée. 

L'influence d'une taille efficace 

réduite sur le niveau et la distribution du 

polymorphisme peut également être stable 

dans le temps, comme c'est souvent le cas 

dans les populations qui se trouvent en 

limite de distribution, leur taille, leur densité 

et leur fécondité sont réduites. C'est le cas 

par exemple des populations de mangroves 

de l'espèce Avicennia marina, sur 

lesquelles des marqueurs microsatellites 

montrent une variabilité génétique moindre 

et une structure génétique plus forte dans 

les populations situées au Nord de l'aire de 

répartition (en Asie du Sud Est) que dans 

les populations australiennes situées dans 

le centre de distribution de l'espèce a15 . Il est 

intéressant de noter que cette réduction de 

taille efficace dans les populations 

marginales semble s’accompagner d’un 

bouleversement du système reproducteur. 

Dans les populations du centre de 

distribution aucun écart a la panmixie n’a 

été détecté, tandis que de forts déficits en 

hétérozygotes sont observés en limite, 

particulièrement au Nord, suggérant 

l’existence d’auto-fécondation dans ces 

populations isolées et de tailles réduites. 

Allelic richness 

F is 

8 

6 

4 

2 

0 

Limite Nord 

1.0 

0.5 

0.0 

LimiteNord 

Centre de distribution 

Centre de distribution 

Limite Sud 

Limite Sud 

Figure 4: Richesse allélique et estimations de F is 

dans les population du Centre de distribution (Nord 

de l’Australie), comparée à celles des limites Nord 

(Sud-Est Asiatique) et du Sud (Sud de l’Australie) 

de l’aire de distribution des arbres de mangrove 

Avicennia marina. 

I.2 Sélection 

Depuis les années 1970, la théorie neutraliste de l’évolution de Kimura désignait la 

dérive comme le principal moteur de l’évolution, au contraire des développements de la 

théorie Darwiniste qui avait auparavant conduit à considérer la sélection comme le 

processus prédominant. Les études empiriques en génétique des populations ont donc 

pendant deux décennies été pratiquées en considérant comme acquise la neutralité des 

marqueurs polymorphes utilisés. Toutefois, les avancées de la biologie moléculaire et le 

développement parallèle d’un nombre croissant de marqueurs utilisés en génétique des 

28



populations ont peu à peu conduit à repositionner la sélection au centre du débat en 

évolution. Il s’est agit dans un premier temps d’indices indirects apportés par la 

comparaison de multiples marqueurs supposés neutres comme les allozymes ou les 

microsatellites, puis des développements des outils génomiques qui ont permis 

d’identifier un certain nombre de gènes soumis à des processus sélectifs identifiés. 

L’utilisation de multiples locus de différents types a d’abord permit d’identifier des 

marqueurs au comportement atypique (Figure 3), puis de développer et d’utiliser des 

tests d’hypothèse permettant de mettre en évidence l’existence de sélection (Beaumont 

& Nichols 1996, Depaulis & Veuille 1998, Lemaire et al. 2000, Luikart et al. 2003). Nous 

avons utilisé ce type d’approche comparative sur les échantillons de P. margaritifera 

(ADN mitochondrial, 8 allozymes, marqueurs nucléaires anonymes a3 ) et mis en 

évidence le comportement atypique de trois marqueurs allozymiques a5 . L’utilisation d’un 

test de neutralité (Raufaste & 

Bonhomme soumis) a montré pour deux 

d’entre eux un écart significatif à 

l’hypothèse nulle de neutralité, 

suggérant une forme de sélection 

équilibrante induisant un niveau de 

différentiation génétique inférieur aux 

estimations réalisées sur la base des 

autres locus a5 . L’utilisation des 

marqueurs moléculaires supposés 

neutres, en génétique de la 

Fst 

conservation notamment, fait l’objet 

Figure 5:Identification des locus atypiques: Distribution 

d’une interprétation qui peut sembler 

hypothétique du Fst (divergence génétique) entre les 

paradoxale : l’interprétation des 

locus neutres échantillonnés dans le génome. Les 

effets locus spécifiques révèlent quelques locus 

données repose sur l’hypothèse de leur 

atypiques ( zones rouge et verte ) montrant une valeur 

neutralité, mais que le niveau de 

de Fst très différente de la plupart des autres locus. 

polymorphisme est par la suite 

Modifié à partir de Luikart et al. (2003) 

interprété comme un estimateur du 

‘potentiel évolutif’ des espèces. Il existe donc une hypothèse sous-jacente implicite, mais 

non démontrée, qui est que le niveau de polymorphisme aux marqueurs neutres devrait 

permettre une estimation indirecte satisfaisante de celui caractérisant les gènes soumis à 

sélection, et être un bon indicateur de la résilience des populations. 

Dans le cadre d’un projet (DivStab) et d’une thèse (celle de Sonia Massá) nous 

avons choisi de tester ces hypothèses. D’une part nous avons mis au point des 

expériences visant à estimer l’impact de la diversité génétique aux marqueurs neutres 

sur la résistance et la résilience de la phanérogame marine Zostera noltii à de fortes 

températures. En parallèle nous développons des banques EST’s qui seront criblées 

pour sélectionner des marqueurs impliqués dans la réponse aux chocs de température 

chez cette plante s28 et retenir des marqueurs polymorphes. A terme il s’agira de tester 

Nombre de loci 

29



l’existence d’une corrélation entre le polymorphisme aux deux types de marqueurs 

utilisés, afin de tester l’hypothèse qu’un set de marqueurs neutres de type allozymes ou 

microsatellites présente un polymorphisme représentatif du génome dans son 

ensemble, y compris du polymorphisme potentiellement soumis à sélection. 

I.3 Impact des activités anthropiques 

Enfin, il est difficile, voire impossible, de s’intéresser à l’écologie et à l’évolution 

des populations marines aujourd’hui sans prendre en compte et tenter d’évaluer 

l’importance d’un autre facteur influençant de plus en plus fréquemment la répartition 

spatiale de variabilité génétique dans les populations naturelles : les activités 

anthropiques agissant directement (exploitation) ou non (modification de l’habitat, 

introduction d'espèces) sur les populations naturelles a2,a4,a10, s23 . 

Dans le cas de la nacre perlière par exemple, l’analyse d’échantillons prélevés dans 

deux archipels avant et après des épisodes de transferts liés à l’aquaculture en milieu 

naturel a mis en évidence l’impact de l’importation de souches différenciées sur la 

composition génétique des stocks sauvages. La divergence génétique initiale, quoique 

faible entre deux archipels a complètement disparu, et la contribution des quelques 

individus importés dans les fermes (par rapport à l’importance des bancs naturels) 

semble forte. Ces résultats suggèrent la possible existence d’heterosis qui augmente la 

migration efficace (Ingvarsson & Whitlock 2000) ou, de façon plus probable, un succès 

reproducteur accru dans les conditions d’élevage en fermes dû à la forte densité 

(Levitan et al. 1992) des individus qui y sont stockés a10 . 

AXE 2 

0,3 

0,2 

0,1 

0,0 

-0,1 

-0,2 

-0,3 

MA -O 

MH-N 

MP-O 

MA -N 

TA-O 

MG-N 

MP-N 

TA-N 

MH-O 

AR-N 

MG-O 

AR-O 

HO-O 

HO-N 

-0,4 

-0,5 

-0,4 -0,3 -0,2 -0,1 0,0 0,1 0,2 0,3 0,4 0,5 

AXE 1 

Figure 6: analyse factorielle 

des correspondances (AFC) 

sur la base des données 

moléculaire des populations de 

Polynésie échantillonnées 

dans les années 1980s (en 

bleu) et en 2000s (en orange). 

Les flèches rouges indiquent le 

changement drastique de 

composition génétique des 

atolls dans lesquels des 

translocations on été réalisées 

dans les années 1990s à partir 

de naissain collecté 

principalement dans les deux 

atolls cerclés de rouge. 

30



Enfin, dans le cas de la Posidonie nous avons pu montrer dans le cadre de la 

thèse d’Elena Diaz-Almela que la pollution liée aux installations aquacoles à proximité 

des herbiers, responsable de fortes mortalités, modifie la composition clonale, et donc 

génétique, des prairies en favorisant les clones les plus grands qui semblent survivre 

mieux aux perturbations a20 . 

En conclusion, j’ai pu durant ces différentes expériences tester, et observer les 

limites de validité, d’ un certain nombre d’hypothèses a priori quant à la structure des 

populations marines appréhendée avec des marqueurs supposés neutres (Encadré 2). 

Encadré 2: Hypothèses a priori testées et réfutées dans certains 

cas. 

HYPOTHESES a priori 

Marqueurs 

neutres 

Absence de 

barrière 

Grande 

taille de 

population 

Forte 

dispersion 

Sélection 

a5 

a5 

Barrières 

Physiques 

(courants, 

fragmentation 

de l’habitat) 

a1,2,6 

Faible taille 

efficace, 

variance du 

succès 

reproducteur 

a4,10,11,14,15, 23 

Faible 

dispersion 

réalisée 

a14,20,22 

31



Durant ce parcours de recherche, j’ai rencontré de façon directe deux limites 

importantes à l’analyse et à l’interprétation des données moléculaires dans le cadre de 

la génétique des populations. La première est la prise en compte des spécificités 

conférées par la clonalité dans l’étude et l’interprétation de la génétique des populations 

d’organismes clonaux. La seconde est l’écart récurrent aux hypothèses sous-jacentes et 

aux conditions requises pour l’interprétation des données moléculaires avec les outils 

classiques de génétique des populations. 

J’ai commencé à aborder chacun de ces problèmes lors de mes différentes 

expériences post-doctorales, et étant donné leur large spectre d’applications 

potentielles, ainsi que mon implication dans un projet Européen portant sur l’utilisation 

de la théorie des réseaux en évolution, ces deux problématiques se trouvent à la 

charnière entre activités passées et perspectives de développement futur. J’ai donc 

choisi de les détailler davantage avant d’expliquer comment elles s’inscriront dans les 

perspectives et futurs projets dont les modèles d’étude s’orienteront maintenant 

davantage vers le milieu océanique profond. 

32



II. CLONALITE, ECOLOGIE ET EVOLUTION 

La première est l’existence d’un système reproducteur complexe, la clonalité, 

qui pose d’abord le problème de l’accès au niveau individuel sur le plan technique, 

mais également conceptuel. Quelle est l’unité démographique chez un organisme 

clonal (l’entité individuelle ou l’ensemble des unités dérivées du même évènement de 

reproduction sexuée et partageant le même patrimoine génétique ; chez les plantes : 

ramet ou le genet ?). Doit-on considérer le problème de façon différente selon que 

les unités sont indépendantes physiquement les unes des autres (comme les 

bactéries, ou certains organismes pathogènes) ou conservent un certain niveau 

d’interconnexion et d’interdépendance (plantes clonales, colonies coralliennes…) ? 

Comment identifier les lignées clonales, pour commencer ? 

La complexité de ces problèmes est reflétée en premier lieu par l’absence 

d’outils standardisés permettant de prendre en compte de façon spécifique la 

clonalité dans les analyses de données génétiques. Le premier volet de ce travail a 

donc consisté à proposer des méthodes d'analyses de données permettant de 

prendre en compte de façon spécifique les caractéristiques des organismes clonaux. 

Il s'agissait de définir des descripteurs génétiques adaptés à l’étude des organismes 

clonaux a17,21,22 , pour i) reconnaître les lignées clonales a12,19,22 , ii) extraire les 

informations dérivées des données génétiques en terme de diversité et de structure 

clonale a13,22 , iii) rendre possible la comparaison des résultats obtenus lors de 

différentes études sur différents organismes a13,21,22, s25 , et pour iv) intégrer les 

données démographiques avec le patron de croissance et de diversité clonale a22,s26 . 

Ces différentes approches sont rapportées dans les cinq manuscrits qui 

suivent. 

33



II.1 Assessing genetic diversity in clonal organisms: Low diversity or low 

resolution? Combining power and cost efficiency in selecting markers. 

Journal of Heredity, 2005. 

Dans un premier temps nous nous sommes attachés à démontrer l'importance des 

méthodes moléculaires et statistiques pour accéder au niveau individuel et réaliser 

des estimations non biaisées de la clonalité. On peut citer à titre d'exemple le cas de 

Posidonia oceanica, qui a longtemps été considérée comme extrêmement clonale 

sur la base d'analyses moléculaires réalisées avec des marqueurs trop peu 

polymorphes (Figure 7a). Nous avons utilisé cet exemple pour proposer des 

méthodes statistiques permettant de s'assurer du pouvoir discriminant des 

marqueurs utilisés et de la capacité d’accéder de détecter tous les génotypes 

présents dans les échantillons analysés (Figure 7b). Il s’agit d’un pré requis pour 

espérer faire des inférences sur le niveau de clonalité des populations ou analyser 

plus avant les données avec les outils statistiques classiques en génétique des 

populations. 

a) 

b) 

Diversité clonale: (G-1)/(N-1) 

Diversité clonale: (G-1)/(N-1) 

0.8 

0.6 

0.4 

0.2 

0.0 

1.0 

0.8 

0.6 

0.4 

0.2 

allozymes RAPD's trinucleotides dinucleotides 

0.0 

0 2 4 6 8 10 

Nombre de locus 

Formentera 

FECS 

PortLigat 

Denia 

Tavolara 

Otranto 

Paphos 

Malta 

Figure 7 : Niveaux de diversité clonale estimée 

(en fonction du nombre N d’échantillons analysés 

et G de génotypes multilocus distincts identifiés) 

obtenus dans les herbiers de Posidonia oceanica 

avec les différents types de marqueurs utilisés 

(a) dans différentes études en comparant les 

valeurs moyennes obtenues avec les allozymes, 

les RAPD’s et les marqueurs microsatellites 

trinucléotides ou dinucléotides, et (b) en 

comparant les échantillons des huit mêmes 

herbiers avec un nombre croissant de marqueurs 

microsatellites di (bleu) ou tri (orange) 

nucléotides utilisés pour identifier les différents 

génotypes présents dans les échantillons. La 

différence de pouvoir discriminant est flagrante, 

et les marqueurs les plus discriminants 

permettent d’obtenir une asymptote, c'est-à-dire 

une diversité génotypique reflétant de façon 

fiable la diversité clonale, avec environ 6 à 7 

locus. On peut noter que le problème n’est pas 

seulement quantitatif (sous estimation de la 

diversité clonale et non discrimination des lignées 

clonales) mais également qualitatif : l’herbier de 

Malte présentait la plus faible diversité 

génotypique selon les marqueurs tri-nucléotides 

et est en fait le porteur de la plus importante 

diversité clonale. 

34

Journal of Heredity 2005:96(4):434–440 

doi:10.1093/jhered/esi043 

Advance Access publication March 2, 2005 

ª The American Genetic Association. 2005. All rights reserved. 

For Permissions, please email: journals.permissions@oupjournals.org. 

Assessing Genetic Diversity in Clonal 

Organisms: Low Diversity or Low 

Resolution? Combining Power and Cost 

Efficiency in Selecting Markers 

S. ARNAUD-HAOND, F.ALBERTO, S.TEIXEIRA, G.PROCACCINI, E.A.SERRÃO, AND C. M. DUARTE 

From CCMAR, CIMAR—Laboratório Associado, FCMA- Univ. Algarve, Gambelas, P-8005-139, Faro, Portugal 

(Arnaud-Haond, Alberto, Teixeira, and Serrão); Instituto Mediterraneo de Estudios Avanzados, CSIC-Univ. Illes Balears, 

C/ Miquel Marques 21, 07190 Esporles, Mallorca, Spain (Duarte); and Stazione Zoologica ‘‘A. Dohrn,’’ Laboratorio di 

Ecologia del Benthos, 80077 Ischia, Naples, Italy (Procaccini). 

Address correspondence to S. Arnaud-Haond at the address above, or e-mail: sarnaud@ualg.pt or eserrao@ualg.pt. 

The increasing use of molecular tools to study populations of 

clonal organisms leads us to question whether the low 

polymorphism found in many studies reflects limited genetic 

diversity in populations or the limitations of the markers 

used. Here we used microsatellite datasets for two sea grass 

species to provide a combinatory statistic, combined with a 

likelihood approach to estimate the probability of identical 

multilocus genotypes (MLGs) to be shared by distinct 

individuals, in order to ascertain the efficiency of the markers 

used and to optimize cost-efficiently the choice of markers 

to use for deriving unbiased estimates of genetic diversity. 

These results strongly indicate that conclusions from studies 

on clonal organisms derived using markers showing low 

polymorphism, including microsatellites, should be reassessed 

using appropriate polymorphic markers. 

The development of molecular techniques over the last 

decade has provided new tools to examine genetic variability 

within and among populations (Avise 1989, 1994; Parker 

et al. 1998; Sunnucks 2000). However, the efficient use of 

new molecular techniques often lags behind their accelerating 

development (Parker et al. 1998). In a variety of cases, 

conclusions previously drawn from the first markers used, 

commonly allozymes or random amplified polymorphic 

DNA (RAPD), have been significantly revised in light of 

results obtained using new markers, either due to evidence 

for selection on some markers, or to very low levels of variability 

limiting their ability to estimate population parameters 

(Beaumont and Pether 1996; Charlesworth 1998; Lemaire 

et al. 2000; Parker et al. 1998; Pogson et al. 1995). 

Limited marker resolution becomes even more critical 

when studying clonal organisms for which multilocus 

genotypes are the only way to discriminate genetically distinct 

individuals, such as plants or bacteria (Hagen and Hamrick 

1996; Hamrick and Godt 1989). Aquatic plants, many of which 

are highly clonal, are particularly prone to erroneous inferences 

on the genetic structure of their populations derived from the 

use of low-power markers. Indeed, initial evidence based on 

allozymes indicated widespread genetic monomorphism in 

submerged plants (Barrett et al. 1993), suggesting a much larger 

role for clonal than for sexual propagation. However, 

subsequent research using more powerful markers provided 

evidence of a wider range of clonal diversity, from apparently 

monoclonal meadows (Waycott et al. 1996) to multiclonal and 

highly genetically diverse ones (Alberte et al. 1994; Laushman 

1993; Reusch et al. 2000; Waycott 1995). 

Very low levels of genetic variability have been reported 

for the Mediterranean sea grass Posidonia oceanica, inferred to 

be highly clonal, both when using allozymes (Capiomont et al. 

1996) and RAPD markers (Procaccini et al. 1996; Procaccini 

and Mazzella 1996). The recent application of new RAPD 

primers (Jover et al. 2003) and of tri- and heptanucleotide 

microsatellites (Procaccini and Waycott 1998) revealed higher 

clonal diversity (Table 1 and Figure 1), suggesting that 

previous reports of low genetic variability were largely derived 

from the limited power of allozymes and of the first RAPD 

markers that were used. Yet the level of genetic variability 

revealed by those microsatellites remained low, still suggesting 

a predominance of clonal growth in the maintenance of 

natural populations (Procaccini et al. 2001). Recently 

developed dinucleotide microsatellites suggested a much 

higher level of clonal diversity in the P. oceanica meadow used 

to test for polymorphism (Alberto et al. 2003a). The 

increasing revelation of the genetic diversity of clonal 

organisms, as new tools are introduced, questions the extent 

to which inferences derived from any one marker type provide 

434 

35

Brief Communications 

Table 1. 

Sampling methods applied in the studies of population genetics of P. oceanica using distinct molecular markers 

Markers SD N md Md SA L(p)/P A References 

Allozymes H 4–51 — — 5000 8(2) 2 Capiomont et al. (1996) 

RAPD 1 Lr 16 10 40 — 65(1)/11 1 Procaccini et al. (1996) 

RAPD 2 H 9–15 5–10 — — 28(26)/2 2 Jover et al. (2003) 

M tc Lr 20–47 5–8 145–232 — 5 4 Procaccini et al. (2001) 

M d (popul. 1–4, 8) R 38–50 0.5–1.5 67–79 1600 8 12.5 Present study 

(popul. 5–7) Lr 29–40 5–8 145–232 — 

Details are given as to the markers used (M tc : tri- and heptanucleotide microsatellites, M d : dinucleotide microsatellites), the sampling design (SD; Lr: linear 

transect with equally spaced sampling points; R: random coordinates in 80 m 3 20 m; H: haphazard sampling), sample size (N), the approximate minimum 

and maximum distances (md, Md, respectively) in meters between sampled shoots, as well as the area sampled when nonlinear sampling was used (SA, in 

m 2 ), the number of loci (L), and when distinct, the number of polymorphic loci (p) analyzed, or the number of RAPD primers used (P), the average number 

of alleles per polymorphic loci (A), and the corresponding references. 

reliable accounts of the genetic diversity of these populations 

or reflect the limitations of these markers (Reusch 2001). 

Here we propose a combined approach using both the 

exploration of all marker combinations and the likelihood 

probability of identical multilocus genotypes (MLGs) to be 

shared by distinct individuals to ascertain a priori the dependence 

of the estimates of genetic diversity of clonal organisms on 

the number and efficiency of the markers used. This approach 

can also be used to optimize, in terms of cost-efficiency, the 

choice of markers to derive unbiased estimates of genetic 

diversity of the clonal organism studied. We demonstrate this 

approach using microsatellite markers for two clonal sea grass 

species, P. oceanica and Cymodocea nodosa (Alberto et al. 2003a,b). 

Materials and Methods 

For P. oceanica, approximately 40 shoots were collected from 

each of eight localities (Table 2). A portion of each shoot was 

desiccated and preserved in silica crystals. For C. nodosa, 38 

shoots were collected in patches in Alfacs Bay (northern 

Spain). A sample of 45 seedlings collected in Cadiz Bay was 

used as a control for marker power, since no clonal replicates 

are expected in seedlings. 

Clonal diversity 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0.0 

allozymes RAPD's trinucleotides dinucleotides 

Figure 1. Estimated levels of clonal diversity (R ¼ G ÿ 1/ 

N ÿ 1), where G is the number of genotypes and N is the 

number of samples analyzed, reported in P. oceanica meadows 

with allozymes (A), RAPD (R), tri- and heptanucleotide 

microsatellites (T), and dinucleotide microsatellites (D). 

Genomic DNA was isolated following the CTAB method 

(Doyle and Doyle 1988). Samples from eight meadows of 

P. oceanica were fully genotyped for four trinucleotides, one 7- 

nucleotide (Procaccini and Waycott 1998), and eight dinucleotides 

(Alberto et al. 2003a) microsatellites. The samples of C. 

nodosa were genotyped for 12 dinucleotide microsatellites, as 

described in Alberto et al. (2003b). 

The clonal or genotype diversity is often estimated as P d ¼ 

G/N, whereG is the number of distinct genotypes identified 

and N is the number of shoots analyzed. However, for a 

monoclonal stand (i.e., with a single genotype), this index would 

indicate a different value of genotypic diversity depending on 

the sample size, and thus we chose to modify it slightly by using 

R ¼ G ÿ 1/N ÿ 1, which will always be zero for a single clone 

stand and one for maximal genotypic diversity, when every 

sampled unit is a new genet (Dorken and Eckert 2001). The 

index maintains the same order of magnitude and can still be 

approximately compared with the classical P d values reported in 

the literature, although care must still be taken when comparing 

values from studies using different sampling designs. 

Genotypic diversity as revealed by any possible combinations 

of any number of the available markers was then 

computed in order to select the most parsimonious marker 

combination allowing efficient discrimination of genets. 

Submatrices were produced from the complete genotype 

matrix by selecting all combinations C L l of l from the L available 

loci (with 1 l L and L ¼ 5 for tri- and heptanucleotides, 

and L ¼ 8 for dinucleotides). For each rank of l, the average 

genotypic diversity R was computed, as well as the standard 

error, and the combination of l loci revealing more distinct 

genotypes was retained. This exercise was performed using a 

routine written in C (Gencount, available from F. Alberto upon 

request) on each population and both sets of markers (tri- þ 

heptanucleotides, and dinucleotides). The curves describing the 

dependence of the average R (6 SE) on l were drawn and 

compared. The minimal combination of l markers allowing the 

discrimination of the maximum MLGs with a satisfying 

likelihood probability was retained and compared among 

populations in order to find a minimal consensus combination 

allowing the discrimination of all MLGs in any population. 

In addition, and to test whether all of the samples with 

identical genotypes belong to the same genet, we used the 

round-robin method (Parks and Werth 1993) to estimate allelic 

36 

435

Journal of Heredity 2005:96(4) 

Table 2. 

Sampling details for P. oceanica populations used in this study 

Geographic zone (from west to east) Sampling location SD N s N g 5triR 5diR R 

Northern Spain Port Lligat R 40 13 0.17 0.24 0.31 

Southern Spain Las Rotes R 50 34 0.23 0.58 0.67 

Balearic islands (Formentera) Illetes R 36 23 0.22 0.54 0.63 

Balearic islands (Formentera) Es Calo de Oli R 40 15 0.23 0.34 0.36 

Italy (Sardegna) Tavolara Lr 40 20 0.20 0.41 0.49 

Italy (southeastern) Otranto Lr 29 24 0.21 0.70 0.82 

Malta Malta Lr 39 33 0.14 0.78 0.84 

Cyprus Paphos R 38 26 0.21 0.58 0.68 

Sampling location, sample size (N s ), sampling design (SD; Lr: linear transect with equally spaced sampling points; R: random coordinates in 80 m 3 20 m). number 

of genotypes (N g ), average clonal diversity with five di- and tri- þ heptanucleotide microsatellites (5 3 R) microsatellites, and with all the eight dinucleotides (R). 

frequencies and genotype probabilities in each population. This 

subsampling approach avoids overestimation of rare allele 

frequencies by estimating the allelic frequencies for each locus 

on the basis of a sample pool composed of all the genotypes 

distinguished on the basis of all the loci, except the one being 

estimated. This procedure is repeated for all loci, and the 

unique genotype probability is then calculated as follows: 

p gen 

¼ Yl 

ð f i Þ2 h ; 

i¼1 

where l is the number of loci, f i is the frequency in the 

population of each allele (two per locus) at the ith locus, 

and h is the number of heterozygous loci. When the same 

genotype is detected more than once, the probability of these 

being derived from distinct reproductive events (i.e., different 

genets) can be estimated by the binomial expression 

p sex 

¼ XN N ! 

x!ðN ÿ xÞ! 3 ½ p genŠ x 3 ½1 ÿ p gen Š N ÿx ; 

x¼n 

where N is the number of sampling units and n is the number 

of separated fragments with identical genotype to a previously 

encountered ramet (Parks and Werth 1993; Stenberg et al. 

2003; Tibayrenc et al. 1990). In order to test whether all 

replicates of a MLG belong to the same genet, significance 

was considered for P sex from the first re-encounter (n = 1). 

In order to evaluate the importance of somatic 

mutations, the frequency distribution of the number of 

different alleles between all pairs of MLGs was plotted for 

each population. 

Results 

The analysis of clonal diversity performed on eight P. oceanica 

populations with four trinucleotide and one heptanucleotide 

nuclear microsatellites (Figure 2a) showed low levels of diversity 

ranging from 0.14 (Malta) to 0.23 (Formentera Illetas). Much 

higher diversity was revealed when using the same number 

(five) of dinucleotides (Figure 2b); the level of diversity 

ranged from 0.24 (Port Lligat) to 0.78 (Malta). With all eight 

dinucleotide loci, the variability was between 0.31 (Port 

Lligat) and 0.84 (Malta). In the same way, the number of 

alleles per locus was 3 with the septanucleotides, approximately 

4 with trinucleotides, and 12.5 with dinucleotides. 

For C. nodosa, all seedlings had different MLGs 

(Figure 2c) as expected, whereas lower clonal diversity of 

approximately 0.44 was observed for the northern Spain 

population. However, both samples reached the maximum 

clonal diversity with a minimum combination of four markers. 

In all the P. oceanica samples, all identical genotypes 

identified with dinucleotides were estimated to have a 

probability of less than .01 of having originated from two or 

more distinct events of sexual reproduction (i.e., distinct 

genets), except for three genotype pairs from Paphos and Port 

Lligat (where .01 , P sex , .05), whereas this was not the case 

for tri- and heptanucleotide microsatellites. In all populations, 

some MLGs defined with these last markers showed a higher 

probability (P sex . .05), thus indicating that several distinct 

individuals may be included in the same MLG groups (data not 

shown). Indeed, this was confirmed by the discrimination 

within those groups of several individuals bearing distinct 

MLGs when using dinucleotide microsatellites. In the same 

way, the relationship between the number of tri- and 

heptanucleotide microsatellites and the apparent genetic 

diversity (Figure 2a) indicated that even using the full set 

underestimates the genetic diversity of P. oceanica populations. 

In contrast, we observed an asymptotic relationship between 

the number of dinucleotide markers used and the clonal 

diversity revealed by these for most populations of both P. 

oceanica and C. nodosa, which reached unity for the C. nodosa 

seedlings, as expected (Figure 2). This relationship indicates 

that it is possible to select one or several subsets of markers 

yielding accurate estimates of clonal diversity for these species. 

The optimal combination of markers, leading both to an 

asymptotic shape and to low P sex values (,.05), was found to 

be, for P. oceanica, a set of seven dinucleotides, and for C. nodosa, 

a set of six dinucleotides. 

Finally, the frequency distribution of the number of 

different alleles between all MLG pairs showed unimodal 

distributions in all populations, with no apparent trend toward 

higher frequency or extra peak at very low values, as might be 

expected if somatic mutation was a common event (see the 

appendix). The seed sample of C. nodosa can be considered as a 

control distribution in which no somatic mutation should be 

expected. Slightly more low values can be observed in P. 

oceanica than in C. nodosa, which may be attributed to their 

distinct mating system, P. oceanica being monoecious and able 

to self-fertilize, whereas C. nodosa is dioecious. 

436 

37


a) 


b) 


c) 


0.30 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

0 1 2 3 4 5 6 

Number of loci 

1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

0 2 4 6 8 10 


1.0 

0.8 

0.6 

0.4 

0.2 

0.0 

0 2 4 6 8 10 12 14 


Discussion 

Formentera 

FECS 

PortLigat 

Denia 

Tavolara 

Otranto 

Paphos 

Malta 

Formentera 

FECS 

PortLigat 

Denia 

Tavolara 

Otranto 

Paphos 

Malta 

Alfacs 

Cadiz 

Figure 2. Curves describing the genotypic resolution of 

microsatellites with (a) tri- and heptanucleotide and (b) 

dinucleotide motifs in P. oceanica, and in (c) C. nodosa, based on 

analysis of all possible combinations C l n 

of n loci (n ¼ 1,...,l; l ¼ 

number of loci available), giving the average clonal diversity R 

(6SE) for each n. Clonal diversity estimated by R ¼ G ÿ 1/N ÿ 

1, where G is the number of genotypes and N is the sample size. 

Very different conclusions can be reached concerning the level 

of genetic polymorphism, either in terms of alleles or genotypic 

diversity, depending on the markers used (Figure 1 and Table 2), 

not only between allozymes (R ¼ 0.14) and RAPD (an average of 

0.07 in the first study and 0.49 in the second study), but 

dinucleotide (0.60) and tri- and heptanucleotide (0.38) microsatellites. 

One may be concerned by the influence of distinct 

sampling strategies on this result. However, the area sampled for 

allozymes studies was larger than for any other studies (Table 1), 

which then cannot explain the lowest level of genotypic diversity 

observed with those markers. More important, samples collected 

from eastern to western Mediterranean localities have common 

allozyme MLG profiles. As for studies using RAPD or 

microsatellites, the sampling scheme and average distance 

between shoots were similar, except in the study with 

dinucleotide microsatellites, in which the distance between 

shoots was less (Table 1), which would nevertheless tend 

to increase the chance to sample shoots from the same clones 

and would therefore not cause the higher genotypic diversities 

observed here for these markers. However, in the present study, 

a very different estimation of R could be derived from analysis of 

the same number of markers (five) on the same population 

samples with dinucleotide (0.52) and tri- and heptanucleotide 

(0.20) microsatellites (Figure 2a,b and Table 2). 

Reports of limitations linked to microsatellite markers 

are more scarce than limitations linked to allozymes or RAPD 

(Allendorf and Seeb 2000; Beaumont and Nichols 1996; Parker 

et al. 1998). However, different levels of genetic variability 

depending on microsatellite motif have been reported in some 

eukaryote genomes, and the relative mutation rate of dinucleotides 

versus tri- and heptanucleotides is estimated to be between 

1.5 and 2.1 (Anderson et al. 2000; Chakraborty et al. 1997). This is 

thought to be due to both the differential mutation rate during 

replication and to a higher rate of recombination and consequent 

mismatch repair (Chakraborty et al. 1997; Li et al. 2002). The 

average number of alleles per locus is a linear function of 4N e l, 

where N e is the effective population size and l is the mutation 

rate (Kimura 1983). In our study, this number is 2.5 to 3 times 

higher for dinucleotides than for trinucleotides, instead of 1.5 to 

2.1, suggesting a ratio of respective mutation rates (l di /l tri ) 

somewhat higher than estimated thus far, although this could be 

a consequence of variance due to the limited number of 

microsatellites used. This mutation rate should lead to careful 

screening for possible bias due to somatic mutations at 

dinucleotide loci. However, in the present study, the frequency 

distribution of the pairwise number of allele differences in all 

populations did not show evidence for a significant bias of 

genotypic diversity due to somatic mutations. Although slightly 

more low values can be observed in P. oceanica than in C. nodosa, 

this may be due to their distinct mating system, P. oceanica being 

monoecious and able to self-fertilize, whereas C. nodosa is 

dioecious. A higher frequency of related individuals may then be 

observed in P. oceanica meadows. Yet no secondary peak toward 

very low values was observed, but instead, an unimodal 

distribution shape appeared to be the rule. 

The perils of drawing inferences on the population 

structure of clonal organisms using markers of insufficient 

resolution are evident when comparing the results obtained 

in P. oceanica with different microsatellites. In particular, the 

population sampled in Malta exhibited the lowest number of 

clones with tri- and heptanucleotides (R ¼ 0.14), suggesting 

this meadow relies on clonal (versus sexual) reproduction 

(Table 2 and Figure 2a). However, the population in Malta 

clearly emerges as one of the most genetically diverse (R ¼ 

0.84) when examined with the more powerful dinucleotide 

microsatellites, suggesting high level of sexual reproduction 

in this particular meadow (Table 2 and Figure 2b). Hence the 

use of markers of limited power introduces both quantitative 

and qualitative errors in assessments of the genetic diversity 

of populations of clonal organisms, as not only was clonal 

38 

437


diversity underestimated, but the relative ranking of clonal 

diversity among the populations examined was also in error. 

The combined method reported here provides a 

convenient method for selecting the best combination of 

microsatellites to obtain accurate descriptions of the genetic 

diversity of populations of clonal organisms, provided 

sufficient highly polymorphic markers are available. The 

results presented confirm the expected asymptotic shape of 

the relationship between the number of microsatellites used 

and the genetic diversity they reveal (Figure 2b,c), and 

demonstrate that failure to observe such asymptotic patterns 

is indicative of insufficient power (Figure 2a). In addition, all 

the calculated probabilities for any pair of identical genotypes 

to be derived from two distinct events of sexual reproduction 

(P sex ) were less than .05, further indicating that unambiguous 

estimates of clonal diversity of the meadows examined could 

be derived with the markers used. The usefulness of this 

combined approach is stressed by the fact that it would have 

warned of the likelihood of drawing erroneous inferences on 

the population structure of P. oceanica with the use of the 

tri- or heptanucleotide markers, where no genotypic diversity 

asymptotic value was reached and identity probabilities were 

not significant for all groups of MLG. The application of the 

method to C. nodosa seedlings (Figure 2c), for which the 

relationship between the number of microsatellite markers 

used and the estimated genetic diversity converged to the 

expected asymptotic value of one, further confirms that the 

approach provided here allows assessment of the accuracy of 

inferences on the genetic diversity of clonal organisms 

derived using different sets of markers. 

Although genotyping as many distinct loci as possible 

will ensure the maximal genotype diversity of the samples 

analyzed, this practice is seldom realistic because of resource 

limitations, both time and money. Under these circumstances, 

the procedure can also been used in a pilot study to 

select the minimum combination of markers delivering 

accurate estimates of genetic diversity (i.e., asymptotic values 

and significant P sex values), thereby helping to optimize the 

cost-efficiency of research efforts in terms of genotyping 

and in terms of time and money. The most cost-effective 

combinations include seven and six markers for P. oceanica 

and C. nodosa, respectively. 

In conclusion, the results presented here demonstrate 

the risks of delivering quantitatively and qualitatively 

erroneous inferences on the genetic diversity of populations 

of clonal organisms when the markers available have 

insufficient power. These results show that conclusions 

about low sexual reproduction in populations of clonal 

species derived with the use of markers showing low 

polymorphism (i.e., a small number of alleles, or not evenly 

distributed) need to be reassessed using markers capable of 

revealing the distinct genotypes of the population. The 

approach provided here, applicable to any clonal organism, 

allows the combined assessment of the asymptotic trend of 

the markers and of the significance of the associated 

likelihood probability of identity in order to ascertain the 

detection of all distinct genotypes sampled, and thus to 

provide accurate estimates of population genetic diversity, 

while estimating the most cost-effective combination of 

markers to achieve this goal. 

Appendix 

Frequency distribution of the pairwise number of allele 

differences between MLGs in each of eight P. oceanica 

samples and in two C. nodosa samples. The x-axis represent 

438 

39


the number of allele differences and the y-axis is the 

frequency distribution for each x rank. 

Acknowledgments 

This research was funded by projects M&Ms (EVK3-CT-2000-00044) 

and LIFE-Posidonia (2000/NAT/E/7303) of the European Union. The 

European Science Foundation (ESF) and Fundacao para a Ciencia ea 

Tecnologia (FCT), Portugal funded a postdoctoral (to S.A.-H.) and a 

doctoral (to F.A.) fellowship. We thank E. Díaz-Almela, R. Santiago- 

Doménech, and E. Álvarez-Perez for help with sample collection, and G. 

Pearson and three anonymous referees for useful comments on a 

preliminary version of this manuscript. 

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Corresponding Editor: James Hamrick 

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II.2 

Within-population spatial genetic structure, neighbourhood size and 

clonal subrange in the seagrass Cymodocea nodosa. Molecular Ecology, 

2005. 

Dans le cadre de la thèse de Filipe Alberto, l'étude de la dispersion clonale 

versus sexuée chez Cymodocea nodosa a été réalisée dans des herbiers présentant 

des niveaux de perturbation contrastés. Nous avons développé et appliqué le 

principe de clonal subrange (qui corresponds à l’éventail des distances minimales 

d’extension clonale) afin d’estimer les distances de dispersion par voie clonale 

(Figure 8a). Cette méthode permet d’obtenir la distribution de la probabilité d’identité 

génétique en fonction de la distance, et de visualiser la distance maximale observée 

entre deux unités d’échantillonnage appartenant à la même lignée clonale. 

0.25 

0.20 

0.15 

0.10 

0.05 

0.00 

-0.05 

-0.10 

○Coancestry (f ij ): ramets 

● Coancestry (f ij ): genets 

■ Probabilité d’identité clonale 

5 10 15 20 25 30 35 40 

Figure 8: Autocorrélation spatiale et Clonal 

subrange : distribution des coefficients de 

parenté sur le jeu de données complet 

(ramets), ou en excluant les distances entre 

réplicats (uniquement entre genets), et 

probabilité d’identité génotypique/clonale en 

fonction de la distance entre échantillons. 

Logiquement, la probabilité devient nulle à 

l’endroit ou les courbes de parenté entre 

ramets et entre genets convergent. 

Nous avons également adapté les méthodes classiques d’autocorrélation 

spatiale à des jeux de données dans lesquels le même ‘individu génétique’ peut 

apparaître plusieurs fois sur différentes coordonnées géographiques. Trois 

méthodes ont testées pour exclure les réplicats et pratiquer les analyses à l’échelle 

des genets : choix des coordonnées centrales, choix des plus proches, ou 

permutations. L’approche sur permutation, qui permet d’obtenir une enveloppe de 

distribution des coefficients de parenté ou distances génétiques versus distance 

géographique, nous a paru préférable car elle s’applique sous des conditions moins 

restrictives (avec moins d’hypothèses a priori sur la dispersion) que les deux autres. 

Nous avons donc choisi cette approche pour estimer les tailles de voisinages, bien 

que dans le cas de la Cymodocée, les résultats se soient avérés identiques 

qualitativement (significativité du patron) et similaires quantitativement (distance de 

dispersion ou taille de voisinage) avec les trois méthodes testées. 

42

Molecular Ecology (2005) 14, 2669–2681 

doi: 10.1111/j.1365-294X.2005.02640.x 

Within-population spatial genetic structure, neighbourhood 

Blackwell Publishing, Ltd. 

size and clonal subrange in the seagrass Cymodocea nodosa 

FILIPE ALBERTO,* LICÍNIA GOUVEIA,* SOPHIE ARNAUD-HAOND,* JOSÉ L. PÉREZ-LLORÉNS,† 

CARLOS M. DUARTE‡ and ESTER A. SERRÃO* 

*CCMAR, CIMAR-Laboratório Associado, University of Algarve, Campus de Gambelas, 8005–139 Faro, Portugal, †Area de Ecología, 

Universidad de Cadiz, Facultad de Ciencias del Mar y Ambientales, 11510 Puerto Real, Cadiz, Spain, ‡IMEDEA (CSIC-UIB) Instituto 

Mediterraneo de Estudios Avanzados, C/Miquel Marques 21, 07190 Esporles, Mallorca, Spain 

Abstract 

The extent of clonality within populations strongly influences their spatial genetic structure 

(SGS), yet this is hardly ever thoroughly analysed. We employed spatial autocorrelation 

analysis to study effects of sexual and clonal reproduction on dispersal of the dioecious 

seagrass Cymodocea nodosa. Analyses were performed both at genet level (i.e. excluding clonal 

repeats) and at ramet level. Clonal structure was characterized by the clonal subrange, a 

spatial measure of the linear limits where clonality still affects SGS. We show that the 

clonal subrange is equivalent to the distance where the probability of clonal identity 

approaches zero. This combined approach was applied to two meadows with different 

levels of disturbance, Cadiz (stable) and Alfacs (disturbed). Genotypic richness, the 

proportion of the sample representing distinct genotypes, was moderate (0.38 Cadiz, 0.46 

Alfacs) mostly due to dominance of a few clones. Expected heterozygosities were comparable 

to those found in other clonal plants. SGS analyses at the genet level revealed extremely 

restricted gene dispersal in Cadiz (Sp = 0.052, a statistic reflecting the decrease of pairwise 

kinship with distance), the strongest SGS found for seagrass species, comparable only to 

values for selfing herbaceous land plants. At Cadiz the clonal subrange extended across 

shorter distances (20–25 m) than in Alfacs (30–35 m). Comparisons of sexual and vegetative 

components of gene dispersal suggest that, as a dispersal vector within meadows, clonal 

spread is at least as important as sexual reproduction. The restricted dispersal and SGS pattern 

in both meadows indicates that the species follows a repeated seedling recruitment 

strategy. 

Keywords: clonal plant, clonal subrange, Cymodocea nodosa, microsatellites, seagrass, SGS 

Received 1 February 2005; revision received 15 April 2005; accepted 6 May 2005 

Introduction 

Genetic structure can be defined as the development of a 

nonrandom distribution of alleles at a given spatial scale 

resulting from limited dispersal, selection, genetic drift 

and population history. An important concept related to 

dispersal is isolation by distance, which predicts the expected 

pattern of spatial genetic structure (SGS) under restricted 

dispersal and local genetic drift (Vekemans & Hardy 2004). 

The amount and scale of gene flow determines the role of 

local adaptation and population SGS in the evolutionary 

Correspondence: Ester A. Serrão, Fax: +351 289818353; E-mail: 

eserrao@ualg.pt 

process (Wright 1977; Fenster et al. 2003). In plants, SGS is 

the result of the joint effect of pollen and seed dispersal 

and, for the numerous plant species exhibiting clonality, of 

the pattern of clonal growth. Indeed, clonality is expected 

to greatly influence the patterns of SGS, both because of the 

expected aggregated distribution of clone mates with an 

identical genotype, and because clonal growth is also a 

component of spatial dispersal (Gliddon et al. 1987). The 

strength of the influence of clonality on SGS will depend on 

the type and rate of clonal growth (Marbà & Duarte 1998), 

intermingling among clones, fragmentation and the lifespan 

of the genets. For example if the clone becomes fragmented 

and is present at long distances from the initial seedling 

germination site, then clonal reproduction increases the 

© 2005 Blackwell Publishing Ltd 

43

2670 F. ALBERTO ET AL. 

genet dispersal (Chung & Epperson 1999, 2000; Chung 

et al. 2000). Species or stages that exhibit a ‘guerrilla’ type 

of clonal growth (irregular shape and widespread ramets: 

Lovett Doust 1981) are expected to show high clonal intermingling 

and dispersal. Conversely, a ‘phalanx’ strategy 

(regularly shaped radiating circles of densely clumped 

ramets) will lead to clone mate clustering and consequently 

to increased SGS. A key question specific to clonal organisms 

is the effect of clonality on SGS, i.e. the identification of the 

spatial scales over which clonal processes affect the genetic 

structure of the population, which we hereafter refer to as 

the clonal subrange of the SGS. Despite this, some studies 

purposely increase the porosity of the sampling program so 

as to avoid any effects of clonality on the resulting depiction 

of the SGS (i.e. selecting a minimum sampling distance greater 

than the expected spread of the clones). This approach 

results in loss of information on both the role of clonality as 

a driver of SGS, and on the SGS at small spatial scales. 

Spatial genetic autocorrelation analysis examines the 

genetic relatedness between pairs of individuals with 

regard to their relative positions in space (cf. Epperson 

2003 and Vekemans & Hardy 2004). Theoretical models of 

isolation by distance predict patterns of SGS at drift– 

dispersal equilibrium, and recent theoretical and methodological 

advances allow new inferences about gene dispersal 

and neighbourhood size (Rousset 1997; Hardy & Vekemans 

1999; Vekemans & Hardy 2004). For neutral genetic markers 

the expected outcome of SGS on spatial autocorrelograms 

is a linear decrease in the mean genetic kinship coefficient 

with the logarithm of spatial distance for two-dimensional 

populations. The slope of the regression equation describing 

this decline can be used to estimate SGS parameters 

such as gene dispersal and Wright’s ‘neighbourhood size’ 

(Fenster et al. 2003). 

Effective sexual recruitment is the result of pollen and 

seed dispersal, seed germination and establishment, 

and seedling growth. Recruitment behaviour and genet 

dynamics in clonal plants have been described in an 

idealized and simplified way by Eriksson (1993, 1997) as a 

continuum from ‘repeated seedling recruitment’ (RSR) 

into adult populations, and unique ‘initial seedling recruitment’ 

(ISR) at the beginning of population history. In the 

RSR strategy seedlings recruit regularly within stands of 

established adults, whereas in the ISR strategy they only 

establish during the initial colonization period, and further 

development of the meadow is due to clonal growth (Eriksson 

1993, 1997). It has been hypothesized that repeated seedling 

recruitment would be more common in clonal marine 

plants than in their terrestrial counterparts (Inglis 2000), 

since the micro-environment provided by established 

meadows may increase seedling survival (Terrados 1993). 

Here we analyse the SGS and clonal structure of a dioecious 

seagrass (Cymodocea nodosa) in two meadows by 

means of spatial autocorrelation using pairwise kinship 

coefficients estimated from microsatellite alleles and four 

different methods of analysing the data. Based on C. nodosa 

seed morphology and position, we expected sexual dispersal 

to be weak and the meadows to exhibit ‘repeated seedling 

recruitment’, while the high vegetative growth rate of 

this species suggests that clonal propagation should be an 

important dispersal vector. Therefore, our objective in the 

present study was to analyse the relative importance of 

clonal and sexual dispersal. To achieve this, we evaluated 

the potential of spatial autocorrelograms to estimate dispersal 

parameters such as (i) the neighbourhood size and 

gene dispersal, and (ii) the clonal subrange; a measure of 

the spatial scale at which the probability of clonal identity 

is near zero (Harada & Iwasa 1996; Harada et al. 1997), 

estimating the spatial range over which clonality directly 

affects SGS. 

Materials and methods 

Model species 

Cymodocea nodosa (Cymodoceaceae) is a dioecious, rhizomatous 

seagrass (Hemminga & Duarte 2000) that exhibits 

fast clonal growth, with maximum linear clonal extension 

rates in excess of 2 m/year (Duarte & Sand-Jensen 1990). It 

occurs throughout the Mediterranean basin and in the 

North Atlantic from central Portugal to Cap d’Arguin in 

Senegal, as well as in the Canary Archipelago and the Madeira 

Islands. Cymodocea nodosa exhibits basicarpy, producing 

two seeds at the base of the female shoots where, in the 

absence of disturbance, they remain buried under the 

sediment until germination occurs (Buia & Mazzella 1991). 

This suggests highly restricted seed dispersal, and although 

there are anecdotal observations of seeds cast upon the 

shore and seeds transported by positively buoyant detached 

shoots, these mechanisms appear to represent rare dispersal 

events. To date, no direct estimates of seed dispersal are 

available for this species and the potential for pollen 

dispersal remains undetermined. While C. nodosa seedlings 

have been found at the periphery of established patches, 

the probability that they could grow and develop a new 

patch is estimated at about 10% (Duarte & Sand-Jensen 1990), 

although this figure may be higher inside established 

meadows (Terrados 1993). 

Study sites and sampling 

Two contrasting meadows, separated by over 1000 km 

along the Spanish coast, were sampled in June 2003 to 

examine the spatial structure of C. nodosa microsatellite 

genotypes (see Fig. 1). The Cadiz population occurs on the 

southwest margin of the tidal bay of Cadiz in the Atlantic, 

where it extends from 0.5 to 3 m water depth. The Alfacs 

population grows in the tideless Alfacs Bay in the 

© 2005 Blackwell Publishing Ltd, Molecular Ecology, 14, 2669–2681 

44

SPATIAL GENETIC STRUCTURE IN CYMODOCEA NODOSA 2671 

Fig. 1 Sampling sites of Cymodocea nodosa 

for spatial genetic structure analyses. 

(A) Alfacs Bay in the Mediterranean coast of 

Spain, associated with the Ebro River delta. 

(B) Cadiz Bay in the South Atlantic coast of 

Spain. 

Mediterranean, at 0.5 m depth. The two populations are 

also subject to contrasting disturbance regimes: Alfacs Bay 

is periodically disturbed by the migration of subaqueous 

dunes (Marbà et al. 1994; Marbà & Duarte 1995) and the 

local landscape, dominated by C. nodosa patches, is characterized 

by an extinction–recolonization balance (Vidondo 

et al. 1997). In contrast, in Cadiz Bay the C. nodosa meadows 

are continuous and apparently undisturbed. 

At each site, sampling was performed along a grid of 

20 × 38 m. The internal grid spacing was 2 m yielding a total 

of 220 sampling units per population. For each sampling 

unit, the meristematic portion of 3–5 shoots, belonging to 

the same rhizome/genet, was preserved and dried on 

silica crystals before transportation to the laboratory. 

Microsatellite genotyping 

After DNA extraction (Doyle & Doyle 1988) samples were 

genotyped for nine microsatellite loci (Alberto et al. 2003). 

Three polymerase chain reaction (PCR) multiplexes with 

fluorescently labelled primers (MA, MB and MC) followed 

by two electrophoresis multiplexes (MA + MC and MB) 

were sufficient to analyse all loci on an ABI 377 automated 

sequencer using the genescan software (Applied Biosystems). 

Approximately 10 ng of DNA were amplified in a 15-µL 

volume, containing 60 µm of each dCTP, dGTP, dATP and 

dTTP, 2 mm of MgCl 2 

, 200 mm Tris-HCl (pH 8.4), 500 mm 

KCl and 1 U Taq DNA polymerase (Invitrogen, Life 

Technologies). Each reaction contained one of the following 

multiplexes of fluorescently labelled C. nodosa microsatellite 

primers: (MA) Cn2–86/6-FAM, Cn2–38/HEX 

and Cn2–14/6-FAM; (MB) Cn2–16/HEX, Cn2–18/6-FAM, 

Cn4–29/NED and Cn2–45/6-FAM; (MC) Cn2–24/NED 

and Cn4–19/NED. Individual primer concentration ranged 

from 0.06 to 0.23 µm. Cycling conditions consisted of an 

initial denaturing step of 4 min at 94 °C, followed by 24 

cycles of ‘touchdown’ PCR consisting of 30 s at 94 °C, 30 s 

at 55 °C (reduced by 0.2 °C in each subsequent cycle), and 

30 s at 72 °C, 10 additional cycles consisting of 30 s at 94 °C, 

30 s at 50 °C and 40 s at 72 °C, and a final elongation step 

at 72 °C for 10 min. 

Clone identification 

Observed identical multilocus genotypes (MLGs) can 

either be the result of sampling the same clone/genet at 


45


two different spatial coordinates, or two different genotypes 

originated by two distinct sexual reproduction events 

but sharing the same alleles for all genotyped loci (Arnaud- 

Haond et al. 2005). The probability of encountering the 

latter depends on the population frequencies for the alleles 

in that genotype and the number of loci used to fingerprint 

samples. To address this issue, we estimated the probability 

of a given multilocus genotype occurring n times as a 

consequence of different sexual reproduction events (P sex 

), 

according to Parks & Werth (1993). Detailed description of 

P sex 

estimation and genet assignment using an appropriate 

set of markers is reported elsewhere (Arnaud-Haond et al. 

2005). Once clones were identified genotypic richness was 

estimated for each site according to Dorken & Eckert (2001) 

as: 

G − 1 

R = 

N − 1 

(eqn 1) 

Where G is the number of distinct genotypes and N the 

sample size. The distribution of clone size was described 

using the distance between the farthest clone mates as a 

conservative estimate of the linear size of the clone. In 

clonal plants this distribution is typically skewed with only 

a few clones having large dimensions. Hämmerli & Reusch 

(2003a) found for the seagrass Zostera marina that clonal 

size increased with heterozygosity, suggesting that more 

outbred genets would be better competitors for space 

occupation. We examined the hypothesis of a relationship 

between genet heterozygosity and clone size (number of 

clonal replicates) using a Monte Carlo simulation provided 

by the program clonality version 1 (Prugnolle et al. 2004). 

This program tests if MLGs repeated in the sample (i.e. 

clonal growth) have an increasing effect on population 

heterozygosity, resulting in a decrease of the inbreeding 

coefficient F IS 

. The program first estimates F IS 

, using Weir 

& Cockerham’s (1984) unbiased estimator f, without removing 

repeated MLGs (sample N) and detects how many 

MLGs have multiple copies and how many copies for each 

of these. It proceeds by reducing the data to a single copy for 

each MLG (sample U), and a new sample is then generated 

(sample R1) by amplifying randomly chosen genotypes 

of sample U so that the sample size and the amount of 

repetitions of multilocus genotypes are kept identical to 

those found in sample N (see Fig. 1, Prugnolle et al. 2004). 

The procedure is repeated 5000 times (samples R1 to R5000) 

and a corresponding f Ri 

is computed each time (f R1 

to f R5000 

). 

A P value is obtained by computing the proportion of 

times that f Ri 

≤ f. 

Population genetic statistics 

Allele frequencies, expected heterozygosities (H E 

) and 

inbreeding coefficients (f ) were estimated using the software 

genepop (Raymond & Rousset 1995). Hardy–Weinberg 

equilibrium and genotypic linkage disequilibrium (using a 

single copy per genet) were tested for each population 

using the exact Hardy–Weinberg test (Weir 1990) and the 

Fisher exact test, respectively, both available in genepop. 

Spatial genetic structure 

Two main types of analyses were performed with both data 

sets, one with the repetitions of MLGs kept throughout, 

and another using a single copy per MLG; they are hereafter 

called ramet- and genet-level analyses, respectively. At the 

ramet level one tries to characterize the potential amplifying 

effects of clonality on SGS, created by the genetic correlation 

between clone mate pairs. The genet-level analysis circumvents 

the latter problem; however, clonality is a component 

of dispersal and so affects the observed SGS pattern even 

when repeated MLGs are removed. 

The genetic co-ancestry between pairs of individuals can 

be summarized over a range of distance intervals in terms 

of multilocus estimates of kinship (F ij 

). In order to do so, we 

used a kinship coefficient used in Loiselle et al. (1995) and 

implemented in the software spagedi (Hardy & Vekemans 

2002). Average kinship coefficients were estimated for the 

following distance classes: 0–2; 2–4; 4–6; 6–8; 8–10; 10–12; 

12–14; 14–16; 16–18; 18–20; 20–25; 25–30 and 30–45 m. 

Correlograms were constructed by plotting mean pairwise 

kinship coefficients as a function of spatial distance class. 

Pairwise kinship coefficients were regressed on the logarithm 

of spatial distance to estimate a regression slope (blog). 

For each population, spatial locations were randomly 

permuted among individuals 10 000 times in order to test, 

for each spatial distance class, whether the observed mean 

kinship values were different from those expected under a 

random distribution of genotypes. To test the significance 

of the observed SGS pattern, a distribution of regression 

slopes was also constructed using a permutation test, and 

P values for the observed regression were estimated as the 

fraction of this distribution greater than the observed slope 

(tests available in spagedi). Ramet- and genet-level analyses 

are detailed below. 

Ramet-level analysis 

Coupled to the traditional ramet-level analyses an additional 

method was performed using all sampled ramets but 

now considering only the kinship values for pairs between 

different genets (using the option 4.3.3.5.3, in spagedi 

software, where categories corresponded to different clones). 

In this analysis all spatial information for a given multisampled 

genet is kept since all repetitions from a given 

genotype are used, but the potential inflating effect on SGS 

produced by clone mate pairs is removed. Hereafter we 

will refer to this method as the among-genet analyses. 

Where clonal growth results in the spatial clustering of 


46


clone mates, the ‘ramet level’ will produce higher kinship 

values than the ‘among genet’ within the spatial range 

where clone mates are clumped, beyond which both 

take similar values. In fact, if we plot both correlograms 

together, we show that the point where the two curves 

merge is an estimate of the spatial range at which clonality 

has non-negligible effects on the SGS, here defined as 

clonal subrange. In order to further illustrate the clonal 

subrange we determined the probability of clonal identity 

(F r 

) as a function of spatial distance (Harada & Iwasa 1996). 

For that purpose we computed, for a set of distance 

intervals, the fraction of pairs of ramets sharing the same 

multilocus genotype. The values were plotted on top of 

the above-described autocorrelogram. This analysis was 

performed using an r 1.6.1 (The r Development Core 

Team, 2002) code. 

Genet-level analysis 

SGS was characterized after removing clonal replicates 

from the data set and considering the central coordinates of 

each clone (average of x and y coordinates of clone mates). 

Using a genet’s central coordinates for its spatial representation 

can be justified as this point is the most parsimonious 

position of the clone’s birthplace. However this assumes 

isotropic growth and no disturbance causing loss of a 

sector of the clone. The first assumption is not supported 

by available information that shows that the origin of the 

patch/clone is always displaced relative to the geometric 

centre (Duarte & Sand-Jensen 1990; Vidondo et al. 1997). A 

resampling technique was used to analyse the variance 

in the estimates resulting from the selection of different 

spatial coordinates to represent the clone. A random 

representative ramet from each genotype repeated in the 

sample was resampled to create a matrix with a single copy 

of each genet, the procedure was repeated 100 times to 

estimate the dispersion of the estimates. The proportion of 

these 100 data sets yielding significant mean kinship values 

for the first distance class and/or significant regression 

slopes was recorded. 

Indirect estimation of dispersal, and neighbourhood 

size 

The intersection between the correlogram curve and the 

x-axis of the plot is often considered as an estimate of the 

distance within which individuals reproduce with their 

close relatives, or the radius of a patch (Epperson 2003). 

However, this method is highly dependent on the spatial 

scale of sampling (Fenster et al. 2003). Thus, we rather 

estimated the neighbourhood size (Nb), the number of 

individuals that characterize the strength of genetic drift in 

the population (Vekemans & Hardy 2004). A redefinition 

of the concept is proposed by Fenster et al. (2003), defining 

neighbourhood area as a circular area containing such Nb 

individuals, within which biparental inbreeding remains 

insignificant. If the SGS pattern is produced by an isolationby-distance 

process, at drift–dispersal equilibrium in a 

two-dimensional space, Nb can be estimated from blog 

and is equal to –(1 – F (1) 

)/blog (Hardy & Vekemans 1999; 

Fenster et al. 2003), where F 1 

is the average F ij 

between 

individuals belonging to the first distance class (here F 1 

= 

F [2m] 

). However, under the above-mentioned conditions, 

kinship is expected to decrease linearly with the logarithm 

of spatial distance for a restricted range (σ to 20σ, where σ 

is the axial standard deviation of gene dispersal distances; 

Rousset 1997). As σ is unknown, an iterative approach 

available in spagedi was used to estimate blog using the 

observed genotype density as the effective population 

density (D). The neighbourhood area was calculated as 

the surface that would contain the Nb individuals for 

each population. Because blog depends to some extent 

on the sampling scale used and it is negative, we also 

estimated the Sp statistic which absolute value reflects 

the rate of decrease of pairwise kinship with distance 

(Vekemans & Hardy 2004). This allowed us to compare 

the SGS pattern in C. nodosa with the strength of patterns 

observed among other species. The Sp statistic is equal to 

–blog/(1 – F (1) 

). 

Finally we evaluated the relative importance of clonal 

growth and sexual reproduction to gene dispersal. First we 

computed the axial variance of gene dispersal mediated by 

clonal growth σ2 

veg 

(Gliddon et al. 1987), as one-half the 

mean squared distance between a ramet and the central 

coordinates of the clone to which it belongs. To calculate 

σ2 

veg 

, we used all sampled genets, not only those that had 

more than one ramet sampled, and in order to do so an 

arbitrary distance of 1 m (half the minimum distance 

between consecutive sample units) was considered for 

genets which appeared only once in the sample. Then we 

applied the Gliddon et al. (1987) model of parent–offspring 

dispersal variance (σ 2 ): 

1 

σ = σ + σ + σ ⇔ σ = σ + σ 

2 

2 2 2 2 2 2 2 

p s veg sex veg 

(eqn 2) 

where σ2 p 

is the pollen dispersal variance and σ2 

s 

the seed 

dispersal variance and consequently the sexual mediated 

dispersal variance is σ2 sex 

= 12 / σ2 p 

+ σ2 

s. The genet-level 

SGS-based estimate of Nb (see above) is also a function of 

the sexual and vegetative components of dispersal variance 

and can be used to estimate the total variance σ 2 = Nb/4πD. 

An estimate of σ2 

sex 

can then be obtained by subtracting 

from σ 2 the above-estimated 

2 

. 

σ veg 

Scoring errors and/or somatic mutations effects on SGS 

We evaluated the potential bias caused by scoring errors or 

somatic mutations, on the strength of positive autocorrelation 


47


at smaller distance classes. This problem can arise if clone 

mates are erroneously assigned as different, thus being 

included in the analyses where clonal repeats are excluded. 

We produced an r 1.6.1 (The r Development Core Team, 

2002) routine to detect which pairs differ by only a single 

allele while being neighbours in the 2-m sampling scale. 

When such pairs were found the smaller genet was removed 

from the data set as a potential source of error. A total of 

18 genotypes in Cadiz and 14 genotypes in Alfacs were 

removed. Separate analysis after the exclusion of these 

genotypes did not differ from those with the complete 

data set. 

Results 

Genotypic richness and clonal structure 

The number of alleles amplified was 41 and 39, and a total 

of 83 and 95 different microsatellite multilocus genotypes 

(MLG) were identified for Cadiz Bay and Alfacs Bay, 

respectively. All repetitions of the same MLG, for all MLGs 

observed in more than one sample unit, had P sex 

values < 0.05 

and so were considered to result from repeated sampling 

of the same clone. Therefore, the number of different MLGs 

found corresponds to the number of clones present in our 

samples. The spatial organization of clonal repetitions 

(Fig. 2) shows that both meadows were dominated by a 

few large clones, which appeared aggregated in space. In 

Cadiz Bay and Alfacs Bay, respectively, only four and two 

clones had more than 10 repeats (representing 41% and 

33% of the 220 sampled shoots) and only 24 and 18 clones 

appeared more than once (70% and 57% of the sampled 

shoots). This resulted in an extremely skewed distribution 

of clone size, as observed by the distribution of the linear 

dimension of the clones (Fig. 3), with a median clonal 

dimension of 3.6 m and 3.4 m in Cadiz Bay and Alfacs Bay, 

respectively. Genotypic richness was below 0.5 for both 

meadows, although Alfacs Bay showed higher levels 

(0.46) than Cadiz Bay (0.38). The highly skewed clone size 

distribution, and the low number of clones with more than 

one observation, indicate that this moderate genotypic 

richness is the result of the dominance of a few, large 

Fig. 2 Sampling grids of Cymodocea nodosa in 

Cadiz Bay and Alfacs Bay. The minimum 

distance between consecutive sampling points 

was 2 m. The numbers shown code the different 

genets; different patterns are used to 

represent each of the clones with more than 

one copy. Some sampling sites had no cover; 

these are represented by blank grid units. 


48


Fig. 3 Distribution of the linear dimensions 

of Cymodocea nodosa clones for Cadiz Bay 

and Alfacs Bay. The linear dimension is the 

minimum clone size estimated as the distance 

between the farthest clonemates. 

Table 1 Number of alleles (Na), expected heterozygosity (H E 

) and inbreeding coefficient (F IS 

) for genet-level analysis and ramet-level 

analysis of Cymodocea nodosa at Cadiz Bay and Alfacs Bay. Significant departures from the null hypothesis of Hardy–Weinberg equilibrium 

are coded: ***P < 0.001; **0.001 

Cadiz Bay 

Alfacs Bay 

Loci 

Na 

Genet level Ramet level Genet level Ramet level 

H E 

F IS 

H E 

F IS 

Na H E 

F IS 

H E 

F IS 

Cn2–86 4 0.748 −0.220*** 0.736 −0.281*** 6 0.748 −0.022** 0.730 −0.172*** 

Cn2–38 5 0.611 −0.046 0.572 −0.101* 4 0.485 −0.016 0.486 −0.142 

Cn2–14 3 0.498 −0.477*** 0.482 −0.548*** 4 0.617 0.015 0.576 −0.135** 

Cn2–24 10 0.787 0.249*** 0.819 0.152*** 5 0.380 −0.187 0.451 −0.286*** 

Cn4–19 5 0.456 0.040 0.472 0.170*** 6 0.652 0.005* 0.632 −0.166*** 

Cn2–16 3 0.523 −0.110 0.508 0.047 2 0.259 0.475*** 0.174 0.350*** 

Cn2–18 3 0.447 −0.219 0.480 −0.331*** 2 0.478 −0.162 0.478 −0.421*** 

Cn4–29 3 0.498 −0.357*** 0.516 −0.356*** 6 0.524 −0.120 0.496 −0.009*** 

Cn2–45 5 0.666 −0.191 0.664 −0.172*** 4 0.662 −0.251*** 0.646 −0.419*** 

Multilocus 41 0.582 −0.129*** 0.583 −0.144*** 39 0.534 −0.064*** 0.519 −0.197*** 

clones, despite the presence of many unique clones in the 

samples. 

Heterozygote excess 

Both meadows showed significant heterozygote excesses 

before and after clonal replicates were removed (all P < 

0.001) although the F IS 

values were higher for the latter 

analysis (Table 1). We did not find any significant association 

between clone size and heterozygosity, P = 0.58 and P = 

0.07 for Cadiz and Alfacs, respectively. When testing 

for genotypic linkage disequilibrium, and after applying 

Bonferroni correction, only nine (Cadiz) and six (Alfacs) 

pairs of loci, from a total of 36 pairs, rejected the null 

hypothesis of genotypes at one locus being independent 

from genotypes at the other locus. The pairs of loci involved 

were not consistent across the two sites. 

Ramet-level analysis and clonal subrange 

Clonal structure was characterized by analysing the spatial 

autocorrelation of microsatellite genotypes using the 

kinship coefficient (F ij 

) at the ramet and among genet 

levels. The distance class where these correlograms merge 

(see Fig. 4) represents the clonal subrange, the distance 

range beyond which clonality has negligible effects on 

genetic structure, as less than 1% of the pairs are clonal. 

For Cadiz, the clonal subrange extended across shorter 

distances (20–25 m) than it did in Alfacs Bay (30–35 m). 

The probability of clonal identity (F r 

), plotted on Fig. 4, 

declined with increasing distance, from around 25% for 

both meadows in the first distance class (2 m) to reach zero 

and 2.5% at 30 m in Cadiz and Alfacs, respectively. At 

the point where the ramet level and among genets level 

correlograms merge, F r 

takes values lower than 1% (as less 


49


Alfacs (F (2,m) 

= 0.021; P > 0.05). Yet for the random selection 

method 100 and 68 of the 100 generated data sets had 

positive significant F (2,m) 

values for Cadiz [F (2,m) 

± SE (over 

100 data sets) = 0.089 ± 9 × 10 −3 ] and Alfacs [F (2,m) 

± SE 

(over 100 data sets) = 0.029 ± 4 × 10 −3 ], respectively. 

Within the sampled range, the average kinship coefficients 

between pairs of individuals declined linearly with 

the increasing logarithm of the spatial distance (Table 2) 

and there was a significant SGS pattern (all slope tests were 

significant, see Table 2 and Fig. 5). The steeper regression 

slopes (blog) for Cadiz Bay (−0.044; P < 0.001), than for 

Alfacs (−0.012 to −0.014; P < 0.001), resulted in smaller Nb 

estimates and a stronger Sp statistic in Cadiz (Table 2). The 

estimated vegetative component of gene dispersal σ2 

veg 

was 

10.9 and 17.6 in Cadiz Bay and Alfacs Bay, respectively. 

The sexual component of dispersal ( σ2 

sex 

) estimated on the 

basis of those values was consequently lower in Cadiz (7.1) 

than in Alfacs (20.5). Finally, the relative importance of the 

sexual and vegetative components of gene dispersal, 

described by the ratio σ2 sex 

/ σ2 

veg 

, was 0.65 in Cadiz and 1.16 

in Alfacs. 

Discussion 

Spatial genetic structure and sexual reproduction 

Fig. 4 Analysis of Cymodocea nodosa clonal structure by means of 

spatial autocorrelation analysis of kinship coefficients for Cadiz 

Bay and Alfacs Bay. Three distinct analyses were used: (i) a rametlevel 

analysis which includes all ramets sampled; (ii) an amonggenet 

analysis, where only pairs between different genets are 

allowed (see Methods); and (iii) the probability of clonal identity. 

The spatial distance where the ramet-level and among-genet 

correlograms merge corresponds to a probability of clonal identity 

close to zero and estimates the radius of the clonal subrange. 

than 1% of the pairs at that distance interval share the same 

genotype). These distances correspond to the dimensions 

of the largest clones found in each of the populations 

(Fig. 3). 

Genet-level analysis and indirect dispersal estimates 

A summary of results from the autocorrelation of genetic 

variation using kinship coefficients and different methods 

of data analysis is presented in Table 2. The coefficient 

estimates from the ramet and among genets levels are presented 

only for comparison with the genet-level estimates. 

Ramet-level estimates are higher due to the correlations 

between clone mates. When the central method was used 

to represent the spatial coordinates of the clones the average 

kinship coefficient was significantly positive at the first 

distance class for Cadiz (F (2,m) 

= 0.119; P < 0.001) but not for 

The pronounced SGS observed in Cadiz suggests extremely 

limited dispersal for Cymodocea nodosa in that bay. This 

pattern is the strongest observed so far for any seagrass 

species (Reusch et al. 1999a; Hämmerli & Reusch 2003b) 

and the Sp values found here are among the strongest 

patterns reported for land plants, observed in selfing 

herbaceous species (Caujapé-Castells & Pedrola-Monfort 

1997; Bonin et al. 2001; review in Vekemans & Hardy 2004). 

Yet, under equal seed dispersal, dioecious species such as 

C. nodosa are expected to show lower SGS than selfing 

species, because of pollen flow (Vekemans & Hardy 2004). 

Seed dispersal in C. nodosa is expected to be limited as 

a consequence of seed size (Eriksson 1997; Inglis 2000), 

negative buoyancy, position at the base of the shoot buried 

in the sediment (Buia & Mazzella 1991), and association 

with female plants (Caye & Meinesz 1985). Nevertheless, 

the strong SGS observed here may also be related to 

limited pollen dispersal. Restricted pollen dispersal has 

been suggested from the observation that the abundance 

of seed production is related to the proximity of male to 

female plants in the seagrass beds (Caye & Meinesz 1985), 

although the authors did not quantify the spatial scale 

of this observation. Terrados (1993) did not detect pollen 

limitation, although this was only over distances of less 

than 0.5 m. 

The weaker SGS pattern observed in Alfacs may be 

partially explained by the different disturbance regimes 

affecting both sites. The model used to estimate dispersal 


50


Table 2 Summary of kinship autocorrelation in two Cymodocea nodosa meadows using different methods of analysing a data set from a 

clonal organism (see methods). Mean F ij 

kinship values found for the shortest distance interval (F (2,m) 

). The slope of the regression of mean 

kinship with the logarithm of spatial distance (blog) and the Sp statistic with the jackknife estimated standard error (Sp). Finally the 

estimated neighbourhood size (Nb) and the area containing such number of individuals based on the observed genet density in each 

meadow. Nb values for the genet-level (central) analysis are estimated using an iterative procedure (see methods). Significant values of 

F (2,m) 

, blog and Sp are shown in bold (α = 0.025). For the random method (last row) standard errors are given based on the distribution of 

parameters obtained after analysing 100 of these data sets 

Method F (2,m) 

blog Sp (SE) Nb Area m 2 (radius m) 

Cadiz Bay 

Ramet level 0.133 −0.083 0.096 ± 0.022 10.4 101 (5.7) 

Among gemet 0.051 −0.049 0.052 ± 0.020 19.4 187 (7.7) 

Genet level (central) 0.119 −0.044 0.052 ± 0.016 23.5 226 (8.5) 

Genet level (random) 0.089 ± 9 × 10 −3 −0.044 ± 1 × 10 −3 0.048 ± 2 × 10 −3 19.8 ± 0.8 191 ± 6 (7.8 ± 0.2) 

Alfacs Bay 

Ramet level 0.094 −0.044 0.050 ± 0.007 20.6 198 (7.9) 

Among gemet 0.030 −0.024 0.025 ± 0.008 40.4 390 (11.1) 

Genet level (central) 0.021 −0.014 0.015 ± 0.004 56.8 548 (13.2) 

Genet level (random) 0.029 ± 4 × 10 −3 −0.012 ± 0.000 0.012 ± 0.000 80.9 ± 4.15 780 ± 40 (15.8 ± 0.5) 

Fig. 5 Genet-level analysis correlogram 

showing mean kinship coefficients (circles) 

between individual Cymodocea nodosa genets 

as a function of spatial distance at Cadiz 

Bay and Alfacs Bay. The first row shows the 

correlogram produced when central coordinates 

were used to represent the spatial 

coordinates of the genet. Broken lines 

delimit 95% confidence intervals around 

the null hypothesis of random distribution 

of genets in space. In the second row the 

correlograms (continuous line) are based 

on the mean kinship coefficients found 

after 100 data files were analysed, each 

containing a single randomly selected ramet 

for each genotype. Dotted lines delimit 

the maximum and minimum values found. 

This procedure generates confidence intervals 

for the kinship and dispersal estimators. 

assumes that the SGS has reached a stationary phase 

representative of the drift–dispersal equilibrium (Rousset 

1997; Vekemans & Hardy 2004), whereas the periodical 

disturbance imposed by the migration of a subaqueous 

dune (Marbà et al. 1994; Marbà & Duarte 1995) can prevent 

the population from reaching the required equilibrium 

state. An SGS pattern takes several generations to develop 

depending on the scale of analyses. In Alfacs Bay this is by 

far longer than the disturbance period (Marbá et al. 1994; 

Marbá & Duarte 1995) suggesting that the SGS pattern 

observed could be a transient one. The analysis of the 

shape of the correlogram, at a spatial scale smaller than the 

axial standard deviation of gene dispersal distances (σ), 

can provide information about the relative contributions 

of seeds and pollen to the overall level of gene dispersal 

(Heuertz et al. 2003; Vekemans & Hardy 2004). In our case 

the initial curvature in Alfacs (downward concave form) 

suggests a more important contribution of seed dispersal 

than in Cadiz; this should be the most likely scenario if 

disturbance cleared space facilitating seed dispersal along 

the sediment surface. Another consequence of disturbance 

and higher population turnover should be a younger 


51


meadow age. Both arguments could explain a weak SGS 

pattern, still influenced by founder events, such as observed 

here for Alfacs Bay. 

Although a few genets dominated the studied meadows, 

the majority of the genets, in both sites, appeared only 

once. This high frequency of young genets suggests that 

both meadows are characterized by successful sexual 

reproduction and low probability of young genets to grow 

to older/larger clones. However, genotypic richness (R), 

which provides an estimation of the balance of sexual and 

clonal reproduction over several generations, was only 

moderate due to the presence of a few dominant clones, 

although equivalent to what has been found for other 

clonal plants (Ellstrand & Roose 1987). Nevertheless our 

results clearly suggest that, for the analysed meadows, 

sexual reproduction is an important means of population 

recruitment for C. nodosa. It is important, however, to keep 

in mind that seagrass populations can show a wide range 

of genotypic richness levels, from monoclonal stands (e.g. 

Waycott et al. 1996; Reusch et al. 1999b; Alberto et al. 2001; 

Billingham et al. 2003) to highly diverse ones (e.g. Procaccini 

& Mazzella 1996; Reusch et al. 2000; Coyer et al. 2004; 

Arnaud-Haond et al. 2005). 

Considered together our findings of restricted dispersal, 

successful sexual recruitment, and skewed genet size 

distribution suggest that sexual recruitment in C. nodosa is 

more important at the local meadow scale than on an intermeadow 

scale. This type of life history trait has been referred 

to as repeated seedling recruitment (RSR in Eriksson 1993) 

and has been reported for other plant species (Auge & 

Brandl 1997; Suzuki et al. 1999; Stehlik & Holderegger 2000; 

Auge et al. 2001; Shimizu et al. 2002; Ziegenhagen et al. 

2003). Seeds lacking specialized dispersal traits are expected 

to exhibit a greater competitive ability than seeds with 

higher dispersal capacity (Eriksson 1997), as they may 

undergo selection resulting from intraspecific competition 

in a crowded environment. If the population shows some 

level of biparental inbreeding, the most homozygous 

seedlings might be affected by some level of inbreeding 

depression influencing the growth traits relevant for 

space competition in dense seagrass stands. Evidence for 

inbreeding depression affecting clonal growth has been 

reported for the seagrass Zostera marina (Hämmerli & 

Reusch 2003a). Such processes may partly explain the 

observed heterozygosity excess in both meadows, which 

persisted even when the clonal replicates were removed 

from the data set (Table 1). The initial seedling growth 

stage corresponds to the most important bottleneck for 

clonal patch development, characterized by high seedling 

mortality (80–90% annually, Duarte & Sand-Jensen 1990, 

1996) and where only half of the clone patches survive 

longer than 0.8 year (Vidondo et al. 1997). Even though we 

did not find any association between clone size and heterozygosity, 

our sampling did not cover the complete size 

spectra of C. nodosa clones. It is thus possible that significant 

relationships between individual heterozygosity and 

the capacity to initiate patch growth would be observed 

had we sampled seedlings in the initial growth stages. 

Such a selection hypothesis awaits support by further 

investigation, but it is interesting to note that we have not 

observed heterozygote excess in young seedlings (less than 

1 year old) from Cadiz Bay, analysed with the same microsatellite 

markers (Alberto et al. 2003). 

Clonal structure and subrange 

The spatial organization of C. nodosa clones found here 

(Fig. 2) reveals that clonal replicates tend to aggregate in 

space. Recently, Sintes et al. (2004) used a clonal growth 

model to follow the colonization of space by a single 

developing C. nodosa clone. These authors showed that 

the growing network is characterized by a guerrilla-type 

growth in the initial colonization phase, but later on, and 

merely due to simple clonal growth rules (rhizome elongation, 

rhizome branching rate, branching angle and spacer length 

between consecutive shoots), the clone becomes compacted 

and circular (4–5 years of age) and changes to a growth 

model typical of compact structures. The aggregation of 

clonal replicates found for the older/larger clones in this 

study validates the predictions of the Sintes et al. (2004) 

model for natural meadows composed of several growing 

clones, although most genets found here are still small 

clones in the early growth phases. This highly skewed 

clone size distribution indicates again high mortality at the 

early growth phases, a type of structure often found for 

clonal plants (Chung & Epperson 1999, 2000; Herben et al. 

2002). At the periphery of larger clones there were smaller, 

apparently fragmented, groups of additional clonal 

replicates, perhaps produced by the decay of the rhizome 

connections. It is likely that through this process older 

compacted clones give rise to smaller clonally integrated 

units, independent of the older/larger original clone. 

Alternatively the apparent fragmentation of large clones 

could simply be due to clone intermingling, as in this study 

the spatial scale is not totally suitable to disentangle genetic 

diversity at scales smaller than 2 m. 

We have here implemented an original method based on 

spatial autocorrelation to study the effects of clonality on 

the SGS pattern. This approach was based on pairwise kinship 

coefficients estimated for the data set containing all 

sampled individuals, analysed in two different ways; considering 

all possible pairs and only the among-genet pairs. 

We show that the point where the two resulting correlograms 

merge (Fig. 4) is equivalent to a probability of clonal 

identity (probability of sampling the same clone at a certain 

spatial distance) close to zero, termed the clonal subrange. 

Such characterization of the clonal subrange can also be 

used to select a minimum distance between adjacent 


52


samples when the objective is to maximize the genotypic 

richness in a given sample or to estimate parameters that 

require removal of clonal replicates. For the meadows analysed, 

the probability of sampling the same clone declined 

to zero at 30 m in Cadiz whereas in Alfacs it was still 2.5% 

at that distance. 

Indirect estimation of dispersal 

Neighbourhood size (Nb) estimates indicate the balance 

between drift and gene flow at a local scale (Fenster et al. 

2003). Smaller Nb estimates, around 20–24 in Cadiz, suggest 

that genetic drift and inbreeding play an important role, 

and that the homogenizing effects of gene flow should 

be low. In order to estimate the neighbourhood area we 

should have used the effective density (D) instead of the 

observed genotype density (Hardy & Vekemans 2002). 

However, the former is extremely difficult to derive for 

clonal plants, where the simple estimation of the number 

of individuals in a population is a challenge (distinction 

between genets and ramets). By using the observed genotype 

density to estimate patch size we are most likely 

underestimating the total number of genotypes (G) in the 

surface analysed, because G is a function of the sampling 

effort. On the other hand, and partially compensating for 

this, the real effective population size is expected to be 

smaller than G (Orive 1993), an effect which in this case is 

magnified by the unbalanced individual contribution to 

reproduction expected from the skewed distribution of 

clone sizes. 

We estimated the axial variance of the clone mates’ spatial 

distribution ( σ2 

veg 

) as an alternative way to quantify the 

contribution of clonal growth to gene dispersal. The model 

of Gliddon et al. (1987) and the Nb value estimated through 

the SGS analysis can be used to extract the sexual component, 

allowing estimation of the relative importance of 

sexual vs. vegetative dispersal. The results obtained suggest 

that, at least for Cadiz Bay, clonal spread might be 

an important gene dispersal vector, equivalent to sexual 

reproduction. However the comparison is based on 

assumptions made concerning the effective density and 

caution must be exercised in its interpretation. Also Gliddon 

et al.’s formula does not assume overlapping generations 

and the regression slope gives a mean estimate of 

dispersal over several generations, whereas the axial variance 

estimated from the width of genets gives the current 

vegetative dispersal. Finally, vegetative dispersal may be 

underestimated because of edge effects associated with the 

sampling scale. Ecological and genetic simulation studies 

such as the ones produced by Sintes et al. (2004) and 

Heuertz et al. (2003) should be employed together in order 

to validate the methods presented here and/or provide 

additional ways of describing the influence of clonal reproduction 

on SGS. 

Acknowledgements 

This research is a contribution of the EU-project M&MS (ref. 

EVK3-CT-2000-00044), and project PNAT/1999/BIA/15003/C of 

the Portuguese Science Foundation (FCT). We thank T. Simões, J.J. 

Vergara and F. Brun for help in field collections and L. Correia for 

technical help, C. Perrin and G. Pearson for a careful reading of 

this manuscript and anonymous reviewers for their suggestions. 

Fellowships to F. Alberto (PhD) and S. Arnaud-Haond (postdoctoral) 

were granted by FCT and the European Social Fund. 

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Filipe Alberto conducted this study as part of his PhD, and is 

interested in population genetics of marine organisms. Licínia 

Gouveia collaborated in this study as part of her undergraduate 

studies in marine biology. Sophie Arnaud-Haond is a post 

doctoral associate interested in marine evolution and population 

genetics, currently focusing on marine plants and clonal organisms 

in general. José L. Pérez-Llorens is interested in ecophysiology of 

marine macrophytes. Carlos M. Duarte leads a team studying 

marine biodiversity from the genetic, species and habitat level 

to global biogeochemical cycles. Ester A. Serrão leads a research 

group that is primarily interested in marine ecology, adaptation 

and population genetics. 


55



II.3 

Standardizing methods to describe population structure of clonal 

organisms. Molecular Ecology, Invited Review, 2007. 

Dans cet article, nous nous sommes attaché à souligner un certain nombre de 

problèmes récurrents en écologie moléculaire des organismes clonaux, sur la base 

d’une revue bibliographique. Il s’agissait de proposer un premier pas vers la 

standardisation des méthodes d’analyses, depuis la stratégie d’échantillonnage, le 

choix des outils moléculaires, l’identification des lignées clonales basée sur une 

combinaison d’approches moléculaires et statistiques, les indices descripteurs de la 

clonalité et la description de ses composantes spatiales. 

Nous avons perfectionné, par rapport à notre première publication en 2005, les 

méthodes de reconnaissance des lignées clonales. Il s’agissait d’une part de 

répondre aux questions 1/ ‘quand considère-t-on des génotypes identiques comme 

un indice d’identité clonale ?’ en améliorant la méthode proposée auparavant afin de 

prendre en compte les écarts à l’équilibre de Hardy-Weinberg car ils sont fréquents 

chez les organismes clonaux et 2/ ‘des génotypes différents impliquent-il des lignées 

clonales différentes ?’ en s’intéressant au problème des mutation somatiques et en 

introduisant la notion de Lignées Multi Locus (MLLs) plutôt que de Génotype Multi 

Locus (MLGs). 

Puis, nous avons fait la synthèse des approches proposées dans la littérature, 

notamment de celles empruntées aux estimateurs de richesse spécifique. Nous 

avons examiné les points forts et faibles de ces indices ou groupes d’indices, et 

proposé de descripteurs de la diversité, de l’architecture et de la croissance clonale, 

notamment des descripteurs permettant la comparaison des caractéristiques 

clonales et démographiques. 

Ces dernières considérations sur les composantes spatiales de la clonalité 

nous ont amené à une discussion sur l’importance de la stratégie d’échantillonnage 

et à proposer des stratégies ‘minimum’ afin de permettre une interprétation fiable des 

données obtenues et de rendre possible des comparaisons ultérieures avec d’autres 

études. 

56

Molecular Ecology (2007) 16, 5115–5139 

doi: 10.1111/j.1365-294X.2007.03535.x 

Blackwell Publishing Ltd 

INVITED REVIEW 

Standardizing methods to address clonality in population 

studies 

S. ARNAUD-HAOND,* C. M. DUARTE,† F. ALBERTO* and E. A. SERRÃO* 

*CCMAR — CIMAR Laboratório Associado, Univ. Algarve, Gambelas, 8005-139, Faro, Portugal, †IMEDEA, CSIC-Univ. 

Illes Balears, C/Miquel Marques 21, 07190 Esporles, Mallorca, Spain 

Abstract 

Although clonal species are dominant in many habitats, from unicellular organisms to 

plants and animals, ecological and particularly evolutionary studies on clonal species have 

been strongly limited by the difficulty in assessing the number, size and longevity of 

genetic individuals within a population. The development of molecular markers has 

allowed progress in this area, and although allozymes remain of limited use due to their 

typically low level of polymorphism, more polymorphic markers have been discovered 

during the last decades, supplying powerful tools to overcome the problem of clonality 

assessment. However, population genetics studies on clonal organisms lack a standardized 

framework to assess clonality, and to adapt conventional data analyses to account for the 

potential bias due to the possible replication of the same individuals in the sampling. 

Moreover, existing studies used a variety of indices to describe clonal diversity and structure 

such that comparison among studies is difficult at best. We emphasize the need for 

standardizing studies on clonal organisms, and particularly on clonal plants, in order to 

clarify the way clonality is taken into account in sampling designs and data analysis, and 

to allow further comparison of results reported in distinct studies. In order to provide a first 

step towards a standardized framework to address clonality in population studies, we 

review, on the basis of a thorough revision of the literature on population structure of clonal 

plants and of a complementary revision on other clonal organisms, the indices and statistics 

used so far to estimate genotypic or clonal diversity and to describe clonal structure in 

plants. We examine their advantages and weaknesses as well as various conceptual issues 

associated with statistical analyses of population genetics data on clonal organisms. We do 

so by testing them on results from simulations, as well as on two empirical data sets of 

microsatellites of the seagrasses Posidonia oceanica and Cymodocea nodosa. Finally, we 

also propose a selection of new indices and methods to estimate clonal diversity and 

describe clonal structure in a way that should facilitate comparison between future studies 

on clonal plants, most of which may be of interest for clonal organisms in general. 

Keywords: clonal diversity, clonal size, clonal subrange, clonality, methods, molecular markers, 

power law, sampling design, spatial autocorrelation, species richness 

Received 15 May 2007; revision 27 July 2007 

Introduction 

Clonality is a life-history strategy, particularly widespread 

in plants, allowing organisms to produce offspring without 

sexual reproduction, hence typically genetically identical 

Correspondence: S. Arnaud-Haond, E-mail: sarnaud@ifremer.fr; 

eserrao@ualg.pt 

Present address: Ifremer, Centre de Brest BP70, Department DEEP, 

29280 Plouzané, France 

— at the exception of possible somatic mutations — to 

themselves. Despite the large number of clonal species present 

across a wide variety of taxa and habitats, evolutionary 

theory and models are mostly based on singular genetic 

individuals. A specific consideration of clonality is largely 

lacking, probably because ecological and particularly 

evolutionary studies of clonal plants have long been deterred 

by the difficulty in discriminating between genetically 

distinct individuals and clonal replicates [i.e. to discriminate 

© 2007 The Authors 

Journal compilation © 2007 Blackwell Publishing Ltd 

57

5116 S. ARNAUD-HAOND ET AL. 

Fig. 1 (a) Time course of the number of studies on clonal plants using molecular markers per year, among the 247 published studies on 

clonal plants reviewed (bars), and the temporal evolution of the percentage of studies using allozymes (−), multibanding (RAPDs, AFLP 

and fingerprints; ···) and microsatellites (---). (b) The distribution of clonal diversity (R) estimated with Allozymes, Fingerprints (RAPDs, 

AFLP), and Microsatellites markers over the 297 studies reviewed, presented as the average (± SE) for the studies using different marker 

types on the left panel, and as the cumulated frequency of increasing R-values on the right panel with lines pointing at the median values 

of R for different marker types. 

between distinct genets and distinct ramets; sensu Harper 

(1977)]. The advent and subsequent development of markers 

powerful enough to resolve genotypic identity has now 

bypassed that bottleneck, stimulating research efforts 

towards the examination of the genetic structure of clonal 

plant populations. This is indicated by the fact that 83% of 

the articles on clonal plants published in that area over the 

past three decades, as revealed by a literature search on 

the ISI Web of Knowledge, were produced after 1995 

(Fig. 1a). The bulk of these articles characterized the 

genetic structure of clonal populations through the computation 

of general indices of genetic structure, such as 

heterozygosity, F estimators or spatial autocorrelation 

analysis, all methods developed for nonclonal organisms 

and therefore not explicitly addressing the issue of clonality. 

Yet the clonal nature of the populations poses specific 

challenges that impinge on their genetic structure, and this 

introduces some uncertainties in the interpretation of 

results derived in the past. Moreover, the implications of 

the clonal nature of the organisms studied are so pervasive 

that clonality affects the study of population genetics even 

at the sampling stage. This aspect has not been specifically 

addressed as yet, possibly leading to errors in the use and 

interpretation of the indices applied. 

58 


Journal compilation © 2007 Blackwell Publishing Ltd

POPULATION STRUCTURE AND CLONALITY 5117 

A substantial fraction of the research effort has attempted 

to characterize the extent of clonality in populations through 

the use of diversity indices, borrowed from the species’ 

diversity literature. These include the ratio of the number 

of genotypes (or clonal lineages) over the number of samples 

(Ellstrand & Roose 1987), the Shannon-Wiener index 

(Pielou 1966; Peet 1974), the complement of Simpson’s 

index (Gini 1912; Simpson 1949) and the corresponding 

evenness indices. However, the use of different indices 

across studies precludes an efficient and useful comparison 

of their results in terms of clonal diversity. In general, none 

of the available software for general population genetics 

analyses includes routines and options for clonal organisms, 

signalling a lack of sufficient awareness of the specificities 

of clonality and the need for a standardized set of indices 

and methods. Some specific software have been developed 

in the last few years, allowing the analysis of some clonal 

components at the intrapopulation levels (Stenberg et al. 

2003; Meirmans & Van Tienderen 2004; Peakall & Smouse 

2006; Arnaud-Haond & Belkhir 2007). Also, none of the 

calculations used so far specifically consider how different 

clones are distributed in space, which is a fundamental 

trait of the genetic structure of clonal populations (van 

Groenendael & de Kroon 1990; Reusch 2001), and it was 

only very recently that a software was released allowing 

those features to be specifically analysed for clonal organisms 

(Arnaud-Haond & Belkhir 2007). Hence, there is a 

need to standardize the methods used to characterize the 

genetic structure of clonal organisms both in order to facilitate 

the gathering and integration of future data and their 

comparison among studies. 

Here we provide an overview, on the basis of a review of 

the published literature, of current methods to assess the 

genetic structure of clonal plant populations and formulate 

new methods where appropriate. We specifically focus 

on indices and statistics to (i) relate genotypic and clonal 

identity, (ii) describe clonal diversity, and (iii) describe the 

spatial pattern of clonal distribution. We examine the properties 

of the statistics most commonly encountered in the 

literature, on the basis of simulated and empirical microsatellite 

data sets of populations of the clonal seagrasses 

Posidonia oceanica and Cymodocea nodosa (Alberto et al. 2003a, b, 

2005) used as test cases. These simulated and empirical data 

sets are also used to examine and discuss the implications 

of clonality for sampling design. 

Literature survey 

We searched the published literature for studies using 

molecular markers to assess population genetic structure 

of clonal plants published between 1973 and 2003. We 

did so by searching the ISI Web of Knowledge for entries 

of published studies including the terms ‘plants’ and 

(‘clonality’ or ‘clonal’ or ‘clone’ or ‘asexual’) and a variety 

of molecular markers [e.g. allozymes, microsatellites, 

random amplified polymorphic DNA (RAPD), amplified 

fragment length polymorphism (AFLP), simple sequence 

repeats), and screening the references obtained for molecular 

analysis of clonal plants. A first screening of the 

literature delivered about 450 studies, of which further 

scrutiny revealed only about 280 to be relevant, 246 of 

which could be retrieved and analysed. Additionally, searches 

on genetic structure of nonplant clonal organisms were 

also conducted, for articles published between 2000 and 

2005, of which 51 were analysed. The list of those references 

can be found in Table S1, Supplementary material, summarizing 

the information extracted from each article. For 

each article, the methods used to estimate and describe clonal 

diversity and spatial clonal distribution, as well as the spatial 

design of the sampling were extracted (Table S1, Table 1). 

The examination of the publication trends shows a major 

growth in the number of published studies on population 

structure of clonal plants using molecular markers (Fig. 1a), 

as well as a shift in the relative use of different markers. The 

publication effort on population structure of clonal plants 

increased abruptly in 1998 coinciding with the advent of 

the use of microsatellite markers (Fig. 1a). All published 

studies used allozymes until the early 1980s, when the 

introduction of fingerprinting approaches in the literature 

led to a shift in methods followed by an uprise in the use 

of microsatellites as the most powerful markers to assess 

clonal membership yet available (Fig. 1a). 

Genotypic vs. clonal membership, estimating 

sexual input 

The genotyping of sampling units, or ramets, with multiple 

independent markers will allow their assignment to several 

groups of multilocus genotypes (MLGs). Two additional 

steps are necessary before being able to reasonably assume 

that (i) all replicates of the same MLG are part of the same 

clone, or genet; and (ii) each distinct MLG belongs to a 

distinct clone, or genet (Halkett et al. 2005b). The first part 

requires estimating the probability of finding identical MLGs 

resulting from distinct zygotes, and the second requires a 

careful analysis of the pairwise differences among MLGs in 

order to detect possible somatic mutations or scoring errors 

that may result in distinct MLGs characterizing sampling 

units actually belonging to the same clone. Procedures to 

accomplish both these steps are detailed below and 

illustrated in Box 1. 

The analysis of clonal populations requires the capacity 

to assess the likelihood that two individuals with the same 

multilocus genotype, within the power of the markers used, 

are indeed part of the same clone and therefore unlikely to 

be derived from distinct sexual reproductive events. These 

tests have been used in about 30% of the reviewed studies. 

For the calculation of this probability, the population allelic 



59


Box 1 Genotypic vs. clonal membership 

a) Assessing whether all replicates of the same MLG 

are part of the same clone 

The probability of a given genotype i under the 

assumption of Hardy–Weinberg equilibrium can be 

estimated as: 

l 

p f 

h 

∑ ( i 

)2 

gen = i= 

1 

(eqn 1) 

where l is the number of loci, f i 

the frequency of each 

allele at the i th locus (estimated using the round-robin 

method, see text), and h the number of heterozygous 

loci in the sample. 

When taking into account departures from Hardy– 

Weinberg equilibrium (using F IS 

), this equation 

becomes: 

l 

∏ 

p ( F ) = [( f g ) × ( 1+ ( z × ( F )))] 2 

gen IS i i i IS() 

i 

i= 

1 

(eqn 2) 

where l is the number of loci, h is the number of 

heterozygote loci, and f and g are the allelic frequencies 

of the alleles f and g at the i th locus (with f and g identical 

for homozygotes), F IS(i) 

is the F IS 

estimated for the 

i th locus (using allelic frequencies estimated with 

the round-robin method), and z i 

=1 if the i th locus is 

homozygous (for f i 

= g i 

) and z i 

=–1 if the i th locus is 

heterozygous. 

When the same genotype is detected n times in a 

sample of N sampling units, the probability that the 

repeated genotypes originate from distinct sexual 

reproductive events (i.e. from different zygotes, thus 

being different genets), derived from the binomial 

expression, is: 

h 

p 

sex 

= 

N 

∑ 

i= 

n 

N! 

p − p 

i N − i) [ gen 

!( ! 

][ 1 

gen 

] 

i N − i 

(eqn 3) 

In this calculation, the probability of the genotype 

p gen 

can be replaced by p gen 

(F IS 

) to consider possible 

departures to Hardy–Weinberg equilibrium, in order to 

obtain a more conservative estimate of p sex 

. 

A Monte Carlo procedure can be applied to ensure 

that the set of loci used provides enough power to discriminate 

all MLGs present in the sample: 

Fig. B1.1: Box plot describing the genotypic resolution 

of microsatellites in a data set of the seagrass Cymodocea 

nodosa containing 220 sampling units genotyped using 

nine microsatellites, analysed for of all possible combinations 

C 

K 

l 

of K loci (K = 1, ... , l; l is the number of loci 

available). the edges of the boxes show the minimum 

and maximum number of genotypes and the central 

line shows the average number of genoptypes identified 

in the sample using X microsatellites (Alberto et al. 

2005). The example illustrated here shows that a set of 

seven loci allows an accurate determination of the 

number of genotypes in the sample. 

b) Ascertaining that each distinct MLG belongs to a 

distinct clone, or genet (Halkett et al. 2005a); defining 

clonal lineages (MLL) 

This procedure can be used if the distribution of genetic 

distances among sampling units does not follow a strict 

unimodal distribution but shows high peaks toward 

low distances, susceptible to reveal the existence of somatic 

mutations or scoring errors in the data set resulting in low 

distances among slightly distinct MLG actually deriving 

from a single reproductive event. The use of the frequency 

distribution of distances to detect such events 

Fig. B1.1 

60 




Box 1 Continued 

Fig. B1.2 

has been proposed four times so far, to our knowledge 

(Douhovnikoff & Dodd 2003; Meirmans & Van Tienderen 

2004; Arnaud-Haond et al. 2005; Rozenfeld et al. 2007). 

In a recent work on Posidonia (Arnaud-Haond et al. 2007) 

we introduced the concept of MLL to design genets 

represented by slightly distinct MLG, due to mutation or 

scoring errors. We propose a two step approach, consisting 

in (i) screening each MLG pair presenting extremely 

low distance, and originating a primary small peak in 

the frequency distribution of distances, making it bimodal 

rather than unimodal (see the dashed line in Fig. B1.2). 

Then we propose (ii) using p sex 

on the set of identical 

loci in order to estimate the likelihood that those 

slightly distinct MLG would actually be derived from 

distinct reproductive events. When such likelihood 

was lower than a chosen threshold (in that case 0.01), 

then the slightly distinct MLG may be considered as 

being derived from the same genet and being slightly 

distinct representatives of the same MLL. Numerous 

distance metrics can be chosen, such as the number 

of distinct alleles, Jaccard similarity in particular for 

multibanding patterns (Douhovnikoff & Dodd 2003) 

or the number of microsatellite motifs (Arnaud-Haond 

et al. 2007) under the hypothesis of a stepwise mutation 

model for somatic mutations. 

Fig. B1.2: (A) Frequency distribution of the pairwise 

number of alleles differences between MLGs for the 

same sample of C. nodosa (Alberto et al. 2005), compared 

with (B) the frequency distribution of the pairwise distances 

in a set of seeds from the same location (Cadiz, 

Spain) in which neither identical MLG nor somatic 

mutation are expected. The x-axis represents the number 

of allele differences and the y-axis is the frequency 

distribution for each x rank. The dashed line in the 

adult distribution represents the threshold below which 

identical MLG have a p sex 

, estimated after excluding the 

slightly different loci, that supports the slightly distinct 

MLG as having originated from the same MLL (i.e. from 

the same zygote). 



61


frequencies can be estimated using a ‘round-robin’ method 

(Parks & Werth 1993; Arnaud-Haond et al. 2005). This subsampling 

approach avoids the overestimation of the rare 

allele frequencies, by estimating the allelic frequencies for 

each locus on the basis of a sample pool composed of all the 

MLGs distinguished on the basis of all the loci, except that 

for which allelic frequencies are estimated. This procedure 

is repeated for all loci, and the unique genotype probability 

(p gen 

) is then estimated under the assumption of Hardy– 

Weinberg equilibrium (Box 1, equation 1). 

A constraint on this procedure is the possible occurrence 

of departures from panmixia in the population studied, as 

may occur due to selfing and biparental inbreeding, or 

high linkage disequilibrium. In these cases, the estimated 

probability p gen 

may be significantly lower than the real 

probability of occurrence of a given repeated MLG originated 

from different zygotes. The corresponding p sex 

may 

in those cases represent an underestimation of the likelihood 

of encountering this particular MLG twice or more. It 

has been proposed that the genetic composition of the population 

could be taken into account to improve the estimate 

of p gen 

, by using samples collected at the zygote stages (for 

example seeds) in order to assess the level of linkage disequilibrium 

and departure from Hardy–Weinberg in the 

population of sexual individuals (Gregorius 2005). Yet, for 

those species, numerous among clonal plants, that experience 

large variance in reproductive success or variable 

selection regimes in space and time, this approach may not 

be realistic, or may even lead to more biased results than 

the classical estimates of p gen 

. We therefore recommend 

the use of F IS 

values obtained using allelic frequencies 

estimated with of the round-robin method, to improve 

estimates of p gen 

by taking into account departures from 

Hardy–Weinberg equilibrium, as first suggested by Young 

et al. (2002: Box 1, equation 2). 

These estimates of p gen 

, or of the upper bound of its confidence 

interval, are often (about 13% of studies) used to 

ascertain whether replicated MLGs result from clonal 

reproduction. This is not appropriate, as the p gen 

is the 

probability of finding a given MLG i 

when analysing only 

one sampling unit, not the probability of finding that MLG i 

in the N sampling units collected and analysed. A similar 

problem occurs with other methods, used in 5% of the articles 

reviewed, estimating the probability for a given MLG i 

to occur n times due to sexual reproduction as pgen 

n . This 

calculation actually delivers the probability of finding n 

times the MLG i 

when analysing exactly n sampling units, 

instead of the probability of MLG i 

occurring n times in a 

sample of N sampling units. Therefore, these calculations 

do not address the question ‘are one or more of the n replicates 

of a given MLG i 

encountered in a sample of N sampling 

units likely to be issued from independent events of 

sexual reproduction?’. To address this question when the 

same genotype i is detected more than once (n) in a sample 

composed of N sampling units, the probability that the 

sampling units with the same genotype actually originate 

from distinct sexual reproductive events (i.e. from separate 

genets) is best derived from the binomial expression 

describing p sex 

(Tibayrenc et al. 1990: Box 1, equation 3, 

Parks & Werth 1993), which has only been used in 6% of the 

articles reviewed. 

In very particular cases of high clonal dominance and 

very low clonal diversity, a limitation exists to this method. 

First, it will not be known whether the estimates of allelic 

frequencies on the basis of very few sampled chromosome 

will accurately represent population allelic frequencies 

(if all existing genets have been included in the sample, as 

in a monoclonal population) or if there are many more genets 

in the population but which the sampling scheme was 

unable to detect. Second, and above all, the low statistical 

power in such a data set is likely to lead to nonsignificant 

probabilities p sex 

, thus not allowing exclusion of the possibility 

that the most common MLGs would have occurred 

independently several times in the studied population as a 

result of distinct events of sexual reproduction. Such situation 

is paradoxical as this implies that in the cases where 

the dominance of clonality would be more obvious, it may 

not be possible to demonstrate its occurrence statistically. 

One recommendation in such cases may be the increase in 

sample size, or the extension of the sampling area, to attempt 

collecting more distinct and rare MLG, if they exist in the 

population . The increase in the number of distinct MLGs 

sampled would indeed increase the reliability of allelic 

frequency estimates and the statistical power to ascertain 

the clonal identity of the numerous identical MLGs. If 

however, a population contains only one or only a few genotypes, 

even with very high sampling effort no further 

MLGs are detected, and although the allelic frequencies of 

the population are exhaustively sampled, statistical power 

associated with p sex 

may be low. The recommendation in 

those cases is to increase the number of variable loci in the 

analysis, towards levels at which the probability of finding 

the exact same MLG but originated from distinct zygotes, 

would be very low. 

It may be wise to proceed with these tests of clonal 

identity for identical multilocus genotypes before engaging 

in analyses that assume these to derive indeed from 

the same clone. A further test for the likelihood of clonal 

identity between two samples with the same multilocus 

genotype may be to sample, using a Monte Carlo procedure, 

subsets of loci and examine the robustness of the 

inferred clonal membership to changes in the power of the 

analysis. Indeed, this procedure allows testing whether 

or not the power to discriminate the maximum number of 

distinct genotypes is satisfactorily reached with the number 

of markers used, thereby allowing the accurate estimation 

of the clonal diversity (Arnaud-Haond et al. 2005, see Box 1, 

Fig. B1.1). 

62 




Once the set of loci has been assessed to be powerful 

enough to resolve all distinct clones in a set of samples (i.e. 

each MLG corresponds to a single clone), the second step 

is to ascertain the clonal membership of each MLG (i.e. each 

clone corresponds to a single MLG). Indeed, the assignment 

of genetic identity of clones has recently been questioned 

(Klekowski 2003). Multiple MLGs belonging to the same 

clone may be found either due to the existence of somatic 

mutation or scoring errors (Douhovnikoff & Dodd 2003), 

which would lead to the overestimation of the number of clones 

in the sample analysed. This potential bias can be tested for 

by inspecting the frequency distribution of genetic distances 

among pairs of MLGs (Douhovnikoff & Dodd 2003; 

Meirmans & Van Tienderen 2004). The occurrence of somatic 

mutation or scoring errors at a significant rate is expected 

to be reflected in the existence of a peak in the frequency 

distribution of genetic distances at very low, non-null, genetic 

distances (Douhovnikoff & Dodd 2003; Van der Hulst et al. 

2003: see Box 1, Fig. B1.2A and B). A threshold of genetic distance 

can in those cases be defined, below which the hypothesis 

that distinct MLGs belong to the same clone cannot 

be rejected (Douhovnikoff & Dodd 2003; Meirmans & Van 

Tienderen 2004). These MLGs will then be assembled into 

groups of distinct ‘multilocus lineages’ (MLLs) corresponding 

to the best possible identification of distinct clonal lineages 

(Arnaud-Haond et al. 2007; Diaz-Almela et al. in press). 

The tendency for studies to use a growing number of 

increasingly polymorphic markers will likely lead to an 

increase in the number of apparent MLGs relative to the 

number of MLLs in the sample, as more somatic mutations 

and scoring errors are expected as marker number and 

resolution increase. This suggests that the procedure described 

above should be routinely used to avoid bias in clonal 

diversity estimates (Loxdale & Lushai 2003). Although the 

concept of clone was first introduced by the ancient Greeks 

to design entities issued from asexual reproduction, and 

did not necessarily imply exact genetic identity (unlike the 

term genet, defined much later by Harper in 1977), it has 

been traditionally used in biology to refer both to biological 

units derived from asexual reproduction and those sharing 

genetic identity. Indeed, the capacity to ascertain genetic 

identity is a recent achievement, and the consequences of 

the ambiguity of the traditional use of the term ‘clone’ are 

only now becoming apparent (Tibayrenc & Ayala 2002). 

At this stage, the concept of ‘clonal lineages’, defined as 

‘the asexual descendants of a given genotype differing 

from the originator only via mutation and mitotic recombination’ 

(Anderson & Kohn 1995) may therefore be more 

precise and operative than that of the more ambiguous 

term ‘clones’. 

Only once these tests have been conducted that the indices 

described below may be considered indices of clonal, 

and not genotypic, diversity, which is a requirement to 

assess the spatial distribution of the clonal lineages. It is 

indeed important to recognize that the terms ‘clonal lineages’ 

(or MLLs) and ‘clonal’ do not necessarily correspond 

to ‘genotypes’ (or MLGs) and ‘genotypic’, respectively. 

This step is also required to obtain reliable estimates of 

the rate of clonal vs. sexual reproduction. The successful 

assessment of the level of ‘individual’ or ‘clonal lineage’ 

(arising from a single zygote) through these two steps is 

also particularly important to further apply classical 

population genetic analyses such as F IS 

or F ST 

, or autocorrelation 

analysis (see below) in order to extract information 

on inbreeding, heterozygote selective values, dispersal and 

migration rate via sexual propagules vs. clonal spread. 

One of the most common problems affecting the estimates 

reported in the literature is the lack of resolution due to the 

limited polymorphism of the markers used. This precludes 

the accurate discrimination of some distinct lineages that 

falsely appear identical, on the basis of the set of markers used, 

leading to the overestimation of clonal input (i.e. the underestimation 

of clonal diversity).The comparison of the average 

clonal diversity derived using five types of molecular markers 

across the studies reporting clonal richness suggest that 

microsatellites and RAPD are more efficient in distinguishing 

among clones on the basis of their multilocus genotypes 

than AFLP or allozymes are (Fig. 1b). Indeed studies with 

microsatellites or RAPD tend to report higher clonal diversity 

than studies using AFLP, with the mean clonal diversity 

across the studies reviewed here increasing from allozymes 

to fingerprints and to microsatellites (Fig. 1b). There has 

been a shift in the use of these markers, from a dominance 

of studies using allozymes to a rapid spread of the use of 

fingerprints and microsatellites (Fig. 1a). However, it is also 

important to note that whatever kind of marker can led to 

erroneous estimates if the polymorphism is insufficient, as 

was observed comparing two sets of distinct microsatellites 

revealing very contrasting results for the seagrass Posidonia 

oceanica (Alberto et al. 2003a, Arnaud-Haond et al. 2005). 

Description of the components of clonal diversity 

As in studies addressing species biodiversity (e.g. Peet 1974), 

several components can be used to estimate clonal diversity 

in a particular population: clonal richness, representing 

either the absolute number or the proportion of distinct 

entities (clonal lineages or genets) present in the sample 

relative to the number of sampling units; clonal heterogeneity, 

which is influenced both by the richness and the relative 

abundance of the entities in the sample; and clonal evenness, 

describing the equitability of the distribution of the sampling 

units (or ramets) among these entities. 

Clonal richness 

The simplest and most widely used (about 72% of the studies) 

index of clonal richness is the number of genotypes of 



63


Box 2 Clonal richness estimates 

The index of clonal diversity proposed by Ellstrand 

& Roose (1987) for a sample of size N in which G 

genotypes are discriminated is estimated as: 

P 

d = 

G 

N 

(eqn 4) 

This modification was proposed by Dorken & Eckert 

(2001): 

G 

R = ( −1) 

( N −1) 

(eqn 5) 

such that the smallest possible value in a monoclonal 

stand is always 0, independently of sample size, 

and the maximum value is still 1, when all the 

different samples analysed correspond to distinct 

clonal lineages. 

These indices provide an estimate of the clonal (vs. 

sexual) input, once the set of loci allowed assessing the 

clonal membership, as previously detailed. Else, this 

index may overestimate clonal input, as it will ignore 

the reproduction of the same multilocus genotype 

through sexual reproduction (Stoddart 1983; Uthike 

et al. 1998). To estimate the extent of this possible bias in 

estimating sexual input, one method was developed 

(Stoddart 1983; Stoddart & Taylor 1988) involving two 

of those components. The first is the estimate of genotypic 

diversity in the sample: 

G 

o 

= 

G 

∑ 

1 

i= 

1 

p 

2 

i 

(eqn 6) 

where p i 

is the observed frequency of the i th of G 

genotypes, as described in Stoddart (1983). This first 

component happens to be also the inverse of the 

Simpson index of genotypic heterogeneity commonly 

used to describe clonal diversity (equation 20). It is used 

in a ratio with the second component, the expected 

genotypic diversity under Hardy–Weinberg and random 

assortment between all pairs of loci: 

Ge * = 1 

⎛ 

(eqn 7) 

D 

P ⎞ 

⎜ + 

N 

⎟ 

⎝ ⎠ 

where D is the sum of all p2 

i 

for all p i 

where (p i 

× N) > 

1 , and P the sum of p i 

for all (p i 

× N) < 1. The clonal input 

is then estimated as: 

G 

G 

o 

e * 

(eqn 8) 

When the data set used is made of markers exhibiting 

high polymorphism and allowing an optimal discriminating 

power, a very high number of genotypes may 

be expected and P will be negligible. The estimator 

(equation 19) will approximate estimator (15) as the 

number of multilocus lineages is more accurately estimated, 

and when reaching full resolution of MLLs P d 

(or R) 

provides then a reliable estimate of the clonal input. 

the population estimated by G, the number of multilocus 

genotypes or lineages detected in a sample. This index is 

obviously dependent on the sample size. As proposed for 

species richness S, the rarefaction method used to compare 

allelic richness estimates (Petit et al. 1998) or a permutation 

approach should be used (Leberg 2002) to compare two 

samples differing in sample size, n and N > n. These 

methods allow the estimation of expected G in the second 

population if only n units would have been sampled. A 

bootstrap approach can be used to subsample n individuals 

from the total sample universe available (N), and reiterate 

this process to estimate the average G, along with confidence 

intervals (Arnaud-Haond & Belkhir 2007). 

After G, the most commonly (about 38% of the studies) 

used index of clonal richness is the ‘clonal diversity’ index 

P d 

as proposed by Ellstrand & Roose (1987), the fraction 

of distinct clonal lineages in the population relative to 

the number of sampling units (Box 2, equation 15). The 

expected confidence limits of P d 

can be derived from tables 

of confidence limits of percentages depending on sample 

size (Sokal & Rohlf 1995, Table P). Examination of these 

tables reveals that P d 

estimates are very sensitive to sample 

size for low percentage values (i.e. strongly clonal populations). 

Indeed, this estimator can be seriously biased when 

analysing data from population with an extreme composition, 

such as monoclonal stands (richness will be overestimated), 

particularly when sample sizes are small. As an 

example, the finding of a single MLG among 20 individuals 

(i.e. a monoclonal set) would still lead to an estimated P d 

of 

0.05, the same as encountering five distinct clonal lineages 

among 100 sampling units. To attenuate this flaw for the 

extreme cases of monoclonal or low richness stands with 

small sample size, a slight modification has been proposed 

by Dorken & Eckert (2001) as R (Box 2, equation 16). Clonal 

diversity ranges across all possible values (from monoclonal 

R = 0 to absence of clonality R or P d 

= 1) across studies 

(Fig. 1b, Table 2), reflecting the variable extent of clonality 

of populations. Moreover, studies including comparative 

analyses of R or P d 

across populations typically display 

broad differences among populations of individual species 

(Table S1). Numerous examples can be observed in 

all kinds of organisms, where the same species can occur 

64 




Table 1 Sampling geometries, strategies, and statistics used for clonal plants in 246 reviewed articles. The symbols are linking this information 

to the text and to the raw data available in Table S1, Supplementary material, detailing the findings of the literature review. The percentage 

of studies using various sampling geometries (shape of the area sampled) and sampling strategies (choice of sampling units) is 

detailed; the frequency of the statistics used to describe clonal richness and diversity, as well as to ascertain clonal identity of the replicates 

of the same MLG are also detailed. Finally, recommendations are suggested as to the use of sampling geometries, strategies and the choice 

of statistics (labelled * and ** corresponds to recommended and highly recommended methods, respectively) 

Description 

Symbols 

Percentage 

of studies 

Recommendation 

(if any) 

Sampling 

Sampling geometry 

Undefined G u 

46.7 Avoid 

Linear L 46.7 Avoid 

Rectangles Q 28.9 * , † 

Square S 10.7 ** 

Circle C 1.5 ** 

Patches p 2.5 * 

Sampling strategy 

Undefined nd 25.9 Avoid 

Haphazard h 26.4 Avoid 

Regular re 21.8 * 

Random coordinates ra 3.0 ** 

Minimum spacing min 18.8 * , ‡ 

Exhaustive exh 6.6 * , § 

Coordinates coord. 33.7 

Statistics 

Richness 

No estimates — 18.8 Avoid 

Number of genotypes G 68.0 * 

Ratio (G/N) P d 

(or IC = 1 – P d 

) 37.1 * 

Ratio (G − 1)/(N − 1) R 1.0 ** , 

Resampling to standardize richness estimates 

sub-sampling 0.7 ** , †† 

to the minimum sample size 

Heterogeneity and evenness 

Simpson complement D* 31.0 ** , ‡‡ 

Simpson (or Fager) evenness V 15.2 * 

Shannon-Wiener H' 5.0 * 

Shannon-Wiener evenness V'H' 1.0 * 

P (getting the most common MLG by chance) PG 1.5 * 

Ascertain clonal identity (Studies not considering by default identical MLG=identical clones) 

25.8 

Probability of a given MLG p gen 

15.2 Avoid, §§ 

p(getting a given MLG n times by chance) p n gen 

6.3 Avoid, 

p(identical MLG to derive from distinct reproductive events) p sex 

4.6 ** 

1/G max simulated 

or (1 − p identity 

) (with the set of loci used) 1−p identity 

2.0 Avoid, ††† 

†If low perimeter/area ratio, note that squares and circles are inducing less edge effect. 

‡If based on pilot studies or prior knowledge of average clonal size. 

§If not detrimental to the population. 

Minimize the bias when N is low (lower than 20). 

††If necessary for comparison purposes. 

‡‡The less redundant with classical richness estimates. 

§§Is the probability of getting a given MLGi when analyzing only one sampling unit, without taking into account the number of sampling 

units, N, collected and analyzed. 

Delivers the probability of getting n times a given MLG when analyzing exactly n and not N (sample size) sampling units. 

†††An average value is not reliable as the probability may be extremely distinct among genotypes, besides, this method does not take into 

account the number of sampling units analyzed. 



65


Table 2 Range of values reported for the main indices of clonal diversity and clonal size (linear) or surface area encompassed in different 

categories of clonal organisms (values for each study are detailed in Table S1) 

Organisms R or P d 

diversity 

Simpson 

Simpson 

evenness Clonal size (m) Clonal area (m) 

Terrestrial plants [0.00, 1.00] [0.00, 1.00] [0.00, 1.00] [0.25, 1000.00] [1.00, 7000.00] 

Aquatic plants [0.00, 1.00] [0.00, 0.99] [0.00, 0.99] 30.00 — 

Marine plants [0.00, 1.00] [0.00, 1.00] [0.00, 1.00] [8.00, 80.00] [31.00, 6400.00] 

Marine invertebrates [0.03, 1.00] — — — — 

both in monoclonal stands and in stands where the clonal 

diversity reaches, or almost, its maximum (Piquot et al. 

1996; Ayre & Hughes 2000; Freeland et al. 2000; Kapralov 

2004; Olsen et al. 2004; Halkett et al. 2005a) These observations 

show that the extent of clonality is highly flexible not 

just among but also within clonal species, suggesting 

considerable plasticity in the apportioning of reproductive 

effort between clonality and sexual reproduction. 

Finally, several methods have been developed and mostly 

used for clonal invertebrates (Stoddart 1983; Stoddart & 

Taylor 1988; Uthike et al. 1998), to estimate the sexual vs. 

clonal input with a limited set of markers (see Box 2, 

equations 17–19). 

Clonal heterogeneity 

Clonal richness indices only describe the proportion of the 

sample that is variable and do not describe the distribution 

of the sampling units among MLLs (i.e. evenness). Indeed 

for the same amount of clonal richness, the sample could 

comprise either very few highly represented clonal lineages 

with several rare ones, or evenly distributed ones. Discriminating 

between these contrasting clonal compositions is 

essential, since clonal heterogeneity is a fundamental feature 

determining the ecology and evolution of the populations. 

This issue parallels the old debate in ecology, when the 

need to combine richness with evenness was proposed to 

describe species heterogeneity in communities (Simpson 

1949; Peet 1974). Indeed species heterogeneity indices have 

been borrowed to describe clonal diversity (Parker 1979; 

Ellstrand & Roose 1987). 

The most widely used index of clonal heterogeneity (28% 

of the studies reviewed) is the Simpson index (Simpson 1949), 

which was developed originally to calculate the probability 

that two individuals selected at random from the sample 

will belong to the same species. When applied to clonal 

diversity, this can be interpreted as estimating the probability 

that two sample units chosen at random from the sample 

universe would belong to the same clonal lineage (Box 3, 

equations 20–22). The reciprocal index (Hurlbert 1971; Hill 

1973), reflects the ‘apparent number of clonal lineages in the 

sample’ (Box 3, equation 23). 

The Shannon-Wiener’s index is the best known and most 

used diversity index in ecology, although it has only been 

used in about 6% of the articles on clonal diversity. It was 

derived independently by Shannon and Wiener (Wiener 

1948, Shannon & Weaver 1949 both in Washington 1984; 

see also Washington 1984 for clarification on the incorrect 

use of the designation Shannon-Weaver). It should be noted 

that this last index is prone to a large sampling variance 

(Pielou 1966). For a given clonal richness, the Shannon-Wiener 

index is not expected to be very sensitive to the variation in 

the dominance of a particular MLL, whereas for a constant 

dominance it is more sensitive than the Simpson’s index to 

the increase in the number of rare MLLs (Peet 1974). 

The choice of index depends on the question posed. 

If the goal is the estimation of genotypic diversity or the 

amount of sexual vs. asexual reproduction in different 

populations, then the Shannon-Wiener’s estimators may be 

most adequate. On the other hand, if the study addresses 

historical processes, such as the way colonization occurred 

in different populations, or ecological processes such as 

intraspecific competition under different environmental 

conditions, the Simpson’s index may be more informative. 

However, the interpretation of spatial or temporal variability 

with either of these indices is often difficult given 

that they vary with both clonal richness and evenness, 

making it often necessary to assess these two components 

independently of each other. In all of the distinct types of 

organisms studied, widely diverse Simpson clonal heterogeneity 

values were reported ranging between 0 and 1 

(Table 2), consistent with the similarly broad ranges of R. 

Clonal evenness 

As the indices of heterogeneity do not reflect equitability, 

the indices of evenness used in ecology have also been 

adapted to estimate the equitability in the distribution of 

clonal membership among samples. The indices of heterogeneity 

of Simpson and Shannon both have a corresponding 

index of evenness (Box 3, equations 26 and 27). Both of these 

most commonly used evenness indices (respectively in 12% 

and 1% studies) vary from 0 to 1 when all MLLs have equal 

abundance. The performance of equitability indices is 

66 




Box 3 Clonal heterogeneity and evenness 

estimates 

Clonal heterogeneity 

Gpop 

Simpson index: λ= 

(eqn 9) 

∑ p2 

i 

i= 

1 

where p i 

is the frequency of the MLLi in the population, 

and G pop 

the number of distinct MLLs in the population. 

An unbiased estimator of λ for a sample of size N is: 

G 

⎡ ni 

ni 

− ⎤ 

L = ∑ ⎢ 

( 1 ) 

⎥ 

i= 

1 ⎣ NN ( − 1) 

⎦ 

(eqn 10) 

where G is the number of MLLs detected in the 

sample, and n i 

is the number of sampled units with 

the MLLi. 

The Simpson index can be modified to vary positively 

with heterogeneity (Pielou 1969), as an index first proposed 

in economical sciences (Gini 1912; Peet 1974), and 

the resulting complement of Simpson index then describes 

the probability of encountering distinct MLLs when 

randomly taking two units in the sample: 

Gpop 

∑ p i 

i= 

1 

Simpson’s complement: D = 1 − 

2 

(eqn 11) 

pop 

for which the unbiased estimator from a sample of size 

N is D* = 1 – L that ranges from 0 to almost 1 − (1/G). 

As proposed for species heterogeneity indices, the 

reciprocal of Simpson index is: 

Simpson’s reciprocal: 1 

(eqn 12) 

λ 

for which the unbiased estimator for a sample of size N 

is 1/L. 

Simpson’s reciprocal ranges from 1 to G, and it can 

be interpreted as the number of equally represented 

MLLs required to obtain the same heterogeneity as 

observed in the sample (Hurlbert 1971; Hill 1973), or 

as the ‘apparent number of clonal lineages in the 

sample’. 

The Shannon-Wiener’s index describes clonal diversity 

as: 

Gpop 

∑ 

H′ =− p logp 

i= 

1 

i 

i 

(eqn 13) 

using the estimator: 

H′′ =− 

(eqn 14) 

This index quantifies the level of uncertainty regarding 

the MLL of a sample unit taken at random (Pielou 

1966). This index of clonal diversity increases with the 

number of MLLs and the evenness in the assignment 

of individuals (ramets) to the MLLs, since this leads 

to a greater uncertainty in predicting the MLL of a 

randomly drawn sample unit. 

Clonal evenness 

A way of describing clonal equitability, which is 

independent of clonal richness but not explicitly 

described by any diversity index (see above), is to use 

an evenness index. So far the most widely used 

evenness index in clonal plant studies is the Simpson’s 

complement index (Hurlbert 1971; Fager 1972): 

(eqn 15) 

with D min 

and D max 

being the approximate minimum 

and maximum values of Simpson’s complement index 

given the sample size N and the sample clonal richness 

G, estimated as: 

This evenness formulation can also be used with the 

Shannon-Wiener index (e.g. Hurlbert 1971), or alternatively 

evenness can also be estimated as V′, the ratio of 

observed to maximal diversity (using either heterogeneity 

index). In this case, when using the Shannon-Wiener 

index, the corresponding evenness index, sometimes 

called Pielou’s evenness (J′, Pielou 1975) and hereafter 

referred to as such, can be estimated as: 

where 

G 

∑ 

i= 

1 

ni 

ni 

log 

N N 

( D− 

Dmin) 

V = 

( D − D ) 

D 

D 

min 

max 

max 

min 

⎡( 2N − G) × ( G−1) 

⎤ N 

= ⎢ 

⎣ N 

⎥ 

⎦ 

× and 

2 

( N − 1) 

= 

J′ = VH ′ ′′= 

( G − 1) 

G 

H′′ 

′′ 

H max 

N 

× 

( N − 1) 

H′ max 

= log G. 

(eqn 16) 



67


Box 4 Power law (Pareto) distribution of clonal 

membership 

The distribution of elements into size classes has been 

shown to follow a power law for a very broad diversity 

of systems and phenomena, all of which (from distributions 

in social sciences to astrophysics and the 

commonality of gene expression) conform to a particular 

probability density distribution referred to as the 

Pareto distribution (e.g. Pareto 1897 in Vidondo et al. 

1997; Ueda et al. 2004). A power law distribution applies 

to systems where the distribution of elements into 

classes is highly skewed, with much fewer large classes 

than small ones. The use of a power distribution allows 

the efficient and parsimonious description of the distribution 

of the studied elements into classes. We therefore 

propose here the use of the Pareto distribution as a continuous 

approximation to describe the discrete distribution 

of sample units, or ramets (elements) into groups of 

clonal sizes (classes), where clonal sizes are defined by 

the number of sampling units belonging to that clone 

(MLL). This relationship is described by the equation: 

N 

≥X 

= aX− 

β 

(eqn 17) 

where N ≥X 

is the number of sampled ramets belonging 

to lineages (MLLs) containing X, or more, ramets in 

the sample of the population studied, and the parameters 

a and β are fitted by regression analysis. In 

practice, the power slope (–β) is derived as the slope of 

the fitted log-log regression equation describing the rate 

of decline in the relative frequency of ramets that belong 

to MLLs of size equal to or larger than a given number 

of ramets X (when both are in log scale; Fig. B4.1). The 

parameter β (–slope) therefore indicates the scaling of 

the partitioning of the ramets among MLL size classes 

(Fig. B4.1). 

Fig. B4.1: (a) Distribution of replicates among MLLs 

in Cymodocea nodosa from Alfacs Bay (Alberto et al. 2005), 

showing the steep decline in number of MLLs with 

increasing clonal membership typical of power law 

distributions; (b) transformed into a log-log reverse 

cumulative distribution. 

Fig. B4.1 

dependent on that of the heterogeneity indices they are 

based upon: if based on the Shannon-Wiener index, they 

will give more weight to the rarer components (species 

or genotypes) than when based on the Simpson index. 

In addition, a review of these and other evenness indices 

(Smith & Wilson 1996) reports that V’H′′ (= J′, equation 27) 

remains sensitive to changes in richness (also shown here 

below) despite intended to be independent of richness. 

As for richness and diversity, Simpson evenness values 

encompass the maximum, or almost the maximum, range 

(Table 2). 

Clonal distribution 

In fact, the problem on hand amounts to the description of 

the distribution of elements (ramets) into classes (clonal 

lineages, or genets), so that the use of a density distribution 

may be more appropriate than the calculation of a compound 

index. An overview of the literature shows that the distribution 

of replicates among lineages, when detailed, is always 

left skewed (all of the 45 studies reporting this information) 

with an exponential decay (Table S1). Transformed in a reverse 

cumulative frequency distribution, this empirical distribution 

can be approximated by a power law distribution, appropriately 

described by the Pareto distribution (e.g. Pareto 1897 in 

Vidondo et al. 1997; Box 4). All of the distributions of clonal 

membership found in the literature review conformed to 

the Pareto. This distribution indeed applied to a range of 

clonal organisms encompassing herbaceous plants and 

trees (Parks & Werth 1993; Hangelbroek et al. 2002; Chung 

et al. 2004; Nagamitsu et al. 2004), corals (Bastidas et al. 2001; 

Le Goff-Vitry et al. 2004), bivalves (Taylor & Foighil 2000), 

and ostracods (Cywinska & Hebert 2002). The model was 

shown to appropriately fit all of the distributions, with all 

68 




Fig. 2 Pareto plots showing the distribution of clonal membership across a range of species of terrestrial plants (Chung et al. 2004; 

Nagamitsu et al. 2004) and marine invertebrates: corals (Bastidas et al. 2001; Le Goff-Vitry et al. 2004), clams (Taylor & Foighil 2000) and 

ostracods (Cywinska & Hebert 2002). Pareto plots represent the fraction of sampling units belonging to clones representing by ≥ X units as 

a function of X on a double logarithmic scale (Y = proportion of sampling units belonging to clonal lineages represented in the samples by 

X or more sampling units, and X = observed clonal sizes quantified as the numbers of sampling units found for every clonal lineage). This 

plot should display a straight line if the distribution of clonal membership conforms to a Pareto distribution, and the Pareto parameters can 

be estimated from the least squares regression line. The coefficient β, describing the Pareto distribution (–1 × regression slope), the 

correlation coefficient (r 2 ) and the significance of the regression (P value) are given for each panel. 

regressions showing high significance and high r 2 values 

spanning from 0.84 to 0.99 (Fig. 2). Hence, the Pareto model 

adequately describes the frequency distribution of clonal 

membership for populations of clonal organisms. 

Now, the next step would be to be able to interpret the 

Pareto distribution in terms of diversity and evenness. 

Simulations, described in detail in the Supplementary 

material, were performed to explore cases where evenness 

would vary when diversity would be fixed, and conversely, 

in order to relate the properties of diversity and evenness 

in the populations studied and the shape and parameters 

of the Pareto distribution. The results (Fig. 3) show that the 

slope of the Pareto distribution, β, increases exponentially 

with increasing evenness of the distribution of sampling 

units into MLLs (with r 2 ranging from 0.62 to 0.93 depending 

on the richness level). A high evenness with clonal lineages 

all having approximately comparable sizes, will therefore 

result in a steep slope (high β value), whereas the outcome 

of a skewed distribution with very few, large clonal lineages 

and many small ones will be a shallow slope (low β 



69


Fig. 3 The relationship between the parameters describing the Pareto distribution, and the richness and evenness level in the samples 

analysed. The data were obtained on the basis of simulations by distributing replicates (N = 50) among groups of genotypes (G=5, 15, 25, 

30, 45) across an increasing level of evenness (E = 1–5). The right panel illustrates the exponential increase of the Pareto parameter β with 

the level of evenness (r 2 between 0.60 and 0.93) for the five levels of richness explored. The left panel shows the exponential decrease in the 

size of the smallest size of genotype groups (in terms of number of replicates) with the increase in richness (r 2 between 0.91 and 0.99) for 

the five levels of evenness used. 

value). Also, the results of the simulation showed that 

the sizes of the smallest and highest MLLs classes decrease 

exponentially with increasing richness (with r 2 ranging, 

respectively, from 0.88 to 0.99 and from 0.89 to 0.99 depending 

on the evenness level), so that as the lowest and highest 

size classes tend to get larger, the richness is expected to 

decrease. The Pareto distribution is therefore influenced 

both by richness and evenness, and provides an intuitive, 

graphical depiction of the heterogeneity in the distribution 

of replicates among lineages, which appears to be of 

universal application to populations of clonal organisms. 

The representation of the Pareto distribution synthesizes 

the information in graphical form, rather than simply as a 

compound numerical estimate as the other indices reviewed 

here do, providing a clear depiction of the size distribution 

of clonal lineages in the population (Fig. 2). The β values 

obtained by compiling these data from the literature were 

spanning between 0.88, indicating a skewed distribution 

with dominance of some big clonal lineages and 2.96 indicating 

much higher evenness, although the minimum (i.e. 

most skewed) we observed, with β = 0.06, is a meadow of 

Posidonia oceanica dominated by a very big genet surrounded 

by several marginally represented MLG; Fig. 4). In the highest 

evenness scenario where all lineages bear the same number 

of replicates, estimation of the Pareto distribution parameters 

by regression is likely not to be possible as only one or two 

points would be available, but in this particular case, the 

interpretation of this finding as revealing extreme evenness 

is sufficient, provided enough lineages have been sampled. 

Furthermore, the maximum clonal size reached in terms 

of number of sampling units, and the frequency of those 

relatively dominant clonal lineages can also be observed on 

the graph (Fig. 2). An additional property, is that the application 

of the Pareto distribution allows calculation of the 

fractal dimension of the process under study, here the distribution 

of clonal size in the population, which equals 1 + 

β (Schroeder 1991), allowing, among other applications, 

the simulation of populations with a genetic structure 

similar to observed ones. Finally, the use of the Pareto distribution 

to describe the distribution of ramets into clonal 

lineages has the additional advantage that it is based on 

linear regression, providing estimates of uncertainty, therefore 

allowing statistical comparisons, which is not readily 

possible for other indices of clonal diversity. 

The use of the Pareto distribution to describe clonal 

diversity is exemplified here for the Mediterranean seagrass 

(P. oceanica) populations sampled. The fitted Pareto 

distributions yielded β values that ranged between 0.033 

± 0.015, for Sa Paret (Cabrera, Balearic islands; Fig. 4), 

a population which was dominated by a large clonal lineage 

that contained most of the shoots sampled (35 of 40 

shoots), and 1.48 ± 0.52, for the Acqua Azzurra (Sicily, Italy) 

three populations where almost all (33) genotypes were 

observed once, except two represented three and four times 

(data not shown). Figure 4 shows contrasting Pareto distributions 

illustrating clonal structure in four populations 

with highly contrasting richness (R spanning from 0.10 to 

0.77) and evenness (as estimated by Simpson evenness V 

ranging from 0.20 to 0.73). Both richness and evenness 

influence the shape of the Pareto distribution and its associated 

β value, as can be observed by comparing samples 

with similar R and distinct V (Campomanes and Playa 

Cavallets) or, conversely, with similar V and distinct R 

(Carboneras and Playa Cavallets), all pairwise comparisons 

revealing contrasting Pareto distribution and associated 

β values. Yet, consistent with the results of simulations 

(Fig. 2), increasing richness from Carboneras to Playa 

Cavallets (R increasing from 0.3 to 0.73) is reflected in 

decreasing maximum MLL size (from 15 to 5) that can be 

easily derived from the Pareto plot (provided comparable 

sample sizes, which is the case here). In the same way, the 

comparison of samples like Campomanes and Playa Cavallets 

70 




Fig. 4 Pareto plot of clonal membership distribution in four populations of the seagrass Posidonia oceanica (Es Castel, Porto Colom, 

Campomanes, Playa Cavallets). The coefficient β, describing the Pareto distribution, the correlation coefficient (r 2 ) and the significance of 

the regression (P value) are given for each panel. 

shows how, R being equal, a higher evenness (as measured 

by Simpson index of evenness V increasing from 0.47 

to 0.77) translate into a steeper Pareto slope (with the associated 

β parameter of Pareto increasing from 0.40 to 1.23). 

Relationship and possible redundancy between the 

different indices of clonal diversity 

The various indices of clonal diversity discussed above 

are not independent of each other, as they are based on 

the same basic information, but differ in the weight each 

assigns to the basic clonal richness and to the equitability of 

the distribution of replicates among clonal lineages. Hence, 

the application of all these indices may be redundant, and 

a small subset may suffice to capture the crucial information 

in terms of richness and equitability. 

The relationship between these different indices was 

assessed using correlation analysis on both empirical and 

simulated data sets. The empirical data set was obtained in 

the 34 populations of the seagrass P. oceanica used here as a 

test case. Monte Carlo simulations were also performed to 

explore the relationship between clonal diversity indices 

(richness, heterogeneity and evenness) and to test for the 

generality of the links between different indices of clonal 

diversity derived from the analysis of the P. oceanica data 

set. The details on the simulations conducted are provided 

in the Supplementary material. 

All correlation estimates were transformed as ‘1-Pearson 

r’ in order to perform a cluster analysis and draw a hierarchical 

tree using this index as an estimate of distance 

among indices. These analyses were performed using 

statistica 6 software (StatSoft 2001). 

The estimates of the indices derived from the simulated 

data set were indeed positively correlated to one another 

(Fig. 5). Qualitatively, the same correlation structure between 

indices was obtained on the basis of the P. oceanica data set, 

with r-values quantitatively similar to those obtained on 

the basis of simulations (data not shown). Examination 

of the correlation structure between the various indices 

showed that the genotypic richness R, the Simpson’s complement 

D, the Shannon-Wiener H′, and Pielou’s evenness 

V′H″ are very redundant (r=0.82–0.95), whereas the Pareto 

β was the least redundant, followed — as expected — by the 

Simpson evenness V. 

The redundancy between these indices is synthetically 

grasped upon examination of the cluster linking them 

(Fig. 5). A relationship with richness had been reported in 

the literature on species diversity for the Pielou evenness 



71


Fig. 5 Cluster analysis of indices describing clonal diversity 

obtained on the basis of simulated data, using ‘1-Pearson r’ as 

clustering distance: clonal richness R, Shannon-Wiener’s heterogeneity 

H′ and Pielou evenness V’H′, Simpson’s complement 

heterogeneity D and evenness VD, and Pareto’s β. The same 

correlation and cluster structure was obtained on the basis of the 

Posidonia oceanica data set, with r values quantitatively similar to 

those obtained on the basis of simulations. 

index, when a small number of species (< 25) is observed, 

and can therefore apply to a large range of studies on clonal 

organisms, where the sample size per locality hardly 

encompasses 30–50, and the number of genotypes will 

therefore seldom be sufficient to avoid this bias (Smith & 

Wilson 1996). The Simpson evenness V (Hurlbert 1971) is 

least redundant with R (Fig. 1b; cf. Peet 1974), and appears 

therefore to be the most suitable index to estimate evenness 

in a given sample. Finally, the use of the Pareto distribution 

delivers the least redundant index (β), and can be useful 

to depict heterogeneity. Hence, the three main types of 

information required to fully describe diversity: richness, 

evenness and heterogeneity are adequately grasped by 

the combined use of R, V and the complement of the slope 

of the Pareto distribution (β), respectively. We therefore 

recommend use of these three metrics to describe clonal 

diversity. 

Spatial analyses of clonal structure 

Sampling geometry and strategy 

In contrast with species consisting of unique genotypes, 

clonal populations have the capacity to spread and multiply 

common clonal lineages in space, so that inferences about 

the spatial genetic structure within clonal populations are 

unavoidably linked to the distribution of the clonal lineages 

in space. Sampling design choices can easily influence and 

bias estimators of clonal diversity. Definition of the sampling 

strategy must consider: (i) sample size, (ii) sampling area 

size, shape, and replication, (iii) sampling regime (random, 

haphazard, regular), and (iv) whether to impose any minimum 

spacing constraints in order to reduce clonal repetitions 

in the sample. Each of these choices critically affects the 

perceived genetic structure of the population and should 

be therefore adopted on the basis on an informed understanding 

of the consequences of alternative choices. 

The choice of sampling design depends on the objectives 

of the study. If the main objective is comparison with previous 

studies, the best choice may be to use the same 

sampling methods and scheme, though possibly fraught 

with other problems, in order to avoid confounding the 

comparison with effects of differential sampling. When 

the objective is to estimate clonal diversity in a population, 

the ideal sampling design would be a random sampling 

along the distributional area of the entire target population 

so that every possible sampling unit would have equal probability 

of being included in the sample. In cases of patchy 

distribution of individuals, random coordinates can be 

generated and adjusted to the nearest possible sampling 

unit once on the field. Only this scheme would minimize 

bias in the estimation of diversity indices (Pielou 1966), 

and deliver the best approximation of the real population 

values (but see the next section on sampling density). This 

ideal sampling regime is, however, often practically difficult, 

particularly in cases of patchy distribution of populations, 

and bias derived from deviations from randomness 

and an uneven distribution across the whole population 

area are often introduced. An appropriate alternative may 

be a hierarchical (multistage sampling at randomly selected 

clusters) or a stratified random sampling design, particularly 

appropriate for heterogeneous populations where 

conspicuous subunits exist that can be defined as sampling 

strata (e.g. areas of different population densities). Each of 

the sampling cluster or stratum should be several times 

larger than the expected clonal size, and each sampled in 

sufficient numbers to yield representative estimates of 

the proportion of distinct clonal lineages within sampled 

areas. The replication of these areas in different zones of the 

population reduces the potential bias due to larger-scale 

heterogeneity within the target population, and in fact allows 

testing for such heterogeneity, an important advantage 

over simple random sampling. 

Any sampling addressing the spatial structure of clonal 

lineages must be conducted along a two-dimensional area 

(i.e. avoiding linear transects, see below) recording the 

corresponding X–Y coordinates (absolute or relative). The 

location of the sampling units (i.e. the sampling coordinates) 

within this area can be selected randomly, haphazardly, 

or under a variety of regular schemes (e.g. simple lattice, 

hierarchical grid). Regular spacing of sampling coordinates 

should be based on information allowing best choice of 

relevant pore sizes (i.e. geographic distance between 

sampling units) in order to avoid bias. For instance, if 

72 




Table 3 Influence of the sampling area geometry on the estimation of the number of genotypes (G) and genotypic richness (R), on the 

importance of edge effect (E e 

) and on its significance tested with a 1000 random resampling procedure (number of p (observe >random) 


Box 5 Spatial components of clonality 

Edge effect 

In order to test whether for the sampling design used, 

apparent unique or rare MLLs are more distributed 

towards the edges of the sampling area, thereby 

inducing a possible overestimation of clonal diversity, 

the following index can be estimated: 

E 

e 

( Du 

− Da) = , 

Da 

A 

with D u 

the average geographic distance between 

unique MLLs and the centre of the sampling area, 

and D a 

the average geographic distance between all 

sampling units and the centre of the sampling area. 

The significance of such index is tested against the 

null hypothesis of random distribution of unique and 

multiply represented MLLs. In practice, the likelihood 

of the observed difference D u 

– D a 

being only due to 

chance and not to edge effect can be tested for by 

permuting x times the positions of the samples (i.e. 

randomly reassigning the sample unit to the sampling 

coordinates), and calculating the index for each permutation 

to obtain an empirical distribution of E e 

. 

If the observed E e 

value lies beyond the critical value 

(function of the chosen alpha) in the distribution of E e 

in the permuted data, then a significant edge effect is 

present that may cause indices of clonal diversity to 

overestimate the population diversity. 

Aggregation index 

In order to test for the existence of spatial aggregation 

of clonemates, or MLGs belonging to identical 

MLLs, the aggregation index A c 

can be estimated as 

follows: 

P P 

c = 

( sg 

− sp) 

Psg 

with P sg 

being the average probability of clonal identity 

of all sample unit pairs and P sp 

the average probability 

of clonal identity among pairwise nearest neighbours; 

these are estimated from the respective observed 

proportions in the sample. This index will typically 

range from 0, when the probability between nearest 

neighbours does not differ on average from the global 

one, to l when all nearest neighbours preferentially 

share the same MLL, in a situation of spatially distant 

distinct clonal lineages. The statistical significance of 

the calculated aggregation index can be tested against 

the null hypothesis of spatially random distribution 

of samples using a resampling approach, whereby the 

individuals sampled are randomly assigned to the 

existing sampling coordinates. 

Sampling density 

Definition of the appropriate area of a sampling cluster or 

stratum and sample size in each area requires an a priori 

estimation of the average sampling density (sample units 

per unit area) that would be high enough to encompass 

several repetitions of the same clonal lineages and of the 

average area that would be large enough to include many 

different clonal lineages (also see discussion of clonal 

subrange below). Without a priori information on clonal 

structure, the only guidance to design sampling strategies 

derives, in addition to the theoretical impact of geometry 

on edge effects, from knowledge on the clonal growth and 

demography of the species (e.g. horizontal spread rates, 

branching angles, lifespans), which can, alone or through 

the use of models (e.g. Lovett-Doust 1981; Sintes et al. 2005), 

provide expectations on the linear extent of the clonal 

lineages. In the absence of such information, limited pilot 

studies may be needed as a basis to design efficient, 

unbiased sampling strategies. These pilot studies should 

be focused on resolving clonal size structure at small 

spatial scales, as to ascertain the sampling density and/or 

‘pore’ size (if relevant) of the subsequent study, since 

clonality is often not an issue for objectives related to the 

largest scales. 

The consequence of various sampling densities could be 

assessed by using a resampling approach, whereby clonal 

richness indices (G and R) would be estimated for multiple 

random combinations of sampling units, for sample sizes 

ranging from 1 to N (N = total sample size in a given area; 

Fig. 6). 

Inspection of the plot of the number of genotypes (G) vs. 

the number of sampling units would theoretically allow, 

as in the case of the selection of the number of markers 

required (see above), selection of the minimum sampling 

density yielding asymptotic R values. However, an associated 

feature to the power law distribution of clonal membership 

size observed in all studies examined (Figs 2 and 4) is 

that no asymptotic R value is obtained until an exhaustive 

sample of the population is reached. In the test case examined 

here, no asymptotic value could be observed in any 

of the samples of 40 Posidonia oceanica populations, except 

two populations with an overwhelmingly dominant single 

clone each (data not shown), and no asymptotic stabilization 

of R with increasing sampling effort was observed 

in the most intensively sampled population, even after 

74 




Fig. 6 (a) The relationship between the indices of clonal richness G and R and the number of sampling units N based on data sets of Posidonia 

oceanica (N = 149) and Cymodocea nodosa (N = 220), illustrating the absence of an asymptotic value of R and its strong dependence on 

sampling density. (b) Spline fit describing the rate of change in R with increasing N (∂R/∂N) for both samples (P. oceanica: λ = 1000; r 2 = 0.21, 

P < 0.05; C. nodosa: λ = 1000; r 2 =0.78, P < 0.001). 

sampling about 150 shoots in a quadrat limited to 50 × 

50 m (Fig. 6). A similar lack of asymptotic stabilization of 

R with increasing sampling effort was observed in both 

Cymodocea nodosa populations with about 220 sampling 

units in a 20 × 60 m area each (Fig. 6). Although the value 

of R does not reach an asymptote with increasing sampling 

effort, the rate of change in R with increasing sample size 

N declines as N increases following a law of diminishing 

returns described by the spline fit of R vs. N (Fig. 6), such 

that sample sizes of 50 units provide, in the test cases examined 

here, an adequate approximation of R (Fig. 6). The fact 

that the value of R does not reach an asymptote with 

increasing N is a consequence of the Pareto distribution of 

clonal membership, as the lack of a statistically defined mean 

value, unless the entire population is sampled, is a property 

of power law distributions such as the Pareto distribution. 

Since the review presented here shows that a power law 

distribution of MLL size (in terms of number of replicates) 

appears to be a universal feature of clonal organisms (Figs 2 

and 4), sample sizes should be as large as possible to ensure 

that R values are as stable as possible (e.g. N = 50 for the 

examples in Fig. 6b). We therefore recommend collecting 

excess samples to test whether R stabilizes for a given 

sampling size using a subsampling approach, genotyping 

additional samples until a modest change in R with further 

sample size increments is achieved. In any case, the limitations 

imposed by this undesirable behaviour of R should 

be considered when comparing across-studies using 

different sample sizes or sampling density. It is also important 

to mention that this property being due to the power 

law distribution of replicates among clones, the parameters 

describing the Pareto distribution are mostly unaffected 

by sampling density, once enough genotypes have been 

sampled to allow the construction of a robust regression. 

The Pareto distribution may therefore be much more 

adequate to compare properties of clonal diversity among 

sites and studies with different sampling density, provided 

the sampling areas are comparable. 

Clearly, the sampling strategy, density and spatial design 

strongly affect the estimates of clonal diversity. Remarkably 

however, almost half (49%) of the published reports 

reviewed did not include any mention of the geometry, 

area or procedure (random or haphazard vs. regular) used 

in sampling. Among those that provided sufficient detail, 

the geometry ranged from circles (only six studies, representing 

less than 4% of the studies) to rectangles or squares 

(40% and 18% of the studies, respectively) and linear 

transects (19% of the studies). Sampling was more often 

random or haphazard (38% of the records) than regular 

(27% of the records), and a minimum spacing between 



75


sample units was set at a scale depending on a priori 

knowledge of species average clonal size in 31% of the 

studies (Table 1). Comparative analyses among studies 

are rendered cumbersome, if not impossible, by the 

diversity of methods used, and by the lack of information 

on the sampling strategy used, particularly on the sampled 

area. 

Spatial autocorrelation 

Spatial autocorrelation analyses have been used to 

ascertain the scale-dependence of clonal diversity in clonal 

populations, including those of clonal plants (about 22% of 

studies on genetic structure of clonal plant populations, 

and 8% in other clonal organisms). These inferences were 

derived from spatial autocorrelation analyses representing 

the average genetic distance or kinship coefficient (Loiselle 

et al. 1995; Ritland 1996; Epperson & Li 1997; Rousset 2000) 

between pairs of individuals within specific ranges of 

geographic distance, weighed against the average genetic 

distance or kinship coefficient between all paired samples 

in the population, plotted against distance (Fig. 7). Autocorrelograms 

are commonly used to infer properties not 

specific to clonal plants, such as dispersal scale and neighbourhood 

size. However, spatial autocorrelograms can also 

be used to infer properties specific to the clonal nature of 

the studied organism (Reusch et al. 1999). Spatial autocorrelograms 

have been applied to clonal plants in the past 

including or excluding replicated MLLs, depending on 

the specific question addressed. The comparison of spatial 

autocorrelograms including and excluding replicate MLLs 

(Fig. 7) and the estimation of the probability of clonal 

identity have recently been shown to allow inferences on 

the linear spatial domain over which clonality affects the 

genetic structure of the population, referred to as the ‘clonal 

subrange’ of the population (Harada et al. 1997; Alberto 

et al. 2005). Using these approaches, the clonal subrange 

can be operationally described as the spatial scale below 

which the spatial autocorrelograms derived either including 

or removing pairs among identical MLLs converge (Fig. 7, 

cf. Alberto et al. 2005), and at which the probability of 

clonal identity approaches zero (Harada et al. 1997). This 

clonal subrange represents the characteristic maximum 

size of the clonal lineages in the sample, and is the spatial 

scale beyond which clonality does not affect genetic structure. 

Application of these techniques have inferred linear clonal 

subranges of 20–25–30–35 m for a Mediterranean seagrass 

species (C. nodosa, Alberto et al. 2005), and 140–190 m for 

two terrestrial species, Carpobrotus sp. (Suehs et al. 2004) and 

Aechmea magdalenae (Murawski & Hamrick 1990), respectively. 

For clonal plants, autocorrelation analysis have reported 

important spatial structure in the distribution of clones in 

space, both including all sampling units in the analysis 

(about 80% of the studies report significant autocorrelation) 

or excluding the effect of clonality in the sample by removing 

replicates or distances among pairs of the same MLLs 

(61% of the studies report significant autocorrelation). The 

few studies reporting estimates of neighbourhood size for 

clonal plants show very limited linear dispersal scales, of 

the order of tens to hundreds of metres (Table 2). 

Finally, it may be relevant to screen for the occurrence of 

clonally mediated dispersal (e.g. by means of fragmentation, 

dispersal and re-establishment), which can be an 

additional efficient source of dispersal susceptible to significantly 

affect the genetic neighbourhood (Charpentier 

2001; Hammerli & Reusch 2003). The autocorrelogram may 

show a non-null probability of clonal identity at large 

geographic distance scales preceded by null for several 

distance classes. This may signal the occurrence of dispersal 

by clonal fragmentation, although the absence of 

such profiles cannot be used to infer the absence of this 

process, which may be a rare event, requiring therefore 

large sampling efforts for its detection. 

Aggregation 

Knowledge of the spatial position of the individuals 

sampled also allows the examination of the extent to which 

clonal lineages occur segregated or intermingled in the 

population. The extent of intermingling of clones in a 

population therefore provides insight into the history of 

clonal growth and space occupation, and the competitive 

interactions among clones. A segregated distribution of 

the clonal lineages in space (high aggregation) may for 

example arise from either recent colonization, where clonal 

lineages are still expanding in relatively empty space or 

due to competitive exclusion, as observed by Cheplick 

(1997). Conversely, an intermingled pattern suggests either 

a full occupation of space by a large number of clonal 

lineages due to a long history following colonization and/ 

or high density, and relatively weak competitive interactions 

among clones. We propose that the extent of intermingling 

or aggregation of the clonal lineages can be assessed by 

comparing the probability of clonal identity (set as 0 among 

replicates of the same MLL and 1 among sampling units 

belonging to distinct MLLs) between nearest neighbours 

relative to that between pairs of sampling units drawn at 

random from the population. A spatial clonal aggregation 

index, A c 

, can therefore be estimated as described in Box 5. 

The application of this estimator to the 34 populations of 

P. oceanica sampled across the Mediterranean showed very 

contrasting results spanning from 0.00 ns (implying high 

level of intermingling) to 0.68** (implying high and significant 

level of spatial aggregation of ramets belonging to 

the same MLLs). These two extremes were observed in two 

populations of the Balearic Islands, showing high variability 

in the extent of aggregation of clones in populations located 

relatively close to one another. 

76 




Fig. 7 Spatial autocorrelation analysis of Cymodocea nodosa in Alfacs Bay (from Alberto et al. 2005). (a) Clonal structure and subrange (on 

top). Kinship estimates from all ramet pairs or only for pairs between ramets showing a different multilocus genotype, and probability of 

clonal identity (proportion of pairs between ramets with identical multilocus genotypes), with confidence limits (for P = 0.975 and P = 0.025) 

based on 1000 permutations of spatial coordinates. (b) Genet level analysis (below), using a single copy for each multilocus genotype. The 

slope of the regression of mean kinship estimates as a function of the logarithm of spatial distance is plotted on the left, using as spatial 

coordinates the central zone occupied by multiramet genets, with broken lines delimiting 95% confidence limits around the null hypothesis 

of random distribution of genets in space. On the right side a single ramet per multiramet genet was randomly selected to create a 100-genet 

data file to generate the confidence limits for the correlogram. 

Software 

Some recently published software compute most of the 

indices and metrics detailed in this work (Box 6), mlgsim 

(Stenberg et al. 2003), genalex (Peakall & Smouse 2006), 

genotype and genodive (Meirmans & Van Tienderen 

2004), and genclone 1.0 (Arnaud-Haond & Belkhir 2007). 

Methods to assess clonality and clonal membership are 

available in all of them with slight differences, but only 

the last two, genotype and genodive (Meirmans & Van 

Tienderen 2004) and genclone 1.0 (Arnaud-Haond & 

Belkhir 2007) allow resampling procedures to test for the 



77


Box 6 Software available to analyse molecular data on populations of clonal organisms 

Data sets analysed, methods to assess clonality and clonal membership, clonal diversity estimates, and methods proposed to 

describe spatial components of clonal growth: genalex (Peakall & Smouse 2006), genclone (Arnaud-Haond & Belkhir 2007), 

genotype and genodive (Meirmans & Van Tienderen 2004) and mlgsim (Stenberg et al. 2003). 

mlgsim 

genotype 

and genodive genalex genclone 

Data sets 

Levels of ploidy Haploid ✓ ✓ 

Diploid ✓ ✓ ✓ ✓ 

Polyploid 

✓ 

Markers Dominant ✓ ✓ 

Codominant ✓ ✓ ✓ ✓ 

Clonality and clonal membership 

MLGs discrimination ✓ ✓ ✓ ✓ 

MLLs: use of frequency distribution of Frequency distribution ✓ ✓ 

pairwise differences to group MLGs likely Allelic distance ✓ ✓ 

to be distinct due to somatic 

Length distance (microsatellites) 

✓ 

mutation or scoring errors 

Custom distance 

✓ 

Probability of clonality, or of clonal identity P gen 

✓ ✓ ✓ ✓ 

P sex 

✓ ✓ ✓ ✓ 

and confidence interval 

✓ 

P I 

(exclusion over the sample) 

✓ 

Subsampling frequency to test for 

✓ 

the efficiency of the set of marker used 

Subsampling procedure to correct 

✓ 

✓ 

richness indices for different sample size 

Clonal richness and diversity 

Clonal richness G ✓ ✓ 

P d 

✓ ✓ 

R 

✓ 

Clonal diversity and evenness Shannon diversity and evenness ✓ ✓ 

Simpson diversity and evenness 

✓ 

Hill diversity 

✓ 

Pareto distribution ✓* 

Spatial components of clonality 

(When coordinates are available) Map clone ✓ ✓ 

Clone size ✓ ✓ 

Clonal subrange 

✓ 

Spatial autocorrelation 

✓ 

Edge effect ✓ ¥ 

Aggregation index ✓ ¥ 

*available in the newly released version of genclone, genclone 2.0. 

accuracy of the set of samples and loci used. The same two 

software, genodive and genclone 1.0, allow estimating 

richness and diversity indices, but only the latter allows 

estimating Simpson and derived indices (Hill’s and 

evenness). 

Spatial components can be analysed using either genalex 

or genclone 1.0 by mapping clones or estimating maximum 

clone size and the latter also allows performing 

clonal subrange analysis and implements specific spatial 

autocorrelation methods adapted to the occurrence of 

replicates of the same genotype in the data set. 

Finally, the Pareto distribution and parameter, aggregation 

index and edge effects are proposed in the new 

version of the software genclone, genclone 2.0 (Arnaud- 

Haond, Belkhir, available for download on genclone 

website). 

78 




Prospect 

The rapid growth in the research effort examining the 

clonal structure of populations is providing an important 

empirical basis to probe the implications of clonality. As 

this empirical basis grows ever larger, there is a need to 

standardize procedures to allow comparative analyses to 

be formulated and common patterns in the clonal diversity 

and structure of clonal populations to emerge. As discussed 

above, comparisons across studies are not straightforward 

as most of the descriptors of clonal structure are strongly 

sensitive to sampling choices; hence the need to move 

towards standardized procedures. 

We recommend that studies of clonal diversity and 

structure be based on samples collected at random coordinates 

within sampling areas that minimize the perimeterto-area 

ratio (e.g. circles or squares). We try to dissipate 

present ambiguity in the use of the terms by introducing 

the concept of clonal lineage and MLL instead of clone and 

MLG, in order to include in a clonal lineage (MLL) not only 

an MLG but also any group of MLGs characterized by very 

few genetic differences that appear more likely to be derived 

from somatic mutations or scoring errors rather than from 

distinct zygotes. We describe how Monte-Carlo procedures 

can help ascertain the number of loci required to deliver 

accurate assignments of clonal lineages as well as to elucidate 

potential sampling biases derived from edge effects, 

thereby delivering the most robust estimates of clonal richness 

possible. We recommend the use of genetic richness 

(R), the Simpson evenness index (V), and the complement 

of the slope of the Pareto distribution of clonal membership 

as the most parsimonious set of nonredundant indices of 

clonal diversity. The issue of sampling design and density 

has also been shown to be far from trivial, and the sensitivity 

of most indices to these parameters, rendering risky any 

comparison among studies, that may be interpreted with 

high caution. A spline fit describing the rate of change in R 

with increasing N may be used to get the most accurate 

possible estimates of R and the Pareto distribution may be 

chosen for comparative proposes, as it is the less sensitive 

indices to sampling density. Lastly, the preceding discussion 

emphasizes the critical importance of explicitly considering 

the distribution of clonal lineages in space, allowing 

the analysis of spatial clonal traits such as estimates of the 

clonal subrange and the extent of clonal aggregation. Most 

of features are now available through four principal software 

packages released recently (Box 6), which should 

facilitate the use and standardization of these methods. 

The elements provided here represent a first step towards 

an increasing realization of the consequences of clonality in 

the design and analyses of studies, helping to develop a 

coherent framework for the study of genetic structure of 

clonal plant populations. We believe that consideration of 

the recommendations herein proposed should help move 

this emerging research program further, and we hope they 

will provide new impetus towards further exploration of 

the consequences of clonality for the population dynamics 

and evolution of species. 


This work is a contribution of the EU Network of Excellence 

MARBEF-Marine Biodiversity and Ecosystem Function, the EU 

Project M&MS (EVK3-CT-2000-00044) a project funded by the 

Fundación BBVA, project PNAT/1999/BIA/15003/C of the 

Portuguese Science Foundation — FCT, and fellowships from 

FCT and ESF. We are grateful to Cécile Perrin, Ashwin Engelen, 

Onno Diekmann, Elena Varela and Elena Diaz-Almela for fruitful 

discussions, which helped improve this article, and to Gareth 

Pearson for revising the manuscript. We wish to thank the Editor 

and two anonymous referees for the improvements they suggested 

on previous versions of the manuscript. 

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Sophie Arnaud-Haond is a researcher in IFREMER (France) and 

an associate researcher in CCMar (Portugal). Her research interests 

focus on the influence of mating system and clonality, dispersal 

and selection on the ecology and evolution of marine populations. 

Carlos M. Duarte leads a team in IMEDEA (Spain) studying 

marine biodiversity from the genetic, species and habitat level to 

global biogeochemical cycles. Fillipe Alberto is a post-doctoral 

researcher in CCMar, interested in marine population genetics and 

ecology, marine phylogeography and clonality. Ester Serrão leads a 

research group (CCMar) that is primarily interested in marine 

ecology, adaptation and population genetics. 

Supplementary material 

The following supplementary material is available for this 

article: 

Table S1 Survey list of published studies using molecular markers, 

and information extracted from the literature 

This material is available as part of the online article from: 

http://www.blackwell-synergy.com/doi/abs/ 

10.1111/j.1365-294X.2007.03535.x 

(This link will take you to the article abstract). 

Please note: Blackwell Publishing are not responsible for the content 

or functionality of any supplementary materials supplied by 

the authors. Any queries (other than missing material) should be 

directed to the corresponding author for the article. 



81



II.4 Feed-backs between genetic structure and perturbation-driven decline in 

seagrass (Posidonia oceanica) meadows. Conservation Genetics, 2007. 

Dans le cadre de la thèse d’Elena Diaz-Almela, nous avons appliqué ces méthodes 

d’étude des composantes spatiales de la clonalité afin d’estimer l’impact des 

perturbations démographiques, ici l’existence d’installations aquacoles modifiant la 

turbidité et la présence de matière organique de l’eau, sur la composition clonale des 

prairies. Nous avons utilisé le ‘clonal subrange’ (CR), proposé dans l’article 

précédent avec Filipe Alberto, pour montrer une influence circulaire de la taille des 

clones sur la mortalité. Les prairies présentant le moins de mortalité sont celles ou 

les herbiers contrôles montrent une dominance de grands clones, qui semblent être 

favorisés en terme de survie. 

a) b) 

Herbier Impacté 

Herbier contrôle 

Figure 9: ‘clonal subrange’ (CR) comparé entre zones impactées et zones contrôles, et 

CR par rapport au résidus de la relation mortalité / taux de sédimentation. On observe 

donc ici a) d’une part une relation positive entre taux de mortalité et la taille maximale des 

clones dans les herbiers impactés, et b) d’autre part l’existence d’une relation significative 

entre le taux de mortalité en fonction de l’impact et la taille des clones, suggérant une 

meilleure survie des grands clones. 

82

Conserv Genet (2007) 8:1377–1391 

DOI 10.1007/s10592-007-9288-0 

ORIGINAL PAPER 

Feed-backs between genetic structure and perturbation-driven 

decline in seagrass (Posidonia oceanica) meadows 

Elena Diaz-Almela Æ Sophie Arnaud-Haond Æ 

Mirjam S. Vliet Æ Elvira Álvarez Æ Núria Marbà Æ 

Carlos M. Duarte Æ Ester A. Serrão 

Received: 11 August 2006 / Accepted: 9 January 2007 / Published online: 11 April 2007 

Ó Springer Science+Business Media B.V. 2007 

Abstract We explored the relationships between 

perturbation-driven population decline and genetic/ 

genotypic structure in the clonal seagrass Posidonia 

oceanica, subject to intensive meadow regression 

around four Mediterranean fish-farms, using seven 

specific microsatellites. Two meadows were randomly 

sampled (40 shoots) within 1,600 m 2 at each site: the 

‘‘impacted’’ station, 5–200 m from fish cages, and the 

‘‘control’’ station, around 1,000 m downstream further 

away (considered a proxy of the pre-impact genetic 

structure at the site). Clonal richness (R), Simpson 

genotypic diversity (D*) and clonal sub-range (CR) 

were highly variable among sites. Nevertheless, the 

maximum distance at which clonal dispersal was 

detected, indicated by CR, was higher at impacted 

stations than at the respective control station (paired t- 

test: P < 0.05, N = 4). The mean number of alleles (Â) 

and the presence of rare alleles (Â r ) decreased at impacted 

stations (paired t-test: P < 0.05, and P < 0.02, 

respectively, N = 4). At a given perturbation level 

(quantified by the organic and nutrient loads), shoot 

mortality at the impacted stations significantly 

E. Diaz-Almela (&) N. Marbà C. M. Duarte 

Laboratorio de Ecología Litoral, Grupo de Oceanografía 

Interdisciplinar (G.O.I), IMEDEA (CSIC-UIB), C/Miquel 

Marqués no 21, C.P. 07190 Esporles, Spain 

e-mail: elena.diaz-almela@uib.es 

S. Arnaud-Haond M. S. Vliet E. A. Serrão 

CCMAR, CIMAR – Laboratório Associado, 

F.C.M.A. – Univ. Algarve, Gambelas, 8005-139 Faro, 

Portugal 

E. Álvarez 

Dirección General de Pescas, Comunidad de las Islas 

Baleares, Palma de Mallorca, Spain 

decreased with CR at control stations (R 2 = 0.86, 

P < 0.05). Seagrass mortality also increased with Â 

(R 2 = 0.81, P < 0.10), R (R 2 = 0.96, P < 0.05) and D* 

(R 2 = 0.99, P < 0.01) at the control stations, probably 

because of the negative correlation between those 

parameters and CR. Therefore, the effects of clonal 

size structure on meadow resistance could play an 

important role on meadow survival. Large genotypes 

of P. oceanica meadows thus seem to resist better to 

fish farm-derived impacts than little ones. Clonal 

integration, foraging advantage or other size-related 

fitness traits could account for this effect. 

Keywords Clonal sub-range Genetic diversity 

Population decline Genotypic diversity Fish-farm 

impacts 

Introduction 

The interactions between perturbation-driven population 

decline and genetic diversity are currently the 

focus of an intense research activity, both for its fundamental 

interest and for its implications to conservation 

biology. But the dissection of their influence on 

each other is a complex task, because a circular feedback 

is expected between both factors: population 

decline may affect population genetic resources, and 

the genetic diversity present in the population prior to 

perturbation may influence its response. 

Strong reductions in population size are expected to 

erode genetic variability, first through direct loss of 

genotypes and alleles, and thereafter through increased 

random genetic drift and elevated inbreeding within 

83 

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1378 Conserv Genet (2007) 8:1377–1391 

the remnant population offspring (Wright 1931; Nei 

1975; Young et al. 1996). Although most experiments 

and field observations support positive interactions 

between population size and genetic diversity (Leimu 

et al. 2006), the effects of population decline in the 

genetic diversity of the adult remnant populations are 

highly variable (e.g. Young et al. 1996; Lee et al. 2002; 

Edwards et al. 2005; Lowe et al. 2005; Reusch 2006). 

This variability can be accounted for by the role of lifehistory 

traits, such as the generation time or the 

breeding regime in the speed of genetic diversity erosion 

(Young et al. 1996; Collevatti 2001; Lee et al. 

2002; Lowe et al. 2005; Leimu et al. 2006). Moreover, 

intermediate perturbation levels may enhance genetic 

diversity in populations, producing space available for 

new genotypes to install, as has been described among 

several clonal plants, in which developed and stable 

populations show dominance by a few clones (McNeilly 

and Roose 1984; Watkinson and Powel 1993). 

Among seagrasses (clonal plants), there is evidence 

that perturbation-induced regression may reduce meadow 

genetic polymorphism (Alberte et al. 1994; Micheli 

et al. 2005). Therefore, the empirical evidence suggests 

the existence of species-specific thresholds of population 

reduction and isolation under which population genetic 

diversity would not be significantly affected (Leberg 

1992; Young et al. 1996; Lowe et al. 2005). 

At a given perturbation level, populations bearing high 

genetic diversity are expected to be more resistant (i.e. to 

be less affected by a given perturbation), and to exhibit 

faster recovery than homogeneous ones because the 

probability of occurrence of resistant variants is expected 

to be higher and/or through processes of functional 

complementarity (Loreau and Hector 2001; Reuschand 

Hughes 2006). Overall, a majority of empirical studies 

indicate positive interactions between population genetic 

diversity and fitness (Leimu et al. 2006). But more studies 

are needed to confirm this tendency (Leimu et al. 2006), 

especially for the population fitness components of 

resistance to and recovery from perturbations. In the 

seagrass Zostera marina, higher genetic diversity (in 

terms of allelic richness and/or heterozygosity) increased 

survival, growth and flowering rates of transplants 

(Williams 2001; Hämmerli and Reusch 2003). 

Among clonal plants, another component of population 

genetic diversity is genotypic diversity (clonal 

diversity), the number and evenness of genetic individuals 

(genets) represented among the ramets. Recent 

experiments indicate that genotypic diversity can increase 

resistance (Reusch et al. 2005) and speed of 

recovery (Hughes and Stachowicz 2004) of the clonal 

seagrass Zostera marina facing perturbations (Reusch 

and Hughes 2006). 

The seagrass Posidonia oceanica, is a slow-growing 

(Marbà and Duarte 1998) and extremely long-lived 

clonal plant (Mateo et al. 1997). Its primary reproductive 

mode is vegetative, with sparse sexual reproduction 

(Gambi et al. 1996; Balestri and Cinelli 2003; 

Díaz-Almela et al. 2006). P. oceanica is endemic to the 

Mediterranean coasts (den Hartog 1970), where its 

meadows are the dominant ecosystems between 0.3 

and 45 m depth (Bethoux and Copin-Monteagut 1986; 

Pasqualini et al. 1998). These meadows provide 

important ecosystem functions, both in terms of production 

and biodiversity (Hemminga and Duarte 

2000), which are being jeopardised by their tendency 

towards a substantial decline (e.g. Marbà et al. 2005). 

One of the major threats to P. oceanica meadows is 

the growing marine aquaculture activity (Holmer et al. 

2003). Fish farm effluents produce rapid reductions in 

meadow shoot density, which are particularly intensive 

in the areas next to fish cages (Delgado et al. 1997, 

1999; Ruiz et al. 2001). If there is an effect of this 

perturbation on the genetic diversity and clonal structure 

of P. oceanica meadows, it should be best detected 

in these areas. 

In the present work, we use seven microsatellite 

markers (Alberto et al. 2003; Arnaud-Haond et al. 

2005) to investigate the variability in genetic diversity 

and genotypic structure of P. oceanica meadows situated 

around four fish farms across the Mediterranean, 

for which demographic trajectories have been evaluated 

(Diaz-Almela et al. submitted). Our objectives 

are (1) to elucidate the effects of shoot density 

regression on meadow clonal structure and genetic 

diversity and (2) to derive insights into the possible 

importance of the clonal structure and genetic diversity 

of the meadow previous to perturbation on its resistance 

to fish-farm impacts. 

Materials and methods 

Samples of the seagrass Posidonia oceanica were collected 

in meadows located around four fish farms along 

the Mediterranean (Fig. 1; Table 1), at water depths 

ranging between 16 and 28 m among sites. The farms 

in Cyprus, Italy and Spain were located in open coasts 

about 1 km from shores, whereas the farm in Greece 

was located in a strait about 300 m from shore and was 

the shallowest. All studied meadows near (i.e. 5–15 m) 

the cages exhibited high rates of shoot decline, as reflected 

by the annual balance between shoot recruitment 

and mortality rates assessed by shoot census in 

permanent plots (Table 1). Conversely, shoot populations 

were in steady state or declining at slow rates, 

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Conserv Genet (2007) 8:1377–1391 1379 

80x20m 2 

Co.1 

Control 

1000m 

20m 

In.1 

5-15m 

Im.1 

Im.2 

In.2 

Impacted 

Co.2 

Fig. 1 Above: locations of the fish farm sites analysed in this 

study. Circle: El Campello (Spain), square: Porto Palo (Sicily), 

diamond: Sounion (Greece), triangle: Amathous (Cyprus). 

Below: sampling scheme of the genetic sampling stations 

(Impacted, Control). The genetic sampling areas encompass a 

variable number of demographic census plots, belonging to 

impacted (Im) and intermediate (In) demographic stations, in 

the case of the genetic impacted station, or to a control (Co) 

demographic station, in the case of the genetic control station 

similar to those observed in other P. oceanica meadows 

elsewhere (Marbà et al. 2005), when growing at 800– 

1,200 m away from the cages (Table 1). 

The sampling for genetic structure was performed 

in each site, within two stations (i.e. hereafter called 

‘‘impacted’’ and ‘‘control’’ stations), encompassing 

an area of 80 · 20 m 2 each. These stations contained 

the permanent plots where annual shoot demographic 

parameters were estimated (Table 1). Mean shoot 

densities within the ‘‘impacted’’ stations, located at 

the edge of the meadow nearest to fish cages, ranged 

from 20 (El Campello, Spain) to 165 (Sounion, 

Greece) shoots m –2 (Table 1). The ‘‘control’’ station, 

situated 1,000–1,200 m away from cages, in the direction 

of the main current, had mean shoot densities of 

68 (El Campello, Spain) to 395 (Porto Palo, Sicily) 

shoots m –2 . 

A total of 38–40 ramets (i.e. leaf shoots) were collected 

within each genetic sampling station, at randomly 

drawn coordinates, within a rectangular area of 

80 · 20 m 2 . The base of each leaf bundle, including the 

shoot apical meristem, was preserved in silica crystals 

until DNA extraction. Distributions of distances between 

pairs of collected samples (normal, slightly 

skewed towards low distances) were not significantly 

different among sampling sites and stations. 

Table 1 Location, water depth, distance to fish cages and year of initiation of fish farm activities of each sampling site and station 

Site Coordinates Depth 

(m) 

Distance to 

cages (m) 

Fish farm 

initiated in: 

Demography 

station 

Shoots 

m –2 

Relative 

mortality 

rate (yr –1 )* 

Relative 

recruitment 

rate (yr –1 )* 

Amathous (Cyprus) 

IMPACTED 34°41¢96N 20.5 300 1992 Im. 1, 2 454 ± 42 0.186 ± 0.050 0.141 ± 0.041 

33°12¢00E 

CONTROL 34°41¢99N 19.5 1,200 Co. 1, 2 491 ± 51 0.185 ± 0.067 0.139 ± 0.047 

33°12¢36E 

Sounion (Greece) 

IMPACTED 37°39.586¢N 15.5 10–30 1996 Im.-In. 1, 2 165 ± 25 1.606 ± 0.479 0.095 ± 0.034 

23°57.291¢E 

CONTROL 37°39.550¢N 16.2 1,200 Co 1 365 ± 34 0.070 ± 0.020 0.056 ± 0.013 

23°58.240¢E 

Porto Palo (Sicily) 

IMPACTED 36°42.710¢N 22.5 5–50 1993–1994 Im.-In. 1 156 ± 17 1.241 ± 0.491 0.004 ± 0.003 

15°8.438¢E 

CONTROL 36°43.307¢N 20 1,000 Co. 1, 2 395 ± 35 0.577 ± 0.275 0.027 ± 0.009 

15°8.474¢E 

El Campello (Spain) 

IMPACTED 38°25.300¢ N 28 10–30 1995 Im.-In. 1, 2 20 ± 6 0.617 ± 0.128 0.091 ± 0.027 

0°20.829¢W 

CONTROL 38°24.875¢N 28 1,000 Co. 1 68 ± 4 0.056 ± 0.029 0.106 ± 0.019 

0°21.139¢W 

The demographic stations encompassed by the genetic sampling stations at each site are also provided, as well as the mean shoot 

densities and mean mortality, and recruitment rates at the genetic sampling stations (Mean ± SE) 

85 

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1380 Conserv Genet (2007) 8:1377–1391 

Genomic DNA was extracted following a standard 

CTAB extraction procedure (Doyle and Doyle 1988). 

The sample polymorphism was analysed with the most 

efficient combination (Arnaud-Haond et al. 2005) of 

seven nuclear microsatellites reported by Alberto et al. 

(2003) to allow the resolution of clonal membership, 

using the conditions described by Arnaud-Haond et al. 

(2005). The number of alleles and size range (see 

Appendix) of some of the microsatellite loci was enlarged 

in this study as compared with the initially described 

by Alberto et al. (2003). 

Clone discrimination: 

We used the round-robin method (Parks and Werth 

1993) to estimate the allelic frequencies in each 

population sample. This sub-sampling approach avoids 

the overestimation of the rare alleles, by estimating the 

allelic frequencies for each locus on the basis of a sample 

pool composed of all the genotypes distinguished among 

all the loci, except the one for which allelic frequencies 

are estimated. This procedure is repeated for all loci, 

taking into account Wright’s inbreeding coefficient estimated 

for each loci after the exclusion of identical multi 

locus genotypes (Young et al. 2002), and the probability 

that the same multi-locus genotype is produced by different 

sexual events (P gen (f)) is then estimated as: 

P gen ðf Þ¼ Yl 

i¼1 

½ðf i g i Þð1 þðz i ðF isðiÞ ÞÞÞŠ2 h 

ð1Þ 

where l is the number of loci, h is the number of heterozygous 

loci, f i and g i the allelic frequencies of the alleles f 

and g at the ith locus (with f and g identical for homozygotes), 

the F is estimated for the ith locus with the roundrobin 

method, and z i = 1 the ith locus that is homozygous 

and z i = –1 for the ith locus that is heterozygous. 

When the same genotype is detected more than once 

(n) in a population sample composed of N ramets, the 

probability that the samples actually originate from 

distinct reproductive events (i.e. from separate genets) 

is described by the binomial expression (Tibayrenc 

et al. 1990; Parks and Werth 1993): 

P sex ¼ XN 

i¼n 

N! 

i!ðN iÞ! ½P genŠ i ½1 P gen Š N i ð2Þ 

where n is the number of sampled ramets with the 

same multi-locus genotype, N is the sample size, and 

P gen is defined above. Estimates were performed using 

the software GENCLONE 1.0 (Arnaud-Haond and 

Belkhir 2007) 

Clonal diversity and structure: 

The clonal, or genotype diversity (R) at each station 

has been estimated as: 

R ¼ ðG 

ðN 

1Þ 

1Þ 

ð3Þ 

where G is the number of genotypes in the sample and 

N is the number of ramets analysed, as was 

recommended by Dorken and Eckert (2001) and Arnaud-Haond 

et al. (2005). Using this estimator, the 

minimum value for clonal diversity in a monoclonal 

stand is always 0, independently of sample size, and the 

maximum value is still 1, when all the different samples 

analysed correspond to distinct genotypes. 

The complement of Simpson index (Pielou 1969) for 

genotypic diversity in each station, representing the 

probability of encountering distinct Multi-Locus 

Genotypes (MLG) when randomly taking two sample 

units was estimated as: 

D ¼1 

X G 

i¼1 

 

n i ðn i 

NðN 

 

1Þ 

1Þ 

ð4Þ 

where N is the number of sample units (ramets sampled), 

G the number of multi-locus genotypes, and n i is 

the number of sample units sharing the ith MLG. 

The clonal sub-range (i.e., the maximum distance in 

meters between two identical genotypes belonging to 

the same clone) was estimated for each station (Harada 

et al. 1997; Alberto et al. 2005). All clonal 

diversity and structure parameters were calculated 

with GENCLONE 1.0 (Arnaud-Haond and Belkhir 

2007). 

Genetic diversity and structure: 

Genetic diversity within populations was estimated with 

the mean number of alleles per locus, which was standardized 

(Â) to the lowest sample size collected in a 

station (33 samples in Greece, control station), using 

GENCLONE 1.0 (Arnaud-Haond and Belkhir 2007). 

After identification of ramets belonging to the same 

genets, replicates were removed from the dataset to 

perform the following calculations using the GENETIX 

4.0 package (Belkhir et al. 2004). Unbiased (H E ) and 

observed (H O ) gene diversity estimates (Nei 1987) were 

calculated. A permutation procedure (1,000 permutations) 

was used to test whether a particular estimate of 

the overall inbreeding coefficient (F is ), was significantly 

different from 0. Heterozygosity was also calculated 

for each genotype, and relationships of genotype 

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Conserv Genet (2007) 8:1377–1391 1381 

heterozygosity with genotype frequency and clonal subrange 

were explored through regression analysis. 

Spatial autocorrelation within stations was assessed 

using the kinship estimator coefficient of Ritland (^F ij ) 

as a genetic relatedness statistic (Ritland 1996), 

calculated using the GENCLONE 1.0 software 

(Arnaud-Haond and Belkhir 2007). We performed 

regression analyses of mean ^F ij against the Log e of 

mean geographic distance, within each distance class. 

This allowed the test of the adequacy of two-dimensional 

isolation-by-distance models in each station 

(Rousset 1997). 

The autocorrelation analyses were performed twice 

for each station and site: (i) first including all samples, 

which mostly estimates the genetic neighbourhood of 

ramets of the same genet and (ii) using permutations 

(1,000) in order to include at each permutation only 

one ramet (and one of the possible corresponding 

coordinates, randomly chosen for each permutation 

step) from each genet. This approach removes the 

influence of the spatial pattern of clonal growth from 

estimates of the relationship between genetic and 

geographic distance, allowing us to test for limitations 

to gene dispersal through seeds and pollen. The spatial 

scale (80 · 20 m 2 ) and number of distance classes (6) 

were the same across stations. For each autocorrelation 

analysis the upper levels of distance classes were defined 

in order to include, as much as possible, an even 

number of distance pair comparisons among classes 

(Table 2). Among stations, the minimum geographic 

distance between pairs of samples was of 0.3–0.7 m 

(0.6–1.6 m when genotype replicates were excluded), 

and the maximum distance ranged between 63.4 and 

76.9 m. We tested the significance of the regression 

slopes using 1,000 random permutations of the sample 

coordinates. 

From the slopes of the regressions of genetic distance 

to geographic distance within each distance class, 

we calculated the Sp statistic (Vekemans and Hardy 

2004), following the equation (5): 

Sp ¼ 

ð1 

^b F 

^Fð1Þ Þ 

ð5Þ 

where ^b F is the slope of the linear regression and ^F ð1Þ 

represents the mean Kinship coefficient within neighbours 

(i.e. the lowest distance class). We tested for 

differences between regression slopes from impacted 

and control stations within each site performing F-tests 

of the slopes, for the spatial autocorrelation with genet 

replicates. In the case of the spatial autocorrelation 

without genet replicates, we simply compared the 95% 

Table 2 Number of distance pairs per distance class in each station, with and without genet replicates 

Station No. distance pairs per distance class b F ±SE Sp ±SE 

Cyprus impacted 

Ramets 130 –0.009 ± 0.006 P = 0.08 0.009 ± 0.006 

Genets 27 (18 higher class) –0.011 ± 0.005 ns 0.010 ± 0.005 

Cyprus control 

Ramets 130 –0.006 ± 0.004 ns 0.006 ± 0.004 

Genets 54 (55 lower class) 0.003 ± 0.002 ns 0.003 ± 0.002 

Greece impacted 

Ramets 111 –0.030 ± 0.005*** 0.031 ± 0.005 

Genets 95 –0.030 ± 0.001*** 0.030 ± 0.001 

Greece control 

Ramets 88 –0.010 ± 0.002* 0.010 ± 0.002 

Genets 84 (76 higher class) –0.009 ± 0.002* 0.009 ± 0.002 

Italy impacted 

Ramets 130 –0.022 ± 0.006** 0.022 ± 0.006 

Genets 79 (70 higher class) –0.015 ± 0.002** 0.015 ± 0.002 

Italy control 

Ramets 130 –0.012 ± 0.005* 0.012 ± 0.005 

Genets 69 (61 higher class) –0.014 ± 0.002* 0.014 ± 0.002 

Spain impacted 

Ramets 123–124 –0.020 ± 0.003* 0.020 ± 0.003 

Genets 54 (55 lower class) –0.041 ± 0.009** 0.042 ± 0.009 

Spain control 

Ramets 130 –0.032 ± 0.006** 0.033 ± 0.006 

Genets 42 (43 lower class) –0.044 ± 0.007** 0.046 ± 0.007 

The observed regression coefficient b F between mean ^F ij and the Log e of mean geographic distance within each distance class ± SE and 

the Sp statistic for each spatial autocorrelation analysis. The significant values are in bold. *: P < 0.05; **: P < 0.01; ***: P < 0.001. The 

b F and Sp values underlined or marked in italics indicate significant differences between the stations signalled this way 

87 

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1382 Conserv Genet (2007) 8:1377–1391 

confidence intervals of the permutations performed 

with one genet real coordinate each time. 

Testing for the impact of perturbation on genotypic 

and genetic variability in the meadows: 

In the absence of pre-disturbance samples, we have 

considered the genetic structure at control quadrats to 

provide a proxy for the genetic structure of the meadow 

next to the fish farm prior to disturbance. We based this 

assumption on the fact that the distance between stations 

(800–1,200 m) was relatively low for a species 

forming long-lived large clones (Sintes et al. 2006) in 

which, for a large proportion of meadows, the genetic 

neighbourhood has been shown to exceed the sampling 

area of stations sampled in this work (1,600 m 2 ; Arnaud- 

Haond et al. 2007). Moreover, the sampling was parallel 

to the coast at uniform depths between stations. 

We therefore compared genetic structures at control 

and impacted stations among sites. We considered the 

four sites across the Mediterranean as independent 

replicates to test for a consistent impact of fish farms 

on the genetic and clonal diversity of the seagrass 

meadows. Differences in Clonal sub-range (CR), 

Genotypic richness (R), Simpson Clonal Diversity Index 

(D*), the mean number of alleles (Â) and expected 

(H E ) and observed (H O ) heterozygosities between 

impacted and control stations were analysed performing 

pairwise t-tests over data around the Mediterranean. 

When significant pairwise differences between 

stations were detected in a parameter, we searched for 

correlations between the magnitude of the differences 

and benthic sediment inputs (total, organic matter and 

nutrients), which provides a metric for the intensity of 

fish farm pressures on the farms (Holmer et al. 2007) 

and shoot density between stations. 

Testing for the influence of genetic diversity 

components on demographic responses to 

perturbation: 

Data on meadow shoot recruitment and mortality were 

obtained by direct census of tagged plants within three 

permanent plots installed in each demographic station 

(genetic sampling stations encompassed a variable 

number of demographic stations, see Table 1) and site, 

as described in Diaz-Almela et al. (submitted). In that 

work, shoot mortality and recruitment variability have 

been shown to change exponentially, or in some cases 

following a power-law with the total, organic and 

nutrient benthic input rates measured in situ. Therefore, 

the possible influences of genotypic and genetic 

diversity components on the demographic response at 

a given environmental forcing were assessed by comparing 

the residuals (averaged within each genetic 

station, Table 3) of mortality and recruitment versus 

sediment inputs at impacted stations with the genetic 

and genotypic structure at control stations. Control 

stations were assumed to provide a proxy for the 

genetic and genotypic structure prior to the impact at 

each site. 

Results 

Genetic variability: 

Clonal structure and genetic diversity showed high 

variability among sites (Table 3). Genotypic richness 

(R) ranged between 0.44 (Amathous, ‘‘Impacted’’) and 

0.97 (Sounion, Control, Table 3). The number of 

genotypes differing in just one dinucleotide repetition 

at a unique locus varied among sites and stations (1 at 

Sounion Control station to 16 at El Campello impacted 

station). The frequency of such genotypes did not depend 

on the station, the mean number of samples per 

genotype or the clonal sub range, but it was negatively 

correlated to the allelic diversity, suggesting that those 

very similar genotypes did not derive from somatic 

mutations and arose naturally from the lower number 

of possible allelic combinations. The standardized 

mean number of alleles (Â) present in each station 

ranged between 20 (El Campello, Impacted, Table 3) 

and 48 (Sounion, Control), and the allelic frequencies 

were more similar between stations that between sites 

(see Appendix). The chances of obtaining the same 

multi-locus genotype by sexual recombination were 

very small (all P sex < 0.01). Therefore, identical genotypes 

were considered members of the same clone. 

As clonal richness, Simpson clonal diversity was 

minimum at Amathous (‘‘Impacted’’, D* = 0.880) and 

was highest at Sounion (‘‘Control’’, D* = 0.998, 

Table 3). Conversely, the clonal sub-range was minimum 

at the Sounion ‘‘control’’ station (CR = 12.7 m) 

and maximum at the Amathous ‘‘impacted’’ station 

(CR = 76.6 m, Table 3). Genotypic and allelic diversities 

decreased with increasing clonal sub-range, as 

large clone sizes were linked to the dominance of the 

sample by a few clones (CR and R: R 2 = 0.80, 

P < 0.002; CR and D*: R 2 = 0.49, P < 0.04; CR and Â: 

R 2 = 0.79, P < 0.003, n = 8). 

The variability in genetic structure between stations 

was much lower than among sites. Moreover, common 

Multilocus genotypes (MLG) were found between 

impacted and control stations at Amathous (1 MLG), 

Porto Palo (2 MLG) and El Campello (2 MLG). 

123 

88

Conserv Genet (2007) 8:1377–1391 1383 

Table 3 Genotypic structure parameters at the stations investigated: number of multilocus genotypes discriminated (G) inN genotyped 

samples, the unbiased genotypic richness (R), Complement Simpson diversity (D*) and the clonal sub-range (CR), in meters 

Sampling locations Genotypic structure Genetic structure Mean residuals of mortality with inputs 

N G R D* CR Â ^Fis ^Fð1Þ Total OM N P 

Amathous 

IMPACTED 40 18 0.44 0.880 76.6 29 –0.14 –0.02 –0.85 –0.23 –0.07 –0.18 

CONTROL 40 25 0.62 0.937 65.1 30 0.01 –0.03 –0.24 –0.68 –0.29 –0.30 

Sounion 

IMPACTED 37 31 0.92 0.994 29.9 41 –0.01 0.01 0.98 1.26 0.68 0.24 

CONTROL 33 29 0.97 0.998 12.7 48 –0.02 –0.01 –0.27 –1.01 –1.19 –1.06 

Porto Palo 

IMPACTED 40 34 0.77 0.981 60.5 38 0.06 –0.01 0.19 –0.06 0.01 –0.17 

CONTROL 38 32 0.72 0.971 41.7 40 –0.04 0.00 –0.48 –0.49 –0.18 0.23 

El Campello 

IMPACTED 39 26 0.66 0.961 70.9 20 –0.27 0.02 –0.25 –0.36 –0.20 –0.18 

CONTROL 40 23 0.56 0.953 68.7 28 –0.24 0.04 –0.66 –1.34 –1.23 

Genetic structure parameters: the standardised mean number of alleles (Â), the standardised mean inbreeding coefficient (^F is , marked 

in bold when it deviates significantly from Hardy–Weinberg equilibrium), and the mean Ritland kinship coefficient between neighbour 

samples (^F ð1Þ , without genet replicates). The residuals of regressions between mortality and total, Organic Matter, Nitrogen and 

Phosphorus sedimentation rates are also provided 

Genotype heterozygosity was not correlated to 

genotype frequency or clonal sub-range (data not 

shown). Significant heterozygote excesses were detected 

at the ‘‘control’’ station of El Campello (Spain, 

P < 0.001) and at the ‘‘impacted’’ station of Cyprus. 

The remaining stations did not differ significantly from 

Hardy–Weinberg equilibrium (Table 3). The mean 

Ritland kinship coefficient between neighbours was 

nearly 0 at all stations and sites (Table 3). 

Significant (P < 0.001 to P < 0.05) spatial autocorrelation 

patterns were detected either with or without 

genotype replicates in all sites and stations with the 

exception of Cyprus (Table 2), revealing a negative 

relationship between genetic relatedness and geographic 

distance. The spatial autocorrelation patterns 

varied widely across sites: comparing control stations 

among sites, it was lowest in the shallowest site 

(Greece: Sp = 0.010 ± 0.002, Table 2) and highest at 

the deepest site (Spain: Sp = 0.032 ± 0.006, Table 2). 

The removal of the MLG replicates did not affect the 

strength and patterns of the spatial autocorrelation in 

any consistent way (Table 2). 

Impact of perturbations on genotypic and genetic 

variability in the meadows: 

The slope of the spatial correlation and the Sp statistic 

were not significantly different between stations, 

except in Greece, where Sp at the impacted station 

was three times higher than at control station 

(P < 0.05). Such difference persisted when the autocorrelation 

was performed without MLG replicates 

(Table 2). 

The observed heterozygosity H o was lower at impacted 

than at control stations in every site with the 

exception of Cyprus, in which no significant differences 

were found in shoot density and net population growth 

between the so called ‘‘impacted’’ and ‘‘control’’ stations. 

Nevertheless, the reduction was not significant, 

even excluding this site (Pairwise t-test, two tails, 

P = 0.17, n = 3). 

In turn the clonal sub-range was systematically and 

significantly higher at ‘‘impacted’’ stations than at control 

ones (paired t-test, P < 0.05, n =4,Fig.2). Despite 

their negative relationship with clonal sub-range, no 

consistent variation was found in clonal richness R or 

Simpson clonal diversity D* between impacted and 

control stations across sites (Fig. 2). Nevertheless, the 

mean number of alleles Â (also inversely related to 

clonal sub-range) significantly decreased, as compared 

to their respective control stations (paired t-test, 

P < 0.05, n =4,Fig.2). The mean number of rare alleles 

Â (frequency < 5% at any station of a given site) was 

also significantly lower at impacted stations as compared 

to their respective control stations (P < 0.02, n =4). 

The increase in clonal sub-range at impacted stations 

showed no significant correlations with differences in 

shoot mortality rates or shoot densities between impacted 

and control stations (R 2 = 0.66, P = 0.121, n =4; 

R 2 = 0.43, P = 0.211, n = 4, respectively). The systematic 

reduction in the mean number of alleles at impacted 

stations also showed a non-significant correlation with 

differences in shoot mortality rates (expressed as 

ln(year –1 ), R 2 = 0.73, P = 0.096, n = 4) and with differences 

in sediment input rates (expressed as 

ln(g(DW)m –2 d –1 ), R 2 = 0.49, P = 0.189, n = 4). 

89 

123

1384 Conserv Genet (2007) 8:1377–1391 

Fig. 2 Diagrams of clonal 

richness (R), mean number of 

alleles (Â), Simpson clonal 

diversity D* and clonal subrange 

(CR, in meters) at 

impacted and control stations. 

The symbols correspond to 

the sites indicated in Fig. 1 

R 

Â 

Impacted 

D* 

CR 

Table 4 Coefficient of determination of linear regressions describing the relationship between differential shoot mortality at impacted 

stations (i.e. the residuals of shoot mortality with sedimentation rates) and clonal richness (R), Simpson clonal diversity (D*), mean 

number of alleles (Â) and maximum clonal range (CR, meters) at the respective control stations 

Demographic residuals at impacted stations Genetic structure at control stations (n =4) 

R D* Â CR (m) 

Mortality-Total inputs R 2 = 0.70, ns R 2 = 0.99** R 2 = 0.79, ns R 2 = 0.79, ns 

Mortality-OM inputs R 2 = 0.94* R 2 = 0.70, ns R 2 = 0.78, ns R 2 = 0.85, ns 

Mortality-N inputs R 2 = 0.96* R 2 = 0.67, ns R 2 = 0.81, ns R 2 = 0.86* 

Mortality-P inputs R 2 = 0.83, ns R 2 = 0.61, ns R 2 = 0.62, ns R 2 = 0.70, ns 

ns: P > 0.05; *: P < 0.05; **: P < 0.01 

Possible influence of genetic structure components 

on demographic responses to perturbation: 

The residuals of shoot mortality with total, organic and 

nutrient inputs at the impacted stations were correlated 

with the clonal sub-range (CR) at the control stations 

(Table 4), assumed to be representative of meadow 

genetic structure in the area near the cages, before impact. 

The negative relationship was significant between 

CR and the residuals of shoot mortality with nitrogen 

input rates (R 2 = 0.86, P < 0.05, n = 4; Fig. 3, Table 4). 

The residuals of shoot mortality at the impacted stations 

were positively correlated with R, Â and D* at control 

stations (Table 4). The strongest and most significant 

correlations occurred between residuals of mortality 

with nitrogen (N) inputs at impacted stations and R at 

control stations (R 2 = 0.96, P = 0.014, n = 4; Fig. 3, 

Table 4) as well as between residuals of mortality with 

total sediment inputs at impacted stations and D* at 

control stations (R 2 = 0.99, P = 0.003, n = 4; Fig. 3, 

Table 4). Residuals of shoot recruitment vs. sediment 

inputs at impacted stations did not show any significant 

relationship with D*, R, Â or CR at control stations. 

Discussion 

The effect of disturbances on clonal structure and 

genetic diversity: 

In spite of the high mortality and rapid reductions on 

P. oceanica meadow density near fish cages, most 

123 

90

Conserv Genet (2007) 8:1377–1391 1385 

Fig. 3 Regressions of Clonal 

richness (R), Simpson clonal 

diversity (D*), clonal subrange 

(CR) and mean number 

of alleles (Â) at the control 

stations with the residuals of 

shoot mortality with N 

sedimentation rate 

Residuals of mortality at impacted stations (yr -1 ) 

R D* 

CR 

Â 

Genetic structure at control stations 

variability in genetic parameters was still attributable 

to differences among sites rather than to differences 

between stations, indicating that the recent effects of 

population decline on genetic diversity have been 

lower than the longer term natural factors shaping the 

genetic structure across the species geographic range. 

Indeed, the similar genetic structure found at ‘‘impacted’’ 

and ‘‘control’’ stations within each site, as well 

as the existence of common genotypes between stations 

of the same site, support the assumption of similar 

patterns of clonal structure and genetic diversity 

between stations previous to impact. 

Despite the low shoot densities reached at impacted 

stations (29% of shoot density at ‘‘control’’ station in El 

Campello, which clearly compromise population viability 

in this slow growing species), effects on genetic 

diversity within the remaining meadows were limited to 

a reduction in the allelic richness, particularly affecting 

rare alleles. The lack of significant differences between 

stations for the observed heterozygosity or the 

inbreeding coefficient is consistent with predictions 

(Nei et al. 1975) and experiments (Leberg 1992), indicating 

that population bottlenecks have a stronger 

effect on allelic richness than on population heterozygosity 

(see also Widmer and Lexer 2001). The latter 

would indeed require extreme bottleneck or founder 

effects through several generations to be clearly reduced 

(Leberg 1992). Such patterns of allelic richness 

reduction have also been observed in other long-lived 

species, like logged or fragmented populations of 

tropical trees (White et al. 1999). An extensive survey 

within this group of species indicates that genetic 

diversity loss through fragmentation or selective logging 

is better reflected in the resulting inbreeding in the 

progeny, over longer time scales (Lee et al. 2002; Lowe 

et al. 2005). This suggests that genetic diversity may 

keep on being lost slowly in the subsequent generations 

(Lowe et al. 2005), still affecting the population a long 

time after the perturbation occurred. 

Posidonia oceanica is an extremely long-lived species 

(Mateo et al. 1997) in which genets are expected to 

persist for centuries (Hemminga and Duarte 2000; 

Sintes et al. 2006), when they are allowed by the 

environmental conditions. The sparse sexual reproduction 

of the species (Gambi et al. 1996; Balestri and 

Cinelli 2003; Díaz-Almela et al. 2006) and its slow 

vegetative extension rate (Marbà and Duarte 1998) 

ensures that the genetic structure observed in a so 

91 

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1386 Conserv Genet (2007) 8:1377–1391 

short time scale (all fish farms initiated operation 

Conserv Genet (2007) 8:1377–1391 1387 

observed significant reduction in shoot mortality at 

impacted stations with presumed larger initial clonal 

sub-range and number of shoots per genet suggests that 

mortality rates are slightly lower where clones are large 

and constituted of a high number of ramets. 

While the observation of larger clones at impacted 

stations could be explained as a simple matter of 

probability (i.e. given an equal shoot probability to die, 

it is more likely for little clones to disappear completely 

than for large ones), the increased mortality 

observed within meadows initially composed of little 

clones would suggest that the shoot probability of dying 

decreases with the size of the clone it belongs to. 

A major uncertainty about these inferences is the 

lack of information on the meadow genetic structure 

previous to the impact, which does not allow us to 

validate that of the control areas as a proxy. Experimental 

studies are needed to test for our conclusions. 

Nevertheless the results are based on the observation 

of a consistent pattern across four sites in the Mediterranean, 

where a basic similarity in the genetic 

structure between impacted and control stations supports 

the likelihood of our assumption. A major role 

for chance in producing such patterns appears unlikely. 

Altogether, those observations strongly suggest that 

some size-related fitness traits may influence the seagrass 

resistance to perturbation. 

Among clonal plants, clonal integration (share of resources 

and of probability-to-dye between ramets) has 

been shown to be a size-related adaptive trait (e.g. van 

Kleunen et al. 2000), which would provide a selective 

advantage in environments with a low proportion of 

suitable habitat (Oborny et al. 2000; Oborny and Kun 

2002). It has been invoked to explain enhanced survival 

and accelerated growth of clone patches with clonal size 

in undisturbed conditions among several seagrass species 

(Olesen and Sand-Jensen 1994; Vidondo et al. 1997). 

In P. oceanica, clonal integration has been experimentally 

proven to exist within at least 20–30 cm distance 

(Marbà et al. 2002). The ramets of a clone can 

remain connected during decades (as 40–50 years is the 

maximum life expectancy of P. oceanica shoots, Marbà 

and Duarte 1998) but given the slow horizontal growth 

rate of the species (1–6 cm year –1 ,Marbàand Duarte 

1998) we can hypothesize an upper limit for clonal 

integration in this species of 2.4–3 m, a range greater 

than the size estimated for most genotypes in this study, 

but much lower than the clonal sub-ranges registered at 

all the stations. This would suggest that other size-related 

fitness traits should account for the enhanced resistance 

to perturbation of large clones found in this work. 

Among other benefits, foraging capacity is improved 

by clonal size (Oborny and Kun 2002), which means that 

93 

a larger range of different micro-habitats can be explored 

by the same genetic individual when its number 

of modular units increases, optimizing its capacity to 

reach micro-environments it is better adapted to. Also, 

large clones may have reached such large size because 

they may have surmounted various regimes of selection, 

being better adapted to a larger range of conditions. This 

could be an additional factor accounting for the greater 

survival of large clones relative to small ones when exposed 

to disturbance derived from fish farm operations. 

The lack of correlation between genotype heterozygosity 

and clonal sub-range with neutral markers is not 

enough to reject such hypothesis, because heterozygote 

advantage is not proven to occur in P. oceanica. Therefore, 

under disturbed conditions, such mechanisms (increased 

clonal integration, optimized foraging capacity, 

or dominance of the fittest genotypes) enhancing survival 

of larger clones could make a population constituted 

of a few large clones more resistant to perturbation 

than a diverse population consisting of many little 

clones, counterbalancing the potentially beneficial 

influence of genotypic and genetic diversity in population 

resistance to and recovery from perturbations 

(Reusch and Hughes 2006). 

The experiments by Williams (2001), Hughes and 

Stachowitz (2004) and Reusch et al. (2005) suggest the 

existence of positive effects of genotypic diversity on 

survival and recovery of seagrasses for clones of similar 

size. As genotypic and allelic richness tend to be reduced 

with increased dominance of meadows by a few 

clones, the results of this study point to the existence of 

a trade-off between genetic or genotypic diversity and 

clone size in the potential of seagrass meadows to 

survive perturbations. This hypothesis deserves to be 

tested with experimental or field studies, which simultaneously 

test the effects of genotypic diversity with 

those of clonal size on plant survival and recovery. This 

study shows effects of fish farm-derived mortality on 

the clonal structure and genetic diversity of seagrass 

meadows. What are the consequences of those changes, 

on the scope of recovery after disturbance, is difficult 

to ascertain. Provided seagrass meadows are 

experiencing losses worldwide and will most likely 

continue to undergo in the near future (Duarte et al. in 

press), to understand the feed-backs of genetic and 

clonal structure with disturbance may help to predict 

the trajectories of those meadows. 

Acknowledgments The present work has been financed by the 

MedVeg (Q5RS-2001-02456 of FP5) and THRESHOLDS (contract 

003933-2 of FP 6) of the European Union. We are grateful to 

Rocío Santiago, Fernando Lázaro and Alberto Rabito for their 

assistance in the field. 

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1388 Conserv Genet (2007) 8:1377–1391 

Appendix Allelic frequencies of the seven loci at the four sites (C = Control station; I = Impacted station) 

Locus 1 141 143 145 147 149 151 153 155 157 158 159 161 163 165 167 A Locus 7 176 178 180 182 A 

Amathous C 0.60 0.22 0.16 0.02 4 0.40 0.60 2 

Amathous I 0.03 0.50 0.28 0.17 0.03 5 0.67 0.33 2 

Sounion C 0.02 0.19 0.02 0.52 0.19 0.05 0.02 0.02 8 0.52 0.45 0.03 3 

Sounion I 0.13 0.37 0.49 0.01 4 0.01 0.18 0.81 3 

Porto Palo C 0.02 0.02 0.50 0.22 0.09 0.05 0.07 0.03 8 0.55 0.45 2 

Porto Palo I 0.19 0.02 0.31 0.15 0.06 0.27 6 0.69 0.31 2 

Campello C 0.07 0.09 0.46 0.33 0.02 0.02 0.02 7 0.37 0.63 2 

Campello I 0.46 0.54 2 0.85 0.15 2 

Locus 2 154 156 164 166 172 174 182 184 188 198 A Locus 5 159 161 163 165 167 171 A 

Amathous C 0.94 0.04 0.02 3 0.48 0.52 2 

Amathous I 0.89 0.11 2 0.03 0.33 0.06 0.58 4 

Sounion C 0.08 0.83 0.09 3 0.02 0.16 0.16 0.67 4 

Sounion I 0.04 0.88 0.07 3 0.34 0.03 0.63 3 

Porto Palo C 0.69 0.19 0.03 0.09 4 0.60 0.29 0.10 3 

Porto Palo I 0.50 0.47 0.03 3 0.16 0.44 0.31 0.06 0.03 5 

Campello C 0.20 0.09 0.61 0.11 4 0.17 0.07 0.43 0.33 4 

Campello I 0.02 0.77 0.21 3 0.15 0.1 0.46 0.29 4 

Locus 3 194 198 200 206 208 210 212 214 216 218 220 222 224 226 228 230 232 234 236 238 

Amathous C 0.02 0.06 0.22 0.10 0.16 0.02 0.02 0.08 0.12 

Amathous I 0.28 0.11 0.22 0.06 0.28 

Sounion C 0.02 0.05 0.02 0.09 0.06 0.11 0.06 0.02 0.03 0.02 

Sounion I 0.01 0.12 0.04 0.01 0.04 0.04 0.13 0.03 0.01 0.04 0.10 0.10 

Porto Palo C 0.05 0.03 0.02 0.19 0.24 0.16 0.03 0.07 0.19 0.02 

Porto Palo I 0.02 0.02 0.21 0.24 0.06 0.06 0.11 0.10 0.16 0.02 

Campello C 0.28 0.35 0.11 0.15 0.02 0.09 

Campello I 0.31 0.44 0.04 0.21 

Locus 3 240 242 244 246 248 250 252 254 256 260 262 264 266 268 282 288 A 

Amathous C 0.08 0.06 0.02 0.04 12 

Amathous I 0.06 5 

Sounion C 0.02 0.02 0.03 0.11 0.09 0.03 0.03 0.03 0.08 0.02 0.03 0.02 0.02 0.02 13 

Sounion I 0.03 0.07 0.09 0.03 0.04 0.01 0.01 14 

Porto Palo C 10 

Porto Palo I 10 

Campello C 6 

Campello I 4 

123 

94

Conserv Genet (2007) 8:1377–1391 1389 

Appendix continued 

Locus 4 208 210 218 220 222 226 228 234 236 238 240 242 244 250 252 A Locus 6 168 170 172 174 178 A 

Amathous C 0.02 0.84 0.08 0.06 4 0.74 0.26 2 

Amathous I 0.64 0.33 2 0.83 0.17 2 

Sounion C 0.02 0.38 0.39 0.22 4 0.77 0.23 2 

Sounion I 0.01 0.35 0.01 0.40 0.03 0.18 0.01 7 0.82 0.18 2 

Porto Palo C 0.02 0.03 0.02 0.57 0.07 0.09 0.07 0.02 0.07 0.05 10 0.62 0.33 0.05 3 

Porto Palo I 0.02 0.03 0.63 0.08 0.06 0.02 0.08 0.08 8 0.69 0.19 0.06 0.05 4 

Campello C 0.91 0.09 2 0.20 0.39 0.41 3 

Campello I 1 1 0.02 0.81 0.15 0.02 4 

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97 

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II.5 

GenClone 1.0: a new program to analyse genetics data on clonal 

organisms. Molecular Ecology Notes, 2007. 

Il existait seulement trois logiciels permettant de prendre en compte la 

clonalité dans les analyses de données moléculaires, et la plupart étaient restreint à 

la reconnaissance des lignées clonales. Aucun ne proposait l’étude des 

composantes spatiales de la clonalité (‘clonal subrange’, les nouveaux indices 

proposés dans notre article de revue). Nous avons donc développé un logiciel 

permettant d’utiliser les les différentes méthodes et indices compilés ou proposés 

dans l’article de revue, notamment l’adaptation des analyses d’autocorrélation 

spatiale aux jeux de données moléculaires sur les organismes clonaux organismes 

clonaux. 

GenClone a déjà été mis à jour à deux reprises (nous en somme au mois de 

décembre 2007 à Genclone 2.0), la première fois afin de corriger d’inévitables ‘bugs’ 

signalés par les premiers utilisateurs, et la seconde pour y intégrer les nouveaux 

outils statistiques développés et proposés dans la revue. 

http://si-wagner.ualg.pt/ccmar/maree/software.php?soft=genclon 

98

Molecular Ecology Notes (2007) 7, 15–17 

doi: 10.1111/j.1471-8286.2006.01522.x 

Blackwell Publishing Ltd 

PROGRAM NOTE 

GENCLONE: a computer program to analyse genotypic data, test 

for clonality and describe spatial clonal organization 

SOPHIE ARNAUD-HAOND* and KHALID BELKHIR† 

*Laboratory of ‘Ecology and Evolution of Marine Organisms’, CCMAR, F.C.M.A.-Universidade do Algarve, Faro, Portugal, 

†Laboratoire Génome, Populations, Interactions, Adaptation, Université de Montpellier II, France 

Abstract 

GENCLONE 1.0 is designed for studying clonality and its spatial components using genotype 

data with molecular markers from haploid or diploid organisms. GENCLONE 1.0 performs 

the following tasks. (i) discriminates distinct multilocus genotypes (MLGs), and uses 

permutation and resampling approaches to test for the reliability of sets of loci and 

sampling units for estimating genotypic and genetic diversity (a procedure also useful 

for nonclonal organisms); (ii) computes statistics to test for clonal propagation or clonal 

identity of replicates; (iii) computes various indices describing genotypic diversity; and 

(iv) summarizes the spatial organization of MLGs with adapted spatial autocorrelation 

methods and clonal subrange estimates. 

Keywords: clonality, individuals resampling, locus combination, multilocus genotypes, software, 

spatial distribution 

Received 15 June 2006; revision accepted 10 July 2006 

Clonal species, from unicellular organisms to marine invertebrates, 

are dominant in many habitats. In ecological studies 

on clonal organisms, particularly in clonal plants with cryptic 

rhyzomatic connections, the discrimination of individuals 

issued from sexual or clonal reproduction, or the estimation 

of sexual vs. clonal reproduction, are among the 

most challenging technical issues. Molecular markers are 

particularly useful since genetically identical individuals 

issued from clonal reproduction can theoretically be 

recognized on the basis of their multilocus genotypes. 

However, reliable recognition of clonal identity, on the 

basis of molecular markers, requires specific statistical 

tests and procedures. Two software packages have 

been recently developed, mlgsim (Stenberg et al. 2003) and 

genotype and genodive (Meirmans & Van Tienderen 

2004), that provide some of those required tests. The software 

developed here, genclone 1.0 implements new and 

improved statistical features such as accounting for deviation 

from Hardy–Weinberg equilibrium while testing for clonality, 

Correspondence: Sophie Arnaud-Haond, Fax: +61 7 4725-1570; 

E-mail: s-arnaud@ualg.pt or belkhir@univ-montp2.fr 

http://www.ualg.pt/ccmar/maree/software.php?soft=genclon 

and specially adapted analyses for studying the spatial 

components of clonality. 

genclone requires the following information for each 

individual: (i) a name; (ii) one to two spatial coordinates 

(when available); and (iii) genotype at each locus for 

codominant markers. The options available to users can be 

divided in three sets of analysis, corresponding to the 

three ‘upper panels’. (i) ‘Test’ — for checking for locus and 

‘sampling unit’ reliability for optimal multilocus genotypes 

(MLGs) and genetic individuals recognition; (ii) ‘MLG’ — 

for computing various genotypic richness and diversity 

descriptors; and (iii) ‘Spatial components’ — for describing 

various spatial aspects of clonality. 

Tests 

These procedures use permutation approaches to test for 

data quality. (In other words, the power of the analysed 

sample and loci set to obtain an accurate estimate of the 

maximum number of multilocus genotypes present in the 

dataset and in the sampling area, respectively). All possible 

datasets corresponding to all possible combinations of loci 

(with L the number of analysed loci) and sampling units 

(with N the total number of ‘sampling units’) are generated, 



99

16 PROGRAM NOTE 

and then the minimum, average and maximum number 

of discriminated MLGs for each class of number of locus 

(l) or sampling units (n) are obtained. When performed 

on the loci, this permutation procedure allows us to verify 

if an asymptote is reached when l tends towards L. It 

therefore allows ensuring that the set of loci used permit a 

good estimate of the real number of MLG present in the 

sample analysed. This procedure combined with the test 

for clonal identity detailed hereafter allows to ascertain 

the maximum efficiency of the chosen loci combination 

(Arnaud-Haond et al. 2005). When applied to individuals, 

it allows us to verify if the sampling density (in terms of the 

number of sampling units) is sufficient to reliably estimate 

the true number of MLGs present in the sampled area. When 

the number of individuals or loci is high, the computation 

time can be very long due to the huge number of possible 

combinations. We have therefore developed two complementary 

procedures based on resampling without replacement: 

resampling x times from the set of L loci; and 

resampling x times from the set of N individuals; (where x 

is chosen by the users) and then estimating the average 

number of MLGs which can be distinguished with this 

number x of loci or individuals. These resampling procedures 

also provide estimates of the maximum, minimum 

and average number of MLGs in the subset of data, as well 

as the maximum, minimum and the mean number of alleles 

and the heterozygosity (unbiased estimate, Nei 1978) for 

each subset of data generated. This is an alternative to the 

bootstrap, and to the rarefaction procedure (El Mousadik 

& Petit 1996), commonly used to compare the levels of 

diversity among sample sets of unequal size, and can also 

be used to compare allelic richness and heterozygosity in 

nonclonal organisms (Leberg 2002). The last test in this 

section is a test for clonal propagation based on the round 

robin method proposed by Parks & Werth (1993). This 

allows us to estimate, for each MLG, the probability P GEN 

and the derived binomial P SEX 

. These probabilities are used 

to test both for clonal identity and for clonal propagation 

(Arnaud-Haond et al. 2005; see also Tibayrenc et al. 1990; 

Gregorius 2005). A slightly more conservative test is also 

provided, which is based on estimates P GEN 

(f) and P SEX 

(f), 

of the same probabilities, but now taking into account the 

estimated F IS 

in the population (Young et al. 2002). Finally, 

a genetic distance matrix can be computed (based on the 

number of different alleles among sampling units). The 

frequency distribution of genetic distances can, for example, 

help to screen for scoring errors or somatic mutations 

(Douhovnikoff et al. 2004; Meirmans & Van Tienderen 

2004; Arnaud-Haond et al. 2005). With a high number of 

loci, or loci characterized by a high mutation rate, this 

frequency distribution can also help to define a threshold 

below which MLGs separated by low genetic distance and 

can be considered as belonging to the same ‘clonal lineage’, 

or of the same genetic individual. 

MlG 

This option allows us to compute the usual estimators of 

genotypic richness in a sample of N sampling units. These 

are: G = the number of distinct MLGs; R = the modified 

index of genotypic richness, as proposed by Dorken and 

Eckert (Dorken & Eckert 2001). Additionally, the commonly 

used indices of genotypic diversity, derived from species 

diversity indices, are computed. These are: the Simpson 

complement and, the Shannon-Wiener (Hurlbert 1971; 

Washington 1984) diversity and evenness indices, as 

well as Hill’s Simpson reciprocal (Hurlbert 1971; Hill 1973) 

(which corresponds to the ‘apparent number of genotypes 

in the sample’). 

Spatial components 

These procedures allow us to summarize spatial aspects 

of clonal diversity when geographic coordinates of the 

sampling units are available. A map of MLGs can be drawn 

and exported as a bitmap file. The clonal subrange section 

plots the probability of clonal identity against distance 

(Harada & Iwasa 1996; Harada et al. 1997). Here we use a 

custom definition of distance classes (either as a number 

of distance classes, or as a list of predefined maximum 

distances for each class), and estimate of the ‘clonal subrange’ 

as the maximum spatial distance between two replicates of 

the same MLG (Alberto et al. 2005). Finally, autocorrelation 

procedures adapted to the existence of replicates are 

computed, using Loiselle et al. (1995) and Ritland (1996) 

kinship coefficients. Classical autocorrelation analysis are 

performed at the ‘ramet level’ (i.e. including all sampling 

units), and random permutations of the geographical coordinates 

are performed among sampling units in order 

to test for the significance of the observed spatial structure. 

Following Vekemans & Hardy (2004) F ij 

(the average kinship 

for each distance class) and b (the slope of the regression) 

are estimated and tested for significance. At the ‘genet level’ 

(i.e. including only one copy of each MLG), autocorrelation 

is computed in three ways: (i) using central coordinates for 

each replicated MLG (Hämmerli & Reusch 2003; Alberto 

et al. 2005); (ii) using a weighted approach (Alberto et al. 

2005; Wagner et al. 2005;) to remove the distances among 

pairs of identical genotype from the dataset; (iii) using 

a resampling approach in order to create and analyse 

subdatasets of size g (= the number of MLG identified), 

with each MLG being attributed randomly one of the spatial 

coordinates corresponding to one of the sampling units 

exhibiting this given MLG (Alberto et al. 2005). For this 

last procedure, confidence intervals are computed at 

90% and 95% level, in order to test whether the ‘observed’ 

distribution obtained by resampling significantly depart 

from the ‘random’ distribution generated by randomly 

permuting spatial coordinates among MLGs. 

100 



PROGRAM NOTE 17 


We thank Kevin Dawson, Jim Coyer and Malia Chevolot for their 

help in improving the first versions of genclone and correcting 

the English. 

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101



II.6 Conclusions et Perspectives. 

Dans un premier temps nous avons tenté de standardiser le cadre ‘logistique’ 

(échantillonnage) et ‘statistique’ des études afin d’optimiser les possibilités 

ultérieures d’interprétation des résultats dans un contexte plus vaste, ou de 

comparaison entre des études différentes. Nous n’avons toujours pas résolu le 

problème de l’échantillonnage et de sa densité. Les différentes espèces 

d’organismes clonaux sont réparties dans des populations présentant des densités 

et des niveaux de fragmentation spatiales variables, et étant données les propriétés 

indésirables des estimateurs classiques de diversité, il demeure difficile en l’absence 

de données démographiques de comparer ces indicateurs entre populations d’une 

même espèce, et donc a forciori entre espèces différentes. Nous continuons 

actuellement à explorer les propriétés de la distribution de Pareto face aux variation 

de densité, et testons également la validité d’autres distributions, de type 

exponentielle, à la description de la diversité clonale. 

Je poursuis à présent ce travail de réflexion en tentant de réaliser une autre 

revue et une révision des concepts centraux en écologie et en évolution (taille 

efficace, temps de génération, unité soumise à la sélection naturelle, mutations 

somatiques…), et de la façon dont ils sont affectés par la clonalité. Que signifie le 

concept de taille efficace, quelle est l’unité sur laquelle les processus sélectifs 

s’appliquent (l’unité ou la lignée ?), que signifie le concept de générations – 

chevauchantes ou non- chez des espèces où le génotype issu d’un zygote est 

susceptible de perdurer des centaines de milliers d’années s26 ? 

Il s’agit de faire un premier pas, primaire et synthétique, afin de stimuler une 

réflexion collective nécessaire à la compréhension de la façon dont la clonalité 

influence la dynamique et l’évolution des populations et des espèces et sur notre 

façon d’appréhender ce mode de fonctionnement particulier et ses implications pour 

nos études. 

102



III. LES METAPOPULATIONS CONSIDEREES COMME DES SYSTEMES 

COMPLEXES : NETWORKS GENETIQUES 

A l’instar de beaucoup d’études d’écologie moléculaire, les travaux auxquels 

j’ai contribué ont porté sur une majorité d’espèces dont les populations naturelles 

connaissent des fluctuations ou des perturbation importantes. C’est le cas des 

espèces exploitées (le chinchard, la nacre perlière), menacées (les Mangroves, les 

Phanérogames), invasives (comme l’algue Caulerpa taxifolia, thèse de Tânia Aires) 

ou pathogènes. Il semble en fait que la plupart des espèces étudiées en écologie 

moléculaire appartiennent à l’une de ces catégories, et sont étudiées précisément 

parce qu’elles se trouvent dans une condition démographique particulière impliquant 

un écart au postulat d’équilibre migration-dérive nécessaire à l’interprétation des 

données de génétique des populations avec les outils statistiques classiques. De 

plus, les modèles classiques tendent à considérer toutes les populations comme 

étant équivalentes en terme de taille efficace et de flux migratoires notamment, et 

l’objectif principal est souvent de cibler les populations les plus importantes d’un 

système, qu’il s’agisse d’espèces menacées, invasives ou pathogènes. 

Par ailleurs, le développement croissant des moyens moléculaires 

disponibles, la multiplication du nombre de locus et de la quantité de données qu’il 

est possible de réunir, rendent les jeux de données de plus en plus complexes. Il 

devient de moins en moins satisfaisant d’utiliser des statistiques résumées et de 

réduire l’information réunie sur les centaines de locus et d’individus à un indice 

unique, dont l’interprétation reste par ailleurs sujette à caution. 

Pour conclure, nous étions à la recherche d’une méthode qui permette 

d’exploiter au mieux l’information contenue dans nos jeux de données, avec un 

minimum d’hypothèses sous-jacentes quant à la biologie du système étudié et la 

possibilité de comprendre le système dans son ensemble. Quelques conversations 

‘fortuites’ avec des collègues physiciens spécialisés dans l’étude des systèmes 

complexes, nous ont amené à tenter l’expérience, avec eux, d’adapter une théorie en 

plein essor aux données de génétique des populations: la théorie des réseaux. 

Le problème spécifique posés par les systèmes définis comme ‘complexes’ 

est la compréhension du fonctionnement d’un système composé de n composantes, 

dans un système non Euclidien, c’est à dire lorsque la compréhension du 

fonctionnement de chacune des n composantes prises individuellement ne permet 

pas la compréhension du fonctionnement du système dans son entier. La théorie des 

réseaux consiste à représenter un système complexe sous forme d’agents (les 

noeuds) reliés par des liens qui représentent le flux d’information dans le système, et 

à analyser les propriétés révélées par la topologie du graphe en termes de flux 

d’information, de stabilité et d’évolution du système ( Figure 10). 

103



Figure 10: 

(a) Illustration shématique de réseau empruntée à Watts et Strogartz (1998), avec de gauche à 

droite le passage d’un réseau complètement régulier à un réseau ‘random’, selon la proportion p de 

liens distribués au hasard entre les noeuds (ou vertex). 

(b) Illustration de l’évolution de deux propriétés des réseaux en fonction de p: le ‘clustering 

coefficient’ et le diamètre (L) (voir encadré 3). 

Développée au début du XXe siècle, elle a connu son réel essor avec le 

développement de l’informatique et de la puissance de calcul requise à son 

développement, à la fin des années 1990 (Watts & Strogatz 1998, Albert et al. 1999). 

Elle a depuis été appliquée à des champs de recherche allant de la sociologie (Watts 

1999, 2004) à la biologie cellulaire ou moléculaire (Ueda et al. 2004) ou à l’écologie 

(Bascompte et al. 2003, Bascompte et al. 2005, May 2006), ses applications initiales 

les plus populaires étant celles de la structure et de la dynamique d’internet (Albert et 

al. 1999, Barabasi et al. 2000). Basée sur la théorie des graphes, la théorie des 

réseaux permet dans un premier temps de représenter les liens entre agents d’un 

système complexe sans être contraint par une représentation dichotomique telle que 

nous la connaissons classiquement dans les arbres phylogénétiques représentant 

les réseaux d’individus ou de populations (Figure E.3, Figure 11). 

104



Figure 11 : réseau de populations de Posidonia oceanica en Méditerranée, élaboré sur la base des 

distances de Goldstein et analysé au point de percolation (au niveau minimum de distance avant la 

déconnection du système). Le diamètre des points reflète le niveau de betweeness-centrality, un 

indice utilisé pour résumer la proportion de chemins les plus courts passant par l’agent représenté, et 

qui reflète donc ici le rôle des populations dans le relais de l’information –ici le flux génique- et le 

maintien de la connectivité du système. 

Une fois le graphe établi, il s’agit à partir de cette représentation statique, 

d’obtenir des informations sur la dynamique du flux d’information dans le système. 

Les descripteurs de la topologie du réseau (distribution des liens, chemin le plus 

court, etc… voir Encadré 3) permettent d’extraire un certain nombre de 

caractéristiques révélatrices de cette dynamique. D’une part l’existence de structure 

hiérarchique au sein des populations (écart à la panmixie, existence de sousfamilles) 

peut-être révélée à l’échelle intra-populationnelle. De même à l’échelle d’un 

réseau de métapopulations, sans a priori sur les groupements ni aucune information 

géographique préalable, l’existence de sous-structure peut-être révélée et les 

groupes de sous populations, quelque soit leur nombre, identifiés. Enfin le 

comportement particulier de certains nœuds ou groupes de nœuds -ou encore de 

certains liens- dans le flux de l’information ou le maintien de l’intégrité du système, 

peuvent être révélées par l’analyse de certaines propriétés déductibles de la 

distribution des liens (Encadré 3). 

105



Encadré 3: Différents types de réseaux et propriétés 

principales. 

Les réseaux sont constitués de noeuds (ou vertices) reliés entre eux par des liens 

(edges). Plusieurs types de réseaux peuvent être construits en fonction de l’uniformité 

des noeuds et des liens, ou de la possibilité de pondérer ou de donner une direction aux 

liens 

Figure E 3.1: différentes sortes de réseaux : 

(a) simple et non dirigé, avec un seul type de 

liens et de noeuds 

(b) existence de différents types de liens et 

noeuds 

(c) liens et noeuds pondérés 

(d) lien directionnels. 

Extrait de Newman, 2003 

Descripteurs de la topologie du réseau: 

‘degree distribution’: distribution des liens du systèmes entre les noeuds 

‘shortest path’, ou ‘geodesic distance’: moyenne des plus court chemins d’un 

noeud à l’autre 

‘diameter’: distance la plus importante séparant deux noeuds dans un réseau 

‘clustering’ (ou ‘transitivity’): ratio de triplet de noeuds interconnecté par rapport au 

nombre total de triplés dans le système. Peut-être utilisé comme un estimateur de la 

sous-structure du système. 

Small world: se dit d’un type spécifique de réseau présentant un faible diamètre et 

un fort clustering. Cette topologie typique a été rencontrée dans un très grand 

nombre de réseaux étudiés jusqu’à présent (internet, contact sociaux, etc…) 

Caractéristiques noeuds-spécifiques: 

‘betweeness-centrality’: nombre de chemin les plus courts passant par un noeud 

donné. 

‘degree’: nombre de liens unissant un noeud donné à d’autres agents du système. 

Dans un premier temps, nous avons construit des réseaux simples (de type a dans 

l’encadré 3), c'est-à-dire avec un seul type de nœuds et de lien, non dirigés et non 

pondérés. Nous avons réalisé les premiers essais et la mise au point sur la base d’un 

jeu de données de microsatellites de Posidonia oceanica échantillonnée tout autour 

de la Méditerranée. 

106



A l’échelle des populations, des réseaux d’unités d’échantillonnage (ramets) et de 

clones (genets) ont été réalisés sur la base des distances de Goldstein (1995) 

modifiées pour être adaptées au niveau inter individuel ; à l’échelle de l’aire de 

distribution, les différentes localités d’échantillonnages ont été représentées par des 

noeuds reliées par des liens fonction de la distance de Goldstein (1997) entre les 

localités. Le but de cet exercice était d’établir, parmi les méthodes et les indices 

disponibles dans les analyses de réseaux, les plus adaptés à la description des 

relations génétiques entre individus ou populations, et du flux de gènes dans 

l’espace. Nous espérions grâce à cet exercice identifier des individus ou des 

populations ayant un rôle central dans l’histoire ou la dynamique du système. 

Dans le cadre du projet Européen qui vent de débuter, nous travaillons maintenant à 

l’analyse d’autres jeux de données, et également à la modélisation de systèmes de 

métapopulations (avec des sources-puits, de extinction –recolonisation, etc…) afin 

d’élucider la correspondance entre les propriétés du réseau et la distribution du flux 

génique dans un système de métapopulations. 

107



III.1 Spectrum of genetic diversity and networks of clonal populations. Journal 

of the Royal Society Interface, 2007. 

Au niveau intrapopulation, en considérant les unités d’échantillonnage (ramets) ou 

les lignées clonales (genets) comme les agents et leur distance génétique comme le 

lien qui les unis, les réseaux ont mis en évidence un structure typique dite ‘smallworld’. 

Cette structure s’applique à un très large éventail de systèmes complexes 

analysés jusqu’à aujourd’hui, caractérisés par l’existence d’une faible distance 

moyenne entre nœuds et d’un fort clustering, révélateurs de la propagation 

relativement rapide de l’information au travers du système : dans notre cas d’une 

faible divergence génétique moyenne entre individus d’une même population, et une 

forte sous. 

L’analyse de la topologie des réseaux et le fort clustering ont révélé l’existence 

de ‘sous-familles’ d’individus plus apparentés entre eux que d’un groupe à l’autre, 

révélant un écart à la panmixie. La seule méthode performante permettant de 

détecter ce type de structure hiérarchique au sein des populations est 

l’autocorrélation spatiale. Toutefois cette méthode ne permet de mettre en évidence 

l’existence de sous structure que si la distance génétique (ou au contraire le 

coefficient de parenté) est corrélée (ou inversement corrélée) à la distance 

géographique entre individus. Enfin l’analyse de la distribution des distances entre 

nœuds a permis d’identifier des individus ‘outsiders’, beaucoup plus divergent des 

autres qui pourraient être issus d’un évènement de migration récent. 

108

J. R. Soc. Interface (2007) 4, 1093–1102 

doi:10.1098/rsif.2007.0230 

Published online 1 May 2007 

Spectrum of genetic diversity and networks 

of clonal organisms 

Alejandro F. Rozenfeld 1, *, Sophie Arnaud-Haond 2 , 

Emilio Hernández-García 1 ,Víctor M. Eguíluz 1 , Manuel A. Matías 1 , 

Ester Serrão 2 and Carlos M. Duarte 3 

1 Cross-Disciplinary Physics Department, IMEDEA (CSIC-UIB), 

Instituto Mediterráneo de Estudios Avanzados, Campus Universitat de les Illes Balears, 

07122 Palma de Mallorca, Spain 

2 CCMAR, CIMAR-Laboratório Associado, Universidade do Algarve, 

Gambelas, 8005-139 Faro, Portugal 

3 Natural Resources Department, IMEDEA (CSIC-UIB), 

Instituto Mediterráneo de Estudios Avanzados, C/Miquel Marques 21, 

07190 Esporles, Mallorca, Spain 

Clonal reproduction characterizes a wide range of species including clonal plants in terrestrial 

and aquatic ecosystems, and clonal microbes such as bacteria and parasitic protozoa, with a 

key role in human health and ecosystem processes. Clonal organisms present a particular 

challenge in population genetics because, in addition to the possible existence of replicates of 

the same genotype in a given sample, some of the hypotheses and concepts underlying 

classical population genetics models are irreconcilable with clonality. The genetic structure 

and diversity of clonal populations were examined using a combination of new tools to 

analyse microsatellite data in the marine angiosperm Posidonia oceanica. These tools were 

based on examination of the frequency distribution of the genetic distance among ramets, 

termed the spectrum of genetic diversity (GDS), and of networks built on the basis of 

pairwise genetic distances among genets. Clonal growth and outcrossing are apparently 

dominant processes, whereas selfing and somatic mutations appear to be marginal, and the 

contribution of immigration seems to play a small role in adding genetic diversity to 

populations. The properties and topology of networks based on genetic distances showed a 

‘small-world’ topology, characterized by a high degree of connectivity among nodes, and a 

substantial amount of substructure, revealing organization in subfamilies of closely related 

individuals. The combination of GDS and network tools proposed here helped in dissecting 

the influence of various evolutionary processes in shaping the intra-population genetic 

structure of the clonal organism investigated; these therefore represent promising analytical 

tools in population genetics. 

Keywords: genetic networks; small-world networks; genetic diversity; clonal organisms 

1. INTRODUCTION 

The considerable progress achieved during the last 

decades in molecular biology and biotechnologies has 

greatly enhanced the potential of molecular markers for 

studying the process of evolution in natural populations 

in the framework of population genetics. As the empirical 

basis for population genetics is broadened, it is increasingly 

clear that the theoretical constructs under which 

population analyses are traditionally conducted involve 

assumptions that are often violated in natural scenarios, 

such as the random mating, equilibrium (Wright 1931), 

*Author for correspondence (alex@imedea.uib.es). 

Electronic supplementary material is available at http://dx.doi.org/ 

10.1098/rsif.2007.0230 or via http://www.journals.royalsoc.ac.uk. 

and non-overlapping generations commonly assumed for 

interpreting statistics on population genetic composition 

and structure (Hey & Machado 2003). 

The summary statistics used in population genetics 

to estimate relevant parameters such as departure from 

panmixia or the population structure indeed rely on 

theoretical and mathematical models that involve the 

adoption of a somewhat narrow range of underlying 

parameters and demographic models; this greatly limits 

the scope of demographic situations that can be 

accurately explored (Hey & Machado 2003). Among 

others, three examples in which the assumptions 

underlying the classical Wright–Fisher model (Wright 

1931) of population genetics are violated are the cases 

of endangered and of invasive species, as well as 

pathogenic species exhibiting recurrent fluctuations 

in population size linked to epidemiologic events. 

Received 8 January 2007 

109 

Accepted 8 March 2007 1093 This journal is q 2007 The Royal Society

1094 Genetic diversity of clonal organisms A. F. Rozenfeld et al. 

These are among the type of species most studied in 

evolutionary ecology or molecular epidemiology, precisely 

because they exhibit population dynamics that 

strongly depart from equilibrium, which very much 

limits the interpretation of classical population genetics 

statistics. These constraints on the application of 

conventional metrics of population genetic structure 

are even more evident for clonal organisms, the 

characteristics of which challenge the notions of 

effective population size and generation time (Orive 

1993; Yonezawa et al. 2004). Moreover, a clear concept 

of the unit (genetic individual) on which evolutionary 

forces are acting is lacking (Orive 1995; Fischer & Van 

Kleunen 2002) for clonal species. 

Examination of the genetic structure of populations 

is rooted in a comparison of the extent of genetic 

variability among individuals within populations, as 

well as differences among the populations, typically 

assessed using appropriate molecular markers applied 

to statistically representative samples of individuals. 

Hypervariable markers such as microsatellites are 

commonly considered to be the markers of choice in 

assessing the genetic variability and structure of 

populations. This is particularly true in the case of 

clonal organisms, for which they allow, in a given 

sample, proper assessment of the individual level 

through the isolation of distinct multi-locus genotypes 

or lineages, which is a prerequisite for estimating 

variability and structure in clonal populations. 

Furthermore, most evaluations of genetic composition 

of clonal (and non-clonal) populations are based on 

summary statistics, such as heterozygosity or fixation 

indices, that do not consider the distribution of genetic 

distances among the sampled individuals. In fact, 

except in the case of co-ancestry coefficients mostly 

used to assess spatial autocorrelation, inter-individual 

distances are not commonly used in the literature 

(Douhovnikoff & Dodd 2003; Meirmans & Van 

Tienderen 2004). 

The depiction of a population structure as a 

concerted representation of the genetic distances 

between agents indicates that a network approach is 

suitable for examination of the genetic structure of 

populations in which the links between agents depend 

on the genetic difference between them. The use of 

networks to graphically represent genetic relationships 

has emerged as a useful tool in cases in which the nodes 

are haplotypes (Templeton et al. 1992; Excoffier & 

Smouse 1994; Posada & Crandall 2001; Cassens et al. 

2003; Rueness et al. 2003; Morrison 2005) or populations 

(Dyer & Nason 2004). Here, we propose a 

network representation of the genetic structure of 

populations of a clonal organism, focused on the genetic 

individuals (in clonal plants designed as ‘genets’, 

extending clonally by growing new shoots also called 

‘ramets’) as the interacting agents. We represent the 

intra-population genetic similarities among the genetic 

individuals as networks. In addition, we go one step 

further from the simple graphical representation of 

genetic relationships, and quantitatively analyse the 

properties of the resulting networks using tools (see §5) 

successfully applied to other problems (Proulx et al. 

2005) such as the characterization of food webs (Dunne 

et al. 2002a,b) and the analysis of protein (Jeong et al. 

2001) or gene (Davidson et al. 2002) interactions. 

We demonstrate this approach using a clonal 

seagrass species (Posidonia oceanica) for which a 

large dataset, including microsatellite data for approximately 

1500 shoots sampled from 37 populations across 

the Mediterranean, is available. We first propose a 

metric for genetic distances among individuals based on 

their observed multi-locus microsatellite genotypes, 

which allows us to describe the spectrum of genetic 

diversity within populations. We then explore the 

biological processes that yield the observed spectra, and 

use this knowledge to topologically represent the 

population as a network, from which structural 

diagnostics are then derived. 

2. AVAILABLE DATA 

2.1. Model species 

Posidonia oceanica is a clonal marine angiosperm 

restricted to the Mediterranean Sea, where it develops 

extensive meadows ranging from 0 to 40 m in depth 

(Hemminga & Duarte 2000). It is a very slow-growing 

organism, with the clones extending horizontally through 

the growth of rhizomes at approximately 2 cm yr K1 , 

developing shoots (ramets, the individual module 

repeated to develop clones) at intervals of approximately 

5–10 cm. This monecious species (i.e. both male and 

female flowers on the same shoot; Hemminga & Duarte 

2000) is characterized by sparse episodes of sexual 

reproduction. Individual P. oceanica shoots (ramets) 

live for up to 50 years, and the clones have been aged to 

over 1000 years (Hemminga & Duarte 2000). The plants 

are experiencing basin-wide decline and are subject to 

specific protection and conservation measures (Marbà 

et al. 1996; Moreno et al. 2001; Marba et al. 2005). 

2.2. Multi-locus microsatellite genotypes 

Approximately 40 P. oceanica shoots were sampled in 

each of 37 localities ranging, from west to east, from the 

Spanish Mediterranean coast to Cyprus (table 2 in the 

electronic supplementary material), encompassing a 

distance range of approximately 3500 km. In all the 

meadows, shoots were collected at randomly drawn 

coordinates across an area of 20 m!80 m. Then a 

meristem portion of each shoot was removed, desiccated 

and preserved in silica crystals. 

Genomic DNA was isolated following a standard 

CTAB extraction procedure (Doyle & Doyle 1987). 

The 37 meadows were analysed with the most efficient 

combination (Alberto et al. 2003; Arnaud-Haond et al. 

2005) of seven nuclear markers, using the conditions 

described by Arnaud-Haond et al. (Alberto et al. 2003; 

Arnaud-Haond et al. 2005). This set of microsatellite 

markers allows the unambiguous identification of clonal 

membership (Arnaud-Haond et al. 2005). 

To avoid scoring errors, which would typically 

generate very small apparent genetic dissimilarities 

among individuals actually sharing the same multilocus 

microsatellite genotype and would thus affect our 

estimates of genetic distance, all ramets with a distinct 

J. R. Soc. Interface (2007) 

110

Genetic diversity of clonal organisms A. F. Rozenfeld et al. 1095 

genotype for only two or one alleles were re-genotyped 

for those loci to ascertain their dissimilarity or to 

correct for genotyping errors. The clonal or genotype 

diversity of a meadow was estimated as 

R Z GK1 

N K1 ; 

where G is the number of multi-locus genotypes 

discriminated (considered as many distinct genets) 

and N is the number of samples (i.e. ramets) analysed 

for the meadow. 

The spatial autocorrelation in those meadows was 

tested for using the kinship coefficient proposed by 

Ritland (1996, 2000) and the Sp statistics proposed by 

Vekemans & Hardy (2004). Using the slope of the 

autocorrelogram (^b f ) and the average kinship in the 

smallest distance class ( ^F 1 ), the Sp statistics is 

described as follows: 

Sp Z K^b f 

: 

1K ^F 1 

The significance of Sp values is tested for using a 1000 

permutation test, assigning randomly one of the 

existing coordinates to each genet at each step 

(Arnaud-Haond & Belkhir 2007). 

3. SIMILARITY METRICS 

In order to characterize the genetic structure of the 

different populations, some measure of genetic similarity 

among ramets is needed yielding a distance of 

zero between identical genotypes. In our particular 

case, where the underlying data are based on multilocus 

microsatellite genotypes, one ramet is characterized 

by a series of pairs of microsatellite repetitions 

at k loci with kZ7 in our case. 

More specifically, the genotype of a particular ramet, 

called A, is represented as 

A Z ða 1 ; A 1 Þða 2 ; A 2 Þ/ða k ; A k Þ; 

where a i and A i are the allele length (in number of 

nucleotides) in both chromosomes at locus i. 

Given a second ramet, B, with genotype 

B Z ðb 1 ; B 1 Þðb 2 ; B 2 Þ/ðb k ; B k Þ; 

we define a dissimilarity degree between A and B at 

locus i as 

d i ðA;BÞ ZminðjA i KB i jCja i Kb i j;jA i Kb i jCja i KB i jÞ; 

which provides a parsimonious (i.e. minimal) representation 

of the genetic distance, understood as the 

difference in allele length, between samples A and B. 

This distance is somehow similar to the Manhattan 

distance, defined in geometry as the distance between two 

points measured along axes at right angles, as if we order 

the alleles at each locus in such a way that a i !A i and 

b i !B i , then the minimal (min) function always selects its 

first argument. We define genetic distance among ramets 

by averaging the contributions from all loci 

DðA;BÞ Z 1 k 

X k 

iZ1 

d i ; 

which provides the degree of global dissimilarity between 

A and B. Since we have D(A, A)Z0, genetically identical 

individuals (clones or genets) are at zero distance 

according to this definition. 

To the best of our knowledge, the genetic distance 

metric D proposed here to characterize dissimilarities 

among diploid organisms has not been formally described 

as yet in the literature. It is, however (P. Meirmans 2006, 

personal communication), the distance definition 

implemented for calculating distances among diploid 

organisms in the widely used genetic software GENOTYPE 

(Meirmans & Van Tienderen 2004). 

4. SIMULATIONS 

In this work, we focused on the ranges of inter-individual 

genetic distances generated within the population to 

underline the intra-population factors (clonality, 

mutation and mating system) that can account for 

such distances. 

In order to estimate the impact of the mating system in 

particular, we performed computer simulations to 

explore the genetic distance between parents and offspring. 

For each meadow, we started the simulation with 

the ramets sampled and generated new sets of virtual 

ramets by implementing separately two classes of 

reproductive events: sexual reproduction within 

clones (i.e. selfing) or among genetically different parents 

(i.e. outcrossing). 

In the simulations modelling outcrossing, we randomly 

picked pairs of parents from the considered 

meadow and generated by sexual reproduction 100 new 

individuals, constituting the first generation. Sexual 

reproduction consisted in the construction of a new 

multi-locus genotype (offspring) by randomly selecting, 

for each locus, one of the two microsatellite repeats 

present in each of two distinct parental genets at that 

locus. This procedure was repeated by selecting parents 

from the first generation to produce a second generation, 

then we picked parents from the second one to 

produce a third generation and so on up to 12 

generations. Once a new generation has been produced, 

we computed the distribution of genetic distances 

between its individuals and their corresponding ancestors 

placed at the original population (generation 0). 

The resulting distributions were characterized by their 

mean and s.d., and the process repeated 100 times, to 

improve by averaging the determination of the mean 

intergeneration distances for each meadow. 

The simulations modelling selfing were similar, 

except that a single parental genet was selected from 

the meadow to produce each offspring by random 

recombination of its two microsatellite repeats present 

at each locus. In this case, only one generation of 100 

offspring was produced and the whole process was 

repeated 100 times to better estimate the mean selfing 

distance between parent and offspring. 

5. NETWORK ANALYSIS 

In mathematical terms, a network is represented by a 

graph. A graph is a pair of sets GZ{P, E}, where P is a 

set of N nodes and E is a set of edges connecting the nodes. 


111


As explained below, we will analyse networks, associated 

to each population, in which the nodes are the genets, i.e. 

the different multi-locus genotypes found at the meadow, 

and the links are established among genets at a genetic 

distance smaller than a threshold D th .Eachedgeconnects 

only two nodes (P i and P j ), and therefore can be assigned 

a weight or length equal to the distance or degree of 

dissimilarity between them D(P i , P j ). Depending on the 

maximum value of the distance (D th ) allowed between 

two nodes for them to be connected, the range of possible 

networks is between a fully connected network (when all 

distances are accepted, and therefore all individuals are 

connected), or a network in which only identical nodes are 

connected (D th Z0). Here, we chose to study for each 

population the network built by using as a threshold the 

average distance, d oc , found between parents and offspring 

in the simulations performed in that meadow to 

illustrate the pairwise genetic relationships within a ‘one 

generation’ path. It is worth noting here that the 

estimated percolation point of the networks in most 

populations (33 upon 37) is interestingly close to the 

d oc but slightly lower in general (regression equation is 

yZK0.36C1.03x, r 2 Z0.78 data not shown). The percolation 

point in a network is defined as the point (in our 

case, the value of the genetic distance) at which the 

largest connected part of the network becomes fragmented, 

in a well-defined mathematical sense (Havlin & 

Bunde 1996), i.e. most pairs of nodes are not connected by 

anypossiblelinkorpath,andthenetworkistherefore 

broken in several pieces made of small clusters or isolated 

nodes. This relationship between the percolation point 

and the estimated outcrossing distance suggests that 

precluding mechanisms generating distances slightly 

below the mean d oc wouldleadtothefragmentationof 

the entire system. In fact, the cases where the percolation 

point coincides with or is superior to the d oc estimated 

under the hypothesis of random rearrangement of 

gametes suggest the existence of departure from 

panmixia in the studied population. In some populations, 

the networks indeed appear to be fragmented or partially 

fragmented into clusters (connected components), 

possibly illustrating the occurrence of substructure in 

the meadows analysed. Inside a cluster, there is a path 

connecting any two nodes. On the contrary, there is no 

path connecting nodes belonging to different clusters. We 

define the quantity S as the size (number of nodes) of the 

biggest cluster in the network. We have S%N. 

5.1. Local properties 

The degree of connectivity k i of node P i is defined as the 

number of nodes linked to it (i.e. the number of neighbour 

nodes). If each of these neighbours were connected with 

all the others, there would be E ðmaxÞ 

i Zk i ðk i K1Þ=2 edges 

between them. The clustering coefficient C i of node P i is 

defined as 

C i Z 

2E i 

k i ðk i K1Þ ; 

where E i is the number of edges that actually exist 

between these k i neighbours of node P i . 

5.2. Global properties 

The clustering coefficient of the whole network is the 

average of all individual clustering coefficients. Another 

important descriptor of the network as a whole is the 

degree distribution P(k), defined as the proportion of 

nodes having degree k. The average degree hki may be 

derived from it. 

The path length between any two nodes is defined as 

the minimal number of hops separating them. The 

diameter L of the network is the maximal path length 

present in the network. Finally, the density of links r is 

the ratio between the actual number of links present in 

the network and the number of links in a fully 

connected network (i.e. N(NK1)/2). 

5.3. Random networks 

In this work, we need to compare the networks observed 

with random networks having the same number of 

nodes and links. There are several ways to obtain a 

random network with a specific number of nodes and 

links. The standard random networks introduced by 

Erdös &Rényi (1959) simply distribute randomly the 

links between the nodes, keeping the number of nodes 

and links present in the original network for which 

significance is to be tested. However, this algorithm 

produces its own degree distribution, introducing a bias 

in the numerous cases where the degree distribution is 

not normal. To avoid this effect, the networks are 

usually randomized while keeping the degree distribution 

observed in the original network. In particular, 

starting from the original network, we picked two links 

and permuted the end nodes as described in Maslov & 

Sneppen (2002). By repeating this procedure, we 

obtained uncorrelated random networks with the 

original degree distribution that allow testing for the 

significance of the original parameters. 

6. RESULTS AND DISCUSSION 

6.1. Genetic diversity spectrum 

The meadows sampled differed greatly in clonal diversity, 

ranging from high monoclonal dominance (e.g. Es Castell, 

Spain, RZ0.10) to highly diverse (e.g. Calabardina, 

Spain, RZ0.88; table 2 in the electronic supplementary 

material). The genetic distance between pairs of individuals 

within any population ranged from DZ0 for clonal 

mates to DZ30 for the most genetically divergent 

individuals present in any population. The distribution 

of D within any population is represented as a frequency 

distribution of all pairwise values, which we refer to as the 

genetic diversity spectrum (GDS) of each population. The 

GDS is analogous to the frequency distribution of 

pairwise differences used on some clonal organism to 

detail the influence of clonality, as well as possible somatic 

mutations or scoring errors (Douhovnikoff & Dodd 2003; 

Van der Hulst et al. 2003; Meirmans & Van Tienderen 

2004). Nevertheless, we propose here to extend its 

interpretation beyond that particular application, using 

simulations to screen for the influence of some of the 

evolutionary forces that can contribute to shape the 

pattern of genetic diversity at the intra-population scale. 


112


(a) 

frequency 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

(b) 

0.08 

0.06 

0.04 

0.02 

2 4 6 8 0 5 10 15 20 

genetic distance 

Figure 1. The genetic diversity spectrum (GDS) for two 

representative populations (a) Es Castell which is a strictly 

clonal population (RZ0.1) and (b) Aqua Azzura 5 which has 

high clonal diversity (RZ0.72). 

In particular, we examine the importance of the mating 

system (outcrossing, selfing), which influences the way 

alleles are transmitted from one generation to the next, 

thereby playing a central role in the changing of allele 

frequencies across generations. 

The GDS of the populations studied showed a range 

of shapes across populations (figure 1 and electronic 

supplementary material, figure 5). Three of the 

populations sampled (namely Es Castell, Cala Fornells 

and Es Port) showed spectra indicative of a strongly 

clonal composition, characterized by a large spike at 

zero distance corresponding to the null distance 

between ramets pertaining to the same genetic individual, 

and discrete peaks located at characteristic genetic 

distances between the few distinct clones present in the 

population (figure 1a). Most of the populations 

sampled, however, are characterized by a broad, 

bimodal GDS (e.g. figure 1b), with a smaller (and 

broader) mode at zero distance, indicating the existence 

of nearly identical individuals forming clones, and a 

mode at higher distances within a broad, skewed, bellshaped 

distribution. 

The common characteristic features of the GDS from 

the different P. oceanica populations were highlighted 

by producing a mean GDS, obtained by averaging the 

GDS across all sampled populations. The resulting 

normalized histogram, which we call hGDSi and show 

in figure 2, is strongly bimodal, showing a large peak at 

zero distance (a peak), suggesting that clonal reproduction 

constitutes one of the main factors influencing the 

intra-population genetic structure. The a peak is 

followed, at small genetic distances, by a depression, 

indicating that low genetic distances between 0.57 and 

1.71 (corresponding to 4–12 nucleotides (nt) in case of 

genotypes composed of seven loci) are uncommon. 

A broad peak (b peak) at a modal pairwise genetic 

distance of 4.3 (approx. 30 nt) represents the most 

commonly observed genetic distance between genetically 

dissimilar (i.e. non-clonal) units sampled within 

populations. Above the b peak distance, the frequency 

of distances between individuals declines exponentially 

(figure 2). Provided that enough polymorphic loci are 

used, which is assumed to be the case in our study where 

markers have been previously selected to that aim in a 

pilot work (Arnaud-Haond et al. 2005), the process 

responsible for generating genetic distances of zero 

frequency 

0.12 

0.10 

0.04 

0.02 

0 

a 

(1) 

d oc 

(2) 

d oc 10 –2 

(4) 

d d oc 10 –3 

c d m d s 

(8) 

d oc 

10 –4 

b 

10 –1 1 

(12) 

d oc 

0 5 10 15 20 

0 5 10 15 


Figure 2. The GDS averaged across sampled populations 

(hGDSi). The error bars indicate the SE for each bin. The 

square points (and corresponding error bars) were obtained 

from numerical simulations aimed at identifying mean (G 

s.e.) genetic distances generated by different biological 

processes: d c h0 (clonal reproduction), d m Z0.86 (somatic 

mutations), d s Z1.89G0.11 (selfing, sexual reproduction 

between genetically identical individuals), d oc (outcrossing, 

sexual reproduction between genetically different individuals): 

d ð1Þ 

oc Z3:39G0:17, d ð2Þ 

oc Z4:24G0:23, d ð4Þ 

oc Z4:86G0:28, 

d ð8Þ 

oc Z5:05G0:29 and d ð12Þ 

oc Z5:1G0:29. The upper index 

indicates the number of generations apart for which the 

distance has been measured (1, 4, 8 and 12 generations). In the 

inset, we show the same distribution on a log–linear scale. 

The straight line is a guide for the eye to highlight the 

exponential decay of the tail. 

among individuals can be mostly assigned to clonal 

reproduction. In contrast, the processes generating 

specific classes of greater genetic distances are less 

apparent. However, this knowledge is essential for 

understanding, from a biological and mechanistic point 

of view, the implications of the observed hGDSi on the 

prevalence of various mechanisms that generate genetic 

diversity and structure within the population. The 

simulations performed allowed us to explore the range 

of genetic distances between parents and offspring, 

depending on the reproductive mode. The mean, across 

the populations examined, simulated genetic distance 

(Gs.e.) generated by selfing and outcrossing was 

1.97G0.16 and 3.43G0.17, respectively (figure 2). 

The characteristic genetic distance generated by 

simulated outcrossing (d oc Zdoc ð1Þ ) is close to the modal 

b peak of the hGDSi (figure 2), suggesting that 

outcrossing is the main mechanism generating genetic 

diversity within the populations of this species. In 

contrast, the characteristic genetic distance generated 

by selfing (d s ) is located at the edge of the depression 

between the a and b peaks in the hGDSi, implying a low 

contribution of selfing in generating genetic distances in 

the meadows, and therefore a limited rate of selfing 

when compared with outcrossing and clonality. 

Observation of the hGDSi also suggests that caution 

should be exercised when interpreting the valley 

between the a and b peaks in the spectrum of genetic 


113


distances in terms of somatic mutations or scoring 

errors, as proposed for obligatory outcrossing species 

(Douhovnikoff & Dodd 2003; Van der Hulst et al. 2003), 

when dealing with possibly self-fertilizing species. A 

small genetic distance can also be generated by selfing, 

a possibility that should be considered along with the 

more probable explanation that these distances arise 

from somatic mutations or scoring errors. In the case of 

possible self-fertilizers, simulations may be useful in 

defining the range of distances that can be generated 

sexually and the threshold below which clonality 

may be assumed. After such simulations in the case of 

P. oceanica, the uncommon distances between 0.29 and 

0.86 (corresponding to 2–6 nt for the case of seven loci 

genotypes) are still unlikely (data not shown) under a 

mixed mating system, with the selfing rate not 

exceeding the proportion expected on the basis of 

clone size (in terms of the number of shoots). Since we 

are confident that the double-checking procedure 

applied to the first dataset allowed the correction of 

most scoring errors, these small distances must be 

mostly generated by somatic mutations accumulated in 

the process of multiple clonal reproductive events. 

Indeed, P. oceanica clones are extremely long-lived, 

with clones dated to millennia (Hemminga & Duarte 

2000), over which clones would have divided multiple 

times, hence providing opportunities for somatic 

mutations. Indeed, the frequency of individuals at 

distances between strictly clonal (DZ0) and the 

minimum observed in between the a and b peaks 

(DZ1.43) also declines sharply, as expected from the 

low probability of accumulated mutations. 

Finally, the mean genetic distances from ancestors to 

offspring located n generations apart, obtained from 

simulations, increase very slowly with n, reaching an 

asymptote after approximately eight generations 

(figure 3). Comparison between the largest distances 

obtained by simulations and those observed on the 

hGDSi shows that the end of the distribution tail is not 

likely to be accounted for by sexual reproduction within 

the population. These distances are not likely to be 

generated by the random rearrangement of alleles 

during outcrossing or selfing over generations, but 

rather by external factors that generate diversity. The 

most probable process is a very low rate of immigration 

from other populations, which can suddenly introduce 

individuals genetically very distinct from those present 

in the population. However, examination of the 

contribution of immigration requires GDS evaluation 

across the entire distribution range of the species, 

rather than population-specific analyses such as that 

presented here. 

6.2. Network representation of the GDS 

The discussion above indicates that the GDS is best 

conceptualized as the result of genetic exchanges 

among a network of individual genets. Network 

analysis may, thus, provide a step forward in topologically 

characterizing the genetic relationships 

between population constituents depicted in the GDS. 

A first step to construct such network is to define the 

threshold genetic distance (D th ) representing closely 

percentage of sample unit pairs 

100 

80 

60 

40 

20 

mutations 

19% 

selfing 

28% 

generations 

1 2 4 8 .. 12 

42% 

60% 

53% 

63 .. 63.5% 

100 

80 

60 

40 

20 

0 5 10 15 20 

0 2 4 6 8 10 


Figure 3. The cumulative distribution of genetic distances, 

obtained as the integral of the distribution shown in figure 2. 

We indicate the fraction of between-individual distances up to 

the values associated with different biological processes: 

mutation, selfing and outcrossing of 1, 2, 4 and 8–12 

generations. Inset: the whole range of genetic distances. 

genetically connected individuals, characterized by 

between-individual distances less than or equal to D th . 

Based on analysis of the GDS, we choose to represent 

D th by the one-generation outcrossing distance (i.e. 

D th Zd oc ), which approximately corresponds to the b 

peak in the GDS of each population and is also very 

closely related to the percolation point. The network 

resulting from the connection of individuals at distances 

less than or equal to D th represents the links 

among individuals that are approximately up to a 

generation apart, on the understanding that the genetic 

distance is only an operational proxy for the kinship 

among the individuals. In this work, the nodes in our 

networks are different genetic individuals, or genets, 

but we comment that networks of ramets can also be 

constructed, with a topology straightforwardly related 

to the one considered here. 

The networks (examples in figure 4) for the 

P. oceanica populations differ greatly in shape and in 

properties (table 1) across the meadows analysed. 

As for the shape, the highly clonal population appears 

as a simple diagram of separate families with two or more 

clones each (figure 4a). In contrast, the network 

corresponding to the more diverse population is readily 

characterized by greater connectivity, showing a number 

of closely connected groups (subfamilies) linked together 

by connections to a small set of central individuals, which 

act as links connecting the different families 

(figure 4b,d,e). In addition, we can distinguish fragmented 

(figure 4c)fromconnected(figure 4f ) networks. 

Comparing the properties, the largest component, S, 

of each network contains most of the individuals of each 

meadow (table 1). The average degree of network 

connectivity hki also differs greatly among populations 

(from 1.20 to 8.74, table 1), with an overall average 

connectivity degree of 5.11, indicating that each 

individual is connected to, on average, five others. To 

indicate the significance of these numbers, we note that 

from the data in table 1, the quantity hki/(GK1) which 

is the average proportion of the genet population 

connected to a given individual, ranges between 


114


(a) (b) (c) 

(d) (e) (f ) 

Figure 4. Network of genets for (a) Es Castell (Cabrera, 

Balearic Islands), (b) Cala Jonquet (Iberian Peninsula), (c) 

Rodalquilar (Iberian Peninsula), (d ) Aqua Azzura 5 (Sicily), 

(e) Roquetas (Iberian Peninsula) and ( f ) Playa Cavallets 

(Ibiza) after elimination of links representing genetic distances 

above the threshold D th Zd ð1Þ 

oc . The value of d ð1Þ 

oc was 

obtained by means of numerical simulations and corresponds 

to the distance generated, on average, by outcrossing across 

one generation in the population. The node size is proportional 

to the number of identical constituent ramets. 

0.13 and 0.55. This implies that each individual differs 

at most one average generation from about 13–55% of 

the individuals in the sample. Together with the 

average link density, this shows that a large number 

of links are already present in the networks at the 

chosen threshold. Indeed, the density of links in the 

network averages 27% (table 1), indicating that two 

randomly selected individuals in a given population 

have on average 27% probability of being less than one 

generation apart. 

The average genetic link density of P. oceanica 

individuals (0.27; table 1) is much higher than observed 

in other complex networks analysed in the literature 

(Albert & Barabasi 2002). Similarly, the clustering 

coefficient C (0.73; table 1) is larger than observed in 

most reported networks, and generally larger than the 

clustering expected if the networks were random (i.e. 

COC r ; table 1). This departure from a random network 

signals the abundance of highly clustered nodes at the 

level of closely related families. The average path length 

(L), representing the minimum number of steps (links 

representing reproductive events or somatic mutations) 

necessary to connect any two individual genets in the 

population, ranged from 1.00 to 3.44, averaging 1.88 

across populations (table 1). This average path length 

suggests that most genets have a high kinship, typically 

below that of cousins, and is comparable to that 

generated by a random network. Taken together, the 

presence of short path lengths not departing from 

values expected in a random network (LzL r ) and of 

higher than expected clustering coefficient (COC r ) 

indicate that the networks of genetic relationships in 

P. oceanica populations have the characteristics of a 

‘small world’ (Watts & Strogatz 1998). Small-world 

networks, as described extensively in the social 

sciences, characterize complex systems in which every 

node can be reached from every other using a small 

number of intermediate steps. This is indicative of a 

high degree of genetic substructure within populations 

of P. oceanica. 

There is an interesting parallel between the substructure 

revealed by the networks shape and parameters, 

and the occurrence of spatial autocorrelation (Arnaud- 

Haond et al. in press) in some meadows (Cala Jonquet, 

Acqua Azzura 5; figure 4). Indeed, autocorrelation is 

used to detect patterns of limited dispersion revealed by a 

significant relationship between genetic and geographical 

distance, but a significant pattern primarily implies a 

substructure of the population in clusters of closely 

related individuals. This is very clearly illustrated by the 

shape of the networks, particularly in meadows where a 

strong pattern was detected such as Cala Jonquet 

(figure 4b), where two subfamilies of five and nine 

highly interconnected individuals are linked together 

by a tiny path of three links and two intermediate 

nodes/individuals. However, failure to detect significant 

spatial autocorrelation does not imply the absence of 

subfamilies in the population, but only a lack of relationship 

between this genetic structure and geographical 

distance, or else low statistical power. Exploration of 

the network of individuals can therefore reveal the 

existence of a substructure of the meadows in various 

families, if it exists, even when no pattern of spatial 

autocorrelation can be detected due to a lack of 

relationship between genetic and geographical 

distances. This is what is observed in most populations 

(table 1) where a small world topology was detected, 

and the networks typically illustrate structures composed 

by highly connected subfamilies (or clusters) 

inter-connected by few central nodes. 

7. CONCLUSIONS 

The results presented here illustrate a novel approach, 

based on analysis of the spectrum of genetic diversity, 

to examine the population genetic structure of clonal 

organisms and for the depiction of inter-individual 

genetic distances by a network. As underlined by 

Dyer & Nason (2004), there are two fundamental 

distinctions between the classical genetic summary 

statistics, which involve decomposing variance, and the 

use of graphs. In the latter case, we do not impose predefined 

hierarchical models that constrain the range of 

temporal scales and evolutionary processes that can be 

accurately screened, but rather take advantage of all 

the information contained in the dataset to let the data 

define their own topology and eventually offer a visual 

illustration. Here, we went one step further than 

graphical illustration by detailing the network properties 

using statistical tools specific to network analyses 

to extract key information on the hierarchical genetic 

structure in the population studied. This approach can 

be extended to explore the genetic structure of virtually 

any populations beyond the specific case of a marine 

clonal plant examined here. In doing so, many new 

elements have been introduced, such as a parsimony 

metric of distance among individual diploid organisms, 

the basis for the construction of the spectrum of genetic 

diversity. We also used simple simulations to explore 

the partition of the contribution of different processes 

to the genetic diversity contained in the spectrum, and 

topological representations of networks of genetic 

relationships derived from the spectrum of genetic 


115


Table 1. Summary of properties measured for population genetic networks of genets (non-similar ramets) at D th Zd oc .(G is the 

number of genets present in the meadow, S stands for the size of major connected components, C for the clustering, L for the 

diameter, C r , L r , 90% CI (C r ) and 90% CI (L r ) for the average clustering and diameter and their 90% confidence interval, after 

random rewiring, hki for the mean degree of connectivity, r for the link density and Sp for the spatial autocorrelation Sp statistics. 

For C, L and Sp, bold values indicate significance as departure from the simulated random distributions ( p!0.05), and n.s. 

stands for non-significant. In the last four rows of the table, we statistically characterize each column: hxi stands for the mean and 

s for the s.d. In the last two rows, we show the minimum and maximum values.) 

G S C L C r 90% CI (C r ) L r 90% CI (L r ) hki r Sp 

Acqua Azzura 3 31 28 0.80 3.13 0.22 [0.15,0.29] 2.17 [2.12,2.22] 4.71 0.16 0.02 

Acqua Azzura 5 29 25 0.78 2.43 0.25 [0.19,0.31] 2.01 [1.95,2.06] 4.76 0.17 0.01 

Addaia 25 17 0.72 1.64 0.41 [0.34,0.48] 1.94 [1.87,2.01] 5.60 0.23 0.02 

Agios Nicolaos 28 18 0.81 2.41 0.32 [0.23,0.41] 1.87 [1.81,1.94] 5.50 0.20 0.06 

Amathous 3 18 11 0.77 1.45 0.27 [0.19,0.36] 2.17 [2.03,2.33] 4.67 0.27 0.01 n.s. 

Amathous 5 25 24 0.73 3.14 0.25 [0.19,0.32] 2.13 [2.07,2.19] 4.56 0.19 0.00 n.s. 

Calabardina 40 40 0.62 3.44 0.18 [0.13,0.24] 2.24 [2.17,2.31] 5.90 0.15 0.00 n.s. 

Cala Giverola 17 10 0.84 1.22 0.72 [0.67,0.77] 1.66 [1.55,1.77] 4.35 0.27 0.01 n.s. 

Cala Jonquet 20 18 0.79 2.95 0.36 [0.27,0.46] 1.90 [1.84,1.97] 4.60 0.24 0.07 

Campomanes 22 12 0.66 1.77 0.26 [0.14,0.40] 2.14 [2.01,2.28] 4.00 0.19 K0.01 n.s. 

Carboneras 16 16 0.78 1.75 0.73 [0.68,0.78] 1.51 [1.48,1.55] 8.50 0.57 0.03 n.s. 

El Arenal 32 27 0.76 2.74 0.28 [0.21,0.36] 2.12 [1.99,2.27] 5.25 0.17 0.02 

Es Castell 05 03 0.00 1.33 0.00 — 2.12 — 1.20 0.30 0.12 

Es Pujols 27 24 0.74 2.76 0.57 [0.50,0.64] 1.94 [1.84,2.04] 5.70 0.22 0.01 n.s. 

Es Calo de s’Oli 15 07 0.73 1.14 0.47 [0.36,0.60] 1.92 [1.74,2.11] 2.80 0.20 0.00 n.s. 

Ses Illetes 21 20 0.78 2.62 0.36 [0.28,0.46] 1.83 [1.78,1.88] 5.52 0.28 — 

Cala Fornells 05 03 1.00 1.00 1.00 — 1.00 — 1.20 0.30 K0.06 n.s. 

La Fossa Calpe 31 29 0.63 2.76 0.24 [0.19,0.29] 2.06 [1.96,2.16] 5.55 0.18 0.00 n.s. 

Las Rotes 34 21 0.75 1.98 0.20 [0.13,0.27] 2.26 [2.20,2.32] 4.65 0.14 0.04 

Los Genoveces 14 13 0.86 1.45 0.84 [0.82,0.87] 1.39 [1.34,1.43] 7.71 0.59 0.03 n.s. 

Magaluf 26 18 0.64 1.88 0.39 [0.29,0.50] 2.13 [2.03,2.24] 4.46 0.18 0.02 n.s. 

Malta 29 24 0.67 2.19 0.33 [0.26,0.40] 1.91 [1.85,1.97] 5.24 0.19 — 

Marzamemi 31 28 0.63 2.48 0.39 [0.31,0.48] 2.05 [1.95,2.15] 5.61 0.19 0.01 

Es Port 05 05 0.90 1.10 0.90 — 1.10 — 3.60 0.90 K0.01 n.s. 

Paphos 26 17 0.74 1.65 0.41 [0.34,0.49] 1.98 [1.91,2.04] 6.54 0.26 0.01 n.s. 

Playa Cavallets 28 24 0.63 2.45 0.32 [0.22,0.42] 1.96 [1.91,2.01] 4.86 0.18 0.00 n.s. 

Port Lligat 12 07 0.95 1.10 0.39 [0.27,0.52] 1.59 [1.55,1.64] 4.17 0.38 0.01 n.s. 

Porto Colom 21 16 0.83 1.42 0.72 [0.66,0.78] 1.51 [1.38,1.65] 7.62 0.38 0.06 

Punta Fanals 26 26 0.70 2.06 0.47 [0.41,0.53] 1.86 [1.80,1.93] 7.54 0.30 K0.01 n.s. 

Rodalquilar 27 14 0.84 1.32 0.37 [0.32,0.42] 1.77 [1.73,1.81] 8.22 0.32 0.00 n.s. 

Roquetas 35 34 0.79 2.47 0.33 [0.29,0.37] 1.82 [1.76,1.89] 8.74 0.26 0.01 n.s. 

C.Sta.Maria 13 20 19 0.73 2.37 0.42 [0.33,0.50] 1.79 [1.74,1.84] 5.50 0.29 0.01 n.s. 

C.Sta.Maria 7 22 16 0.66 1.74 0.57 [0.48,0.65] 1.91 [1.79,2.03] 4.91 0.23 0.01 n.s. 

Cala Torreta 21 10 0.75 1.84 0.13 [0.02,0.24] 2.44 [2.28,2.61] 3.33 0.17 0.06 

Torre de la Sal 15 13 0.71 1.64 0.58 [0.52,0.64] 1.50 [1.37,1.64] 4.93 0.35 0.03 n.s. 

Tunis 34 30 0.77 2.80 0.27 [0.20,0.34] 2.17 [2.10,2.24] 5.41 0.16 — 

Xilxes 12 05 0.47 1.80 0.29 [0.03,0.56] 1.98 [1.24,2.71] 1.50 0.14 — 

hxi 22.84 18.16 0.73 2.04 0.41 — 1.89 — 5.11 0.27 — 

s 8.54 8.82 0.16 0.65 0.22 — 0.31 — 1.77 0.15 — 

min(x) 5.00 3.00 0.00 1.00 1.00 — 2.44 — 1.20 0.14 — 

max(x) 40.00 40.00 1.00 3.44 0.00 — 1.00 — 8.74 0.90 — 

diversity to formally explore the properties of the 

resulting network. Each of these novel approaches is 

rooted in earlier developments in different fields such as 

computational population genetics (Meirmans & Van 

Tienderen 2004), population genetics (Bowcock et al. 

1994; Dyer & Nason 2004), and network analysis 

developed in the realm of complex-systems theory 

(Watts & Strogatz 1998; Albert & Barabasi 2002; 

Maslov & Sneppen 2002), but are brought together here 

to provide a synthetic parsimony analysis of population 

genetic structures. 

This interdisciplinary effort has allowed us to derive 

key features of the population genetics of the clonal 

species studied, such as the low contribution of somatic 

mutations and selfing to genetic diversity, and the 

inference, derived from examination of the spectrum of 

genetic diversity and subsequent network analysis, that 

P. oceanica populations show a rather high kinship level 

and low immigration of propagules produced in other 

populations. The network illustration allowed us to 

underline the high degree of substructure in some 

meadows, clearly composed of several families. The 

analysis of network properties allowed us to describe 

P. oceanica populations as following a typical smallworld 

network shape, a feature already widely described 

in complex systems such as the world wide web and social 

networks (26), characterized by small diameters. A closer 

inspection to networks topology reveals that most of the 


116


meadows are composed by separated subgroups 

(families) interconnected through few ‘central’ nodes. 

The analysis of multiple populations has allowed 

elucidation of the considerable variability in the spectra 

of genetic diversity among populations, while identifying 

characteristic features in the spectrum. It would be 

very interesting to explore the methodology presented 

here by using alternative distances at the intrapopulation 

scale, depending on the question to be 

addressed. The approach demonstrated here can be 

extended to a wide range of organisms to explore 

genetic structure for a number of purposes. For 

instance, the topology of the genetic network for 

pathogens may help reveal important properties such 

as the clustering of strains, particularly when unusual 

transmission of genetic material such as lateral transfer 

are suspected to occur, and elucidate the evolutionary 

processes that shaped different lineages. The results 

presented here reveal the spectral and network analysis 

of genetic diversity as a promising tool to ascertain the 

genetic structure of populations and the role of different 

processes in shaping it. 

This research was funded by a project of the BBVA 

Foundation (Spain), by project NETWORK (POCI/MAR/ 

57342/2004) of the Portuguese Science Foundation (FCT) 

and CONOCE2 (FIS2004-00953), and SICOFIP (FIS2006- 

09966) of the Spanish MEC. S.A.H. was supported by a 

postdoctoral fellowship from FCT and the European Social 

Fund and A.F.R. by a postdoctoral fellowship of the Spanish 

Ministry of Education and Science. We also thank MARBEF 

European network and CORONA for fruitful group discussions. 

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166.3.1529) 


118



III.2 Population genetics networks: identifying weak and strong links in a 

metapopulation system. Soumis. 

Au niveau inter-population, nous retiendrons de cette expérience deux éléments 

fondamentaux et encourageant pour la suite de nos travaux : 1) le réseau des 

populations de Posidonia oceanica (Figure 11) permet tout d’abord une 

représentation des liens entre les populations qui n’est pas contrainte par un schéma 

dichotomique comme le sont les représentations phylogénétiques habituelles, et 

corresponds mieux à la réalité des connexions entre populations 2) L’étude d’un 

certain nombre de propriétés du réseau, notamment le betweeness centrality, permet 

d’identifier certaines populations ou groupes de populations qui semblent avoir une 

influence majeure sur la connectivité du système de métapopulations. A l’échelle 

Méditerranéenne, ces populations se trouvent au niveau de la zone de contact entre 

les bassins Est et Ouest. De même, à l’échelle des côtes Espagnoles, cet indice, 

ainsi que le connectivity degree, désignent les Baléares comme une région jouant ou 

ayant joué un rôle phare dans l’établissement et/ou le maintien des populations de 

Posidonies. Or c’est au Baléares que la prédominance dans certaines prairies de 

clones ayant potentiellement atteint plusieurs dizaines de milliers d’années a été 

mise en évidence s26 . Ces résultats sont d’autant plus encourageants, si l’on 

considère la Figure 11, qu’aucune donnée géographique n’est utilisée dans la 

construction des réseaux, mais uniquement les distances génétiques entre les 

agents (individus ou populations). Dans ce contexte, la cohérence entre la topologie 

et les propriétés du réseau et ce que nous savons de l’histoire et de la topographie 

de la Méditerranée est un résultat encourageant pour le futur développement de 

l’application de la théorie des réseaux en écologie moléculaire et en évolution. 

119

1 

Network analysis identifies weak and strong links in a metapopulation 

system 

Alejandro F. Rozenfeld 1 , Sophie Arnaud-Haond 2,4 , Emilio Hernández-García 3 , Víctor 

M. Eguíluz 3 , Ester A. Serrão 2 and Carlos M. Duarte 1 

1 IMEDEA (CSIC-UIB), Instituto Mediterráneo de Estudios Avanzados, C/ Miquel 

Marqués 21, 07190 Esporles, Mallorca, Spain. 

2 CCMAR, CIMAR-Laboratório Associado, Universidade do Algarve, Gambelas, 

8005-139, Faro, Portugal 

3 IFISC, Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-UIB), Campus 

Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain. 

4 IFREMER, Centre de Brest, BP70, 29280 Plouzané, France 

Classification : Biological Sciences – Ecology, Physical Sciences – Applied 

Mathematics 

Corresponding Author : Alejandro Rozenfeld, alex@ifisc.uib.es, Instituto 

Mediterráneo de Estudios Avanzados, C/ Miquel Marqués 21, 07190 Esporles, Mallorca, 

Spain. 

Manuscript information: 15 pages, 6 figures, 2 tables. 

120

2 

Abstract 

The identification of key populations for conservation or eradication is a major 

challenge in population ecology, particularly when dealing with threatened, invasive, 

and pathogenic species. Network theory was applied to map the genetic structure in a 

metapopulation system using microsatellite data from populations of the threatened 

seagrass Posidonia oceanica, as a model, sampled across its whole geographical range. 

This approach allowed the characterization of hierarchical population structure, and the 

identification of populations acting as hubs critical for relaying gene flow and 

sustaining the metapopulation system. This development opens major perspectives in a 

broad range of applications of molecular ecology and evolution such as conservation 

biology and epidemiology, where targeting specific populations is crucial. 

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3 

Understanding the connectivity between components of a metapopulation system and 

their role as weak or strong links remains a major challenge of population ecology (1-3). 

Advances in molecular biology fostered the use of indirect approaches to understand 

metapopulation structure, based on describing the distribution of gene variants (alleles) 

in space within the theoretical framework of population genetics (4-7). Yet, the 

premises of the classical Wright-Fisher model (4, 6), such as “migration-drift 

equilibrium” (8), “equal population sizes” or symmetrical rate migration among 

populations, are often violated in real metapopulation systems. Threatened or pathogen 

species, for example, are precisely studied for their state of demographic disequilibrium 

due to decline and local extinctions in the first case, or to their complex dynamics of 

local decline and sudden pandemic burst in the second. Furthermore, the underlying 

hypotheses of equal population size and symmetrical migration rates hamper the 

identification of putative population “hubs” centralizing migration pathways or acting 

as sources in a metapopulation system, which is a central issue in conservation biology 

or epidemiology. 

Network theory is emerging as a powerful tool to understand the behavior of 

complex systems composed of many interacting units (9-11). Although network theory 

has been applied to a broad array of problems (12-14), only recently has it been adapted 

to examining genetic relationships among populations or individuals (15, 16). Yet, 

relevant properties of networks, such as resistance (9) to perturbations (i.e. node 

paralysis or destruction), the ability to host coherent oscillations (17) or the predominant 

importance of nodes or cluster of nodes in maintaining the integrity of the system or 

relaying information through it can be deducted from the network topology and specific 

characteristics (10, 11). Here we apply network theory to population genetics data of a 

threatened species, the Mediterranean seagrass Posidonia oceanica, to demonstrate its 

122


Orive, M. E. 1995 Senescence in organisms with clonal 

reproduction and complex life-histories. Am. Nat. 145, 

90–108. (doi:10.1086/285729) 

Posada, D. & Crandall, K. A. 2001 Intraspecific gene 

genealogies: trees grafting into networks. Trends Ecol. 

Evol. 16, 37–45. (doi:10.1016/S0169-5347(00)02026-7) 

Proulx, S. R., Promislow, D. E. L. & Phillips, P. C. 2005 

Network thinking in ecology and evolution. Trends Ecol. 

Evol. 20, 345–353. (doi:10.1016/j.tree.2005.04.004) 

Ritland, K. 1996 Estimators for pairwise relatedness and 

individual inbreeding coefficients. Genet. Res. 67, 175–185. 

Ritland, K. 2000 Marker-inferred relatedness as a tool for 

detesting heritability in nature. Mol. Ecol. 9, 1195–1204. 

(doi:10.1046/j.1365-294x.2000.00971.x) 

Rueness, E. K., Stenseth, N. C., O’Donoghue, M., Boutin, S., 

Ellegren, H. & Jakobsen, K. S. 2003 Ecological and genetic 

spatial structuring in the Canadian lynx. Nature 425, 

69–72. (doi:10.1038/nature01942) 

Templeton, A. R., Crandall, K. A. & Sing, C. F. 1992 

A cladistic analysis of phenotypic associations with 

haplotypes inferred from restriction endonuclease mapping 

and DNA sequence data. III. Cladogram estimation. 

Genetics 132, 619–633. 

Van der Hulst, R. G. M., Mes, T. H. M., Falque, M., Stam, P., 

Den Nijs, J. C. M. & Bachmann, K. 2003 Genetic structure 

of a population sample of apomictic dandelions. Heredity 

90, 326–335. (doi:10.1038/sj.hdy.6800248) 

Vekemans, X. & Hardy, O. J. 2004 New insights from finescale 

spatial genetic structure analyses in plant populations. 

Mol. Ecol. 13, 921–935. (doi:10.1046/j.1365-294X. 

2004.02076.x) 

Watts, D. J. & Strogatz, S. H. 1998 Collective dynamics of 

‘small-world’ networks. Nature 393, 440–442. (doi:10. 

1038/30918) 

Wright, S. 1931 Evolution in Mendelian populations. 

Genetics 16, 97–159. 

Yonezawa, K., Ishii, T. & Nagamine, T. 2004 The effective 

size of mixed sexually and asexually reproducing populations. 

Genetics 166, 1529–1539. (doi:10.1534/genetics. 

166.3.1529) 


123

4 

power to characterize population genetic structure and to identify populations that are 

critical to the dynamics and sustainability of the whole system. These results open 

major perspectives in evolutionary ecology, and more specifically in conservation 

biology and epidemiology where the capacity to target populations deserving major 

efforts of conservation or control is crucial. 

Results and Discussion 

We build networks of population connectivity for a system of 37 meadows of the 

marine plant Posidonia oceanica, sampled across its entire geographic range -the 

Mediterranean Sea-, by using seven microsatellite markers (18). The network was built 

by considering any pair of populations as linked when their genetic distance (Goldstein 

distance (19)) is smaller than a suitably chosen distance threshold (see Methods). We 

highlight these links as the relevant genetic relationships either at the Mediterranean 

(the full dataset) or at the regional (28 populations along Spanish coasts) scales. 

The topology of the network obtained at the Mediterranean scale (Fig. 1) 

highlights, without any a priori geographical information being used, the historical 

cleavage between Eastern and Western basins (18) and the transitional position of the 

populations from the Siculo-Tunisian Strait (see Fig. 1). The average clustering 

coefficient, =0.96, is significantly higher than the one expected after randomly 

rewiring the links (=0.76 with σ 0 =0.02, after 10000 randomizations) revealing the 

existence of clusters of populations more interconnected than expected by chance. The 

values of betweenness centrality, quantifying the relative importance of the meadows in 

relaying information flow through the network, immediately highlight a meadow in 

Sicily (present in 21% of all shortest paths among populations), together with another 

one in Cyprus (16%), as the main stepping-stones between the pairs of populations 

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5 

sampled in the Western and Eastern basins, respectively (Fig. 1 and Table 1). These 

results are in agreement with the genetic structure revealed with classical population 

genetics analysis (Analysis of Molecular Variance “AMOVA”), revealing past vicariance 

(18) and a secondary contact zone in the Siculo-Tunisian Strait. The metapopulation 

structure, clustering and ‘transition zones’ derived from the network analysis arise 

without any a priori input on clustering as needed for AMOVA, and without using 

geographic information in the analysis of allelic richness previously performed to 

support the existence and localization of a contact zone (18). 

Closer examination of the network conformed by the populations along the 

Spanish coasts (Figure 2, Table 2), more extensively and homogeneously sampled than 

the rest of the Mediterranean (Table 1), showed that the degree distribution, P(k), i.e. 

the proportion of nodes with k connections to other nodes, decays rapidly for large k 

(Fig 3.a) and that the six highest values are all observed in samples collected in the 

Balearic Islands (Fig 2, Table 2). The average clustering coefficient of =0.4 was 

significantly higher than that obtained in the corresponding randomized networks 

(=0.13 with σ 0 =0.05 after 10000 realizations), whereas the local clustering decays 

as a function of the degree k (Fig 3.b) which indicates that the central core is 

substructured into a small set of hubs, with high connectivity and low clustering, linking 

groups of closely connected nodes (i.e. with high clustering). Examination of the 

relationship between the degree of a node and the average degree of the populations 

connected to it showed an abundance of links between highly connected and poorly 

connected nodes (Fig 3.c), a property termed dissortativity, present in many biological 

networks (20), and reveals a centralized topology. Observation of Fig. 2 indicates that 

seagrass populations along the Spanish continental coasts are genetically closer to 

Balearic populations than to geographically closer populations. Additionally, the highest 

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6 

values of betweenness centrality (Table 2) are also attained at the Balearic populations, 

suggesting that the meadows of this region play or have played a central role in relaying 

gene flow at the scale of the Spanish coasts. Moreover, the betweenness centrality 

increases exponentially with the connectivity degree k (Fig. 3d). All these findings 

reveal a star-like structure where hubs are connected in cascade and the central core is 

the set of Balearic populations. A clear perspective of this pattern is shown by the 

resulting Minimum Spanning Tree of populations (Fig. 4, see Methods). The biological 

implication is a great centrality of the Balearic Islands, acting or having acted as a hub 

for gene flow thorough the system. 

Populations with high degree k might either be sources sustaining the system (i.e. 

spreading propagules), or sinks receiving gene flow from all the other populations, or 

both. The extremely low rate of sexual recruitment inferred in populations with low 

clonal diversity (R) renders those, if highly connected, much more likely to disperse than 

to receive. The presence in the Balearic Islands of the two populations with the lowest 

observed clonal diversity and the highest connectivity (Es Port, R=0.1; k=10; and 

Fornells R=0.1; k=15), likely representing populations supplying “genetic material” to 

neighbor populations, suggests again that the Balearic islands is a key region for the 

dynamics and connectivity of the metapopulation system at the scale of the Spanish coast. 

Furthermore, 8 among 16 continental populations show extreme low connectivity (k=0), 

thereby allowing identification of those least likely to be rescued by other populations, 

once threatened. As in any genetic approach to metapopulation management, the role of 

currently observed connectivity in future population rescuing is more important if 

current connectivity is limited by dispersal ability rather than by competitive interactions 

that could change in the future in decaying populations. Furthermore, given the 

particular millenary nature of P. oceanica clones, current genetic structure is likely to 

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7 

integrate patterns of gene flow over past centuries, and thus may not reflect present-day 

dynamics. 

Both networks, that at the scale of the whole Mediterranean (Fig. 1) and that for 

the Spanish coasts (Fig. 2), presented “small world” properties (21), i.e. a diameter 

(L=1.39 and L=1.63 respectively) shorter than expected for random networks 

(=1.47 with σ 0 =0.01 and =2.53 with σ 0 =0.15 respectively, after 10000 

randomizations) whereas their clustering was much higher (see numerical values above), 

suggesting a highly hierarchical substructure. This provides clear evidence for the 

appearance of “short-cuts” in gene flow at multiple geographical scales along the 

history of this species, indicating rare events of large scale dispersal having a significant 

impact on the genetic composition of populations. 

These results demonstrate that network analyses are powerful tools to examine the 

structure of gene flow across different geographical scales. The use of specific network 

properties such as the betweenness centrality and the degree distribution allowed to 

identify populations relaying gene flow, or acting as sources supplying the system, as 

well as those less connected, increasing vulnerability to local extinction. The 

identification of key populations to maintain the gene flow across the species range is 

essential to guide conservation strategies for this endangered seagrass. In particular, this 

methodology successfully revealed the existence of an East-West cleavage in the 

Mediterranean and of a contact/transition zone in the Siculo-Tunisian Straight without 

any other a priori information such as geographic data or expected clustering of 

populations. Furthermore, network analysis tools provided graphical representations of 

the genetic relatedness between populations in a multidimensional space (15), free of 

some of the constraints (e.g. a tree-like structure or binary branching) compulsory in 

classical methods describing population relationships. Addressing gene flow using 

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8 

network theory may prove a ground-breaking milestone in critical areas such as 

conservation biology, dealing with threatened or invasive species, and epidemiology, 

where the definition of target populations to be conserved or eradicated is of crucial 

importance. 

Materials and Methods 

Molecular data. About 40 Posidonia shoots collected at each of the 37 sampled 

populations (Fig.1 and Table 1) were genotyped with a previously selected set of seven 

dinucleotide microsatellites (22) allowing the identification of clones (also called genets 

for clonal plants). Clonal diversity was estimated for each population as described in 

Arnaud-Haond et al. (22), and replicates of the same clone were excluded for the 

estimation of inter-population distances. The matrix of interpopulation distances was 

built using Goldstein metrics (19), thus taking into account the level of molecular 

divergence among alleles, besides the differences in terms of allelic frequencies. 

Networks. We first built a fully connected network with the 37 populations 

considered as nodes. Each link joining pairs of populations was labeled with the 

Goldstein distance among them. We then removed links from this network of genetic 

similarity, starting from the one with the largest genetic distance and following in 

decreasing order, until the network reaches the percolation point (23, 24), beyond which 

it loses its integrity and fragments into small clusters. This means that gene flow across 

the whole system is disabled if connections at a distance smaller than this critical one, 

Dp, are removed. The precise location of this percolation point is made with the 

standard methodology adequate for finite systems (23, 24), i.e., by calculating the 

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9 

average size of the clusters excluding the largest one, 

S 

* 1 

= 

N 

∑ 

s 

s< 

Smax 

2 

n s 

, as a function 

of the last distance value removed, thr, and identifying the critical distance with the one 

at which * has a maximum. N is the total number of nodes not included in the 

largest cluster and n s is the number of clusters containing s nodes. Here we find Dp=91, 

as shown in Fig. 5. 

Once the network at percolation point is obtained, we analyzed its topology and 

characteristics (See Fig.1 and Table 1), and interpret those biologically. The first 

column in Table 1 contains also the estimated clonal diversity R of the different 

populations, defined as the proportion of different genotypes found with respect to the 

total number of collected shoots. 

At the Spanish coasts scale, no percolation point is found using the above procedure, 

meaning that the genetic structure in this area is rather different from the one at the 

whole Mediterranean scale. To construct a useful network representation of the 

meadows genetic similarity, the following alternative process was applied in order to 

determine a relevant distance threshold, thr, above which links are discarded. At a very 

low threshold (thr=16, see Movie 1 provided as Supplementary Online Information) 

only the inner part of a central core, constituted by some meadows from the Balearic 

Islands, is connected. As the threshold is increased new meadows (from the central 

Spanish coast) become connected (thr=20). Beyond that value, more peripheral 

meadows are connected from the northern and southern Spanish coasts. The 

geographical extension of the connected cluster (Fig. 6) grows with the distance 

threshold and an important jump occurs at thr=22, when the northern and southern 

coasts get connected for the first time. 

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10 

Further distance threshold increase does not contribute to geographical extension. 

Therefore, we find the value thr=22 and the resulting network as appropriate for 

topological characterization, since at this point the network contains a rich mixture of 

strong and weak links spanning all the available geographic scales within the 

Mediterranean Spanish coasts. 

Estimates of global and local properties of the network. The degree k i of a 

given node i is the number of other nodes linked to it (i.e., the number of neighbor 

nodes). The distribution P(k) gives the proportion of nodes in the network having 

degree k. 

We denote by E i the number of links existing among the neighbors of node i. This 

quantity takes values between 0 and 

E 

(max) 

i 

= 

ki 

( ki 

−1) 

, which is the case of a fully 

2 

connected neighborhood. The clustering coefficient C i of node i is defined as: 

C 

i 

= 

E 

E 

i 

(max) 

i 

= 

i 

2E 

k ( k 

i 

i 

−1) 

The clustering coefficient of the whole network is defined as the average of 

all individual clustering coefficients in the system. The degree dependent clustering 

C(k) is obtained after averaging C i for nodes with degree k. 

Real networks exhibit correlations among their nodes (20, 25-30) that play an 

important role in the characterization of the network topology. Those node correlations 

are furthermore essential to understand the dynamical aspects such as spreading of 

information or their robustness against targeted or random removal of their elements. In 

social networks, nodes having many connections tend to be connected with other highly 

connected nodes. This characteristic is usually referred to as assortativity, or assortative 

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11 

mixing. On the other hand, technological and biological networks show rather the 

property that nodes having high degrees are preferably connected with nodes having 

low degrees, a property referred to as dissortativity. Assortativity is usually studied by 

determining the properties of the average degree of neighbors of a node as a 

function of its degree k (20, 29, 31). If this function is increasing, the network is 

assortative, since it shows that nodes of high degree connect, on average, to nodes of 

high degree. Alternatively, if the function is decreasing, the network is dissortative, as 

nodes of high degree tend to connect to nodes of lower degree. In this last case, the 

nodes with high degree are therefore central hubs ensuring the connection of the whole 

system. 

The betweenness centrality (32) of node i, bc(i), counts the fraction of shortest 

paths between pairs of nodes which pass through node i. Let σ 

st 

denote the number of 

shortest paths connecting nodes s and t and σ (i) 

the number of those passing through 

the node i. Then, 

σ 

st 

( i) 

bc( i) 

= ∑ . 

σ 

s≠t≠i 

st 

st 

The degree-dependent betweeness, bc(k), is the average betweeness value of nodes 

having degree k. 

Minimum Spanning Tree. Given a connected, undirected graph, a spanning tree 

of that graph is a subgraph without cycles which connects all the vertices together. A 

single graph can have many different spanning trees. Provided each edge is labeled with 

a cost (in our analysis the genetic distance among the connected populations) each 

spanning tree can be characterized by the sum of the cost of its edges. A minimum 

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14 

Figure Captions: 

Figure 1: The network of Mediterranean meadows in which only links with Goldstein 

distances smaller than the percolation distance Dp=91 (see Fig. 5) are present. Nodes 

representing populations are roughly arranged according to their geographic origin. The 

precise geographic locations are indicated as diamonds in the background map. One can 

identify two clusters of meadows, corresponding to the Mediterranean basins (east and 

west), separated by the Siculo-Tunisian Strait. The size of each node indicates its 

betweenness centrality (i.e. the proportion of all shortests paths getting through the 

node). 

Figure 2: The network constructed with the Spanish meadows at “geographic 

percolation” state (see Fig. 6). Nodes are shown at the populations geographic locations. 

Node sizes characterize their betweenness centrality (i.e. the proportion of all shortests 

paths getting through the node). 

Figure 3: Main topological properties found by analysing the structure of the network 

of meadows at the Spanish basin scale (Fig. 2). (a) The complementary cumulative 

degree distribution P(degree>k), (b) the local clustering C(k), (c) the average degree 

in the neighbourhood of a meadow with degree k, and (d) the degreedependent 

betweenness, bc(k), as a function of the connectivity degree k. 

Figure 4: Minimum Spanning Tree based on Goldstein distance among Spanish 

meadows. This is the subgraph which connects the populations at the Spanish coast 

scale minimizing the total genetic distance along links. 

Figure 5. The average cluster size excluding the largest one, as a function of the 

imposed genetic threshold, at the whole Mediterranean scale. This identifies Dp=91 as 

the percolation threshold. 

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12 

spanning tree is then a spanning tree with minimal total cost. A minimum spanning tree 

is in fact the minimum-cost subgraph connecting all vertices, since subgraphs 

containing cycles necessarily have more total cost. Figure 4 shows the minimum 

spanning tree for the Spanish meadows. The star-like structure centered at Balearic 

populations is evident. 

We acknowledge financial support from the Spanish MEC (Spain) and FEDER 

through project FISICOS (FIS2007-60327), the Portuguese FCT and FEDER through 

project NETWORK(POCI/MAR/57342/2004), the BBVA Foundation (Spain), and the 

European Commission through the NEST-Complexity project EDEN (043251). 

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16 

Figure 1: The network of Mediterranean meadows in which only links with Goldstein 

distances smaller than the percolation distance Dp=91 (see Fig. 5) are present. Nodes 

representing populations are roughly arranged according to their geographic origin. The 

precise geographic locations are indicated as diamonds in the background map. One can 

identify two clusters of meadows, corresponding to the Mediterranean basins (east and 

west), separated by the Siculo-Tunisian Strait. The size of each node indicates its 

betweenness centrality (i.e. the proportion of all shortests paths getting through the 

node). 

135

17 

Figure 2: The network constructed with the Spanish meadows at “geographic 

percolation” state (see Fig. 6). Nodes are shown at the populations geographic locations. 

Node sizes characterize their betweenness centrality (i.e. the proportion of all shortests 

paths getting through the node). 

136

18 

Figure 3: Main topological properties found by analysing the structure of the network 

of meadows at the Spanish basin scale (Fig. 2). (a) The complementary cumulative 

degree distribution P(degree>k), (b) the local clustering C(k), (c) the average degree 

in the neighbourhood of a meadow with degree k, and (d) the degreedependent 

betweenness, bc(k), as a function of the connectivity degree k. 

137

19 

Figure 4: Minimum Spanning Tree based on Goldstein distance among Spanish 

meadows. This is the subgraph which connects the populations at the Spanish coast 

scale minimizing the total genetic distance along links. 

138

20 

Figure 5. The average cluster size excluding the largest one, as a function of the 

imposed genetic threshold, at the whole Mediterranean scale. This identifies Dp=91 as 

the percolation threshold. 

139

21 

Figure 6. The maximal geographic distance connected (at the Spanish coasts scale) as a 

function of the imposed distance threshold (thr). Above thr=22 the maximal geographic 

distance covered by connected populations nearly duplicates. 

140

22 

Table 1: Local properties of the whole Mediterranean network for thr=Dp=91. 

Information is given for the betweenness centrality (bc) and clustering (C), as well as 

clonal diversity estimates (R) for each sample. 

REGION Name R bc C REGION Name R Bc C 

Addaia 0,67 0,0010 0,980 Cala Jonquet 0,5 0,0031 0,946 

Menoría 

Fornells 0,1 0,0031 0,946 

Port Lligat 0,28 0,0031 0,946 

Cala Giverola 0,43 0 0,997 

SPANISH BALEARIC ISLANDS 

Mallorca 

Cabrera 

Ibiza 

Magaluf 0,68 0,0010 0,981 Punta Fanals 0,68 0,0010 0,981 

Porto Colom 0,5 0,0031 0,946 Torre Sal 0,5 0,0010 0,981 

Es Castel 0,1 0,0010 0,981 Xilxes 0,35 0,0010 0,981 

Es Port 0,1 0,0031 0,946 Las Rotes 0,73 0,0010 0,981 

Santa Maria 13 0,56 0,0010 0,981 El Arenal 0,86 0,0031 0,946 

Santa Maria 7 0,54 0,0010 0,981 Campomanes 0,7 0,0031 0,946 

Playa Cavallets 0,73 0,0031 0,946 

SPANISH IBERIAN PENINSULA 

LaFossaCalpe 0,77 0,0031 0,946 

Calabardina 0,88 0,0003 0,997 

Es Pujols 0,67 0,0031 0,946 Carboneras 0,32 0,0010 0,981 

(ORDERED FROM NORTH TO SOUTH) 

Formentera 

EsCalo de S’Oli 0,36 0,0010 0,981 Rodalquilar 0,53 0,0010 0,981 

Ses Illetes 0,6 0,0031 0,946 Los Genoveces 0,34 0,0010 0,981 

Sa Torreta 0,51 0,0031 0,946 Roquetas 0,69 0,0010 0,981 

CENTRAL BASIN 

Sicily 

Tunis 0,85 0 1 Amathous ST3 0,44 0 1 

Malta 0,74 0 1 Amathous ST5 0,62 0,0008 0,667 

A. AzzuraST3 0,77 0,205 0,897 

A. AzzuraST5 0,72 0,0017 0,963 

Marzamemi 0,81 0,0003 0,995 

EAST BASIN 

Cyprus 

Greece 

Paphos 0,68 0,1579 0,333 

A. Nicolaos 0,69 0 1 

141

23 

Table 2: Local properties of the network constructed with the Spanish meadows. 

Information is given for the connectivity degree (k), betweenness centrality (bc) and 

clustering (C), as well as clonal diversity estimates (R) for each sample. 

REGION Name R k bc C REGION Name R k bc C 

SPANISH BALEARIC ISLANDS 

Ibiza Cabrera Mallorca Menorca 

Formentera 

Addaia 0,67 0 0 0 

Cala Jonquet 0,5 0 0 0 

Port Lligat 0,28 0 0 0 

Fornells 0,1 15 0,180 0,32 Cala Giverola 0,43 0 0 0 

Magaluf 0,68 8 0 1 

Punta Fanals 0,68 1 0 0 

Torre Sal 0,5 0 0 0 

Porto Colom 0,5 9 0,0046 0,89 Xilxes 0,35 8 0 1 

SPANISH IBERIAN PENINSULA 

Es Castel 0,1 5 0 1 Las Rotes 0,73 5 0 1 

Es Port 0,1 10 0,0075 0,8 El Arenal 0,86 8 0 1 

Santa Maria 13 0,56 10 0,0075 0,8 Campomanes 0,7 0 0 0 

Santa Maria 7 0,54 10 0,0075 0,8 LaFossaCalpe 0,77 2 0 1 

Playa Cavallets 0,73 12 0,0037 0,58 Calabardina 0,88 0 0 0 

Es Pujols 0,67 1 0 0 Carboneras 0,32 0 0 0 

EsCalo de S’Oli 0,36 0 0 0 Rodalquilar 0,53 0 0 0 

Ses Illetes 0,6 0 0 0 Los Genoveces 0,34 1 0 0 

Sa Torreta 0,51 2 0 1 

Roquetas 0,69 1 0 0 

(ORDERED FROM NORTH TO SOUTH) 

142


4. Perspectives et Projets de Recherche 

IV. 

PERSPECTIVES : BIODIVERSITE ET EVOLUTION DES ECOSYSTEMES 

PROFONDS 

J’ai intégré le Laboratoire Environnement Profond de l’Ifremer, dirigé par 

Daniel Desbruyères, en janvier 2007. Durant ces derniers mois, je me suis dans le 

familiarisée avec l’environnement profond, en m’intégrant à l’équipe et en prenant 

des premiers contacts avec la communauté internationale constituée de chercheurs 

en écologie et en microbiologie spécialisés dans ce domaine. 

L’étude de la structure génétique en milieu marin et l’adaptation de l’analyse 

de données aux systèmes reproducteurs et à l’état de déséquilibre des populations 

étudiées seront le fil conducteur qui dirigera ma migration vers les profondeurs, et a 

déjà été exposé dans cette synthèse. Plus que des projets de recherche détaillés, il 

s’agit donc ici de proposer une brève description du contexte particulier imposé par 

l’Environnement Profond, en particulier pour ce qui concerne l’écologie et les 

ressources génétiques, et de conclure sur les axes de recherche que je souhaite 

contribuer à développer à l’aide des approches décrites précédemment. 

IV.1 Du milieu terrestre au milieu profond : un abysse de recherche et 

d’efforts. 

Le milieu marin recouvre 70% de la surface de la terre -le milieu abyssal 90% 

de son volume- et la quasi-totalité des phylums vivants sont apparus et/ou 

représentés dans les océans. Pourtant, seulement 250.000, environ, des 1.8 millions 

d’espèces décrites à l’heure actuelle sont marines (Bouchet 2006). 

Il ne s’agit pourtant pas du révélateur d’une extrêmement faible diversité, ni 

d’un plus faible intérêt du milieu marin par rapport au milieu terrestre. Au contraire, la 

plupart des estimations montrent que les sédiments profonds pourraient abriter des 

taux de diversité spécifiques très élevés (Grassle & Maciolek 1992, Gage 1996). Par 

ailleurs, le nombre d’espèces exploitées excède le nombre d’espèces terrestres de 

beaucoup, et ce malgré une domestication récente mais extraordinairement rapide 

(Duarte et al. 2007), et le grand nombre de molécule d’intérêt isolées sur des 

organismes marins souligne un fort potentiel en terme de biotechnologies (Munro et 

al. 1999). L’origine de ce contraste pourrait plutôt se résumer en quatre mots : ‘trop 

loin, trop petit’. C’est en effet grâce aux progrès technologiques permettant de 

contourner la difficulté d’accès à l’environnement profond (développement des 

submersibles habitables et des robots sous marins autonomes -AUV- ou téléopérés 

–ROV-) que l’existence d’oasis de vie en milieu abyssal a été révélée il y a à peine 

plus de 30 ans. C’est aussi grâce à l’essor de la biologie moléculaire, que les 

microorganismes qui dominent la vie –et la production primaire- dans les océans ont 

été caractérisés durant la dernière décennie (Duarte 2006). 

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Ces difficultés techniques peuvent partiellement expliquer que les 

écosystèmes marins aient fait l’objet de moins d’effort de recherches que les 

écosystèmes terrestres jusqu’aux dernières décennies. Il est toutefois préoccupant 

de constater que malgré le développement des technologies nécessaires à leur 

étude, ce déséquilibre de l’effort de recherche soit toujours d’actualité et que 

seulement 0.1% des mers fasse l’objet de mesures de protection contre bientôt 10% 

du domaine terrestre classé dans le cadre de la Convention pour la Diversité 

Biologique (Hendriks et al. 2006), et ce alors que les écosystèmes clés déclinent 

deux à dix fois plus vite dans le milieu marin que dans le milieu terrestre (Lotze et al. 

2006). Ce qui est vrai pour le milieu marin l’est a forciuri pour le milieu profond, qui 

en terme de volume représente 90% de la planète et fait pourtant l’objet d’efforts de 

recherche encore moins important que le milieu côtier. De plus l’environnement 

profond est confronté à un problème supplémentaire à celui de sa difficulté d’accès : 

son statut juridique, car la majorité des écosystèmes profonds se trouve hors des 

zones de juridictions nationales. Au-delà des connaissances restant à acquérir, la 

mise en place de mesures de gestion et/ou de protection nécessite donc un long 

processus de négociation internationale pour la mise en place d’une gouvernance 

consensuelle (Arnaud-Haond et al. 2007, Fig. 12). Ce processus a été entamé en 

2007 à l’ONU, dans le cadre du Processus consultatif non officiel sur les océans et le 

droit de la mer, dont la VIIIe session a porté sur les ressources génétiques marines 

(http://www.un.org/Depts/los/consultative_process/8thmeetingpanel.htm ). 

Malgré son éloignement, il apparaît clairement que des menaces sérieuses 

pèsent sur l’environnement profond, liées aux activités humaines (Davies et al. 2007) 

et aux conséquences potentielles du changement global. On peut citer la 

surexploitation des stocks de pêche, la destruction de l’habitat par les activités 

halieutiques, l’extraction de pétrole, les projets d’activités minières et 

d’enfouissement de dioxyde de carbone, l’impact de l’acidification des océans et du 

réchauffement global sur les cycles biogéochimiques et sur les écosystèmes 

sensibles tels que les récifs coralliens profonds (Roberts et al. 2006). Or une métaanalyse 

récente suggère que la perte de la biodiversité en environnement profond 

pourrait avoir un impact exponentiel sur le fonctionnement des écosystèmes 

(Danovaro et al. 2008). 

Le défi à relever aujourd’hui en ce qui concerne la biodiversité marine consiste 

donc à renforcer l’effort de recherche pour caractériser le plus rapidement possible la 

biodiversité et comprendre le fonctionnement des écosystèmes principaux. Il s’agit 

de tirer le meilleur parti des avancées technologiques et moléculaires, non seulement 

pour combler les lacunes de notre connaissance concernant l’habitat dominant sur 

notre planète et pour y découvrir de nouvelles richesses à exploiter, mais également 

pour anticiper et prévenir la disparition d’une biodiversité et d’un ensemble 

d’écosystèmes dont l’importance et le potentiel restent encore à découvrir. 

144



Figure 2: Poster présenté au 1er colloque national sur les Aires Marines Protégées, à Boulogne sur 

Mer, du 20 au 22 Novembre 2007 

145



IV.2 Quelques un des écosystèmes profonds principaux 

Les moyens d’exploration de l’environnement profond, après les 

circumnavigations et les explorations abyssales de la fin du XIX e et au début du XX e 

siècle, ont mis fin au paradigme, selon lequel le plancher océanique était un désert 

homogène sans vie, ou peuplé de communautés pauvres dépendant intégralement 

des apports de matière organique depuis la zone photique. C’est une mosaïque 

d’écosystèmes divers et riches, sinon tous d’une biodiversité exubérante, du moins 

d’une biomasse élevée qui a été mise en évidence durant les 30 dernières années, 

depuis la découvertes des sources hydrothermales en 1977 (Encadrés 4 et 5). Le 

caractère exceptionnel de la découverte des sources hydrothermales, des sources 

de fluides froids et des écosystèmes temporaires que constituent les bois coulés ou 

les cadavres de mammifères (Encadré 4) ne réside pas uniquement dans la 

découverte de l’hétérogénéité et de la richesse insoupçonnée de l’environnement 

profond, mais également dans celle d’une nouvelle voie métabolique de production 

de la matière organique. La chimiosynthèse, décrite quelques années auparavant 

chez les bactéries des geysers d’eau chaude mais considérée jusqu’alors comme 

‘anecdotique’, détrône la photosynthèse comme unique voie métabolique de 

production de matière organique et source de vie. Les écosystèmes 

chimiosynthétiques sont peuplés d’espèces formant des réseaux trophiques 

complexes basés sur une production primaire réalisée par des bactéries et des 

archae, résultant dans une forte propension à la symbiose dans ces environnements 

extrêmes (Van Dover 2000). 

Finalement, des écosystèmes profonds associés ou non à ces écosystèmes 

chimiosynthétiques, et dont la production primaire reste pour certains à élucider 

(chimiosynthèse ou apport de matière organique depuis la zone superficielle) ont 

également été découverts. On peut citer principalement les monts sous-marins 

(structures, souvent d’origine volcanique, qui s’élèvent à environ 1000 mètres ou plus 

au dessus du plancher océanique) et les récifs coralliens profonds/froids qui y sont 

parfois associés (Encadré 5). 

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147



148



IV.3 Axes de Recherche 

Taxonomie et biogéographie. 

Bien que la controverse sur l’étendue exacte de la diversité spécifiquemarine, 

en particulier dans les profondeurs, gronde encore entre les défenseurs du ‘record 

absolu’ (Grassle & Maciolek 1992) et ceux du ‘à peine le double de ce qui est déjà 

connu’ (May 1992), il semble admis que la plus grande part reste encore à découvrir 

et à décrire (Bouchet 2006 , voir aussi le site du Census of Marine Life: 

http://www.coml.org). 

L’inventaire de la biodiversité et la biogéographie reposent encore 

principalement sur la reconnaissance morphologique des organismes (Etter et al. 

1999, Desbruyères et al. 2006). L’utilisation, en complément de la taxonomie 

morphologique, de marqueurs moléculaires dans le cadre d’approches de type 

‘Barcoding of Life’ (Hebert et al. 2003) permet i) d’une part le positionnement des 

espèces dans l’arbre de la vie pour une meilleure compréhension de leur phylogénie 

et de leur évolution, et ii) d’autre part de détecter l’existence d’espèces cryptiques 

non distinguées sur la base de critères morphologiques (Etter et al. 1999, Samadi et 

al. 2006), ou au contraire d’espèces synonymes (Samadi et al. 2006) décrites 

comme des espèces distinctes dans des écosystèmes ou des zones géographiques 

différentes. 

Cette tâche de soutien à la caractérisation de nouvelles espèces s’inscrit dans 

le cadre plus large de la compréhension de l’histoire biogéographique des taxons 

(voir ci-dessous). 

Biogéographie : facteurs influençant la dispersion à différentes échelles, et 

influence de la symbiose. 

La distribution spatiale de la biodiversité en environnement profond est 

hétérogène, non seulement entre les différents types d’écosystèmes, mais 

également au sein d’un même type d’écosystème (Desbruyeres et al. 2000, 

Desbruyeres et al. 2001, Olu-Le Roy et al. 2004). La compréhension de cette 

répartition de la biodiversité passe par la description de l’histoire évolutive de ces 

écosystèmes et des grands évènements qui ont façonné leur distribution 

biogéographique (Desbruyeres et al. 2000, Van Dover et al. 2002). L’analyse de la 

divergence génétique intraspécifique et/ ou intra-générique, et l’utilisation de 

l’horloge moléculaire, peuvent permettent, en complément de l’analyse de la 

distribution spatiale des espèces, d’obtenir des informations sur l’histoire de la 

colonisation et de la divergence des différents écosystèmes et provinces 

biogéographiques (Van Dover et al. 2001, Van Dover et al. 2002). 

149



Concrètement, plusieurs grandes questions sont posées à différentes échelles 

de temps, parmi lesquelles ont peut citer, par échelle de temps croissante: 

1/ l’histoire de la colonisation des différents types d’écosystèmes profonds. 

L’origine des taxons actuels fait l’objet d’observations aux implications 

contradictoires. Par exemple au niveau des sources hydrothermales où plus de 80% 

des espèces sont endémiques, la morphologie de nombreux invertébrés et 

l’existence de fossiles datant de l’ère primaire (250-500 Ma) supportent l’hypothèse 

d’une une origine très ancienne des taxons et d’un possible rôle de refuge des 

sources hydrothermales pendant des grandes vagues d’extinctions dans la zone 

photique. Les données moléculaires disponibles jusqu’à présent suggèrent au 

contraire des radiations beaucoup plus récentes (secondaire ou tertiaire) pour la 

plupart des taxons étudiés (Little & Vrijenhoek 2003), impliquant des évènements 

d’extinction et recolonisation depuis la surface (Jacobs & Lindberg 1998) ou les 

autres écosystèmes chimiosynthétiques. Pour certains taxons comme le genre 

Bathymodiolus, le scénario d’une colonisation progressive de l’environnement 

profond depuis la surface avec une acquisition progressive d’endosymbiontes 

(Craddock et al. 1995) par le biais d’écosystèmes temporaires comme les bois 

coulés ou les cadavres de mammifères (Distel et al. 2000) semble supporté par les 

données moléculaires. Pour d’autres taxons comme les Alvinellidae par exemple 

apparemment endémiques des sources hydrothermales, la question de l’origine et de 

l’époque approximative de la colonisation reste posée (Van Dover et al. 2002). 

2/ L’identification et la datation des évènements de vicariance et de dispersion 

passés qui ont façonné les provinces biogéographiques qui peuvent être 

différenciées à l’heure actuelle. Au-delà de la question de l’origine des taxons la 

question se pose également la façon dont ceux-ci ont évolué et se sont dispersé, et 

de l’évolution isolée ou ‘concertée’ des écosystèmes profonds. Les sources 

hydrothermales par exemple (dont la biogéographie est pour le moment plus étudiée 

et mieux connue que celle des sources de fluides froids) sont distribuées le long de 

rides médio océaniques et des bassins d’arrière arcs. Six provinces 

biogéographiques principales sont reconnues à l’heure actuelle (Van Dover et al. 

2002) sur la base de la composition des communautés associées aux sources 

hydrothermales. Les évènements de vicariance ont probablement joué un rôle 

important dans la différentiation de ces provinces. Ainsi, les communautés 

hydrothermales de la faille de Juan de la Fuca au Nord de la Ride Est Pacifique 

(EPR) et celles de la partie Sud de l’EPR et des Galapagos partagent un grand 

nombre de taxons supérieurs et des conditions physico-chimiques similaires, 

suggérant une origine commune et une séparation (Tunnicliffe et al. 1998) qui 

pourrait coïncider avec la scission de la ride Est Pacifique il y a environs 30 millions 

d’années. Enfin les évènements de dispersion passés peuvent également expliquer 

la composition actuelle des communautés, comme la forte proximité des 

150



communautés de la Ride Centre Indienne (CIR) et du bassin d’arrière arc (BAB) 

Ouest Pacifique qui suggère une voie de dispersion passée dont la localisation 

exacte reste à déterminer (Van Dover et al. 2001, Desbruyeres et al. 2006). 

3/ L’identification des déterminants principaux de la divergence intra-spécifique, 

c'est-à-dire de la dispersion ‘actuelle’ ou ‘récente’. Les patrons de structure 

génétique observés le long des dorsales océaniques, par exemple, ne suivent pas 

les modèles d’isolement par la distance ou de stepping-stones qui étaient attendu 

étant donnée la fragmentation de l’habitat et sa répartition dans un espace à une 

dimension. Les études portant sur les taxons principalement représentés ont mis en 

évidence ou suggéré l’influence, selon les espèces et les régions biogéographiques, 

de facteurs tels que le mode de dispersion larvaire (Vrijenhoek 1997, Shank & 

Halanych 2007), les barrières au flux génique (Vrijenhoek 1997), la sélection 

environnementale (Jollivet et al. 1995a, Jollivet et al. 1995b), les processus de 

coévolution... Toutefois, les problèmes d’accès à un échantillonnage suffisant, de 

propriétés (neutralité, polymorphisme) des marqueurs utilisés, et d’écart à l’équilibre 

migration-dérive, compliquent la généralisation de ces observations. Les progrès de 

la biologie moléculaire et des outils analytiques, conjugué à l’augmentation du 

nombre d’échantillons disponibles après plusieurs dizaines d’années de campagnes 

océanographiques, permettront probablement dans un avenir proche de contourner 

un certain nombre de ces difficultés et de comprendre un peu mieux les facteurs qui 

affectent la dispersion et la composition génétique des populations d’environnement 

profond. 

Le premier projet portera sur la phylogéographie et de la dynamique des 

populations de crevettes des genres Alvinocaris distribuées dans les océans 

Pacifique et Atlantique et Rimicaris dans l’océan Atlantique. Ce choix a été motivé 

par l’intégration de ce projet dans le projet ANR ‘DeepOases’ et par la collaboration 

avec le laboratoire de Microbiologie des Environnements Extrêmes, où l’étude des 

bactéries épimbiontes est réalisée (Zbinden & Cambon-Bonavita 2003, Zbinden et al. 

2004). Elle permettra l’étude des phylogénies comparées des crevettes et de 

certaines lignées bactériennes associées, afin de tester l’existence de patrons 

caractéristiques de processus coévolutifs. De plus, la répartition relativement 

étendue de ces espèces permettra une étude phylogéographique globale, et des 

études de génétique des populations de l’espèce Rimicaris exoculata plusieurs 

échelles spatiales. 

La phylogéographie des espèces du genre Alvinocaris sera réalisée à l’aide 

de marqueurs mitochondriaux et ITS, car leur distribution étendue permettra de tester 

certaines hypothèses de colonisation des rides océaniques Pacifique (EPR) et 

Atlantique (MAR) ainsi que des échanges actuels et passés entre cold seeps et 

151



dorsales. Plus précisément il s’agit d’estimer i) le rôle de la fermeture de l’isthme de 

Panama dans la divergence entre les espèces, ii) la date des évènements de 

radiation et iii) la possibilité que les sources de suintement froid aient joué un rôle de 

‘stepping-stone’ dans la colonisation des sources hydrothermales. 

Dans le cas de Rimicaris exoculata, il s’agira de décrire le patron de structure 

génétique à différentes échelles spatiales le long de la ride Médio Atlantique (à 

l’échelle de la cheminée, du site, du champ hydrothermal, et entre champs 

hydrothermaux) afin d’estimer la distance à partir de laquelle de la restriction au flux 

de gènes peut-être mise en évidence. Cette estimation sera réalisée sur la base 

d’échantillons déjà disponibles, et d’échantillons qui seront prélevés lors des 

prochaines campagnes, afin d’estimer la stabilité temporelle des patrons de 

structuration observés. 

La répartition spatiale fragmentée de certains écosystèmes profonds, et 

l’instabilité temporelle des écosystèmes chimiosynthétiques (plus modérée pour les 

sources de fluides froids) suggèrent des évènements récurrents d’extinctionrecolonisation 

(Jollivet et al. 1998) et laissent penser que l’équilibre migration-dérive 

n’est probablement pas atteint pour la plupart des espèces (Jollivet et al. 1999). Par 

ailleurs, les tailles de populations varient probablement en fonction de l’aire habitable 

et du temps écoulé depuis la dernière extinction/recolonisation (Vrijenhoek 1997). 

Quant aux mouvements migratoires, ils sont vraisemblablement influencés à la fois 

par les distances à parcourir, le mode de dispersion larvaire et les obstacles ou 

courants existants entre ces habitats fragmentés. Ces caractéristiques compliquent 

l’analyse des données de génétique des populations avec les outils statistiques 

classiques (Jollivet et al. 1999). L’un des aspects de mon travail consistera donc à 

continuer le développement de l’utilisation de la théorie des réseaux ce type de 

données. Par ailleurs, l’étude simultanée des ‘hôtes’ et des invertébrés ou 

microorganismes associés permettra d’utiliser une développement spécifique de la 

théorie des réseaux : les réseaux bipartites, qui permettent d’étudier l’interaction 

entre la connectivité de deux types de réseaux dont les agents sont associés entre 

eux (Watts 2004). 

Composante génétique de la biodiversité et conservation 

Dans le cadre de notre partenariat avec la nouvelle Agence des Aires Marines 

Protégées, l’un de nos objectifs est de contribuer à la mise en place d’Aires Marines 

Protégées en Environnement Profond, par l’identification de zones prioritaires à 

classer et l’identification des critères permettant l’évaluation de l’intérêt des zones à 

protéger et de l’efficacité des mesures mises en place. 

152



Un premier chantier concerne les zones de canyons et les écosystèmes de 

coraux profonds, afin de contribuer aux objectifs de Natura 2000 et de la Convention 

OSPAR. Un projet Européen (CoralFISH, Encadré 6) a été accepté dans le cadre du 

FP7, qui apportera à la fois en terme de cartographie de l’habitat et d’étude d’impact 

des éléments d’informations nécessaires à la mise en place d’AMP profondes sur les 

écosystèmes coralliens de la façade Atlantique. 

153



Un projet Life+ (SAME) a été déposé pour valoriser et complémenter les 

résultats obtenus dans le cadre de CoralFISH. 

Intitulé complet du projet : Etude des interactions entre coraux, poissons et 

pêcheries, dans le but de développer des outils de gestion et de modélisation 

prédictive pour une gestion basée pour les écosystèmes dans les eaux profondes 

Européennes, et au-delà (Encadré 6). 

Il s’agira pour moi (leader du WP4, Encadré 6) d’étudier la composition clonale 

et génétique des populations de corail profond de l’espèce Lophelia pertusa, et la 

structure génétique des populations du polychète associé Eunice norvegica. 

Appartenant à la famille des Sclératinaires, L. pertusa est systématiquement 

rencontrée en association avec le polychète E. norvegica (Roberts 2005), et il est 

proposé que cette association durable soit basée sur l’habitat fourni au polychète par 

la structure corallienne, et à la protection des coraux contre l’érosion par les 

structures tubulaires du polychète. L. pertusa a été observée de part et d’autre de 

l’Océan Atlantique, en Mer Méditerranée, et dans les Océans Indien et Pacifique. La 

distribution étendue des récifs de Lophelia permet d’envisager une étude de la 

phylogéographie et de la dispersion de ces deux espèces à plusieurs échelles 

spatiales. 

Cette étude sera basée sur des marqueurs microsatellites déjà disponibles 

pour L. pertusa (Le Goff-Vitry et al. 2004), et qui seront développés pour E. 

norvegica. Elle visera à i) étudier la variabilité géographique et/ou écotypique de la 

balance entre reproduction sexuée et asexuée chez L. pertusa, et l’impact des 

pêcheries profondes sur la variabilité clonale et génétique de cette espèce, ii) établir 

le patron de structure génétique et le schéma de dispersion comparés des deux 

espèces et estimer le probabilité de recolonisation en cas d’extinction locale et iii) 

éventuellement de recueillir des informations sur le mode de transmission et la 

possible co-évolution entre les lignées de coraux et de polychètes. 

Finalement, l’étude des organismes clonaux que sont les coraux me permettra 

également de poursuivre le travail de réflexion engagé sur les implications de la 

clonalité sur l’évolution des populations et des espèces. 

154

Sophie ARNAUD-HAOND -Candidature à une Habilitation à Diriger des Recherches, Février 2008 

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