Ecology of Leaf Longevity (Ecological Research Monographs)

Ecological Research Monographs
Series Editor: Yoh Iwasa
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Kihachiro Kikuzawa ●
Martin
J. Lechowicz
Ecology of Leaf Longevity

Kihachiro Kikuzawa, Ph.D.
Professor
Ishikawa Prefectural University
Nonoichi, Ishikawa 921-8836
Japan
kikuzawa@ishikawa-pu.ac.jp
Martin J. Lechowicz, Ph.D.
Professor
Department of Biology
McGill University
1205 Dr. Penfield Avenue
Montreal, Québec
Canada H3A 1B1
martin.lechowicz@mcgill.ca
ISSN 2191-0707 e-ISSN 2191-0715
ISBN 978-4-431-53917-9 e-ISBN 978-4-431-53918-6
DOI 10.1007/978-4-431-53918-6
Springer Tokyo Dordrecht Heidelberg London New York
Library of Congress Control Number: 2011926414
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Cover
Front Cover : Leaf senescence of Fagus crenata (Japanese beech)
Back Cover :
Left: Bud break of Fagus crenata
Center : Bud break and new leaf emergence of Mallotus japonicus
Right: Bud break of Alnus hirsuta
Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)

Preface
The functional ecology of foliage is organized by seasonality. In temperate regions,
leaves in deciduous forests often turn brilliant colors in autumn. In spring, buds of
leaves burst and new shoots elongate. Similarly, in seasonal tropical environments
species respond to the timing of rainy and dry periods, and in the aseasonal tropics
subtle environmental cues can influence the timing of leafing and shoot growth.
Detailed consideration reveals the diversity underlying such broad patterns of foliar
phenology. Even in the canopy of a single forest, leaf dynamics are variable within
and among species. Although at a glance leaves seem to simultaneously appear in
spring and drop in autumn in a deciduous forest, some individual leaves in fact
develop later in the season and some leaves fall during the growing season. The evergreen
habit of trees can be achieved through leaves that persist over many years but is
also maintained by overlapping cohorts of fairly short-lived leaves that keep the plant
canopy evergreen. These complex patterns of leaf demography suggest the necessity
of monitoring the dynamics of leaves per se, not merely describing the broad patterns
of phenology at the tree or forest level. By monitoring individual leaves we can obtain
estimates for a fundamental demographic parameter, that is, leaf longevity, and in this
way move phenology from the realm of descriptive lore to that of a modern science
providing quantitative and predictive understanding of plant function.
A focus on the phenology of leaves is entirely merited if for no other reason than
that leaves are the most essential of photosynthetic organs. Photosynthesis is the
most important chemical reaction in the world, converting radiant energy to the
chemical energy that underpins life on Earth. Among the readily observed traits that
characterize leaves, arguably the most broadly relevant is leaf longevity. Leaf
longevity is central to leaf function and is a critical factor deciding plant fitness in
a given environment. Variations in leaf longevity create a contrast between deciduous
and evergreen species that define the nature of entire biomes. Leaf longevity
correlates with the primary production of plant communities and gains increasing
importance in relationship to global climatic change. In the past several decades,
scientists have accumulated information on interspecific variation in leaf longevity
for thousands of species and have produced various hypotheses and theories about
leaf longevity and its consequences. This monograph is an attempt to review and
synthesize our present understanding of leaf longevity.
v

vi Preface
Our own interest in leaf longevity stems from work on plant phenology that we
pursued independently in the 1970s and 1980s, when the scientific study of the basis
of phenological patterns was just beginning to take hold. We began our respective
phenological studies on trees in the mixed-wood forests of northern Japan and eastern
North America. Our interests were largely phenomenological at first, addressing
questions such as why some tree species shed green leaves early in the season
whereas others shed leaves only in autumn, or why some trees burst into bud earlier
in spring than others. We sought explanations for these phenomena from the points
of view of physiological ecology and variation in tree life history. Our thinking was
drawn from phenology to more specific questions about leaf function by Brian
Chabot and David Hicks’ seminal 1982 review entitled “Ecology of Leaf Lifespan”
and by subsequent ecophysiological work on cost–benefit analyses, especially that
of Hal Mooney and Chris Field. Gradually we gravitated to deeper explanations of
variation in leaf longevity rooted in the evolution of plants through natural selection
under the constraints of resource availability and teamed up to organize several
symposia at international meetings in ecology and botany. Our collaborations were
strengthened when M.J.L. had the opportunity to spend time with K.K. in Japan, first
as a guest researcher at the Hokkaido Forest Research Institute and then as a visiting
professor at Kyoto University. Through those extended visits as well as shorter ones,
we carried forward an exchange of ideas that laid the framework of this book.
Box 1 Evolution Through Natural Selection
(continued)

Preface
Box 1 (continued)
In the mid-nineteenth century, Charles Darwin proposed the concept of natural
selection, the foundation of modern evolutionary theory. Darwin recognized
that there was some level of variation in the characteristics of individuals
within a population, and that this variation in traits could affect differences in
the survival and reproduction of individuals. He reasoned that over generations
traits favoring greater survival and reproduction in the local environment
should accumulate, or, in other words, that adaptation and fitness should
increase through a process of natural selection. In Darwin’s time no one knew
the genetic basis of variation in traits, but now we know that the strength of
natural selection depends on the heritability of traits – the degree to which
characteristics can be passed from parent to offspring. Contemporary evolutionary
theory combines Darwin’s seminal idea of natural selection with our
knowledge of genetics to explain everything from the origins of complex adaptations
involving many interacting traits to the origins and interactions among
species that create the diversity of life on Earth. In 1973, Theodosius
Dobzhansky famously remarked that “nothing in biology makes sense except
in the light of evolution.”
This book considers foliar phenology through the lens of leaf longevity, which we
believe can yield important insights into the functional ecology of plants. Our
emphasis is on woody plants, which we know best and which also are best studied,
but the principles discussed often apply as well to herbaceous species. We take
pains to trace the development of ideas in the literature, partly in respect of pioneering
work and also because the diverse streams of research that come together
to form our contemporary view are best appreciated in historical perspective. We
also purposely draw on Japanese-language publications reporting work relatively
little known outside Japan. The book is intended to provide a comprehensive and
coherent starting point for those just embarking on research about leaf longevity
and its consequences at the levels of the whole plant, plant communities, and
ecosystems.
Box 2 Phenology
Phenology is defined as the study of the timing of biological events and their
relationship to seasonal climatic changes (Lieth 1974). People were conscious
of the seasonal development and activity of organisms long before the scientific
study of phenology emerged: survival depended on their knowing the
vii
(continued)

viii Preface
Box 2 (continued)
timing of the runs of salmon up a river or the coloring of leaves as a sign of
the approaching winter. In recent centuries, more precise records of phenological
events began to be kept that have proven invaluable in analysis of climate
change. The record of the blooming dates for cherries in Japan stretches
back over 800 years and in modern times has become an integral part of
meteorological reporting much appreciated by Japanese people. Based on
observations of sample trees at each weather station, blooming time is predicted
as an advancing front moving gradually northward as spring arrives in
Japan. Similar records document the first observation of the butterfly Pieris
rapae and the first song of the bush warbler (Cettia diphone), as well as observations
of the timing of leaf emergence and senescence that define leaf
longevity.
Phenology and Seasonality
Traditional views tie phenology to seasonality defined in terms of climatic
patterns during the annual cycle defined by the planet’s transit around the
sun. In middle and high latitudes where there are great differences in climate
throughout the year, it is certainly reasonable to expect phenological events
to reflect the responses of organisms to temporal variation in abiotic constraints
on their survival and reproduction. On the other hand, climatic variation
at lower latitudes can be considerably less, for example, in some
equatorial forests with little or no seasonal variation in precipitation, temperature,
or daylength. In these situations, and perhaps more generally, we
should consider that the timing of biological events may have more to do
with interactions among organisms than with any abiotic factors. The timing
of emergence and senescence of individual leaves in a plant can be determined
as much by interactions among leaves in a growing plant canopy as
by seasonal variation in climatic conditions (Kikuzawa 1995). Similarly,
synchronous leaf emergence by many different species in a plant community
may have been favored by natural selection, not in response to climatic constraints
but because this reduced the risk of herbivory (Aide 1988, 1992). The
interdependence of plants and the organisms that pollinate their flowers and
disperse their fruits provides countless additional examples of this phenomenon.
We should not forget that interactions within and among organisms can
affect phenology quite apart from the abiotic effects of seasonal climatic
change.

Preface
Box 3 Primary Production
Gross primary production (GPP) and net primary production (NPP) are terms
associated with ecosystem science that characterize the capture of solar energy
in photosynthesis by the primary producers in the system. The total photosynthetic
assimilation of carbon by a plant community is termed gross primary
production (GPP), usually expressed as ton C ha −1 year −1 . Some part of this
assimilated carbon is used in respiration associated with growth and maintenance:
the GPP minus the carbon lost to respiratory processes is termed net
primary production (NPP). The annual biomass increment associated with the
growth of leaves, branches, stems, roots, and reproductive structures, plus some
volatile compounds and exudates, comprise NPP. Precisely estimating NPP is
no easy task! Turnover of leaves and fine roots during the year, ephemeral
structures such as flowers and bud scales, biomass lost to herbivores and disease,
and transfers to mycorrhizal fungal symbionts all must be accounted for.
At the ecosystem level, NPP can be discounted for the respiratory losses associated
with the secondary production of organisms directly (herbivores, disease)
or indirectly (carnivores, parasites) consuming NPP and those decomposing
organic matter to obtain an estimate of net ecosystem production (NEP).
M.J.L. has enjoyed the hospitality and opportunities for intellectual growth provided
by his many colleagues in Japan, but especially by K.K. This time away has been the
perfect complement to the collegiality that characterizes the Department of Biology
at McGill University, an academic home that could not be more congenial and stimulating.
His debt is greatest, however, to his wife, friend, and colleague, Marcia
Waterway, whose patient forbearance with his idiosyncrasies is exceeded only by her
willingness to share her insights and ideas. M.J.L. also is grateful for enlightened and
open-ended funding policies in the Discovery Grant Program at the Natural Sciences
and Engineering Research Council of Canada that let him pursue research on diverse
and often esoteric topics that only sometimes turn out to have practical value.
K.K. would like to express his thanks to Hiromi Kikuzawa for her encouragement
and assistance in fieldwork throughout this study. Colleagues in the Hokkaido
Forestry Research Institute encouraged his study for more than 20 years. Students
in the Center for Ecological Research and Graduate School of Agriculture in Kyoto
University and in the Laboratory of Plant Ecology in Ishikawa Prefectural University
helped during his fieldwork both in Japan and in Borneo. The Ministry of Education,
Science, Sports and Culture of Japan provided essential financial support.
Nonoichi, Japan Kihachiro Kikuzawa
Montreal, Canada Martin J. Lechowicz
July 2010
ix

Contents
1 Foliar Habit and Leaf Longevity ............................................................. 1
2 Leaves: Evolution, Ontogeny, and Death ................................................ 7
Shoot Growth, Buds, and Leaf Emergence ................................................. 9
Budbreak and Leaf Development ............................................................... 14
Photosynthetic Functionality in Mature Leaves .......................................... 16
Age-Dependent Decline in Photosynthetic Capacity .................................. 19
Senescence and Abscission ......................................................................... 21
3 Quantifying Leaf Longevity ..................................................................... 23
Defining Leaf Longevity ............................................................................. 23
Estimating Leaf Longevity from Leaf Turnover on Shoots ........................ 25
Estimating Leaf Longevity from Census of Leaf Cohorts over Time ......... 30
Estimation of Leaf Longevity from Leaf Turnover at the Stand Level ....... 34
Revisiting the Basic Concept of Leaf Longevity ........................................ 35
4 Theories of Leaf Longevity ...................................................................... 41
Costs and Benefits of the Evergreen Versus Deciduous Habit .................... 41
Leaf Longevity to Maximize Whole-Plant Carbon Gain ............................ 43
Modeling Self-Shading Effects on Leaf Longevity .................................... 46
Carbon Balance at the Time of Leaffall ...................................................... 48
Time Value of a Leaf ................................................................................... 49
Leaf Longevity and Leaf Turnover in Plant Canopies ................................ 52
Directions for Future Theory ...................................................................... 55
5 Phylogenetic Variation in Leaf Longevity ............................................... 57
Leaf Longevity of Ferns .............................................................................. 59
Leaf Longevity of Gymnosperms ............................................................... 60
xi

xii Contents
Leaf Longevity of Angiosperms ............................................................... 61
Evergreen Broad-Leaved Woody Species ............................................ 61
Temperate Deciduous Trees and Shrubs .............................................. 63
Tropical Trees and Shrubs .................................................................... 63
Leaf Longevity of Herbaceous Plants ....................................................... 64
6 Key Elements of Foliar Function ........................................................... 67
Photosynthesis and Foliar Nitrogen Content ............................................ 70
Assembling the Elements of Foliar Function ............................................ 71
Photosynthetic Function and Leaf Longevity ........................................... 72
Deciding the Core Set of Cardinal Traits .................................................. 75
7 Endogenous Influences on Leaf Longevity ........................................... 77
Timing of Leaf Emergence and Leaf Longevity ....................................... 77
Plant Growth Rates and Leaf Longevity ................................................... 78
Seedling Growth and Leaf Longevity ....................................................... 80
Variation of Leaf Longevity with Timing of Leaf Emergence .................. 81
Canopy Architecture and Leaf Longevity ................................................. 82
Canopy Heterogeneity and Leaf Longevity .............................................. 84
8 Exogenous Influences on Leaf Longevity ............................................. 87
Insolation and Leaf Longevity .................................................................. 88
Aridity and Leaf Longevity....................................................................... 90
Nutrients and Leaf Longevity ................................................................... 92
Effects of Environmental Stress on Leaf Longevity ................................. 94
Biotic Stressors: Herbivory and Disease ................................................... 94
Abiotic Stressors: Ozone and Natural Oxidants ....................................... 96
Abiotic Stressors: Salinity......................................................................... 96
Abiotic Stressors: Flooding....................................................................... 97
9 Biogeography of Leaf Longevity and Foliar Habit ............................. 99
Biogeography of Foliar Habit ................................................................... 100
Contemporary Distribution of Deciduous and Evergreen Habits ............. 101
Theory for the Geography of Foliar Habit ................................................ 102
Modeling Foliar Habit in Relationship to Climate ................................... 108
10 Ecosystem Perspectives on Leaf Longevity .......................................... 109
Leaf Turnover and Leaf Longevity in the Ecosystem ............................... 110
Nutrient Resorption and Leaf Longevity .................................................. 111
Photosynthetic Nitrogen Use Efficiency and Leaf Longevity .................. 115

Contents
xiii
Defense of Leaves and Leaf Longevity ................................................ 116
Timing of Leaf Emergence, Leaf Longevity, and Leaf Defense .......... 118
Linking Leaf Longevity and Ecosystem Function ............................... 119
References ........................................................................................................ 121
Subject Index ................................................................................................... 141
Organism Index ............................................................................................... 145

Chapter 1
Foliar Habit and Leaf Longevity
Mixed wood forest in spring leafing period, Ithaca, New York, USA
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_1, © Springer 2011
1

2 1 Foliar Habit and Leaf Longevity
The origins of the study of leaf longevity lie in the distinction between evergreen
and deciduous plant species, which is not as simple as it first seems. The evergreen
habit basically is defined by the retention of functional leaves in the plant canopy
throughout the year, as opposed to the deciduous habit in which a plant is leafless
for some part of the annual cycle. This simple evergreen–deciduous dichotomy
most often is applied to woody trees, shrubs, and vines. Herbaceous perennials that
retain leaves through winter are sometimes referred to as evergreen, or more often
as wintergreen, in contrast to summergreen (Sydes 1984; Ohno 1990; Tessier
2008), but the evergreen–deciduous dichotomy has had less attention in herbaceous
species than in woody plants.
Box 1.1 Plant Canopy
The plant canopy can be thought of as a three-dimensional array of leaves for
the capture of solar energy. The term applies at two spatial scales, but in all
cases it refers to an array of leaves in space. At the level of individual plants,
the structure of the canopy is determined by the way that leaves are arrayed
along herbaceous stems or woody branches. The canopy of individual trees is
also referred to as the tree crown, the array of branches above the trunk. At the
level of a plant community, canopy structure depends on the canopy architecture
of neighboring plants and the way that individuals adjust their canopy
architecture in response to neighbors. In grasslands, the low-growing canopy
is often a heterogeneous mix of vertically oriented grasses and laterally
branching, broad-leaved herbs. In forests the canopy can be multilayered,
with taller trees forming the forest canopy but other, less tall, trees forming a
distinct subcanopy.
Box 1.2 Foliar Habit
Foliar habit refers to the common distinction between evergreen and deciduous
plants, which in fact is not as straightforward as most people think. Foliar habit
is a characteristic of the plant canopy as a whole, not of individual leaves. A
plant is commonly referred to as evergreen if it retains at least some leaves
throughout the year, in contrast to deciduous plants, which are bare of leaves for
some part of the annual cycle of the seasons. Depending on the timing of emergence
and fall of individual leaves and the number of leaves retained in the plant
canopy, some subdivisions of the evergreen and deciduous habits are possible.
Variations on the evergreen habit
Leaf exchanger: Leaves are exchanged within a year; thus, leaf longevity is
shorter than 1 year but there are always viable leaves in the plant
canopy.
(continued)

1 Foliar Habit and Leaf Longevity
Box 1.2 (continued)
Semievergreen: Immediately after new leaf emergence, old leaves fall; leaf
longevity is essentially 1 year, and a leafless period is not very apparent.
Brevideciduous: Some leaves are shed during part of the year, but never
more than 50% of the leaves, so the plant canopy appears evergreen.
Semideciduous: More than 50% of leaves are lost at some time in the year,
but the plant canopy is never completely bare.
Heteroptosis: Some branches of a tree become completely leafless during
unfavorable periods but others retain leaves throughout the year.
Variations on the deciduous habit
Summergreen: Leaves are shed in autumn, and in woody plants the canopy
is completely bare through winter; this is a typical deciduous habit in
temperate regions.
Wintergreen: Leaf emergence occurs at the end of summer and leaves are
retained through winter, but are shed at the onset of the next summer,
and the plant is completely bare during summer.
Drought deciduous: Leaves are shed during the dry season in tropical
forests and deserts.
Spring ephemeral: Plants have leaves only in early spring that wither by
summer. This habit is usually found in herbaceous plants but has been
recorded in a small tree (Aesculus sylvatica) in North America
(DePamphilis and Neufeld 1989).
It is important to recognize that the distinction between evergreen and deciduous
species applies at the level of the entire plant canopy, not individual leaves. It is
possible for a plant canopy to be evergreen by replacing relatively short-lived leaves
frequently throughout the year. Of 13 evergreen species in California chaparral, 5
had leaves that survived less than a year but which maintained an evergreen canopy
through a prolonged period of leaf production from early spring into summer
(Ackerly 2004). Although the basic evergreen–deciduous dichotomy at the canopy
level is reasonably clear, some intermediate terms have arisen to describe peculiarities
in leaf turnover that can lead to differing degrees of evergreenness (Sato
and Sakai 1980; Eamus 1999; Eamus et al. 1999a; Eamus and Prior 2001; Franco
et al. 2005; Saha et al. 2005; Negi 2006; Williams et al. 2008). Primary among
these alternative terms is the recognition of a brevideciduous habit in which there
is a brief period in the year when old leaves are falling and new leaves are emerging
simultaneously. This intermediate habit also is referred to as “leaf exchanger”
(Whitmore 1990), “incomplete deciduousness” (Hatta and Darnaedi 2005), and
“semievergreen (Singh and Kushwaha 2005). The canopy in such species is never
entirely leafless, even briefly, and therefore cannot be considered truly deciduous,
but then neither can it be considered any more than marginally evergreen. From
developmental, phylogenetic, and functional points of view, this brevideciduous
3

4 1 Foliar Habit and Leaf Longevity
habit appears to be more a variant of the deciduous habit rather than a true
evergreen habit. Even a north temperate forest tree such as Carpinus caroliniana
that is commonly perceived as unambiguously deciduous in response to harsh
winter conditions is brevideciduous at the southern limits of its native geographic
range (Borchert et al. 2005). The widespread tropical tree, Shorea robusta, which is
usually considered evergreen, in fact shows similarly plastic rangewide responses
in the timing of leaf turnover (Singh and Kushwaha 2005). There is clearly a degree
of plasticity and ambiguity in what at first seems a straightforward dichotomy
between the deciduous and evergreen habits. Similarly, the simple association
between the deciduous habit and strongly seasonal climates is belied by its
occurrence in aseasonal tropical forests as well (Hatta and Darnaedi 2005).
In these same tropical forests, some of the evergreen species maintained relatively
constant leaf numbers through either steady or episodic turnover of leaves throughout
the year, while others were evergreen but allowed their leaf numbers to drop to
only 30–60% of full canopy at some point in the year (Hatta and Darnaedi 2005).
Although the evergreen–deciduous dichotomy has been recognized since ancient
times, it is only in the 20th century that appreciation for the diversity in leaf demography
that underlies observations at the scale of whole trees and forests has emerged
to make sense of these variations within the basic dichotomy.
The pioneering phytogeographic studies of Alexander von Humboldt and Aimé
Bonpland (1807) were the first to stimulate scientific interest in the contrast
between evergreen versus deciduous trees and forests. Western botanists already
were familiar with the broad-leaved deciduous forests of central Europe and
needle-leaved conifer forests of northern Europe, but Humboldt and Bonpland
called attention to the somewhat surprising existence of tropical forests dominated
by broad-leaved evergreen species of flowering plants. Since then, the distinction
between the evergreen and deciduous habit has figured in the classification of
vegetation types by phytogeographers and ecologists (Grisebach 1838, 1884;
Warming 1909; Whittaker 1962; Walter et al. 2002; Woodward et al. 2004). By the
late nineteenth century a complementary stream of inquiry had arisen that sought
to explain the environmental basis for predominance of the evergreen habit and the
frequently allied condition of small, tough, long-lived leaves referred to as sclerophylly
(Beadle 1954, 1966; Loveless 1961; Monk 1966; Mooney and Dunn
1970a,b). Schimper’s (1903) classic book entitled Plant-Geography Upon a
Physiological Basis consolidated the earliest work in this field and raised questions
that continue to be investigated to the present day. It is these attempts to discover
the adaptive value of evergreenness and sclerophylly that eventually led to the study
of leaf longevity in its own right.
Recognizing that the evergreen habit and sclerophylly were associated with dry
and infertile sites, most of the work following Schimper (1903) focused on the
evergreen and deciduous habits as alternative strategies for managing water and
nutrient resources. Mooney and Dunn (1970a,b), for example, adopted a wholeplant
perspective on adaptation to explain the occurrence of evergreen and deciduous
species along gradients of moisture availability in the Mediterranean climates of
Chile and southern California. They observed that as the summer dry period

1 Foliar Habit and Leaf Longevity
became longer, dominance in the chaparral vegetation shifted from evergreen to
deciduous species. They concluded that so long as the dry period was not too
prolonged, the deeply rooted, sclerophyllous evergreens with their relatively low
photosynthetic rates were more productive over the year than the shallow-rooted,
mesophyllic deciduous species. Conversely, when the dry period was not long, the
high photosynthetic rates typical of mesophyllic leaves conferred an advantage on
the deciduous species that were better able to exploit the cool, wet winter season
and to avoid water loss by being leafless in the hot, dry summer. In a related cost–
benefit analysis of leaves as photosynthetic organs, Orians and Solbrig (1977) were
the first to offer a functional explanation at the leaf level for sclerophylly and the
evergreen habit. They postulated that plants adapted to hydric conditions should
have drought-deciduous, mesophyllic leaves, photosynthesize rapidly when water
was readily available, and cease photosynthetic activity quickly as conditions
became drier (Fig. 1.1). On the other hand, they expected plants adapted to xeric
conditions to have evergreen leaves persistent through drought periods, with
relatively low photosynthetic rates even when water was readily available, but able
to withstand drought through conservative stomatal regulation and low cuticular
water loss associated with sclerophylly. In this their ideas followed Mooney and
Dunn (1970a,b), but they also specifically suggested that the association of sclerophylly
with the evergreen habit arose in the time required to recover leaf construction
costs. Given the low photosynthetic capacity of sclerophyllous leaves, only an
evergreen habit allowing amortization over more than a single year could recover
Fig. 1.1 The Orians and Solbrig (1977) expectations for photosynthetic activity in response to
water availability as a function of different plant strategies. Soil water availability is on the
abscissa, from wet to dry; photosynthetic activity is on the ordinate. Four hypothetical species are
illustrated: darker shading shows the portion of the moisture gradient where each species is
respectively at an advantage in terms of potential productivity. X indicates a species with xeromorphic
(sclerophyllous) leaves; m indicates a species with mesomorphic leaves
5

6 1 Foliar Habit and Leaf Longevity
the relatively high costs of leaf construction in sclerophylls. Mesophyllic leaves,
which cost less to construct and have higher photosynthetic capacity, were associated
conversely with the deciduous habit. The Orians and Solbrig (1977) model
embodies ideas about trade-offs in foliar design still prevalent today and stands as
the first theoretical model associating leaf photosynthetic function and leaf longevity
with the distinction between evergreen and deciduous plants.
A seminal review a few years later by Chabot and Hicks (1982) marks a turning
point in consideration of the nature and causes for the evergreen versus deciduous
habits. Their review consolidated ideas emerging in the previous decade, decisively
shifting the discussion from questions of resource availability and resource
management at the whole-plant level to leaf longevity as a central trait in foliar
function that determined whether a plant was evergreen or deciduous. Picking up
on the perspective of Orians and Solbrig (1977), they presented a cost–benefit
analysis of leaf carbon economy based on the premise that leaves are fundamentally
photosynthetic organs, which over their lifetime must repay to the plant the carbon
cost of their construction. More formally, they introduced the following equation
for the carbon economy of a single leaf:
∑ fi ∑ ui
(1.1)
G = P − P −C−W −H −S
where G is the net carbon gain by a single leaf that is exported to other parts of the
plant over a year, P fi is the carbon gain by a leaf at age i during any favorable period
for photosynthesis over the year, and P ui is the net carbon exchange of the leaf
during any periods unfavorable for photosynthesis. Because the photosynthetic gain
during an unfavorable period is by definition zero or nearly so, the net gain during
an unfavorable period typically will be negative consequent to respiratory carbon
losses associated with maintenance and defense of the leaf. The term C is the
construction cost to produce the leaf. Although the actual construction of a leaf
occurs over some finite period of time, Chabot and Hicks (1982) imposed the
cumulative construction cost at the time of leaf expansion when the leaf becomes
photosynthetically active; the leaf construction cost therefore is independent of
time. Similarly, any damage by wind (W) or herbivores and pathogens (H) is also
accumulated and considered independent of time during the leaf lifespan. Finally,
Chabot and Hicks (1982) recognized that some part (S) of the photosynthate
produced by the leaf might be stored or utilized in foliar tissues rather than translocated
to another part of the plant, and hence would not contribute to the net gain
of the plant from that individual leaf. Reasoning in this conceptual framework and
reviewing available data, Chabot and Hicks (1982) argued that leaf longevity thus
should be determined by the balance between costs represented in the negative
terms of (1.1) and benefits represented in the positive terms. Their conceptual
framework and the literature they reviewed firmly placed the leaf in the context of
the plant as a whole, inviting subsequent analyses of how variation in leaf demography
contributes to the distinction between evergreen and the deciduous habits.

Chapter 2
Leaves: Evolution, Ontogeny, and Death
Bud burst of Alnus hirsuta
The evolutionary origin of leaves traces back to the gradual modification of branching
systems in the earliest land plants. The vascular plants belonging to the phylum
Rhyniophyta that first colonized land more than 400 million years ago had only
simple dichotomously branching axes without organs we would recognize as either
leaves or roots (Sussex and Kerk 2001). The early evolution of the land plants
involved a combination of progressive changes in branching architecture (overtopping)
and the associated flattening (plantation, fusion) of some branch elements to
form laminar photosynthetic organs that we recognize as leaves (Sussex and Kerk
2001; Boyce and Knoll 2002; Donoghue 2005). Over the course of the Paleozoic,
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_2, © Springer 2011
7

8 2 Leaves: Evolution, Ontogeny, and Death
four different vascular plant lineages evolved leaves: the ferns, sphenopsids, progymnosperms,
and seed plants (Boyce and Knoll 2002). The leaves of extant members of
these lineages are the primary photosynthetic organs in the great majority of plant
species. The earliest leaves in all four lineages were small, narrow, and single veined
(“microphylls”), arrayed along highly dissected branching systems but larger and
broader multiveined leaves (“macrophylls”) gradually become predominant in the
fern, gymnosperm, and angiosperm lineages (Boyce and Knoll 2002). The earliest of
these land plants are believed to have been evergreen, but by the early Carboniferous
Archaeopteris may have had some deciduous characteristics (Addicott 1982; Thomas
and Sadras 2001). The unambiguous origin of a seasonally adapted deciduous habit
arose only later in the polar forests of a “greenhouse Earth” where plants had to contend
with dark but warm winters and fire-prone conditions (Brentnall et al. 2005). By
the Permian there is some evidence for the seasonally programmed turnover of leaves
in the Glossopteris flora of polar regions (Taylor and Ryberg 2007) and strong evidence
for deciduous polar forests by the Cretaceous (Taggart and Cross 2009).
The leaves of contemporary plant species typically are arrayed along a stem segment
to form a shoot. The basic unit of shoot construction is a metamer consisting of a
leaf and bud at a node along a stem and an associated internodal stem segment
(Barlow 1989). Shoots composed of some number of metamers (Fig. 2.1) can be
considered the modular units of organization in the aboveground portion of plants
a
b
c
Fig. 2.1 Growth of a plant by the accumulation of modules. (a) The shoot, a stem section with
leaves, is the basic modular unit of plant vegetative growth. (b) A plant canopy grows by accumulation
of modules, sometimes only by apical extension, and other times (c) by lateral branching
from dormant buds. Some leaves and shoots typically will be shed as growth of the entire plant
proceeds, and the developing plant canopy can take on a degree of asymmetry as shoots interact
with one another and respond to their immediate microenvironment (d)
d

Shoot Growth, Buds, and Leaf Emergence
(White 1979; Jones 1985; Maillette 1987; Hallé 1986; Watson 1986; Room et al.
1994). Shoot growth arises in meristematic tissues associated with the apex of the
shoot (apical buds) or in terms of branching with the base of leaves (lateral buds).
Buds contain a short stem with leaf primordia and embryonic leaves, essentially
a partially developed, preformed shoot (Kikuzawa 1982; Jones and Watson 2001).
Embryonic leaves have partially developed lamina with distinguishable venation;
leaf primordia are too early in development for features of the mature leaf to be
discerned. Buds are usually, but not always, enveloped by bud scales, which confer
a degree of protection from dessication and herbivory (Kikuzawa 1982; Nitta and
Ohsawa 1998). Shoots have a degree of autonomous regulation over their dormancy,
growth, and senescence but also interact with other shoots in a coordinated way to
form the plant canopy as a whole (Thomas 2002; Barthélémy and Caraglio 2007).
Shoot Growth, Buds, and Leaf Emergence
Leaf emergence of Alnus hirsuta
The emergence of leaves, the growth of shoots, and the development of buds
containing future shoots are inextricably interlinked. At budburst, shoot extension
occurs in concert with leaf emergence, and as the bout of extension growth ends,
the development of buds containing future shoots ensues. This sequence is illustrated
by data from Maruyama (1978) on shoot elongation of deciduous broadleaved
saplings in a Japanese beech forest showing two contrasting modes of shoot
growth (Fig. 2.2). One mode is illustrated by Fagus in which the shoot elongates
and leaves emerge more or less simultaneously in a short burst of growth; this has
been referred to as Fagus-type (Maruyama 1978), flush-type (Kikuzawa 1983,
9

10 2 Leaves: Evolution, Ontogeny, and Death
Shoot Elongation %
100
80
60
40
20
Fig. 2.2 Three temporal patterns of shoot elongation of tree species in a deciduous broad-leaved
forest in Niigata, Japan. Shoot elongation and bud development are relativized to their maximum
size (100%) and plotted against calendar months. (After Maruyama 1978; redrawn by Kikuzawa)
1984, 1988), or determinate (Kozlowski 1971; Marks 1975; Lechowicz 1984) shoot
growth. Populus is an example of a succeeding-type (Maruyama 1978; Kikuzawa
1983, 1984, 1988) or indeterminate (Kozlowski 1971; Marks 1975; Lechowicz
1984) shoot growth in which the shoot elongates and leaves emerge over a relatively
long period. The period of indeterminate shoot growth can be fairly short, as
in Lindera, or quite extended, as in Populus (Maruyama 1978; Kikuzawa 1983).
The noteworthy contrasts between these determinate and indeterminate modes of
shoot growth, respectively, are (1) the episodic versus ongoing extension growth
and leaf emergence and (2) the temporal separation versus overlap of bud development
from extension and leaf emergence. The same two basic patterns of shoot growth
prevail in tropical forests (Koriba 1947a,b, 1958; Lowman 1992), in savanna species
in the western Himalayas (Zhang et al. 2007), in herbaceous plants (Kikuzawa
2003), and in ferns (Hamilton 1990). These patterns of shoot growth and leaf emergence
should be observed in any type of vegetation in the world because they arise
in the developmental controls on shoot growth, not the diverse environmental factors
that trigger the onset of growth (Kikuzawa et al. 1998).
Box 2.1 Bud Scale
0
Fagus
Lindera
M J J A S O
Calendar Month
Buds are a plant structure protecting vulnerable meristematic tissues and
embryonic leaves from cold or desiccation during a dormant period. The
modified, scale-like leaves that form the outer layers of many buds are called
bud scales. Buds form at the base of existing leaves and do not develop into
leaves until the parent leaf falls.
Populus

Shoot Growth, Buds, and Leaf Emergence
The basic patterns of shoot growth are also reflected in contrasting degrees of
development of the embryonic shoot within the bud. In determinate species, leaves
in the bud are unexpanded but already nearly completely developed before budburst,
and all the leaves appear simultaneously as a flush associated with rapid stem elongation;
this type of simultaneous shoot growth is always associated with fully
preformed shoots (Hallé 1978). Expansion of the embryonic leaves in a preformed
shoot may be arrested after their initial development for weeks or even years before
budburst (Foster 1929, 1931; Garrison 1949a,b, 1955; Barthélémy and Caraglio
2007). In species with indeterminate shoot growth, in contrast, single leaves appear
successively along a slowly growing shoot (Kikuzawa 1978, 2003). Leaves of
species with successive, indeterminate shoot growth may be either preformed in the
bud (Kikuzawa 1982) or newly produced (neoformed) during the growing season.
Some trees, such as species in the genus Betula, have both determinate “short
shoots” and indeterminate “long shoots” within their canopy (Kikuzawa 1983).
Leaves on the short shoots and the initial leaves on long shoots are preformed in the
overwintering bud, and later leaves on the extending long shoots are formed only
in the season they emerge (Macdonald and Mothersill 1983; Macdonald et al. 1984;
Caesar and Macdonald 1984). These patterns of simultaneous leaf emergence in
species with determinate shoot growth and successive leafing in species with
indeterminate shoot growth, as well as the combination of the two shoot growth
syndromes in some species, are found in evergreen trees in temperate regions (Nitta
and Ohsawa 1997), herbaceous plants (Yoshie and Yoshida 1989; Kikuzawa 2003),
and tropical trees (Lowman 1992; Kikuzawa 1978; Miyazawa et al. 2006).
There also is some relationship between the structure of buds and the nature of
shoot growth and leaf emergence in deciduous broad-leaved trees (Kikuzawa 1983,
1984, 1986). Species that have buds covered by well-developed, distinct bud scales
inevitably have determinate shoot growth (flushing with simultaneous leafing), but
not all species with determinate shoot growth necessarily have true bud scales. For
example, Styrax obassia has a naked bud, but it also has determinate shoot growth.
The incipient shoot forming within the bud of any species with true bud scales is
referred to as heteronomous (Fig. 2.3) because the shoot contains two types of metameric
units: one forms the bud scales themselves and the other forms true leaves
(Kikuzawa 1983, 1986). On the other hand, species with indeterminate shoot growth
(successive leafing) generally lack true bud scales and are referred to as homonomous:
all the metameric units comprising the shoot are basically identical, producing
leaves that may or may not have stipules or other ancillary structures derived from
the leaf lamina functioning as bud scales in the outermost metameric whorl. In Alnus
hirsuta, for example, the stipules of the outermost leaf function as scales enveloping
the bud as opposed to the distinct bud scales in Ulmus davidiana (see Fig. 2.3).
In the Aceraceae there is a morphological series suggesting the evolutionary transition
from homonomous to heteronomous buds (Sakai 1990). Dipteronia, the closest and
more primitive relative of Acer, is homonomous, lacking bud scales entirely
(Fig. 2.4). In Acer species with determinate shoot growth the distinction between
normal leaves and bud scales is clear – the bud is fully heteronomous. In Acer species
with indeterminate shoot growth, however, the distinction between heteronomy
11

Fig. 2.3 Cross sections of the two types of buds in deciduous broad-leaved trees. (a) Homonomous
bud of Alnus hirsuta with repetitions of the same basic unit of two stipules and a lamina. (b) Heteronomous
bud of Ulmus davidiana with bud scales at the outermost part of the bud distinctly different
from the embryonic leaves to the interior of the bud. (After Kikuzawa 1983)
Fig. 2.4 The linkage between the development of bud scales and shoot elongation patterns (Sakai
1990). Dipteronia, the closest relative of Acer, has no bud scales. In Acer species, young bud
scales have a rudimentary blade at their tips, which disappears during development, suggesting
bud scales originated from normal leaves. Minute rudimentary blades exist at the tip of the inner
bud scales in Acer species with indeterminate shoot growth but not in those with determinate
shoot growth

Shoot Growth, Buds, and Leaf Emergence
and homonomy is blurred. The innermost bud scales have rudimentary leaf blades at
their tips that suggest true bud scales in Acer are derived through the evolutionary
modification of laminar tissues (Sakai 1990). Similar correlations between the
degree of bud scale development and leaf emergence patterns can be observed in the
Betulaceae (Kikuzawa 1980, 1982).
The situation is somewhat similar in evergreen broad-leaved tree species, but it
is less clear cut because the timing of shoot growth and bud development is not as
constrained seasonally as in broadleaf deciduous trees. Three types of buds can be
recognized: naked buds lacking scales, hypsophyllary buds covered with soft green
leaf-like hypsophylls, and scaled buds covered by many hard imbricate brown
scales (Nitta and Ohsawa 1998). Buds with well-developed scales are typically
found in canopy tree species such as Castanopsis cuspidata, Quercus acuta, and
Machilus thunbergii, and naked and hypsophyllary buds in subcanopy or understory
species such Cleyera japonica, Eurya japonica, and Maesa japonica. Species
with naked buds do not form winter buds, instead having shoot growth with acropetal
production of leaves throughout the growing period. Species with hypsophyllary
buds have shoot growth during April and June in the warm temperate forests
of Japan, forming a terminal bud at the same time that then has no further morphological
development until budbreak the following March or April. Species with bud
scales have rapid shoot growth in May and June; during this stem elongation
period, the shoot tip has an immature hypsophyllary bud. After the completion of
stem elongation in early summer, a bud protected with many scales gradually develops
through the summer, fall, and winter within this immature hypsophyllary bud
(Fig. 2.5).
Fig. 2.5 Development of buds from May through October and mature bud condition in December
in two evergreen broad-leaved trees: Cleyera japonica (left) and Quercus acuta (right). The
hypsophyllary buds of Cleyera japonica are produced in spring within the mother bud but show
no further morphological development until the following spring. In Quercus acuta, the hypsophyllary
bud formed in spring develops into a scaled bud throughout the period until budburst the
next year. Open bars, hypsophyllary-bud phase; closed bar, scaled-bud phase. (After Nitta and
Ohsawa 1998)
13

14 2 Leaves: Evolution, Ontogeny, and Death
Budbreak and Leaf Development
The timing of budbreak in species of temperate regions is usually in response to a
combination of photoperiodic cues and spring warming (Lechowicz 2001). The control
of budbreak in tropical species is less clear, but in species from seasonal climatic
regimes the water balance of the plant itself serves as a cue (Borchert 1994). At the
time of budbreak, embryonic leaves expand by absorbing water, in some cases with
further cell divisions (Dengler and Tsukaya 2001; Barthélémy and Caraglio 2007).
The duration of the period of leaf expansion depends on four factors: (1) the number
of primordial cells, (2) the rate of cell division, (3) the duration of the phase of cell
division, and (4) the size of the individual mature cells (Gregory1956). Newly
emerged leaves often are brightly colored and only become green at full expansion
(Dominy et al. 2002). Full expansion of the leaf typically requires of the order of
10–15 days from budbreak, but this timing varies substantially and is influenced by
both environmental conditions and phylogenetic considerations. It should be noted
that terrestrial monocotyledons with graminoid growth forms, such as sedges
(Hirose et al. 1989) and grasses (Bowes 1997), as well as the gymnosperm Welwitschia,
all have a different mode of leaf development in which basal meristems continuously
form new leaf tissues. Hence in these plants, the leaf has different age tissues
with the tip oldest and the base youngest (Mooney and Ehleringer 1997).
Because leaves are the primary organs of plant productivity, the logical benchmark
for leaf maturation is attainment of full photosynthetic capacity. Instantaneous rates
of photosynthesis are influenced by environmental conditions such as ambient
temperature, vapor pressure deficit, atmospheric CO 2 level, and soil water potential,
as well as plant condition and stage of development, but ultimately are most dependent
on irradiance (Larcher 2001; Lambers et al. 1998). Given the very dynamic
nature of photosynthetic rates, what single value might serve as an index of leaf
maturation and more generally as an index of leaf function? It is reasonable to focus
initially on the response of photosynthetic rate to irradiance, the flow of photons on
which this biochemical process depends. Although the net photosynthetic response
to irradiance varies among and within plant species, the basic shape of the response
curve is consistent (Fig. 2.6). At very low irradiance, respiratory loss of CO 2 is
greater than photosynthetic gains, but as irradiance increases photosynthesis
predominates and net gains of CO 2 increase to an asymptote. This asymptotic rate
of net photosynthesis under saturating irradiance and otherwise optimal conditions
is referred to as photosynthetic capacity, A max . Photosynthetic capacity is commonly
taken as the cardinal value most useful in assessing foliar function and plant adaptation
(Wright et al. 2004).
In many, but not all, species photosynthetic capacity develops steadily after
budbreak, reaching its maximal value when the leaf is fully expanded (Saeki 1959;
Šesták 1981; Hodanova 1981; Castro-Diez et al. 2005; Warren 2006). This pattern
is typical of relatively short-lived leaves, but in species with longer-lived leaves
months can pass until full photosynthetic capacity is attained. For example, in Abies
veitchii, leaves appear in June but maximum photosynthetic capacity is reached

Budbreak and Leaf Development
Fig. 2.6 Typical light-
response curves of early,
mid and late successional
species are shown. (From
Bazzaz 1979)
Maturation period (d)
140
120
100
80
slope=0.701***
Ca
Photosynthesis
(mg CO2 dm –2 h –1 )
30
20
10
0
–10
15
Early
Mid
3 6
Late
9
Light Intensity (1000 ft-c)
Cj Cs
Fig. 2.7 Leaf maturation period and leaf mass area across different evergreen broad-leaved tree
species (Miyazawa et al. 1998): Ad, Actinidia deliciosa; As, Annona spraguei; Bn, Brassica
napus; Ca, Coffea arabica; Cp, Connarus panamensis; Cs, Castanopsis sieboldii; Cu, Cucumis
sativus; Dp, Desmopsis panamensis; Ma, Morisonia americana; Ol, Ouratea lucens; Qr, Quercus
rubra; Tc, Theobroma cacao; Xm, Xylopia micrantha. Open squares, species attaining full photosynthetic
capacity before full leaf expansion; open triangles, species attaining full photosynthetic
capacity at full expansion; closed squares, delayed greening; d, days
only in August (Matsumoto 1984); in Pinus pumila, full photosynthetic capacity is
attained only in September or even the following spring (Kajimoto 1990). Evergreen
broad-leaved trees such as Machilus thunbergii, Castanopsis sieboldii, and
Quercus glauca show similar delay in foliar development (Kusumoto 1961;
Miyazawa et al. 1998). In general, broad-leaved evergreen species with heavier, longerlived
leaves take longer to develop their full photosynthetic capacity (Miyazawa
et al. 1998; Fig. 2.7).
Ad
Cs*
60
Ma
40
Xm
Ol
Dp
Qr
Cp Qm
Mt
Ns
Qg
20 Cu As Tc
0
0
Bn
Pv
50100 150 200 250
LMA (g m −2 )

16 2 Leaves: Evolution, Ontogeny, and Death
Photosynthetic Functionality in Mature Leaves
Once a leaf has attained full photosynthetic function, various factors constrain its
performing to full capacity at all times. The overall situation is illustrated by a
diurnal and seasonal record of photosynthesis in a Mediterranean shrub, Phlomis
fruticosa (Fig. 2.8). The light reactions of photosynthesis obviously are precluded
at night, and the diurnal trace of photosynthesis generally is in proportion to insolation
from dawn to dusk if other conditions are favorable. In this evergreen
Mediterranean shrub, photosynthesis is low during the summer dry season and relatively
high in winter and spring when water is more available. Leaves function at
their maximum photosynthetic capacity (A max ) only near midday in early June, falling
well below their photosynthetic potential throughout midsummer and early fall. In
the transition from late spring to early summer as soil water supplies diminish and
atmospheric vapor pressure deficits increase, first midday and then late afternoon
photosynthesis is depressed despite high levels of insolation (Kyparissis et al.
1997). Such midday depression of photosynthesis in response to limited water
supplies is well known in species from temperate (Ishida and Tani 2003), tropical
(Zots and Winter 1994, 1996; Zots et al. 1995; Ishida et al. 1999), and even arctic
(Gebauer et al. 1998) climates. These and innumerable other examples document
the fact that over their lifetime leaves do not work to their full instantaneous
photosynthetic capacity, A max .
Acknowledging this reality, Kikuzawa introduced the concept of the mean labor
time of a leaf, the cumulative amount of photosynthesis achieved by a leaf over its
lifetime compared to the potential value if a leaf were able to work to its full capacity
20
16
12
8
Time of day
JUN7
JUN26
JUL6
JUL17
AUG11
SEP24
OCT19
NOV11
OCT23
APR4
MAR18
JAN30
DEC14
NOV28
Fig. 2.8 Diurnal and seasonal record of photosynthesis for mature leaves of an evergreen
Mediterranean shrub, Phlomis fruticosa, growing at low elevation. The leaves were produced in
April–May 1992 and measured from June 1992 to April 1993, just before this leaf cohort began
to senesce and abscise. (From Kyparissis et al. 1997)
0
10
5
APR19
25
20
15
P n (µmol m −2 s −1 )

Photosynthetic Functionality in Mature Leaves
all the time (Kikuzawa et al. 2004). Mean labor time provides a complement to the
use of A max as a cardinal trait characterizing variation in leaf function. It is essentially
a single, summary variable that subsumes all the environmental and ontogenetic factors
that can reduce photosynthesis below its maximum value over the lifetime of a
leaf. Mean labor time (m) expressed as an average per day is defined by
a h
17
m = 24 G / G
(2.1)
where G h is a hypothetical lifetime photosynthetic rate of a leaf, assuming that the
leaf works 24 h at A max throughout its lifetime; G a is the actual photosynthetic rate
of the leaf throughout its lifetime. This definitive equation can be decomposed into
terms representing the various factors that lead to photosynthetic performance
below full capacity:
G G a pclear GpGpLGa m = 24 = 24 (2.2)
G G G G G
h h pclear p pL
where G pclear is the lifetime carbon gain of a single leaf, supposing that every day
through its life is a clear day. Even if a day is cloudless, the solar angle changes
with time of day, hence the leaf still cannot attain maximum photosynthetic rate
throughout the day; this ratio of G pclear and G h is designated the diel effect. The
term G p represents the lifetime carbon gain under actual weather conditions.
There are cloudy days and rainy days over the lifetime of a leaf when insolation
is reduced compared to a clear sky condition and the photosynthetic rate is
depressed; this ratio of G p and G pclear is designated the overcast effect. The term
G pL represents the carbon gain by a leaf under realized insolation over its lifetime,
including the effects of shading by surrounding plants and self-shading of
leaves within the plant canopy; this ratio of G pL and G p is designated the shading
effect. The final term is the ratio of actual photosynthesis of a leaf over its lifetime
and the potential photosynthetic rate under its realized insolation regime.
The ratio of G a and G pL represents the influence of environmental factors other
than insolation that suppress, such as the midday depression resulting from
water balance limitations or the effects of suboptimal temperatures for maximum
photosynthetic gains. This ratio of G a and G pL is designated the depression
effect. The mean labor time of leaves of Alnus sieboldiana was calculated to be
around only 5 h per day on average over their lifetime (Kikuzawa et al. 2004).
Estimates for herbaceous and woody species derived by various methods are
similarly low: for a Cecropia species, only 1.0 h day −1 ; Cleyela, 1.1 h day −1 ;
Castilla, 1.5 h day −1 ; Annona, 1.9 h day −1 ; Urera, 2.5 h day −1 ; Helocarpus,
2.6 h day −1 ; Polygtonatum, 2.7 h day −1 ; Fagus, 2.8 h day −1 ; Polygonum, 3.3 h day −1 ;
Antirrhoea, 3.5 h day −1 ; Anacardium, 4.5 h day −1 ; and Luehea, 6.1 h day −1 (calculated
from Kikuzawa et al. 2009; Kitajima et al. 1997, 2002; Ackerly and Bazzaz
1995; Kikuzawa, unpublished data). The average of all these values is 2.9 h day −1 ,
which raises some questions about the use of A max alone as a cardinal value for
characterizing foliar function.

18 2 Leaves: Evolution, Ontogeny, and Death
Nonetheless, despite all the variability in photosynthetic rate through the day
and across the growing season, there is in fact a surprisingly good correlation
between the highest photosynthetic rate on a given day and the actual carbon gain
on that day (Zots and Winter 1996; Rosati and DeJong 2003; Koyama and
Kikuzawa 2009). Zots and Winter (1996) reported a linear relationship between
daily photosynthetic gains of single leaves (A ) and their maximum photosynthetic
day
rate on a given day, Â (Fig. 2.9):
max
A = kA · ˆ + c
(2.3)
day max
where k and c are constants. Note that if the value of  max is in fact the true photosynthetic
capacity (A ) and if c is zero, then the proportionality constant k equals
max
the leaf mean labor time. This relationship within days, however, does not assure
that the highest photosynthetic rate achieved by a species under ideal conditions,
its true photosynthetic capacity, A , will in turn correlate consistently with the
max
maximum photosynthetic rate ( Â max ) achieved on a given day. The value of the mean
labor time concept as a complement to the concept of photosynthetic capacity is
that it emphasizes the necessity of identifying the true maximum photosynthetic
rate for a species as opposed to a transient value associated with conditions over a
given time interval.
ˆ
Fig. 2.9 The Aday − Amax
relationship. The daily photosynthetic gain by a leaf (A , mmol m day −2
12 h−1 ) on a given day plotted against the maximum net photosynthetic rate of the leaf on that day
( Â ). Note that Â
max
max here is not the true value of photosynthetic capacity (A ) for the species,
max
but only the highest photosynthetic rate on each day of observation. (From Zots and Winter 1996)

Age-Dependent Decline in Photosynthetic Capacity
Age-Dependent Decline in Photosynthetic Capacity
Once a leaf attains full photosynthetic capacity, A max then gradually decreases with
leaf age (Hardwick et al. 1968; Jurik et al. 1979; Oren et al. 1986; Martin et al.
1994; Mediavilla and Escudero 2003a; Castro-Diez et al. 2005; Warren 2006; Reich
et al. 2009). In herbaceous plants with short-lived leaves, the decline is linear and
relatively fast (Leopold and Kriedmann 1975; Šesták 1981; Hodanova 1981; Erley
et al. 2002; Kikuzawa 2003). In deciduous broad-leaved trees, once full photosynthetic
capacity is attained it is maintained fairly steady until immediately before
leaffall and then declines quickly (Jurik 1986; Koike 1990), although in some Alnus
and Betula species with fairly rapid leaf turnover the time trend is closer to that of
herbaceous species (Koike 1990; Kikuzawa 2003; Miyazawa and Kikuzawa 2004).
Kitajima et al. (2002) also reported this fairly rapid linear decline in photosynthetic
capacity associated with high leaf turnover in two early successional tropical trees
in Panama (Fig. 2.10). In five trees with longer-lived leaves in this seasonally dry
tropical forest, the decline in photosynthetic capacity with leaf age was more
gradual (Kitajima et al. 1997), as was also the case for tropical species in a Costa
Rican plantation (Hiremath 2000). Similarly, in evergreen conifers with longer-
No. Distal Leaves A (µmol CO 2 .m −2.s −1 )
30
25
20
15
10
5
0
10
8
6
4
2
Cecropia Urera
slope = −0.287**
slope = 0.091*** slope = 0.145***
slope = −0.191**
0
0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70
Leaf Age since Full Expansion (d)
Fig. 2.10 Linear decline in photosynthetic capacity associated with rapid growth and high leaf
turnover in early successional Panamanian trees. d, days. (From Kitajima et al. 2002)
19

20 2 Leaves: Evolution, Ontogeny, and Death
lived leaves, there is a more gradual linear decline of photosynthetic rate with age
(Matsumoto 1984; Koike et al. 1994). The general tendency is for photosynthetic
capacity to decrease with leaf age, but the rate of decrease lessens as leaf longevity
becomes greater (Fig. 2.11).
There are two hypotheses to explain the decline of photosynthetic capacity
with time: (1) acclimation to the changing light regime of individual leaves as
the canopy develops (Hikosaka 1998; Gan and Amasino 1997) and (2) diminished
function resulting from age-related changes and senescence of foliar
tissues (Guarente et al. 1998; Warren 2006). The two hypotheses are not mutually
exclusive. Reduced insolation can induce translocation of nitrogen from a
shaded, older leaf to a younger sunlit leaf (Hemminga et al. 1999), with consequent
degradation of photosynthetic function in the older leaf. Even if the
microenvironment around a leaf is stable over its lifetime, cumulative damage
and reduced internal conductance of CO 2 (Hensel et al. 1993; Guarente et al.
1998; Nooden 2004; Warren 2006) can lead to gradually lower photosynthetic
capacity in older leaves.
Photosynthetic decline rate, nmol /g/s/day
10
1
0.1
0.01
10
Ha
Ki02
Ps
Po Ki02
As Am
Ah Ki97
Bp
Fc Ki97
Ki97
Ki97
Leaf Longevity days
Ki97
1000
Fig. 2.11 Relationship between the rate of decline in photosynthetic capacity with time and leaf
longevity. Data are for Acer mono (Am, Kikuzawa and Ackerly 1999), Alnus hirsuta (Ah, Kikuzawa
and Ackerly 1999), Alnus sieboldiana (As, Kikuzawa 2003), five tropical tree species (Ki97,
Kitajima et al. 1997), two tropical pioneer tree species (Ki02, Kitajima et al. 2002), Betula platyphylla
(Bp, Kikuzawa and Ackerly 1999), Fagus crenata (Fc, Kikuzawa 2003), Heliocarpus
appendiculatus (Ha, Ackerly and Bazzaz 1995), Polygonatum odoratum (Po, Kikuzawa 2003),
and Polygonum sachalinensis (Ps, Kikuzawa 2003)
100

Senescence and Abscission
Senescence and Abscission
Whatever may be the rate of gradual decline in photosynthetic capacity, there is a
point in time for all leaves when much more rapid changes in both physiology and
appearance mark their impending death and abscission (Vincent 2006; Lim et al.
2007). Leaf senescence can be triggered by exogenous factors (seasonal changes in
climate, pathogen attack, herbivory) or by endogenous factors (self-shading, fruiting).
Whatever the trigger, senescence is intrinsically a process of genetically regulated
degradation (Nam 1997; Weaver and Amasino 2001; Nooden 2004; Vincent 2006)
involving upregulation of more than 800 genes (Lim et al. 2007). Senescence
allows orderly preparations for seasonal changes in environmental conditions,
including recovery of nutrients from senescing leaves and their recycling within the
plant. Many senescence-associated genes encode proteins that accomplish parts of
the recycling program such as proteases, nucleases, and proteins involved in metal
binding and transport (Guarente et al. 1998). Senescing foliage in broadleaf deciduous
forests often colors as chlorophyll degrades, no longer masking yellow and orange
secondary photosynthetic pigments, and as reddish anthocyanins are produced
de novo (Lee et al. 2003; Ougham et al. 2005). Coloring during senescence in species
with indeterminate shoot growth is weakly developed and usually initiated within
the tree crown or in the lower canopy, whereas in species with determinate shoot
growth coloring is strong and tends to occur first in the upper canopy (Koike 1990,
2004). The anthocyanins confer a degree of protection against photooxidation of
systems involved in the orderly breakdown and recycling of materials from the
senescing leaf (Pietrini et al. 2002). Leaf photosynthesis invariably declines
strongly with the onset of senescence (Makino et al. 1983; Hidema et al. 1991;
Hanba et al. 2004), and foliar nitrogen content decreases steadily as photosynthetic
systems shut down (Mae 2004).
21

Chapter 3
Quantifying Leaf Longevity
Deciduous broad leaved-forest mixed with some conifers. Midori-numa, Daisetsu-san,
Hokkaido, Japan
Defining Leaf Longevity
Leaf longevity and “leaf lifespan” are sometimes used as equivalent terms, and at other
times “leaf longevity” designates the potential longevity of leaves and “leaf lifespan”
their realized longevity. To keep things simple, we here consistently refer only to leaf
longevity, qualifying the context as may be necessary. With an emphasis on times
when a leaf can carry out its photosynthetic function, we define leaf longevity as the
period from the emergence to the fall of a leaf. Because leaf development is a continuous
process, a reasonably consistent operational definition of leaf appearance and
leaffall is necessary. It is impractical to include the period of leaf initiation and early
development before budburst in estimations of leaf longevity, and in any case these
earliest stages in leaf development are not directly relevant to photosynthetic function
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_3, © Springer 2011
23

24 3 Quantifying Leaf Longevity
(Vincent 2006). The onset of full photosynthetic function would be the most logical
starting point from which to estimate leaf longevity, but this is not practical in broad
comparative studies because of species-specific variation in the relation between foliar
development and foliar function (Niinemets and Sack 2004). We generally resort to
recording a phenophase consistent with records in phenological networks (Koch et al.
2007; Morisette et al. 2009) that is associated with a late stage of foliar development,
such as expansion and flattening of the leaf blade in broadleaf deciduous trees
(Kikuzawa 1978). Similar uncertainties are involved in scoring the timing of leaffall.
Senescence of fully formed leaves is generally more drawn out than budburst and early
leaf development and hence is less amenable to timing precisely (Worrall 1999). Leaf
abscission, which might offer an unambiguous terminal event, is often preceded by
significant declines in photosynthetic capacity as leaves change color during senescence
(Diemer et al. 1992; Hensel et al. 1993), and some trees retain dead leaves
(marcesence: Abadia et al. 1996). Any scoring system based on changing color or even
abscission also can be disrupted by a stress event such as an early freeze that abruptly
kills leaves outright regardless of their degree of senescence or development of their
abscission layer. We review here the common methods for estimating leaf longevity,
touching on ways to minimize uncertainty associated with scoring leaf emergence and
leaffall when that is possible for a given method.
Box 3.1 Heterophylly
(continued)

Estimating Leaf Longevity from Leaf Turnover on Shoots
Box 3.1 (continued)
Heterophylly refers to conspicuous differences in shape, size, or function
among the leaves on a plant. For example, the leaves that appear on a shoot of
Cercidiphyllum japonicum early in the season are round and heart shaped at
the base, but those appearing later in the season are flat at the base and more
triangular in shape. Such early and late leaves often differ not only in shape
but also in longevity and physiological function.
Estimating Leaf Longevity from Leaf Turnover on Shoots
The shoot is the modular unit of leaf production, and hence the natural focus for
sampling coherent sets of observations to derive estimates of leaf longevity.
Monitoring the emergence and fall of leaves on a particular shoot at frequent
intervals over an extended time period is the definitive method for estimating leaf
longevity. Counts of leaves are usually recorded at the midpoint of census intervals,
so the more frequent the observations, the more precise is the estimate of leaf
longevity. Frequent counts of all the leaves on a shoot are tedious, but the accumulated
data are highly informative. The method gives a complete record of temporal
variation in leaf production and leaf longevity, which can be especially important
for species with indeterminate shoot growth (Fig. 3.1). Since the date of emergence
and the date of fall are known for each individual leaf, both the mean and the variance
in leaf longevity can be calculated. These demographic data can be reworked to
describe the probability of leaffall as a function of leaf age (Dungan et al. 2003).
Seasonal or interannual differences in leaf longevity or differences between early
and late leaves in heterophyllous species can also be analyzed by partitioning the
data accordingly. In principle, a census can be carried out over many years, but in
practice this approach often is restricted to observations within a growing season.
Data most commonly are summarized initially in a leaf survival curve (Fig. 3.2),
which can illustrate in detail the differences in leaf demography that underlie the
calculation of leaf longevity. The total number of leaf-days (the area under the
curve showing the number of living leaves) divided by the total number of leaves
produced is the mean leaf longevity over the period of observation, which typically
would be one complete growing season (Kikuzawa 1983).
There are various alternative calculations for estimating the mean leaf longevity
from a census of the numbers of leaves emerging and falling over a time interval.
A graphical framework introduced by Navas et al. (2003) helps us to understand the
ways that the relative timing of leaf emergence and leaffall can affect estimates of
mean leaf longevity. If for simplicity the increase (leaf emergence) and decrease
(leaffall) in numbers of leaves are approximated by straight lines over time, then
leaf longevity (L) can be considered in a graphical framework (Fig. 3.3) linked to
the following equation (Navas et al. 2003):
( )
25
L = tp + tL / 2 + t
(3.1)

26 3 Quantifying Leaf Longevity
Fig. 3.1 Shoots of Alnus hirsuta in Bibai, Hokkaido, northern Japan, in mid-June (a) and in early
July (b). By mid-June four leaves (1–4) have fully expanded and a fifth leaf (5) has protruded from
the pair of bracts and is just beginning photosynthetic activity. By early July, more leaves have
been produced (6–8) and the first and the second leaves (1, 2) have fallen. There are two leaf scars
and six leaves (third to eighth) on the shoot; the ninth leaf is just appearing, but because it is still
enclosed by bracts it is not yet counted (Kikuzawa 1980)
Fig. 3.2 Leaf survival curves for representative deciduous broad-leaved trees: Alnus hirsuta (a),
Magnolia obovata (b), and Quercus mongolica var. grosseserrata (c). Open circles represent the
cumulative number of leaves that have emerged through the growing season; closed circles begin
with the onset of leaffall and track the number of leaves still attached at each subsequent census.
The mean leaf longevity over the period of observation is the area under the line showing the
number of attached leaves divided by the total number of emerged leaves

Estimating Leaf Longevity from Leaf Turnover on Shoots
Cumulated number of leaves
produced or lost
a
A
La
Lb
Lc
E
B
F G
C
Lc
tP t
tP t
tL tL
Time of leaf production or loss (d) Time of leaf production or loss (d)
c
A C
Lb
E
H
B
La
D A C
B D
tP t
tL Time of leaf production or loss (d)
where t p is the duration of the period of leaf emergence (i.e., the time from the
appearance of the first leaf to the last), t L is the duration of the period of leaffall
(i.e., the time from the first fallen leaf to the last), and t is the length of the period
from the end of leaf emergence to the start of leaffall when leaf numbers are stable.
If leaffall starts within the period of leaf emergence (i.e., the leaf emergence line
and leaffall line overlap), then t is scored as a negative value. Craine et al. (1999)
adopt essentially the same framework. When leaf longevity is too long for the
continuous observation of all the leaves on a shoot to be practical from emergence
b
G
D
E G
Fig. 3.3 A framework for assessing the determinants of variation in leaf longevity (after Navas
et al. 2003). The panels illustrate different patterns for the relative timing of leaf emergence and
leaffall. This graphical framework relates the duration of the period of leaf emergence (t p , the time
from the appearance of the first leaf to the last), the duration of the period of leaffall (t L , the
time from the first fallen leaf to the last), and the length of the stable period t from the end of
leaf emergence to the start of leaffall. If leaffall starts within the period of leaf emergence (i.e., the leaf
emergence line and leaffall line overlap), then t is scored as a negative value. (a) The case when
there is a time interval between the periods of leaf emergence and leaffall. (b), (c) Cases in which
the emergence and fall of leaves overlap in time: t is long in (b) but short in (c). In (b), t p and t L
are equivalent, but in (c) t L is far longer than t p , L a , L b , and L c in (a) indicate the leaves in the same
cohort having different longevity as a result of differences in the timing of leaffall. Symbols and
calculations are explained further in the text
H
27

28 3 Quantifying Leaf Longevity
to fall, we can calculate leaf longevity based on observations over any reasonable
interval (Williams et al. 1989) by using this equation:
( 2 / ( 2 1) / )( 2 1)
L = N d− N −N b t − t
(3.2)
where N 1 is the standing number of leaves at the initial observation (t 1 ), N 2 is the sum
of N 1 and newly produced leaves during t 2 − t 1 , d is the rate of leaffall during the observation
period t 2 − t 1 , and b is the rate of leaf production during this period. When
N 2 − N 1 is equal to b, this can be reduced to the following equation (Fonseca 1994):
( 1 ) / 1)(
2 1)
L = N + b d− t − t
(3.3)
These equations assume stable leaf numbers during the period of observation,
which allows leaf longevity to be estimated using either the leaf production rate or
the rate of leaffall. If b = d in either equation, then leaf longevity can be estimated
even more simply as follows (Southwood et al. 1986; Navas et al. 2003):
L = N1/ d
(3.4)
where t 2 − t 1 is 1 (year, month, day, etc.). In a situation in which the number of
leaves fluctuates somewhat around an essentially stable state within the period of
observation, King (1994) provides an alternative version of (3.4) utilizing the average
number of leaves (N av ) instead of the initial leaf number (N 1 ):
( 2 1) av ( 0.5(
)
L = t − t N / b+ d
(3.5)
Finally, consider (3.2)–(3.5) in relationship to the graphical framework (see
Fig. 3.3) introduced by Navas et al. (2003). Because b = N/t and d = N/t, the number
of leaves (N i ) at any time t i is given by
and leaf longevity by
i i i
{ ( p ) }
N = bt −dt − t + t
(3.6)
( ) p
L = N / d = b/ d− 1 ti+ t + t
(3.7)
If b = d, (3.7) reduces to L = t p + t, which is the same as (3.1) from Navas et al.
(2003). These various calculations of leaf longevity are all variants on a theme that
arise in the juxtaposition of alternative sampling designs and interspecific contrasts
in leaf demography. All the calculations use data on the relative timing of leaf
emergence and leaffall in different demographic scenarios that can be visualized in
the graphical framework introduced by Navas et al. (2003).
In all the calculations of leaf longevity based on repeated census of leaf emergence
and leaffall, the precision of the leaf longevity estimate ultimately depends
on the census interval. The longer the interval between observations, the less
precise will be the estimate of leaf longevity. Leaves may emerge or fall at any

Estimating Leaf Longevity from Leaf Turnover on Shoots
time in the period between intervals, so the common practice of referencing the
data to the day midway between two sequential observations can introduce
considerable error as the observation interval increases beyond a week. Up to
about a week the uncertainty in the timing of leaffall or emergence is of the order
of the few days potential error associated with the intrinsic ambiguity in observations
of the phenophases themselves. In a study of leaf emergence and fall on
the shoots of trees observed at intervals as long as a month, Dungan et al. (2003)
introduced an approach to minimizing the error associated with longer intervals
between observations. They observed shoots at weekly or biweekly intervals
early in the seasons, so that they could fit their observations on leaf production
and mortality to sigmoid growth functions. These functions can be combined to
estimate the number of living leaves at any time, including times between actual
observations. Fitting their leaf survivorship data to a gamma function, Dungan
et al. (2003) then used failure-time analysis to estimate the probability that a leaf
would survive to any given day after budburst and report leaf longevity as the
age at which the probability of a leaf dying reaches 50%, the leaf half-life.
Strictly speaking the leaf half-life and mean leaf longevity may not be perfectly
identical because of seasonal changes in half-life, but when leaf longevity is
longer than about 80 days it appears that half-life can provide a convenient
surrogate for mean leaf longevity (Diemer 1998a; Dungan et al. 2003). A reanalysis
of the Navas (2003) data confirmed the utility of this method and showed it to
be more accurate than estimates based on the midpoint between consecutive
observations (Dungan et al. 2008).
Box 3.2 Leaf Cohort
Some plants produce leaves sequentially through the growing season, others
all at once in a single episode early in the growing season. Any leaves emerging
together at some time form an even-aged cohort: these may be all the leaves
that will be produced in a year or just those produced at one time by a sequential
leafing species. Following the death of individual leaves in a cohort over
time provides a survivorship curve, which often yields insights into foliar
function and canopy architecture. In successive leafing species, multiple
cohorts of leaves coexist on the plant at any time in the season, each cohort
following its own survivorship curve. A cohort produced early in the growing
season has older leaves than a cohort produced in midseason, and more leaves
in the older cohort may have senesced and fallen by the time the midseason
cohort leaves emerge. In other words, in successive leafing species the leaves
on a plant are multiaged, and the total number of leaves at any time in the
season is the difference between all the leaves that have emerged across all
cohorts and those that have fallen.
29

30 3 Quantifying Leaf Longevity
Estimating Leaf Longevity from Census of Leaf
Cohorts over Time
The primary alternative to following the emergence and fall of leaves on shoots is
to focus on the leaves themselves, following the fate of cohorts of leaves over time.
Whether estimates of leaf longevity are derived by shoot- or cohort-based methods,
the calculations depend fundamentally on records of the birth and death of leaves.
The cohort approach adapts methods of life table analysis well established in population
biology (Krebs 2008) that provide estimates not only of leaf longevity but
also age-dependent leaf mortality rates. The approach of Dungan et al. (2003) can
be used in shoot-based studies to derive age-dependent probabilities for leaf death
as well. The distinction between shoot-based and cohort-based approaches to estimating
leaf longevity has more to do with context and sampling design than with
any fundamental difference in the basis for estimation of leaf longevity. Both
dynamic and static sampling designs can be used in cohort-based estimates of leaf
longevity (Krebs 2008).
Estimates in dynamic analyses are derived by following single cohorts of leaves
from birth to death, which may impose a long and arduous sampling program. For
example, Xiao (2003) provides an example of a dynamic life table analysis based
on following a cohort of 1,000 leaves of Pinus tabulaeformis at annual intervals
over a 5-year period (Table 3.1). The first column in the resulting life table records
leaf age in years, with age zero denoting the start of the census. The second column,
l x , is the number of the initial cohort surviving at age x. The third column, d x , is the
mortality during age x, which is given by (l x - l x+1 ). L x , the average of l x between two
needle ages, is given by (l x + l x+1 )/2, and defines the height of the histogram in
Fig. 3.5. T x is the summation of L x from the older to younger age, which is equivalent
to the area of the histogram, T x = T x+1 + L x . T x divided by l x represents the average
expected life at age x. The line in Fig. 3.5 is the l x curve, which illustrates the survivorship
of the 1,000 leaves over time. The average life expectancy at age zero is
the mean longevity of leaves. In the case of this pine species, the mean leaf longevity
Table 3.1 Dynamic life table for needles of Pinus tabulaeformis
(after Xiao 2003)
Age (years) l x d x L x T x e x
0 1,000 240 880 2,000 2.00
1 760 282 619 1,120 1.47
2 478 246 355 501 1.05
3 232 205 130 146 0.63
4 27 24 15 16 0.59
5 3 3 1 1 0.33
l x , The number of the initial cohort surviving at age x; d x , the mortality
during age x; L x , the average of l x between two needle ages; T x ,
the summation of L x from older to younger age; e x , the average
expected life at age x

Estimating Leaf Longevity from Census of Leaf Cohorts over Time
is 2.0 years. Graphically, this is equivalent to the total area of the annual histograms
divided by the initial leaf number, which is essentially the same as the method
shown in Figs. 3.2 and 3.3.
Such long-running observation series intended for a dynamic life table analysis
sometimes are stopped for practical reasons when half the leaf cohort has died
(Kohyama 1980; Diemer 1998a,b); truncating the observations precludes calculation
of the age-dependent probabilities of leaf death, but the observed leaf half-life
provides a useful estimate of leaf longevity in its own right (Diemer 1998a; Dungan
et al. 2003). On the other hand, the dynamic life table approach applies equally well
to short series of observations over days, weeks, or months rather than years. Miyaji
and Tagawa (1973, 1979) constructed dynamic life tables for leaves of Tilia japonica
and Phaseolus vulgaris, both species with short-lived leaves. The longer observations
continue, the more risk that dynamic life table analyses will be confounded
by stochastic variation in the risk of mortality across the years of observation.
Dynamic life table analyses are not only confounded by stochastic variation but
also biased by differential rates of leaf mortality in better versus worse leaf
microenvironments (Takenaka 2003). Thus even if leaves are selected randomly to
establish the sampled cohort, the sample will concentrate into “better” places over
time. Static life table analyses are not immune to the problem of stochastic interannual
variation, but they do not suffer this sampling bias.
The data required for static life table analyses are gathered in one round of
sampling, which makes this approach logistically appealing. Static life table
analyses do not follow a single leaf cohort over its lifetime but instead reconstruct
the life table from different aged cohorts of leaves observed at a point in time.
Unfortunately, the record of growth cycles in tropical regions usually is too obscure
or ambiguous to apply the static life table approach with confidence. In tropical
forests, the number of leaves on a branch whorl does give information about leaf
emergence pattern, but the seasonal timing of leaf emergence is not fixed in species
or even on branches in a single tree (Kikuzawa et al. 1998). For example, in
Araucaria araucana the mean interval between successive whorls was not exactly
1 year, and varied among individual trees depending on their light regime (Lusk and
Le-Quesne 2000). On the other hand, the required sampling is relatively easy to
apply with evergreen trees in boreal and temperate regions where the basic approach
has a long history of use (Pease 1917). In these strongly seasonal climates, clearly
visible terminal bud scars typically demarcate annual growth increments along the
shoot (Fig. 3.4); it is easy to reconstruct the ages of growth segments along a
branch, and hence the age of the leaves on each segment. We then can infer the
number of leaves in each annual cohort by counting the number of leaves still
attached and the number of leaf scars left by fallen leaves in each shoot growth
increment. This static approach, however, assumes no year-to-year variation in leaf
demographic parameters, which can be problematic because of interannual climatic
variation, age-dependent loss of leaves to herbivory, or trade-offs in resource
allocation between production and reproduction. Kayama et al. (2002), for example,
found this assumption did not hold for some evergreen conifers. Interannual
variation can also confound leaf longevity estimates from a dynamic life table
31

32 3 Quantifying Leaf Longevity
Current Shoot
1-year Leaves
2-year Leaves
Fig. 3.4 Number of leaves in annual whorls of shoot growth in Osmanthus chinensis, a broadleaf
ornamental evergreen tree in Japan
Fig. 3.5 Decline in initial
cohort of needles over time
(in Xiao 2003; drawn by KK
after Xiao)
analysis, as it would in a shoot-based analysis of species with long-lived leaves as
well. In all the census methods for estimating leaf longevity, error variance inevitably
increases with leaf longevity.

Estimating Leaf Longevity from Census of Leaf Cohorts over Time
Box 3.3 Allocation and Partitioning of Resources
Both the products of photosynthesis and the mineral resources available
for plant growth are in finite supply. Hence, there inevitably are limits
and trade-offs imposed on plant function. Carbohydrates and mineral
resources used in growth are not available for reproduction. Plants partition
resources differentially to satisfy competing demands, with the result
that cumulative allocations to plant parts differ. For example, biomass
allocated to leaves, stems, roots, flowers, and fruits arise in the partitioning
of net primary production (NPP) and reflects trade-offs imposed
by the requirements for survival and reproduction in a given environmental
regime.
Box 3.4 Allometry and Isometry
The form and function of organisms can vary with their size. For example,
the allocations to root, stem, and leaves can shift with total plant size. Such
size-dependent changes can be expressed by a power function of the form
A = aW b where A is a measure of some aspect of form or function, W is an
appropriate measure of size, and a and b are allometric constants. This equation
is mathematically equivalent to log (A) = log (a) + b log (W), which
graphs as a straight line. If b is exactly unity, then A is directly proportional
to W and the relationship is said to be isometric. In an isometric relationship,
a twofold increase in size results in a twofold increase in form or function.
In many biological cases, however, form and function change
disproportionately with size: b is not unity and the relationship is said to be
allometric. For example, allometric relationships are used in forest science
to estimate the biomass of standing trees. The biomass of entire trees (W) or
their parts such as leaves (W L ), branches (W B ), stems (W S ), or roots (W R ) all
are correlated to measures of body size such as trunk diameter at breast
height (D) and tree height (H). Using the equation W = aD b , we can estimate
the total biomass of a tree simply by measuring its diameter at breast height.
Species differ in the degree to which their total biomass changes disproportionately
with size, but in all cases the relationship is allometric and b is less
than unity.
33

34 3 Quantifying Leaf Longevity
Estimation of Leaf Longevity from Leaf Turnover
at the Stand Level
Litter traps set on the forest understorey of Alnus japonica (Hakusan, Ishikawa, Japan)
Leaf longevity is occasionally estimated as the inverse of leaf turnover rates at the
stand level. Leaf biomass, estimated by allometric methods (Clark et al. 2001) and
assumed to be in steady state, is compared to the biomass of falling leaves collected
in leaf traps (Tadaki 1965; Edwards and Grubb 1977; Oshima 1977;
Kikuzawa et al. 1984; Takiya et al. 2006). Under the assumption of steady-state
leaf numbers in the canopy, leaf longevity can be estimated as the inverse of the
ratio of leaf biomass (g m −2 ) to annual leaffall adjusted for the length of the
growing season. For example, the standing leaf biomass in an Alnus inokumae
plantation was 163 g m −2 , while annual total leaf fall was 315 g m −2 during a
growing season (Kikuzawa et al. 1984). This finding indicates a leaf turnover rate
of about 2 over the season, and hence an average leaf longevity of the order of 93
days, about one-half the length of the growing season. There are, however, serious
problems with this method. First, from a practical point of view the approach is
too time consuming to acquire species-specific estimates except in monospecific
stands. Second, the assumption of steady-state leaf biomass is commonly unrealistic.
Third, the variance associated with the allometric estimates of canopy biomass
will often be of the order of magnitude as the biomass of fallen leaves. Fourth, the
biomass of individual leaves at abscission is not equal to their biomass in the
canopy. This method of estimating leaf longevity is best avoided in comparisons
at the species level.

Revisiting the Basic Concept of Leaf Longevity
Box 3.5 Photoinhibition
The photosynthetic systems in leaves have two basic components, one utilizing
chlorophyll and various accessory pigments to capture solar energy, and the
other a series of biochemical pathways that uses the captured energy to build
carbohydrates with carbon derived from atmospheric carbon dioxide. When a
leaf is constructed, these two systems are created in ways suited to the environmental
regime in which the leaf will function as a photosynthetic organ.
Photoinhibition arises when transient environmental conditions lead to more
solar energy being captured than can be utilized in the biosynthetic reactions.
For example, this can occur in winter for evergreen shrubs in the forest understory
when photosynthetic enzymes are inactive consequent to low temperature,
but high light levels occur in the usually shaded forest understory because
of leaffall in a deciduous forest canopy (Miyazawa and Kikuzawa 2004, 2006;
Miyazawa et al. 2007).
Revisiting the Basic Concept of Leaf Longevity
In ending this chapter, we return to the concept of leaf longevity itself, which loses
its close functional connection to photosynthesis when leaves survive periods unfavorable
to photosynthetic activity such as winter in high latitudes or periods of severe
drought. Considering seasonal variation in conditions favorable to photosynthesis,
we have proposed a concept of functional leaf longevity (Kikuzawa and Lechowicz
2006). Functional leaf longevity is the number of days when a leaf can actually carry
out photosynthesis over its lifetime. In principle, functional leaf longevity is defined
as leaf longevity minus unfavorable days (winter or dry season) during the leaf lifetime.
In leaves of deciduous trees or annuals in temperate regions, functional leaf
longevity is generally the same as leaf longevity. In other instances, a favorable
period within a year can be unambiguously defined and recognized. This is the
case for arctic and alpine species associated with snowbeds; the period when plants
are snow covered is considered to be unfavorable for photosynthesis, although some
light penetrates snow to about 30 cm (Starr and Oberbauer 2003). For example,
Kudo (1992) examined the effect of differences in favorable period created naturally
by the timing of snowmelt on the leaf longevity of dwarf evergreen and summergreen
plants on Mt. Daisetsu, central Hokkaido. The snow-free period varied twofold,
from 60 to 120 days year −1 , depending on topographically induced variation in
snow depth. In the case of other evergreen species, often it is not as easy to evaluate
functional leaf longevity because some evergreen leaves do photosynthesize during
winter. For example, understory evergreen plants in winter may suffer photoinhibition
(Miyazawa et al. 2007) but still are photosynthetically active in what might at
first be considered an unfavorable season. Camellia japonica, an understory evergreen
tree in the deciduous forests of central Japan, actually has higher daily photosynthesis
in winter than summer when the deciduous canopy is leafless (Miyazawa
35

36 3 Quantifying Leaf Longevity
and Kikuzawa 2004, 2006). Deciding general criteria for defining an unfavorable
period caused by drought is no less straightforward than for winter cold. The different
phenological adaptations of species can affect variation in the degree of unfavorable
conditions for photosynthesis even among co-occurring species. For example,
an unfavorable period resulting from drought in Australia has been defined as the
occurrence of at least 3 consecutive months with less than 25 (or 50) mm precipitation
(Eamus and Prior 2001). Eamus et al. (1999b) compared photosynthetic rates
throughout a year for some tree species in a seasonal tropical forest subject to an
unfavorable dry season under this criterion. Two evergreen species (Eucalyptus tetrodonta,
Eucalyptus miniata) showed relatively stable photosynthetic rates with only
modest declines in the dry season. In contrast, decline in the photosynthetic rate of
leaves retained during the dry season on semideciduous Erythrophleum chlorostachys
was greater, and in fully drought deciduous species such as Cochlospermum
fraseri and Terminalia ferdinandiana was zero because they are leafless. Despite
these complications, in principle it makes sense to discount leaf longevity for periods
unfavorable to photosynthetic activity.
Available data suggest there also are some unappreciated and potentially useful
linkages between functional leaf longevity and gross primary production at the
ecosystem level (Kikuzawa and Lechowicz 2006); this is apparent in the relationship
between the standing biomass of foliage and foliage longevity estimated as
the inverse of leaf turnover in diverse seasonal and aseasonal forests (Fig. 3.6).
Considering the traditional definition of leaf longevity without regard to favorable
or unfavorable conditions for photosynthesis, then leaf production rates (the slope
of this relationship) in forests from seasonal and aseasonal climates appear to
Fig. 3.6 Evidence that functional leaf longevity can provide a clearer relationship to ecosystem
function than leaf longevity uncorrected for time unsuitable for photosynthetic activity (Kikuzawa
and Lechowicz 2006). Left: Relationship of standing leaf biomass and leaf longevity in diverse
forests; the slopes differ significantly between aseasonal and seasonal forests. Right: The difference
in slopes is no longer significant when functional leaf longevity is considered. Closed circles,
aseasonal forest; open circles, seasonal forest

Revisiting the Basic Concept of Leaf Longevity
different significantly. However, if we discount periods in the seasonal climate
unfavorable for photosynthetic activity, then the rate of leaf production in the
system is not appreciably different between regions (Fig. 3.6).
This conceptually simple adjustment in gauging the longevity of leaves has
interesting implications for estimating the photosynthetic production at the ecosystem
level. Gross primary production can be expressed as the product of leaf
biomass and average photosynthetic capacity rate over the favorable season:
P = k · B · Amean · d
37
(3.8)
where P is gross primary production (g m −2 year −1 ), B is leaf biomass (g m −2 ), A mean
is the average maximum photosynthetic rate (A max ) over the favorable season, and
d is the duration (s year −1 ) of the favorable season and k is a constant. The duration
can be partitioned into duration within a day (mean labor time, m h day −1 ) and
duration within a year (days in which plants can carry out photosynthesis within a
year, the favorable period length, f days year −1 ).
d = m · f
(3.9)
Here we incorporate functional leaf longevity L f into (3.8), multiply the right-hand
side of (3.8) by (L f /L f = 1), and substitute (3.9) into (3.8) to obtain:
( )
P k B/ L · A m · L · f
= f mean f
(3.10)
The first term in (3.10), B/L f , is the rate of daily leaf production; this is not so appreciably
different among forests (Fig. 3.6). The next term, A mean m · L f , is the lifetime
photosynthetic gain by a single leaf. Thus, gross primary production (GPP) of a plant
community potentially can be expressed simply as the product of only three terms:
( ) (
× ( favorable period length)
)
GPP = Life time gain by a leaf × daily leaf production rate
(3.11)
If lifetime photosynthetic gain for individual leaves can be taken as a constant
across species, then gross primary production could be determined simply by the
length of favorable period (f ). These rather remarkable, if speculative, possibilities
are not without support in published data. Kira (1969) summarized the gross
primary production data of forests in the world and concluded that gross primary
production can be explained by the leaf area index (LAI) and the length of growing
season (Kira 1970). Here, LAI is the total leaf area per unit land area of the forest
and is equivalent to the product of leaf biomass and specific leaf area (SLA: m 2 g −1 ).
The length of the growing season is the favorable period length (f ). When we plot
the relationship between gross primary production and favorable period length
using Kira’s data, we obtain a significant relationship (see Fig. 3.7), suggesting the
strong contribution of f in determining gross primary production. Whether or not
these possibilities are sustained by further work, it is clear that the functional linkages
between leaf longevity and ecosystem productivity merit close investigation.

38 3 Quantifying Leaf Longevity
Fig. 3.7 Relationship
between gross primary production
of forests and the
favorable period length. Data
were plotted from those of
Kira (1969)
Box 3.6 Leaf Construction Cost
The construction of leaves requires investments not only in materials but also
in the energy required to acquire those materials and assemble the leaf. The
constituent elements of the diverse chemicals in a leaf were acquired and
assembled into foliar tissues at some cost in respiratory energy, which in turn
was acquired through photosynthesis. Net primary productivity is essentially a
measure of the photosynthetic gains that accrue from investments in leaves, so
it only makes sense to measure the cost of those investments in a unit linked
directly to photosynthesis. Thus, leaf construction cost is usually quantified by
an estimate of the amount of glucose (the immediate product of photosynthesis)
required to construct a unit quantity (1 g or 1 m 2 ) of leaf tissue.
Estimating the material cost of the carbon in a leaf is fairly straightforward
because leaf tissues typically are about 50% carbon. Because glucose is 40%
carbon, at least 1.2 g glucose can provide the carbon needed to construct each
gram of leaf tissue. The more difficult problem is estimating the additional
respiratory energy involved in acquiring other foliar constituents and actually
assembling the leaf. These energy components include, for example, the
respiratory costs of acquiring nitrogen, phosphorus, potassium, sulfur, and
other mineral elements contained in biochemicals critical to leaf function
such as chlorophyll and photosynthetic enzymes. There are two approaches to
this problem: one is based on measurements of respiration of growing leaves
and the other on analysis of the constituents of leaf tissue. Although it can be
technically difficult, one can measure the respiration associated with growing
leaves (Merino et al. 1982), which can be partitioned into components
(continued)

Revisiting the Basic Concept of Leaf Longevity
Box 3.6 (continued)
proportional to growth rate (dW/dt) and leaf weight (W): R = r (dW/dt) + uW.
Then, the parameter r multiplied by the final leaf mass gives an estimate of the
respiratory energy used for construction of the leaf. Alternatively, in principle
one can identify and quantify all the biochemical components of a leaf and
sum up their individual costs of construction (Penning de Vries et al. 1974),
but this is not very practical. A more practical variant on this approach, which
has proven reliable, estimates the energy required to construct a leaf by measuring
the energy released on combustion of the leaf tissue (Williams et al.
1989; Griffin 1994).
39

Chapter 4
Theories of Leaf Longevity
Leaf scars of Alnus japonica
Costs and Benefits of the Evergreen Versus Deciduous Habit
The approach to theoretical work on leaf longevity is inspired by optimization
models that came into vogue during the late 1960s to try to understand alternative
modes of adaptation (Lewontin 1978). Reasoning in this conceptual framework
and reviewing available data, Chabot and Hicks (1982) argued that leaves with
higher construction cost should be longer lived because the period of photosynthetic
gains to pay back the construction cost will be longer than for a leaf constructed
at less cost. Using seven Mexican shrubs in the genus Piper (Piperaceae),
Williams et al. (1989) set out to test this idea that leaf longevity should be determined
by the time required for a leaf to pay back the costs of its construction. They
found that, in contrast to Chabot and Hick’s supposition, leaf construction cost
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_4, © Springer 2011
41

42 4 Theories of Leaf Longevity
was negatively correlated with leaf longevity, not positively. Because construction
costs measured as g[glucose]·g[leaf] −1 varied relatively little among their seven
Piper species, only 1.2–1.6 g g −1 , they also examined the correlation of leaf
longevity and leaf mass per unit area (LMA, g m −2 ), another presumed indicator
of leaf construction cost. The LMA of the Piper species had manifold greater
variation, ranging from 15 to 50 g m −2 , but also no significant correlation with leaf
longevity in these Piper species. These results led Williams et al. (1989) to
consider instead the ratio of cost and gain as a predictor of leaf longevity. Their
proposed relationship is given by the equation:
L = k · C / a
(4.1)
where L is leaf longevity, C is leaf construction cost, k is a constant that prorates
cost of construction to a daily basis over the leaf lifetime, and a is the mean daily
photosynthetic rate of the leaf over its lifetime. They reported a significant positive
correlation between this cost–benefit ratio and leaf longevity (Fig. 4.1). Sobrado
(1991) reported a similar result for six deciduous and four evergreen woody species
in a Venezuelan dry tropical forest using the instantaneous maximum photosynthetic
rate (A max ) rather than the daily photosynthetic rate as a measure of leaf
productivity. Oikawa et al. (2004) obtained a similar positive correlation between
the cost/photosynthesis ratio and leaf longevity among different leaves in the fern
Pteridium aquilinum.
Construction cost
(d)
Daily carbon gain
10000
1000
100
10
1
50 100
200 300 500 1000
Leaf longevity (d)
Fig. 4.1 Relationship between the ratio of (leaf construction cost)/(leaf carbon gain) and leaf
longevity. Symbols code different species of Piper (Piperaceae); d, days. (From Williams et al. 1989)

Leaf Longevity to Maximize Whole-Plant Carbon Gain
Leaf Longevity to Maximize Whole-Plant Carbon Gain
Kikuzawa (1991) adopted and elaborated the idea that leaf longevity should be set
not simply by the magnitude of the construction cost, but also by considering the
influence of leaf production potential on the time required to recoup the cost of leaf
construction. More specifically, Kikuzawa reasoned that leaf longevity should be
selected to maximize lifetime net carbon gain, not for the leaf alone but more generally
for the individual plant that bears the leaf (Kikuzawa 1991).
In this context, consider the carbon gain by a single leaf. It has long been recognized
(Šesták 1981) that, at the time of leaf maturation, the instantaneous photosynthetic
rate of the leaf is at its maximum and then declines with leaf age. Let this
maximum daily photosynthetic rate be a and express the daily photosynthetic rate
at time t after leaf maturation as
43
pt ( ) = a· (1 − t/ b)
(4.2)
where a/b is the rate of decline in photosynthetic rate with time and b is the time
when the rate becomes zero. Thus, b defines the potential leaf longevity (Ackerly
1999). The cumulative net carbon gain per unit area of leaf (G) arises in the summation
of photosynthetic gain per unit time (p) over the leaf lifetime minus the
carbon cost of leaf construction:
t
Gt () = ∫ pt ()dt−C
(4.3)
0
where C is the cost to produce the leaf expressed as g[glucose] · m[leaf] −2 . The
construction cost (C) is estimated as the product of leaf mass per unit leaf area
(LMA, g m −2 ) and a factor (c) to convert a unit weight of glucose to a unit weight
of leaf tissue. This conversion factor, which is itself referred to as a construction
cost in the literature, falls in the range 1.1–1.9 g[glucose] · g[leaf] −1 and can be taken
as a constant value of 1.5 g[glucose] · g[leaf] −1 for most purposes (Griffin 1994;
Diemer and Korner 1996; Villar and Merino 2001; Villar et al. 2006).
Box 4.1 Marginal Gain
Microeconomic models used to maximize economic gain in commercial
enterprises can be adapted to analyses optimizing resource gain in plants.
Plants acquire, store, and allocate different kinds of resources such as carbon
and nitrogen through investments in resource gain capacity such as leaf and
root production (Bloom et al. 1985). In this modeling framework, plants are
predicted to obtain resources at the lowest possible cost and utilize them to
gain the highest possible return. Marginal gain essentially expresses the efficiency
of resource gain, not simply the total amount of gain. For example, a
plant should continue to acquire and invest the resources required to produce
leaves and roots until the marginal gain on the investment becomes equivalent
to the marginal costs of acquiring the resources. Additionally, we can expect
that the plant should adjust the allocation of resources so that growth is equally
limited by all required resources.

44 4 Theories of Leaf Longevity
a
Net Gain (G)
0
−C
0 topt te
−C
0
Time (t)
topt 2topt te b
Fig. 4.2 Leaf longevity is set by the optimal timing for replacing a leaf to maximize its cumulative
photosynthetic gain at the whole-plant level. The potential photosynthetic gain by a single leaf over
its lifetime is illustrated in (a). If an individual plant could retain a single leaf, the optimal time for
replacing that leaf to maximize gain is t opt , or the point at which the line from the origin touches the
curve. C is the construction cost of the leaf and t e is the timing when the instantaneous photosynthetic
rate of the leaf becomes zero. The graph in (b) suggests that replacing the leaf at t opt will yield
a greater total gain than retaining the leaf for a second season. G r and G p represents the cumulative
gain by a leaf when replacing (r) and persisting (p) leaves at t e . (From Kikuzawa 1991)
The qualitative consequences of these relationships for leaf longevity can be illustrated
graphically (Fig. 4.2). At the moment the leaf matures (time 0), there has been
no photosynthetic gain but the cost of leaf construction has been incurred, so the
cumulative gain curve has value (0, –C). Cumulative gain increases monotonically
with time, paying back the invested cost and then achieving net carbon gains. Through
the combination of decreased function with leaf age specific to a species and the
annual progression of environmental conditions in a locality, we expect that generally
the rate of carbon gain will diminish with time, until at some leaf age or environmental
condition photosynthetic function is lost and respiratory costs associated with
maintenance and defense actually lead to a net loss of carbon produced by the leaf.
Thus, the point when the gain curve is a horizontal line is the time of maximum
potential gain by the leaf. If we designate the time of maximum gain as t e , it will be
clear from (4.2) that t e = b, the potential leaf longevity. So long as there are no limitations
imposing a longer period of leaf retention, this is also the optimal timing for leaf
turnover at the whole-plant level if photosynthetic gains are to be maximized.
To better illustrate the basic logic of Kikuzawa’s model, consider a situation in
which a plant can retain only one leaf at a time, and hence the optimal strategy at
the whole-plant level collapses to simply replacing this single leaf. Then the optimal
timing to maximize gain by the plant is not to maximize cumulative gain (G) but to
maximize marginal gain (g), or
G r
G p
0
g = G/ t
(4.4)
r
p

Leaf Longevity to Maximize Whole-Plant Carbon Gain
This optimal timing is the point that a line originating from the origin touches the
cumulative gain curve. To obtain this optimal timing, t opt , we differentiate g with
time t, and obtain the time t when the differential becomes 0. If at this point, the second
differential is negative, then this point is the maximum. The solution is given by
topt (2· bC · / a)
45
0.5
= (4.5)
This result suggests that optimum leaf longevity (t opt ) is determined by three parameters:
(1) the daily photosynthetic rate of a young but fully mature leaf (a), (2) the age
of the leaf when the daily photosynthetic capacity becomes 0 (b), and (3) the unit cost
to produce the leaf (C). This solution, which is consistent with the conceptual model
proposed by Chabot and Hicks (1982), provides a comprehensive framework for the
analysis of leaf longevity; this framework also subsumes terms such as C/a that
Williams et al. (1989) had earlier related to longevity through their empirical studies.
Givnish (2002) criticized the focus on carbon in Kikuzawa’s (1991) model for
leaf longevity, arguing that the only real constraint on leaf retention is the need to
retranslocate nutrients for use in new leaves, either immediately or for storage
through an unfavorable period in the annual cycle. He argued that even if leaves
have only very limited potential to secure further carbon gains, it is nonetheless
useful to take those gains so long as invested nutrients need not be recycled. He
points out that carbon, the main element of photosynthetic gain, is mainly used to
strengthen leaves through investments of cellulose, hemicellulose, and lignin in
cell walls and fiber – large polymers not easily broken down and reused. Givnish
(2002) would prefer a model for leaf longevity at the whole-plant level that considered
jointly the economies of carbon and critical nutrients limiting leaf function
(e.g., N, P), but is this really necessary to gain a fundamental understanding of
variation in foliar design? At least two lines of evidence suggest otherwise: (1)
foliar N and P concentrations are well correlated to photosynthetic function
(Wright et al. 2004) and to one another (Han et al. 2005; Reich et al. 2009), indicating
a close linkage in resource allocation and function at the leaf level, and (2)
species on average recover only about half their foliar N before leaf abscission
(Eckstein et al. 1999; Hemminga et al. 1999; Kobe et al. 2005; Yuan and Chen
2009). The important point that determines leaf replacement is not that the nutrients
concerned are or are not retranslocated, but that there is some limitation to
carbon gain in retaining leaves. It is simplest to assume that allocations of N and P
follow rather than determine investments of carbon and the potential for carbon
gain. On the other hand, Oikawa et al. (2009) show that leaves can be shed before
they have recouped their full cost of construction if recovering foliar nitrogen and
investing it in new leaves confers an advantage at the whole-plant level when
nitrogen is limiting in the environment. If there are limitations set by either
endogenous or exogenous factors on the number of leaves a plant retains at a time,
it is better for a plant to replace leaves; if there are no limitations, plants should
retain leaves until their photosynthetic rate declines to zero. The fundamental
questions about leaf longevity then have more to do with the nature of factors
limiting or impairing leaf function as carbon-gaining organs at the leaf and wholeplant
levels than with ancillary concerns about retranslocation of mineral
nutrients.

46 4 Theories of Leaf Longevity
Modeling Self-Shading Effects on Leaf Longevity
Self-shading in the course of canopy growth is one example of a factor at the
whole-plant level that can influence leaf longevity. In the Kikuzawa (1991) model,
photosynthetic rate is assumed to decline with leaf age, although not for any specific
reason. If there were no photosynthetic decline with leaf age, parameter b in (4.2)
and (4.5), and hence leaf longevity, would go to infinity. There is no need to replace
leaves for a plant if the photosynthetic rate of leaves does not decrease with time
for some reason. It may be, however, that the cause of declining photosynthetic
capacity is not aging per se, but rather the progression of self-shading and a concomitant
decrease of nitrogen contents in leaves caused by retranslocation to more
well-lit leaves in the developing canopy (Ackerly and Bazzaz 1995; Ackerly 1999).
If we assume that the number of leaves on a growing shoot is maintained constant,
then leaf longevity will be given from (3.4) by
L = N / r
(4.6)
where L is leaf longevity (days), N is leaf number per shoot, and r is leaf production rate
per shoot per day. Now let the photosynthetic production rate per shoot per day be D g :
Dg = Na ·
(4.7)
where a is the mean daily photosynthetic rate averaged across all leaves on the
shoot. Photosynthetic carbon gain by the shoot then can be partitioned to new leaf
production (D c ) and to translocation at the whole-plant level (D s ), which will be
used for branch, stem, and root production and reproduction. Let the allocation
ratio to foliar production be F; then
Dc = FNa · ·
(4.8)
If the cost to produce one leaf is C, then leaf production rate per day (r) is given
by r = D c /C where D c is given by
Dc = NC · / L
(4.9)
As translocation is given by D g − D c , then the translocation D s is given by
and by substitutions, r will be given by
Ds = N·( a− C / L)
(4.10)
r= FNa · · / C
(4.11)
The preceding two equations are focal in maximizing translocation (4.10) and
shoot growth (4.11), but we have to know how mean daily photosynthetic gain
(a) changes. If the instantaneous photosynthetic rate declines with time, as shown
in (4.2), mean daily photosynthetic rate will be given by
A= a − a · L/2b (4.12)
0 0
where a 0 is the photosynthetic rate at time 0 and b is a constant. This equation is
the integration of (4.2) from time 0 to time L divided by L.

Modeling Self-Shading Effects on Leaf Longevity
If we consider that photosynthetic rate of individual leaves is determined by the
position of each leaf on a shoot, then photosynthetic rate declines linearly with
position in a way analogous to decline with age in single leaves (4.2). Mean photosynthetic
rate is described by the following equation:
0 0
47
a = a − a · N /2p
(4.13)
where p is a constant, a 0 is the photosynthetic rate of a leaf at the top of the shoot,
and N is the number of leaves counted from the top of the shoot. Substitution of
either (4.12) or (4.13) into either (4.10) or (4.11) gives four equations. Ackerly
(1999) gave solutions for two of the four: (1) to maximize the translocation from
the shoot when photosynthetic rate declines with time and (2) to maximize leaf
production when photosynthetic rate declines with position. The other two cases
give solutions intermediate to these two extremes. The solution of the first model
maximizing translocation is
L = (2 · b· C / a )
(4.14)
* 0.5
0
where L* is the optimal leaf longevity to maximize the translocation from the shoot.
This solution is basically the same as (4.4) for a single leaf. The photosynthetic rate
at L* is given by
0.5
a* = a −(2
a · C / b )
(4.15)
0 0
where a* is the photosynthetic rate at the time of leaffall and usually takes a positive
value. In contrast, in the second case, the number of leaves that maximizes the
leaf production per shoot is given by
and the corresponding leaf longevity is given by
*
N = p
(4.16)
*
L 2· C / a · F
= (4.17)
which is equivalent to the equation given by Williams et al. (1989). The photosynthetic
rate at the terminal leaf lifespan when shoot growth is maximized is then a* = 0.
Box 4.2 Population Growth Rates
Although leaves do not reproduce in the ways that individual plants and animals
do, nonetheless the leaves in a plant canopy can be considered a population
subject to the equations governing population growth. In this case, the
following equation will hold:
∞
1 e ri −
= ∑lb
i i
i=
0
If a population increases without any constraints, it will grow exponentially:
N = N
(1)
x
0 erx
(continued)

48 4 Theories of Leaf Longevity
Box 4.2 (continued)
where N and N are the number of individuals at times 0 and x, respectively,
0 x
and r is the intrinsic rate of population growth. Now let the number of individuals
born 1 year ago be n ; the number surviving from this cohort is then
1
n l , where l is survival rate. An individual bears b offspring; thus, in total the
1 1 1
cohort produces n l b offspring. Similarly, individuals born 2 years previ-
1 1 1
ously will produce n l b and so on. Thus, the total number of new individuals
2 2 2
born in a year is
∞
n = ∑ nlb
(2)
Carbon Balance at the Time of Leaffall
0
0
where we consider that b = 0. 0
Now consider the relationship between the total number of offspring born
in this year (n ) and those born last year (n ). The growth must be exponential:
0 1
n = n e 0 1 r or n = e 1 −rn . 0
−ir
Similarly, ni= e n0(3)
and substitution of (3) into n in (2) will give
i
Which of the leaf longevities given by (4.14) and (4.17), and which of the photosynthetic
rates at the time of leaffall given by (4.15) or a* = 0, are nearer to the
truth? Kikuzawa (1991) held that if there were no constraints on the number of
leaves that could be retained by a single individual plant at a time, then leaves
should be retained for their full potential longevity and thus their photosynthetic
rate at the time of leaffall should be zero. But if there are some constraints to retain
a fixed number of leaves for a plant, then leaves should be shaded at the time of t opt ,
even while photosynthetic rate is positive. Ackerly (1999) tested the two alternatives
and suggested that leaf senescence is primarily a function of the position of
a leaf within a canopy rather than its chronological age. He also examined the
photosynthetic rates at leaf death, which were greater than zero but nearer to zero
i i i
0 e 0
0
ir
∞
−
= ∑
n nlb
Dividing both sides of the above equation by n 0 will give the equation:
∞
1 e ri −
= ∑lb
i i
i=
0
i i

Time Value of a Leaf
than expected from (4.15). Oikawa et al. (2009) reported that leaves were shed even
though their carbon gain was positive, which increased the efficiency of nitrogen
use in the whole plant. But when nitrogen was not limiting, leaves tended to be
retained until their carbon gain became zero. Reich et al. (2009) assessed whether
the daytime carbon balance at the average leaf longevity of ten Australian woodland
species is positive, zero, or negative. Almost all leaves had a positive carbon
balance at the time of their fall. These per-leaf carbon surpluses were of similar
magnitude to the assumed whole-plant respiratory costs per leaf. Thus, the results
suggest that a whole-plant economic framework may be useful in assessing controls
on leaf longevity.
Time Value of a Leaf
Harper (1989) was perhaps the first to consider that the value of a leaf changes
with time. He recognized that the value of a leaf for a plant is not simply the
lifetime summation of its photosynthetic gains but also the gains accrued
through investment of organic matter translocated from the leaf. If organic
matter can be translocated and used for production of new leaves earlier, this is
advantageous for carbon gain at the whole-plant level compared to later translocation
for production of new leaves. The situation is analogous to the process of
population growth, in which individual organisms reproduce new individuals. If
a population is maintained at stable numbers, then population growth rate (r) is
given by
−rx
∫ e lx ( )· m( x)dx= 1
(4.18)
where l(x) is the survivorship by age x, and m(x) is the rate of production of new
individuals at age x per unit time dx. By analogy to age at first reproduction, young
leaves cannot contribute to translocation until they are expanded and fully functional.
Leaves that translocate photosynthates used for production of new leaves
several days earlier thus yield an advantage in carbon gains at the whole-plant level
(Harper 1989). If the photosynthate is stored for later leaf production, however,
then this potential advantage is diminished or lost entirely. For example, stored
photosynthates used for leaf production in the next year would confer no advantage
through earlier translocation because materials from new leaves and those from old
leaves do not differ in value. In trees, for example, earlier translocation is significant
in successive leafing species but not in species with a simultaneous leafing
habit. As a corollary, selection should favor hastened development in successiveleafing
species but not in simultaneous-leafing species; delayed greening thus can
be expected to occur in some simultaneous-leafing species but not in successiveleafing
species.
49

50 4 Theories of Leaf Longevity
Box 4.3 The Monsi–Saeki Model and Its Implications
Masami Monsi and Toshiro Saeki (1953) were pioneers in the development of
models for ecosystem productivity. They presented a canopy photosynthesis
model in which (1) light intensity decreased exponentially with accumulating
leaf area and (2) canopy photosynthetic rate increased asymptotically with
light intensity. Thus, in a given light regime there should be a depth in the
canopy where photosynthetic gains are just balanced by respiratory losses;
any deeper into the canopy respiratory losses surpass photosynthetic gains.
Monsi and Saeki predicted that in a given light regime there should be an
optimum leaf area index (LAI, the area or biomass of leaves per unit ground
area), although they recognized that the optimal LAI might also depend on
interactions among leaf angle, leaf size, and branching architecture that influenced
light interception in different species and plant communities. Monsi
and Saeki’s pioneering work stimulated many studies to see how LAI varied
after canopy closure within and among diverse plant community types. For
example, Tadaki and Hachiya (1968) reported that the LAI in terms of leaf
weight per unit land area was consistently about 3.0 ton ha −1 for temperate
deciduous forests, 8.6 ton ha −1 for evergreen broad-leaved forests, and
16 ton ha −1 for evergreen coniferous forests.
Although Monsi and Saeki developed their model for plant communities,
it has implications for individual plant canopies as well. If leaf biomass in
a community or in the canopy of an individual plant is constant, then any new
leaf production must be associated with the fall of a corresponding amount
of old leaves. Light captured by a new leaf in the upper canopy will reduce the
light penetrating to the deepest level of the canopy, thus tipping the balance
of photosynthetic gains to respiratory losses in the most shaded leaves and
(continued)

Time Value of a Leaf
Box 4.3 (continued)
triggering their senescence. When a new leaf appears at the top of the canopy,
an older, shaded leaf should fall at the bottom of the canopy in the steady state.
Although leaves are fixed in their absolute position on the branch where they
originated, their relative position in the canopy becomes progressively deeper
as leaves develop on growing shoots at the upper and outer peripheries of the
canopy. As the canopy grows over time, absolute leaf positions that once were
at the growing periphery and well lighted inevitably become deeply shaded and
unable to sustain a viable leaf. The change in relative position through the lifetime
of an individual leaf is analogous to the change in the real position of
leaves from the exterior to the interior of the canopy over time. Thus, we can
speak of a canopy ergodic hypothesis that predicts the average light regime, and
photosynthetic rates of leaves across positions at a moment in time are equivalent
to those of a single leaf through time, at least so long as the canopy is reasonably
close to a condition of steady-state growth (Kikuzawa et al. 2009).
Westoby et al. (2000) also considered the topic of the “time value of a leaf,” but
went beyond Harper (1989) to formally incorporate the concept into a theory
predicting leaf longevity. They recognized that the functional value of a leaf as a
carbon-gaining organ decreases over time for a variety of reasons: intrinsic loss of
function with age, shading in the course of canopy growth, the effects of damage
by pathogens or herbivores, and similar considerations. In this context they assessed
the trade-off between investments that could slow losses of leaf function over
time and those that involved transport to create new leaves. Taking the rate of the
age-dependent reduction in foliar function to be k and the organic matter transported
from the leaf to other parts of the plant body as E, they then expressed the
amount of transport from a unit amount of leaf over its lifetime (R) as
Integrating this relationship as
L
0
−kt
( )
leaf longevity (L) then can be expressed as
51
R = ∫ E× SLA × e dt
(4.19)
E × SLA −kL
R = ( 1− e )
(4.20)
k
⎛ 1 ⎞ ⎛ kR ⎞
L = ⎜− n 1
k
⎟ ⎜ −
E×
SLA
⎟
⎝ ⎠ ⎝ ⎠
(4.21)
This analysis suggests that leaf longevity is a function of the lifetime amount of
transported photosynthate (R), the maximum rate of transport at the time of full leaf
expansion (E), the rate of decline in transport rate with leaf age (k), and specific leaf
area (SLA). The analysis lets us visualize the relationship between leaf longevity

52 4 Theories of Leaf Longevity
Fig. 4.3 Relationship between
leaf longevity and specific leaf
area. Lines and curves in the
panels follow from (4.18)
in the text. When k = 0, the
relationships are linear and when
k = 0.08, they are curvilinear;
the instantaneous potential
translocation rate E and lifetime
transportation R are parameters.
(From Westoby et al. 2000)
and SLA (the inverse of LMA) when other factors are held constant (Fig. 4.3).
When k = 0, the logarithm of leaf longevity decreases linearly with log (SLA), but
if k takes a positive value, then the relationships become curvilinear and convex to
the bottom. The anal ysis makes it clear that because photosynthetic rate and thus
translocation rate change with time, it is necessary to incorporate these changes in
modeling of leaf longevity.
Leaf Longevity and Leaf Turnover in Plant Canopies
Leaf longevity (mo) [log scale]
The preceding models have focused on longevity as a leaf-level trait and invoked
canopy-level influences in only a generalized way. There is another literature tracing
back to a seminal paper by Monsi and Saeki (1953) on the characteristics of plant
canopies that deals with leaf longevity secondarily through the rate of leaf turnover
in the canopy. When a plant canopy is in steady state, leaf longevity is the inverse
of leaf turnover in the canopy. The pioneering work by Monsi and Saeki (1953)
focused on the concept of an optimum leaf area per unit land area, an optimal leaf
area index (LAI). They used the then-novel method of stratified clipping to assess
the vertical distribution of leaf area in various plant communities. These data on
canopy structure stimulated development of theory predicting the aggregate characteristics
of leaves in different canopy strata. Because of the close correlation
between foliar nitrogen and photosynthetic capacity and the recognition that nitrogen
a
100
10
1
b
100
10
1
1 10
k = 0.00 mo –1
k = 0.08 mo –1
100
Specific leaf area (mm 2 mg –1 ) [log scale]

Leaf Longevity and Leaf Turnover in Plant Canopies
availability often limited plant productivity in terrestrial ecosystems, considerable
attention subsequently has been devoted to the optimal distribution of nitrogen
across canopy strata (Field 1983; Hirose and Werger 1987a,b). Most of this literature
tracing back to Monsi and Saeki (1953) has taken a static view of the plant
canopy, but recently Hikosaka (2003a,b, 2005) has turned the focus toward the
dynamics of leaf turnover in the context of optimizing a stratified plant canopy. He
considers that leaves are produced from the products of canopy photosynthesis and
that after the canopy reaches a stable state older leaves will be shed in proportion
to the production of new leaves. Simulations using Hikosaka’s model revealed the
negative trends of leaf longevity on canopy light environment and on availability of
soil nitrogen that have been documented in studies at the canopy level. Hikosaka’s
model also showed a positive correlation between leaf longevity and leaf mass per
leaf area (LMA), which is consistent with both models and observations (Fig. 4.4).
Leaf life-span (day)
a
26
24
22
c 30
d
b 200
20
0 1 2 3 4 5 6
0
0 500 1000 1500 2000
Nitrogen uptake rate (mmol m –2 d –1 ) Noon PFD (µmol m –2 d –1 )
25
20
N uptake rate = 0.4
150
100
N uptake rate = 4 40
N uptake rate
= max.
15 0 0.1 0.2 0.3
Slope of P max−n L relationship
(mmol m –1 s –1 )
50
120
80
N uptake rate
= 0.4
53
0
0 50 100 150 200 250
Leaf mass per area (g m –2 )
Fig. 4.4 Relationships between leaf longevity and (a) nitrogen uptake rate from soil, (b) irradiance,
(c) relationship between photosynthetic capacity and foliar nitrogen, and (d) leaf mass per area.
(From Hikosaka 2003a, b)

54 4 Theories of Leaf Longevity
However, because of the assumption that the respiration rate of a single leaf increases
in proportion to nitrogen concentration, this model shows a curious behavior in that
under higher levels of nitrogen absorption from the soil, the entire plant stand will die.
Box 4.4 Herbivory
Herbivory refers to the consumption of living plant material by invertebrate
and vertebrate animals. There is an extraordinary variety of modes of herbivory,
from the sucking of sap to the consumption of leaves and seeds. There
also are strong contrasts in losses to herbivores in terrestrial versus aquatic
ecosystems. For example, leaf consumption by herbivorous animals in terrestrial
ecosystems is usually less than 5% of net primary production, in strong
contrast to aquatic systems, where herbivory is usually greater than 50% of
net primary production (Cyr and Pace 1993).
To make sense of this situation we have to consider why plants defend
against herbivore losses at all. The basic answer is that the more expensive
the cost of constructing the systems for primary production, the more likely
are additional investments in their defense against loss to herbivores or disease.
The leaves of terrestrial plants and the various ancillary structures
such as roots and transport systems that sustain photosynthetic function are
relatively “expensive” to construct and maintain. Terrestrial plants make
substantial investments in systems for primary production that are only
recovered over fairly long time periods, and hence ancillary investments in
defense can ensure returns on investment in the photosynthetic function of
their leaves.
In contrast to terrestrial leaves, the costs associated with constructing and
maintaining net primary production are much less in aquatic systems. Aquatic
plants need not invest in structures for the uptake and transport of water. They
can utilize buoyancy to offset the force of gravity that imposes structural costs
on terrestrial plants. They can absorb nutrients from the surrounding water
directly with no need of root systems. In short, the investments in systems for
primary production required of aquatic plants are much lower than those in
terrestrial plants, generally too low to justify diverting resources to defense.
It is advantageous to produce more individuals, even if many will be lost to
herbivory, to simply outgrow the risk posed by herbivory.
On the other hand, there is no doubt that terrestrial plants invest in a variety
of defenses against herbivory. A significant part of net primary production is
allocated to plant defenses, which are usually divided into several types:
1. Physical defenses
–
–
Hard or fibrous tissues resistant to herbivore attack (Lusk et al. 2010)
Thorns and stinging hairs that deter herbivores
(continued)

Directions for Future Theory
Box 4.4 (continued)
2. Chemical defense
–
–
–
Quantitative chemical defense involving relatively large pools of chemicals
such as phenolics that reduce tissue quality for herbivores
Qualitative chemical defense involving small amounts of poisonous
chemicals such as alkaloids that are toxic to many herbivores
Induced chemical defenses that are produced only after herbivore
attack to discourage continued feeding
3. Biological defenses involving diverse mutualisms
– Production of specialized food bodies or extrafloral nectaries on the
leaf lamina or petiole to attract ants that in turn attack caterpillars
which might feed on the leaf
– Production of volatile chemical signals to attract predators and parasites
of an herbivore
– Specialized structures under the veins on the lower surface of a leaf for predatory
mites that act as guards against herbivorous mites or infecting fungi
4. Other methods to avoid herbivores
–
–
Open leaves synchronously with other plants to satiate herbivores and
reduce the risk of damage
Reduce apparency to herbivores by mimicking less palatable tissues or
species
Despite these substantial and diverse investments in defense against herbivores,
it still is not entirely clear why levels of terrestrial herbivory are so low
relative to those in aquatic systems. The defenses enumerated here fall into a
bottom-up, escape-in-time explanation for the low level of herbivory in
terrestrial systems: basically, that mature plant tissues are well defended and
of little value as a food resource for herbivores except in the brief period when
the tissues are developing. An alternative, top-down explanation is that predatory
animals, parasites, and disease keep herbivore numbers low and plant
defenses have relatively little to do with the outcomes. In fact, it is likely that
both top-down and bottom-up controls play a role in terrestrial as well as aquatic
systems, but the relationships are complex and remain to be fully understood.
Directions for Future Theory
There are at least two main lines along which theories for leaf longevity can usefully
be advanced. We have already alluded to one, the consolidation of theory developed
at the canopy level with that developed at the leaf level. Hikosaka (2005) has taken a
55

56 4 Theories of Leaf Longevity
Metabolic rate
(nmd g−1 s−1 )
10 3
10 2
10
1
10
Photosynthesis
10 2
10 3 10 4
Longevity (days)
Fig. 4.5 Longevity of individual organisms or leaves (X-axis) and metabolic rate per unit leaves.
For mammals, this gradient is nearly −1.0, but for photosynthesis by leaves, the gradient is only
about −0.66. The lower line parallel to photosynthesis is dark respiration. (From Reich 2001)
significant step in this direction by integrating leaf-level theory into his analysis of
canopy dynamics, but until recently (Hikosaka and Osone 2009) his emphasis has
been on the canopy. Although it is true that selection on foliar characteristics is
contingent on plant performance that is determined at the whole-canopy level, there
are constraints at the leaf level which may set limits on canopy design. For example,
Shipley et al. (2006) show that the spectrum of variation in foliar design is rooted in
trade-offs at the cellular and tissue levels within the leaf. There also may be some
fundamental linkages of this sort that extend to the scaling of metabolic activity for all
organisms (West et al. 1997; Brown et al. 2005), including plants (Reich 2001; Enquist
et al. 2007; Price and Enquist 2007). Reich (2001) points out that foliar metabolism
scales with leaf longevity much as animal metabolism scales with lifespan, although
with a different slope (Fig. 4.5). What is uncertain is whether this scaling on leaf longevity
would converge to the slope for animals if whole-plant longevity were the scaling
factor. It is the give and take between functional constraints and opportunities at the
canopy versus foliar levels that will decide whole-plant leaf longevities and alternative
strategies for plant productivity. These interactions merit serious analysis. A fundamental
understanding of the different modes of leaf longevity that underlie the evergreen versus
deciduous habits and an explanation of which environments favor one or both habits
is likely to be found in the interplay of foliar- and canopy-level traits.
A second useful line of inquiry would be to seek a deeper understanding of the
roles of herbivory and disease as factors in the selection of leaf longevity. Chabot
and Hicks (1982) noted the significance of these factors, and they have been widely
acknowledged in subsequent work, but without ever being explicitly incorporated
into a theoretical analysis of variation in leaf longevity. We have considerable data
on the effects of both herbivores and disease on leaf function as well as on the
multitude of strategies for foliar defense, but no simple generalizations emerge
(Jones 2006; Nunez-Farfan et al. 2007; Howe and Jander 2008; Poland et al. 2009).
A more complete theoretical framework rooted in an assessment of foliar function
at the whole-plant level might help make sense of the voluminous but often confounding
data on plant defense against herbivores and disease.

Chapter 5
Phylogenetic Variation in Leaf Longevity
Tree fern canopy (Cyathea arborea)
There are broad patterns of variation in leaf longevity associated with plant growth
form (Fig. 5.1), and leaf longevity spans more than two orders of magnitude
(Fig. 5.2). Longevities as little as a few weeks are recorded for some herbaceous
species and 20 years or more for some woody species (Wright et al. 2004). Lusk (2001)
reported leaf longevities for a conifer in south-central Chile as long as 26.2 years in
shaded sites and 21.5 years in open sites. The extensive compilation of leaf longevities
by Wright et al. (2004) is primarily for woody species (79%), mostly shrubs and
trees, with only a few vines; the herbaceous plants in this compilation include
graminoids as well as forbs. The median value of leaf longevity in this data set is
8.5 months. Biologically noteworthy longevities are illustrated by the temporary
flattening of the rank-order diagram (see Fig. 5.2) at about 3.5 months and again at
6 months. Although there is in general a highly regular and continuous variation in
longevity across species, these clusters of species with similar longevities suggest
the existence of some sort of limiting factor on leaf viability associated with longevities
of these durations. We can speculate that the 6-month longevity reflects the typical
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_5, © Springer 2011
57

58 5 Phylogenetic Variation in Leaf Longevity
Floating leaves of
aquatic plants
Annual plants
Perennial herbaceous
plants
Temperate deciduous
trees
10 100 200
Leaf life span (days)
Fig. 5.1 Leaf longevity of plants of different growth forms. (From Kikuzawa and Ackerly 1999)
Leaf longevity, months
1000.00
100.00
10.00
1.00
0.10
0 200 400 600
Increasing rank
Fig. 5.2 Frequency distribution of leaf longevity for leaves of diverse species from a wide variety
of climate zones. (Data from Wright et al. 2004)
length of the growing season in temperate regions where many of the compiled data
were taken, but what might account for the 3.5-month longevity? This cluster of
species with rather rapid leaf turnover includes many fast-growing herbaceous and
woody species from temperate regions, which reflects a dichotomy between
deciduous species that produce only one set of leaves per season and others which
produce leaves throughout the season. Even within a single climatic regime there are

Leaf Longevity of Ferns
alternative evolutionary outcomes in the organization of foliar phenology that
involve distinct differences in leaf longevity. For some groups of plants sufficient
data have been compiled (cf. Wright et al. 2004) to detect broad differences in leaf
longevity, but other groups are too little studied to identify any characteristic leaf
longevity. Here we briefly review what we know about patterns of leaf longevity
among and within diverse groups of plants, illustrating our points with selected
examples.
Box 5.1 Adaptive Radiation
The diversity of species at any time in Earth’s history arises in the balance
between rates of speciation and extinction. There are background rates of
speciation and extinction, but occasionally events trigger a rapid increase in
the rate of speciation. Such bursts of speciation are referred to as an adaptive
radiation. Adaptive radiations are often associated with colonization of speciespoor
environments such as an isolated oceanic island that allows colonizing
species to diversify and exploit a wider variety of resources and habitats without
facing strong competitive interactions from other species. A well-known
example of adaptive radiation is the finches on the Galapagos Islands, which
now include many species derived from a single ancestor that have diversified
to use different habitats and food resources within and among the islands in
this archipelago far off the coast of South America.
Leaf Longevity of Ferns
The extant ferns trace their ancestry to the early Paleozoic but their current diversity
to an adaptive radiation in the early Tertiary (Schneider et al. 2004). Most
species are herbaceous, but there are some woody ferns that are tropical and
evergreen, with leaf longevity generally a year or longer. Leaf longevities were
328 days for Cyathea furfuraca, 525 days for C. pubescens, and 730 days for
C. woodwardioides (Tanner 1983). Mean leaf longevity averaged 1.1–1.6 years
for Cyathea hornei (Ash 1987) and 2–2.5 years for Leptopteris wilkesiana (Ash
1986). The herbaceous ferns are more diverse in both their climatic affinities and
their leaf longevities. Sato and Sakai (1980) classified 67 herbaceous ferns in
northern Japan into four groups in terms of foliar habit: evergreen, semievergreen,
summergreen, and wintergreen. Evergreen species such as Lepisorus
ussuriensis and Pyrrosia tricuspis produce new leaves in June and July that are
shed from April to August 2 years later. Other evergreen species such as
Asplenium incisum, Blechnum niponicum, and Phyllitis scolopendrium also produce
leaves early in the growing season but shed them after only about 1 year.
Semievergreen species such as Dryopteris crassirhizoma and Polystichum
tripteron produce leaves in late May and early July that begin to senesce by
59

60 5 Phylogenetic Variation in Leaf Longevity
December but only completely die as new leaves are produced. Summergreen
species produce their leaves in May and June and shed them in October; many
species, for example, Athyrium brevifrons and Dryopteris phegopteris, share this
habit with leaf longevity around a half-year. Wintergreen species such as
Scepteridium multifidum var. robustum and Polypodium japonicum with a leaf
longevity of about 10 months produce new leaves in late July to early September
and shed their leaves in late May to early July.
Yoshida and Takasu (1993) reported similar observations of leaf longevity for
ferns in the warm temperate zone of central Japan. Summergreen species such as
Athyrium pycnosorum, A. wardii, Coniogromme japonica var. fauriei, and
Cornopteris decurrenti-alata had leaf longevities from 164 to 210 days. Among
evergreen species, the leaf longevities of Polystichum retroso-paleoceum,
Doryopteris polylepis, and D. lacera were around 1 year. In a semievergreen species
such as P. tripteron only a few old leaves remained 300 days later when new
leaves emerged. True evergreen species such as Microlepia marginata, Rumohr
standishii, Athyrium otophorum, Blechnum niponicum, and Asplenium wrightii
had leaf longevities longer than 1 year and old leaves coexisting with newly produced
leaves. Asplenium wrightii had the longest leaf longevity, more than 1,000
days (Yoshida and Takasu 1993).
Leaf Longevity of Gymnosperms
A branch of evergreen conifer (Abies firma)

Leaf Longevity of Angiosperms
The extant gymnosperms, a lineage tracing back to the Middle Devonian some
365 million years ago (MYA), have their greatest diversity in the Southern
Hemisphere, but it is the species in the Northern Hemisphere that are best
studied (Enright and Hill 1995). Lusk (2001) reported a few leaf longevities
for Southern Hemisphere species ranging from 4.2 years for Saxegothaea
conspicua and 7.3 years for Podocarpus nubigena on up to 23.9 years for Araucaria
araucana and 32 years for Podocarpus saligna. Species in the genera Abies,
Pinus, Picea, and Larix are good examples of the northern conifers, which most
often are evergreen trees with fairly long-lived needle- or scale-like leaves.
In the genus Pinus, leaf longevities can range from as short as 1.5 years in Pinus
taeda to more than 40 years in Pinus longaeva (Ewers and Schmid 1981;
Schoettle 1990). Longevity of leaves in Pinus tabulaeformis varies with latitude,
but at the extreme can be as short as 0.94 years (Xiao 2003). Needle longevity
of Pinus contorta in the Rocky Mountains of Colorado was 13.1 years at
3,200 m versus 9.5 years at 2,800 m (Schoettle 1990). A similar trend also was
observed in Pinus contorta in California: longevity at 15 m was 3.9 years, at
182 m was 4.2 years, and at 2,700 m was 7.9 years (Ewers and Schmid 1981).
In a warm temperate region, the leaf longevity of Abies was of the order of 6–8
years (Furuno et al. 1979). The half-life of leaves of Abies mariesii ranged from
3 to 9 years and up to as long as 13 years, varying among branches within the
canopy (Kohyama 1980). The mean leaf half-life is 7 years in A. mariesii and
only 4 years in Abies veitchii (Kimura 1963; Kimura et al. 1968). Eight species
of Asian, North American, and European Picea grown in northern Japan had leaf
longevities ranging from 5 to 11 years (Kayama et al. 2007). The leaf longevity
of Picea mariana was 5–8 years in Minnesota but 8–15 years in Alaska (Reich
et al. 1996). Niinemets and Lukjanova (2003) reported maximum needle longevities
of 16 years in Abies balsamea, 12 in Picea abies, and 6 in Pinus sylvestris.
Gower et al. (1993) estimated leaf longevities of plantation-grown P. abies at
66 months, Pinus resinosa at 46 months, and Larix decidua at 6 months. Larix
species are among the minority of conifer genera that are deciduous, unless their
needles are protected under snow cover (Gower and Richards 1990). An extensive
compilation of leaf longevity for coniferous trees augments these examples
(Wright et al. 2004).
Leaf Longevity of Angiosperms
Evergreen Broad-Leaved Woody Species
Leaf longevity of evergreen broad-leaved trees in temperate regions is usually
1–5 years. Nitta and Ohsawa (1997) provide a good example for 11 species
in laurel forests near the northern limit of evergeeen broad-leaved forests in
61

62 5 Phylogenetic Variation in Leaf Longevity
10
5
4
3
2
1
Symplocos prunifolia Machilus thunbergii
10
AMJJASONDJFMAMJ J ASOND
AMJJASONDJFMAMJ J ASOND
1994 1995 1994 1995
Month
Fig. 5.3 Survivorship curves for different cohorts of leaves in two co-occurring evergreen broadleaf
trees, Symplocos prunifolia (left) and Machilus thunbergii (right). Log leaf number is plotted
against calendar months. Open circles, leaves that appeared in 1995; open squares, leaves that
appeared in 1994; open triangles, leaves that appeared in 1993; inverted triangles, leaves that
appeared in 1992. (From Nitta and Ohsawa 1997)
Japan. Leaf longevities ranged from 1.5 to 4.3 years, quite similar to the range
of 1.4 to 3.8 years reported for 16 species of broad-leaved evergreen dwarf
shrubs from Europe (Karlsson 1992). In the Japanese forest, the leaf longevity
of Symplocos prunifolia was 1.5 years, with leaves emerging each spring but
only being shed during spring and summer the next year (Fig. 5.3). In Machilus
thunbergii with a mean leaf longevity of 2 years, the emergence of leaves in
spring is more or less simultaneous with shedding of the 2-year-old leaf
cohort, although the period of leaffall can be somewhat longer (Nitta and
Ohsawa 1997). A similar pattern prevails in Castanopsis cuspidata, Quercus
myrsinaefolia, and Quercus acuta. In S. prunifolia, Illicium religiosum, and
Cleyera ochnacea, whose leaf emergence period was long, leaffall period was
also long. Eurya japonica usually shows several periods of leaf emergence
within a year, which are coordinated with periods of leaf shedding. This correspondence
in the timing of leaf emergence and leaffall is associated with
translocation of resources from senescing to emerging leaves (Nitta and
Ohsawa 1997). Navas et al. (2003) studied the leaf longevity of 42 plant species
in the Mediterranean region of south France, including some evergeen
trees. Leaf longevities of the evergreen trees ranged from 488 to 802 days.
Mediavilla and Escudero (2003b) reported leaf longevities of evergreen
Quercus coccifera, Q. rotundifolia, Q. suber, and Ilex aquifolium to be between
1 and 2 years in western Spain.
5
4
3
2
1

Leaf Longevity of Angiosperms
Temperate Deciduous Trees and Shrubs
The deciduous habit is characterized by the complete shedding of leaves during an
unfavorable period, usually in response to freezing or drought stress. In temperate
regions, deciduous (summergreen) trees that shed their leaves during winter often dominate
the forested landscape. The summergreen, deciduous habit is a superficial characteristic
of the tree that can mask the longevity of individual leaves during the
summergreen period. All deciduous trees are superficially similar in that in spring many
leaves appear on the tree and in fall leaves turn color and fall before winter. In reality,
leaves emerging in spring on some species survive until autumn, but in other species all
the leaves that emerged in spring have fallen by summer and been replaced by later
emerging leaves that persist until autumn. For example, Kikuzawa (1983) followed
leaf longevities in 41 tree species in the deciduous broad-leaved forests of Hokkaido,
northern Japan. The shortest longevity was 80 days in Alnus hirsuta and the longest
160 days in Quercus crispula and Fagus crenata. Species of Alnus are well known to
have short leaf longevity (Kikuzawa 1978, 1980, 1983; Kikuzawa et al. 1979; Kanda
1988, 1996; Tadaki et al. 1987). A comparable study of 16 deciduous tree species in the
Great Smoky Mountains of southeastern North America (Lopez et al. 2008) found leaf
longevities ranging from 116 days in Aesculus flava to 180 days in Carya cordiformis.
Some shrub species in the understory of deciduous forests have an unusual summerdeciduous
foliar habit. In Daphne kamtschatica, some leaves appear in early autumn
(September) and overwinter, new leaves also expand the next spring (April), and then
all the leaves are shed in June and July so that the plant is leafless in summer when the
tree canopy casts deep shade (Kikuzawa 1984; Lei and Koike 1998).
Tropical Trees and Shrubs
Even in aseasonal tropical forests, leaf longevity is not particularly long. For
example, we can infer from the data of Edwards and Grubb (1977) on litterfall and
leaf biomass that the leaf longevity of trees in a New Guinea forest averaged only
1.4 years. Hatta and Darnaedi (2005) surveyed leaf longevity of nearly 100 tropical
tree species in Bogor and Chibotas, Indonesia. Most trees had an evergreen habit
but about half had a leaf longevity less than than 1 year. Leaf longevities ranged
from only 2 months in Inga edulis and Cryptocarya obliqua to more than 30 months
in Cinnamomum sintoc. In the understory of the Costa Rican tropical forest some
trees have leaf longevities exceeding 2 years but others less (Bentley 1979).
Homolanthus caloneurus is a pioneer tree in tropical lower montane forest with leaf
longevity of only 0.8 years (Miyazawa et al. 2006). In Venezuelan mangrove forests,
leaf half-lives were only 60 days in Laguncularia racemosa, 100 days in
Rhizophora mangle, and 160 days in Avicennia germinans (Suarez 2003). Sixteen
species in the genus Psychotria, all understory shrubs in tropical forests in Panama,
have a remarkable range of leaf longevities, from 119 days in P. emetica to 870 days
in P. limonensis.
63

64 5 Phylogenetic Variation in Leaf Longevity
Leaf Longevity of Herbaceous Plants
Flower and leaves of an aquatic floating-leaved plant (Nymphaea odorata)
The leaf longevity of Ambrosia trifida ranged from 20 to 90 days depending on time
of emergence, averaging about 50 days (Abul-Fatih and Bazzaz 1980). Leaf longevity
of other annual forbs was comparable: Xanthium canadense, 30–40 days (Oikawa
et al. 2006), Glycine max, 20–60 days (Miyaji and Tagawa 1979), and Linum
usitatissimum, around 20–30 days (Bazzaz and Harper 1977). The leaf longevity of
perennial herbs is not markedly different, although tending to be higher. For example,
Diemer (1998a) compared leaf longevity of perennials at different altitudes in the
Austrian Alps. At 600 m, leaf longevity of 13 species was 71 days, very similar to
the 68-day average for 16 species at 2,600 m. The average leaf longevity of 14
herbaceous species in North American grasslands was 63 days (Craine et al. 1999).
Leaf longevities in 32 Swiss grass species ranged from 19 to 29 days for annuals
versus 30 to 113 days for perennials (Ryser and Urbas 2000). Compiling earlier
studies, Janišová (2007) reported annual grasses having leaves with half-lives in the
range of 19–29 days, short-lived perennials with 30–45 days, and long-lived perennial
with 111–200 days.
Tsuchiya (1991) reported the leaf longevity of floating leaves in aquatic herbs
ranged from 13 to 55 days, averaging 25 days. Average leaf longevity for the
floating-leaved Nymphaea tetragona and Brasenia schreberi were 30 and 25 days,
respectively (Kunii and Aramaki 1987). Some floating-leaved species also produce
emergent leaves with stouter petioles that have longevities from 35 to 57 days,
averaging 45 days. For example, in Nelumbo nucifera the longevity of floating
leaves was only 17 days, but the emergent leaves later in the season live for 30–50

Leaf Longevity of Herbaceous Plants
Leaf life span (days)
60
50
40
30
20
10
0
MAY
1987
JUN JUL
Date of leaf birth
emergent
floating
AUG SEP OCT
Fig. 5.4 Longevity of floating (open circles) and emergent leaves (closed circles) in Nelumbo
nucifera, an aquatic macrophyte that produces floating leaves throughout the season and emergent
leaves held on sturdy petioles later in the season. Leaf longevity of emergent leaves is significantly
longer than that of floating leaves. (From Tsuchiya and Nohara 1989)
days (Tsuchiya and Nohara 1989; Fig. 5.4). Leaf longevities of submerged plants
are longer than those of floating-leaved aquatic plants and are comparable to those
of herbaceous land plants. Leaf longevity ranged from 40 days for Potamogeton
crispus to 100 days for Myriophyllum spicatum (Yamamoto 1994). The leaf longevities
of marine seagrasses are comparable, averaging 70 days and typically
ranging from 25 to 170 days (Hemminga et al. 1999; Kamermans et al. 2001).
65

Chapter 6
Key Elements of Foliar Function
Sclerophyllous leaves of various bog plant species
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_6, © Springer 2011
67

68 6 Key Elements of Foliar Function
Leaf longevity is an integral part of a quintet of highly intercorrelated and
functionally interdependent traits that organize the function of leaves as photosynthetic
organs: photosynthetic capacity, A max ; leaf mass per unit area, LMA;
foliar nitrogen content, N; and leaf dry matter content, LDMC (Wright et al.
2004; Shipley et al. 2006). Photosynthetic capacity, a direct measure of foliar
function, is the natural focal element in the quintet. Leaf longevity, LMA, and
foliar N initially drew attention as correlates of photosynthetic capacity and
only later were recognized as part of a unified set of traits characterizing overall
variation in leaf function: the “leaf economic spectrum” (Wright et al. 2004).
Leaf dry matter content subsequently was identified as a little-studied trait that
in fact underpinned the relationships among A max , LMA, foliar N, and leaf longevity
(Shipley et al. 2006). Considering the innumerable characteristics of
leaves, including some that figure in theories of leaf longevity, what makes
these the cardinal traits central in defining trends in variation of leaf function?
There are basically two reasons these five traits have primacy. First, all these
characteristics bear on the costs of leaf construction and the photosynthetic
functions that repay those costs over the life of the leaf. Second, these traits
show a wider and ecologically more consistent range of interspecific variation
than other characteristics of leaves.
Take leaf construction cost as an example of a foliar trait that one might well
expect to be an important element in any quantification of leaf function given its
central place in theories of leaf longevity. In fact, the cost of leaf construction per
unit mass, which is what we can most readily measure, is a trait that turns out to
be relatively invariant across both evergreen and deciduous species from a wide
variety of ecosystems; hence, it is not particularly useful in interspecific comparisons
of leaf function. Griffin (1994) reviewed leaf construction costs from 87
studies, which ranged from 1.08 to 2.09 g g −1 and averaged 1.54 g g −1 . Reviewing
162 studies, Villar and Merino (2001) reported very similar results: an average of
1.52 g g −1 and a range from 1.08 to 1.92 g g −1 . The difference in leaf construction
costs between evergreen and deciduous habits within plant families is not significant
(Villar et al. 2006). One might instead consider that something as simple as
variation in the total area of the leaf could affect a broad range of variation in leaf
longevity despite the narrow range of leaf construction costs, but this is unlikely
because it is the areal rate of photosynthesis that determines the rate of recovery
of costs. We must look instead to one of the cardinal traits to make sense of this
situation, to LMA. By using LMA, we can convert our measured leaf construction
cost (c) per unit leaf weight to an estimate of the construction cost of leaves per
unit area (C) :
C = c · LMA
(6.1)
As c varies at most twofold whereas LMA varies tenfold or more (Wright et al.
2004), the interspecific variation of leaf architecture reflected in LMA clearly will
have more influence on the time required for recovery of the cost of construction
than simply the costs of the differing materials composing the leaf tissues. This
concept helps illustrate why LMA is among the cardinal traits defining the principal

6 Key Elements of Foliar Function
axes of variation in foliar design (Wright et al. 2004) and, more generally, is an
important index of plant strategies at the whole-plant level as well (Westoby 1998;
Westoby et al. 2002).
Specific leaf area, the inverse of leaf mass per area, was considered a key
element in studies of plant productivity beginning in the early twentieth century
(Blackman 1919; Clifford 1972). It was, however, only in the 1970s when traditional
methods of growth analysis began to be superseded by direct measures of
photosynthesis using infrared gas analysis techniques (Šesták et al. 1971) that the
positive correlation between A max and LMA (Fig. 6.1) gradually came into explicit
discussion, through the interests first of plant breeders (Gifford and Evans 1981;
Marini and Barden 1981) and then of ecologists (Oren et al. 1986; Koike 1988;
Reich et al. 1991). Physiological ecologists were quick to recognize how
anatomical variation in leaves contributed to differences in LMA and could in
turn influence photosynthetic function (Nobel et al. 1975; Koike 1988). For
example, Populus maximowiczii with a high LMA has relatively thick palisade
and spongy mesophyll layers (Fig. 6.2), which facilitate high A max in its sunny,
early successional environment (Koike 1988; Hanba et al. 1999; Terashima
2003). Conversely, Acer palmatum is a species of shaded forest understory environments
with a low LMA and a thin leaf lacking extensive internal air space and
having low A max (Koike 1988). These sort of investigations firmly cemented LMA
(or specific leaf weight, SLW, or its inverse, specific leaf area, SLA) as part of a
growing constellation of traits critically associated with the photosynthetic capacity
of leaves.
Pn (mg CO 2 dm −2 hr −1 )
24
20
16
12
8
4
0
MAY 25
Peripheral
Interiors
Y=6.0+1.6x
r=.83*
JULY 16
Y=−5.0+2.5x
r=.70*
4 8 12 4 8 12
SLW (mg cm −2 )
Fig. 6.1 Relationship between photosynthetic capacity (Pn) and leaf mass per unit area (LMA)
(here, SLW) for individual leaves in the interior or peripheral canopy of orchard-grown apple trees
just after leaf maturation (May 25) and in midsummer (July 16). (Redrawn from Marini and
Barden 1981)
69

70 6 Key Elements of Foliar Function
Fig. 6.2 Cross sections of leaves: left, Populus maximowiczii (Pm); right, Acer palmatum (Ap).
(From Koike 1988)
Photosynthesis and Foliar Nitrogen Content
The relationship between photosynthetic capacity and foliar nitrogen content was
brought into sharp focus in the collation by Field and Mooney (1986) of data on
wild plants (Fig. 6.3) and the associated development of a theory for maximizing
photosynthetic return on allocation of foliar nitrogen (Mooney and Gulmon 1979;
Field 1983). Chlorophyll and photosynthetic enzymes account for the large part of
foliar N (Evans 1989), so it is not surprising that photosynthetic capacity is
positively correlated with foliar nitrogen content. Field’s (1983) theory for optimal
allocation of nitrogen builds on the leaf-level correlation between A max and foliar N
(Fig. 6.3) to address the question of allocation of nitrogen across all the leaves on
the plant. Field argued that the photosynthetic return on nitrogen investments is
maximized when all leaves have the same slope [a in (6.2)] of the line tangent to
the graph of daily photosynthetic gains on foliar nitrogen:
a =∂A ∂ N
day /
(6.2)
Although the optimization is scaled in terms of daily photosynthetic gains, there is
a connection to the leaf-level relationship between A max and foliar N (Field and
Mooney 1986) through the linear relationship between A day and A max for a given leaf
(Field 1991; Zots and Winter 1996; Rosati and DeJong 2003). Daily photosynthetic
gain increases asymptotically with foliar N for a family of curves that originate in

Assembling the Elements of Foliar Function
Net CO 2 uptake (nmol CO 2 g −1 s −1 )
1000
800
600
400
200
k
j
0
0 10 20
i
f
Leaf nitrogen (mmol g −1 )
e
a
d
c
30 40 50
Fig. 6.3 The increase of photosynthetic capacity with foliar nitrogen content; each polygon
bounds observations collated from different studies. (From Field and Mooney 1986)
evolved differences in foliar design among species as well as in the ecophysiological
responses of single leaves in differing microenvironments within a plant
canopy (Fig. 6.4). The photosynthetic return on nitrogen investment at the wholeplant
level is maximized when the tangents to the point where the curves for
individual leaves cross the linear leaf-level relationship between A max and foliar N
all pass through the origin (Hirose and Werger 1987a). Similarly, Koyama and
Kikuzawa (2009) observed this linear relationship applied to not only A max but also
A day in leaves of Helianthus tuberosus.
Assembling the Elements of Foliar Function
By the early 1990s photosynthetic capacity was firmly linked to LMA and foliar N,
but it took a seminal paper by Peter Reich and his colleagues (Reich et al. 1997) to
focus attention on the high degree of coherence in the correlations among these
three foliar traits. They collated data for 280 plant species to show that there were
consistent correlations among A max , LMA, and foliar N (Fig. 6.5). As any one of
these traits characterizing foliar function varied from one species to another, they
varied in concert, and these relationships were conserved across and within growth
forms. This is compelling evidence that A max , LMA, and foliar N are integral parts
of a unified suite of traits that affects the functionality of leaves.
h
g
b
71

72 6 Key Elements of Foliar Function
Net photosynthesis
(nmol g −1 s −1 )
a
1000 1000
100
10
1000
A day
100
Specific leaf area (cm 2 /g)
10
Herbs
Pioneers
7
21
63
Leaf nitrogen (mg/g)
Broad-leaved deciduous
Leaf N or A max
Fig. 6.4 Interrelationships among daily photosynthetic capacity (A day ), maximum photosynthetic
capacity (A max ), and foliar nitrogen (N). The relationship between A day and A max is linear (dashed
line); A day increases asymptotically with foliar N dependent on evolutionarily constrained
responses to the ambient environment of the leaf. The three asymptotic curves are examples of
possible A day –N relationships, in each case with the optimal allocation of N when the tangent lines
to the curves are equivalent. When tangent lines correspond to lines originating at the origin,
nitrogen use efficiency (NUE) is optimum. Because Leaf N is proportional to A max , this relationship
can be taken as a surrogate for the A day –A max relationship reported by Zots and Winter (1996)
Net photosynthesis
(nmol g −1 s −1 )
b
100
10
1000
100
Specific leaf area (cm 2 /g)
Broad-leaved evergreen (leaf life-span > 1 year)
Needle-leaved evergreen
10 7
21
63
Leaf nitrogen (mg/g)
Fig. 6.5 Consistent relationships among three key elements of foliar function for 111 species
from six biomes (a) and for 170 species reported in the literature (b). (From Reich et al. 1997)
Photosynthetic Function and Leaf Longevity
Reich and his colleagues (Reich et al. 1991, 1992) also had been investigating the
relationship between A max and leaf longevity, as had others (Gower et al. 1993;
Yamamoto 1994). Their 1997 paper (Reich et al. 1997) documented not only the

Photosynthetic Function and Leaf Longevity
Fig. 6.6 Relationships
between leaf longevity (leaf
lifespan) and other key elements
of foliar function (Lit
data, data reported in the literature).
(From Reich et al.
1997)
1000
Lit data c
Net
Field data
r
photosynthesis
2 =0.78 b=−0.66 ± 0.03
r2 100
10
=0.75 b=−0.69 ± 0.02
r 2 =0.59 b=−0.34 ± 0.03
r 2 =0.60 b=−0.32 ± 0.02
r 2 =0.57 b=−0.46 ± 0.04
r
Leaf life-span (months)
2 =0.49 b=−0.39 ± 0.03
10
1 10100 e
f
100
10
1
1000
strong negative relationship between A max and leaf longevity but also a negative
relationship of leaf longevity with foliar N and a positive relationship with LMA
(SLA in Fig. 6.6). Longer-lived leaves consistently have more mass per unit area,
lower concentrations of foliar N, and lower photosynthetic capacity, which supports
the inclusion of leaf longevity as a cardinal trait affecting leaf function.
Leaf longevity within a single biome varies about 100-fold among species, but
the broad relationships with photosynthetic capacity, foliar N, and LMA persist
across biomes as diverse as lowland tropical rainforest in Venezuela, subtropical
lowland shore forest in South Carolina, montane cool temperate forest in North
Carolina, desert and shrubland in New Mexico, a combination of temperate forest,
bogs, and prairie in Wisconsin, and a combination of alpine tundra and subalpine
forest in Colorado (USA) (Fig. 6.7). These areas vary greatly in mean
100
(nmol g −1 s −1 ) Leaf nitrogen (mg/g) Specific leaf area (cm 2 /g)
73

74 6 Key Elements of Foliar Function
Fig. 6.7 Relationships between leaf longevity and leaf traits: differences among biomes.
Relationships between leaf longevity and nitrogen concentration, nitrogen content, specific leaf
area (SLA), photosynthetic rate per leaf weight, and leaf area are similar among diverse biomes.
The slopes are similar, but intercepts sometimes differ. (From Reich et al. 1999)
annual temperature from −3°C to 26°C and in altitude from sea level to 3,500 m.
Despite the wide variations in environmental conditions among biomes, the
slopes of these relationships between leaf longevity and other foliar traits do not
differ significantly, but the intercepts do vary (Reich et al. 1997, 1999). The difference
in intercept among biomes is the result of differences in LMA, which
becomes lower when water is in good supply. For example, comparing leaves of
similar leaf longevity, LMA is significantly lower in wet high-altitude regions of
Colorado than in arid New Mexico. Similarly, the intercept of the relationship
between leaf longevity and LMA in Australia is displaced to a lower value by
aridity, but the displacement can also involve a shift up or down along the existing
gradient (Fig. 6.8). The presence of relationships at the global scale does not
necessarily mean the same relationships will be detected in regional data sets
(Santiago and Wright 2007).

Deciding the Core Set of Cardinal Traits
log (leaf longevity)
Translocation
towards lower
precipitation
Translocation
towards
lower soil P
concentration
log (LMA)
Fig. 6.8 Scheme showing translocations of relationship between leaf longevity and leaf mass area
(LMA) by changes in precipitation and soil nutrient conditions. (From Wright et al. 2002; drawn
after Westoby et al. 2002; redrawn by KK)
Deciding the Core Set of Cardinal Traits
These emerging patterns were a stimulus to many studies that led to a much larger
database against which the generality of the relationships could be tested. Peter
Reich, Ian Wright, Mark Westoby, and many others (Wright et al. 2004) pooled data
for more than 2,500 plant species and showed definitively that A max , LMA, foliar N,
and leaf longevity were indeed integral parts of what they called the leaf economic
spectrum. Their data documented the range of values to be expected for the key traits
as well as the correlations among them: A max ranged from 5 to 660 nmol g −1 s −1 , foliar
N ranged from 0.2% to 6.4%, LMA ranged from 14 to 1,500 g m −2 , and leaf longevity
ranged from 0.9 to 288 months. They were able to compare values on a mass versus
area basis and found that the correlations among traits were strongest when expressed
on a mass basis. Shipley et al. (2006) reanalyzed the relationships among
four cardinal traits in the leaf economic spectrum that are highly intercorrelated
(A max , foliar N, LMA, and leaf longevity) and showed that a fifth trait in fact underpinned
the relationships among these four foliar traits: leaf dry matter content
(LDMC). LDMC, the ratio of leaf dry weight to fresh weight, is an index of investments
in structural versus fluid cell content. Niinemets et al. (2007a) reported a
strong correlation between LDMC and leaf longevity for 44 species in deciduous
forests in Estonia and showed that species with higher LDMC had cell walls more
resistant to deformation under turgor pressure. Compared to woody species, herbaceous
species have lower LDMC, shorter leaf longevity, and greater A max (Ellsworth
et al. 2004; Wright et al. 2004; Shipley et al. 2006; Niinemets et al. 2007b).
75

76 6 Key Elements of Foliar Function
There is no end to the number of foliar traits that might characterize essential
elements of foliar function and therefore merit inclusion in a comprehensive
analysis of the leaf economic spectrum. For example, foliar phosphorus and dark
respiration rate are likely candidates (Westoby et al. 2002; Wright et al. 2004), but
the data supporting their inclusion are fewer than are those for the four primary
traits. A subsequent analysis (Wright et al. 2005a, b) gave further support to
inclusion of foliar respiration and also suggested inclusion of photosynthetic
nitrogen use efficiency (PNUE) among the cardinal traits. In the context of theory
for leaf longevity, inclusion of respiration makes some sense as a possible index of
the ongoing costs of foliar maintenance that could augment the initial construction
cost when estimating the timing of leaf senescence. PNUE makes less sense as a
unitary cardinal trait in the context of theory for leaf longevity because it is simply
the ratio of two parameters already accounted for in the syndrome of traits central
to foliar function. In general, it behooves us to look beyond correlations to a
minimal set of traits that can be integrated in a mechanistic model of foliar
function, in the present context a model that can predict leaf longevity.

Chapter 7
Endogenous Influences on Leaf Longevity
Bud break with 1-year old, sclerophyllous leaves of an evergreen tree, Camellia japonica
The functional relationships among key traits defining leaf function do not stand in
isolation from functionality at the level of the whole plant. Hence, variation in leaf
longevity is contingent not only on variation in foliar design, but also on trade-offs
involving other aspects of plant function, which include aspects of functional organization
from the level of single shoots to the entire canopy.
Timing of Leaf Emergence and Leaf Longevity
In temperate regions where the length of the growing season sets a limit on leaf longevity,
deciduous species with indeterminate shoot growth can be expected to have
shorter-lived leaves than species with determinate shoot growth. This is the case in
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_7, © Springer 2011
77

78 7 Endogenous Influences on Leaf Longevity
temperate deciduous broad-leaved forests, where the leaf longevity of species with
determinate shoot growth such as Fagus crenata, Quercus crispula, and Carpinus
cordata was 160–180 days, whereas leaf longevity in Alnus hirsuta with indeterminate
shoot growth was 80–90 days (Kikuzawa 1983, 1988). Because there is no limitation
set by the length of the growing period in aseasonal tropical forests, the same
expectation need not apply, but in fact the leaf longevity of species with indeterminate
shoot growth still tends to be less than those with determinate shoot growth. Leaf
longevity was 1–4 months in Heliocarpus appendiculatus (Ackerly and Bazzaz 1995)
with indeterminate shoot growth. Leaf longevity of Dendrocnide excelsa, a species in
subtropical and cool temperate rainforests with indeterminate shoot growth, was
about 7 months compared to 20 months in species such as Doryphora sassafras,
Ceratopetalum apetalum, and Nothofagus moorei with determinate shoot growth
(Lowman 1992). There is clearly endogenous organization of the timing of shoot
growth and leaf turnover.
In species with indeterminate shoot growth, the birth rate of a leaf (r) is given
by the ratio of standing leaf number (N) on a shoot and leaf longevity (L) from (4.6)
(Ackerly 1996).
r = N / L
(7.1)
Designating 1/r = P, P represents the interval between emergence of leaves,
which is called the plastochron interval (Maxsymowych 1959). Using P, we can
rewrite (7.1) as
L = N· P
(7.2)
Leaf longevity thus can be estimated as the product of number of leaves and the
plastochron interval. Ackerly (1996) compared species with leaf longevity from 32
to 5,200 days and standing leaf number per shoot ranging from 3 to 45 (Fig. 7.1).
For species with indeterminate shoot growth, leaf longevity largely depends on the
rate of leaf turnover, with the oldest leaf being lost as a new leaf emerges. If the
growth rate and loss rate of leaves are equivalent, the canopy will be in steady state.
Moreover, if photosynthetic capacity is determined by the position of leaves as
expected in (4.13), then the canopy photosynthesis at any time should be equivalent
to the photosynthetic gain of a single leaf throughout its life: in other words, there
appears to be an ergodic character to the functional relationships between
the leaf and canopy levels (Kikuzawa et al. 2009). Leaf longevity in this steadystate
condition then is determined by the appearance rate of leaves, which will
reflect the shoot growth rate.
Plant Growth Rates and Leaf Longevity
A negative correlation between the relative growth rate of plants and leaf longevity
is expected when a tree canopy is in a stable state with new leaves produced at the
same rate as leaves dropping; then, leaf longevity is determined simply by the inverse

80 7 Endogenous Influences on Leaf Longevity
leaves (Coley 1983). These relationships are not considered causal in and of
themselves because tree growth is affected by very many other traits, but it is clear
there is a functional linkage between overall growth and leaf turnover. This linkage
is also apparent in the relationship between wood density and leaf longevity.
Fast-growing, early successional species on Barro Colorado Island such as Cecropia
insignis with a wood density of only 0.15 g cm −3 had shorter leaf longevity than
slower-growing, late successional species with wood densities in the range of 0.34–
0.64 g cm −3 (King 1994). Ishida et al. (2008) report the same trend for woody species
on the subtropical Bonin Islands. Chave et al. (2009) have characterized a “wood
economic spectrum” that associates increasing wood density with slower growth
rates, which suggests these relationships may prevail generally across species.
Seedling Growth and Leaf Longevity
The relationship between growth rate and leaf longevity also is expressed at the
seedling stage where the initial growth of current-year seedlings depends on seed
size. For example, seed size varies among deciduous broad-leaved trees in northern
Japan from nearly 10 g in Aesculus turbinata to less than 1 mg in Betula platyphylla
(Seiwa and Kikuzawa 1989). A large-seeded species such as A. turbinata typically
attains the large part of its annual height growth within a month of germination
(Fig. 7.3). In contrast, the height growth of a small-seeded species such as B. platyphylla
has a long lag before shoot growth takes off later in the season. The seedling
shoot growth of the large-seeded species is essentially determinate, the small-seeded
is essentially indeterminate, and the leaf longevities are correspondingly long and
short, respectively (Seiwa and Kikuzawa 1991). The leaf longevity of seedlings,
however, is shorter than that of adult trees for both large- and small-seeded species,
perhaps because the costs of transport associated with each leaf are greater in adults
than in seedlings (Kikuzawa and Ackerly 1999). There is, however, no significant
difference in leaf longevity of saplings and adult trees (Reich et al. 2004).
Fig. 7.3 Growth curves of seedlings from germination for Betula platyphylla (Bp), a smallseeded
species, and for Aesculus turbinata (At), a large-seeded species at open (open circles) and
shaded (closed circles) sites. (From Seiwa and Kikuzawa 1989, 1991; redrawn by KK)

Variation of Leaf Longevity with Timing of Leaf Emergence
Variation of Leaf Longevity with Timing of Leaf Emergence
Leaf longevity can vary among different leaf cohorts within individual plants. In Betula
species, the leaves that emerge initially in early spring and leaves that emerge successively
until summer differ in morphology (Kozlowski and Clausen 1966), photosynthetic
traits (Koike and Sakagami 1985; Koike 1990; Miyazawa and Kikuzawa 2004),
and their parent shoot morphology (long and short shoots: Yagi and Kikuzawa 1999;
Yagi 2000; Ishihara and Kikuzawa 2004). Longevity for early leaves in Betula grossa
was around 160–180 days, significantly longer than the 110–130 days for late leaves
(Miyazawa and Kikuzawa 2004). Similar structural differentiation of long and short
shoots was also observed in Halimium atriplicifolium, but leaf longevity on long shoots
of this Mediterranean subshrub was only marginally longer than on short shoots,
13.2 versus 10.6 months (Castro-Diez et al. 2005). Adenostoma fasciculatum, a shrub
of Mediterranean regions in North America, also has short shoots and long shoots
but with leaves on long shoots living only a year compared to 2 years on short shoots
(Jow et al. 1980). Leaf longevity on the Asian vine Akebia trifolia varied from less than
10 days to more than 1 year, irrespective of emergence timing (Koyama and Kikuzawa
2008). In wild strawberry, Fragaria virginiana, leaves emerging in early spring had
longevities of about 60 days compared to 130 days for those emerging in early summer
and 250 days for those emerging in fall and overwintering (Jurik and Chabot 1986).
Sydes (1984) observed similar contrasts in other herbaceous species between leaves
produced early in the growing season with longevities about 60 days compared
to 200 or even 300 days in leaves produced in fall and overwintering (Fig. 7.4).
Date of leaf-fall
Mar 1, 2005
Nov 1
Jul 1
Mar 1, 2004
Nov 1
Jul 1
Mar 1, 2003
Mar 1, 2003
May 1 Jul 1 Sep 1
Leaves on short shoots
+ Leaves on long shoots
Date of leaf emergence
Nov 1 Jan 1, 2004 Mar 1
Leaves on secondary growth shoots
Mean daily temperature < 5C
81
Leaf lifespan
365 Days
0 Days
Fig. 7.4 Date of leaf appearance, date of leaffall, and resulting longevity for individual leaves of
Akebia trifoliata (n = 1,423). The two oblique lines are isoclines for leaf lifespan of 0 and 365
days, respectively. Shading indicates period unfavorable for photosynthesis. (From Koyama and
Kikuzawa 2008)

82 7 Endogenous Influences on Leaf Longevity
Box 7.1 Self-Shading and Leaf Emergence
There is a dichotomy between plants that produce essentially all their leaves
each year in a single burst (simultaneous-type leaf emergence) and those that
produce leaves in a steady progression throughout all or part of the year
(successive-leafing type). As all potential leaves appear at once at the start of
a growing season in the simultaneous type, all the leaves of this type can carry
out photosynthesis throughout the growing season. However, if many leaves
are attached on a shoot, leaves in lower positions will be shaded by those in
upper positions (self-shading), a disadvantage that can be reduced by the orientation
of shoots and leaves (Kikuzawa et al. 1996). By this means, all the
leaves on a shoot can receive sunlight evenly and the photosynthetic performance
of the shoot will increase, although inclining the shoot will also reduce
the height growth of the plant and increase biomechanical support costs. In
contrast, successive leafing essentially is an alternative method to avoid selfshading
within the plant canopy. The first leaf produced on a growing shoot
will enjoy full sunlight until the shoot extends and the second leaf emerges
and begins to shade the first leaf, and so on as successive leaves emerge.
Consequently, there are some linkages among leaf phenology (leaf emergence
pattern), self-shading, and shoot architecture (Kikuzawa et al. 1996;
Kikuzawa2003) in deciduous broad-leaved species. Simultaneous leafing
species (Fagus crenata, Quercus crispula, Tilia japonica) have strongly
inclined shoots and avoid self-shading whereas successive leafing species
(Alnus hirsuta, A. sieboldiana, Betula platyphylla) have upright shoots
(Kikuzawa et al. 1996). Similar linkages between leaf phenology and architecture
exist in herbaceous species as well (Kikuzawa 2003).
Canopy Architecture and Leaf Longevity
Intrinsic controls on the development of canopy architecture determine the degree
of mutual shading among different branches and leaves within a canopy and hence
influence the longevity of leaves throughout the canopy. If shoot elongation is
rapid and leaf turnover on the elongating shoot high, the inner canopy of the tree
tends to become leafless as the outer canopy expands. The inner canopy of Alnus
sieboldiana, a species that elongates upright apical shoots with short leaf longevity,
illustrates this canopy-hollowing phenomenon (Shirakawa and Kikuzawa 2009).
Crown hollowing incurs an increasing cost in maintaining interior branches to
support the leafy shoots in the expanding outer canopy, perhaps explaining why
crown hollowing occurs mostly in species that never attain heights sufficient to
occupy the upper strata of mature forests. In some early successional trees canopy
hollowing is diminished by production of dimorphic shoots, long shoots that expand

Canopy Architecture and Leaf Longevity
the canopy periphery and short shoots that produce leaves along interior branches
without elongating internodes. Long shoots function in both space acquisition and
leaf display, but short shoots only play a role of leaf display. Short shoots can persist
over many years along interior branches, producing only a few relatively longlived
leaves and thus reducing canopy hollowing in species of Betula and Populus
(Critchfield 1960; Pollard 1970; Isebrands and Nelson 1982) and some Acer species
as well (Critchfield 1971; Sakai 1987). Such differentiation of leaf display and
space acquisition through variation in shoot structure and leaf longevity is a general
phenomenon, with the short shoot–long shoot dichotomy only a particular case of
a broader range of structural variation in shoots (Takenaka 1997; Yagi and
Kikuzawa 1999). For example, shifts in the relationships between bud dormancy,
needle longevity, and total needle area per unit shoot length in some evergreen
trees alter the balance between leaf display and space acquisition in canopy development
and reduce canopy hollowing (Takenaka 1997). In some evergreen broadleaved
tree species such as Cleyera japonica, leaves at the inner canopy have
prolonged longevity, or burst bud only after some years of dormancy, thus avoiding
canopy hollowing (Suzuki 2002).
The balance between leaf display and space acquisition in canopy development
is inextricably linked to leaf longevity through the feedback to leaf lifetime carbon
gain. Maximizing the capture of light energy is not simply a question of growing
taller to shade competing neighbors, but also a question of how effectively a plant
captures light from the part of the overall plant canopy surface that it occupies.
There is a trade-off between growing taller to shade neighbors and spreading
laterally to claim more surface area in the upper canopy of the plant stand. For
example, a tree maximizing only height growth could simply extend its apical
shoots straight and upright, but many canopy tree species in mature temperate
deciduous forests such as Fagus, Quercus, or Acer in fact have determinate shoot
growth and apical shoots declined toward the horizontal. These trees avoid selfshading
among leaves within the canopy by branch and shoot angles that allow
light penetration to deeper layers of the canopy (Posada et al. 2009). On the
other hand, successional tree species with indeterminate shoot growth such as
Alnus or Betula elongate their apical shoots strongly upward, growing tall more
quickly but with a higher degree of self-shading in their canopy (Kikuzawa et al.
1996). Such successive leafers can attain higher photosynthetic rates by receiving
full sunlight at the time of first leaf appearance. When the first leaf’s photosynthetic
rate declines with aging, a second leaf appears and again receives full
sunlight at the shoot apex but also shades the preceding leaf on the shoot and so
forth. Thus, successive leafing, high but early decline of photosynthetic rate, and
short leaf longevity are functionally linked with one another. In contrast, leaves
appearing simultaneously on a determinate shoot mutually shade one another
from the initial stage of leaf appearance, and thus plants avoid self-shading by
more horizontal placement of shoots, branching angles, leaf angles, and the like.
Simultaneous leafing, lower but persistent photosynthetic rates, relatively long
leaf longevity, and a more horizontally oriented canopy structure are also parts of
83

84 7 Endogenous Influences on Leaf Longevity
a functional syndrome (Kikuzawa 1995a, b). Similarly contrasting morphological
and phenological characteristics related to light interception are found in the
essentially horizontal leaves of herbaceous forb species (Kikuzawa 2003),
although not in unbranched graminoid species that typically orient their leaves
near vertical in turf or cespitose clumps.
Box 7.2 Impact of Deep Versus Partial Shading
The way that individual leaves react to shading depends on the light regime in
which the entire plant exists. If the entire plant is subjected to low insolation,
as in forest understory species, then leaf longevity is relatively long and leaves
lower in the canopy do not translocate resources to less-shaded leaves higher
in the canopy. Conversely, leaves on trees in the forest canopy exist in a broad
range of insolation regimes from well lighted in the upper canopy to progressively
more and more partially shaded deeper in the canopy. In this case, leaf
longevity is shortened in proportion to shading, and resources are translocated
to the upper, brighter portion of the canopy. These responses reflect a balance
between optimization of resource gain and loss at the leaf level versus the
whole-plant level.
Canopy Heterogeneity and Leaf Longevity
The insolation regimes of leaves set by intrinsic controls on canopy architecture in
a uniform and stable light regime can be disrupted by external influences that create
asymmetry such as adjacent objects, forest edges, or gaps. In such instances, variation
in leaf longevity within individual plants does not appear to follow the general
pattern seen between individuals and species but is actually reversed: leaf longevity
on shaded shoots is shortened compared to sunlit shoots. For example, Miyaji and
Tagawa (1973) reported that shaded leaves in the lower canopy of a Tilia japonica

Canopy Heterogeneity and Leaf Longevity
sapling were shed earlier than sunlit leaves in the upper canopy. Takenaka (2000)
observed individual Cinnamomum japonicum growing at more than 10%, 5% to
10%, and less than 5% full sunlight in the understory of evergreen broad-leaved
forest. Each tree had some shoots in each of the three insolation classes. Takenaka
(2000) compared leaf longevity on shoots in more-shaded positions of better insolated
individuals, and vice versa. He found that the better insolated were individuals,
the stronger was the contrast in shoot growth and leaf turnover between their
well- and poorly insolated shoots. Leaves on poorly insolated shoots were shed
more rapidly than on more-sunlit shoots. This situation in which faster-growing
shoots inhibit slower-growing ones is a form of apical control referred to as correlative
inhibition (Cline 1997; Umeki and Seino 2003). If this more rapid shedding of
shaded leaves within individual plants is simply the direct consequence of the shading
rather than apical control (Cline 1997), there should be a correlation between
leaf longevity and plant size in a dense plant population. That is not the case. There
is no significant correlation between mean leaf longevity and individual plant size
and hence shading in dense plantings of Xanthium canadense; mean leaf longevity
ranged from 20 to 50 days irrespective of plant size (Hikosaka and Hirose 2001).
In summary, individual plants shorten leaf longevity on poorly insolated shoots
when only part of the plant is shaded, but not when the entire plant is shaded.
There is evidence, however, that in more mature trees the relationship between
leaf longevity and insolation reverts to the norm. Mizobuchi (1989) reported that in
large, open-grown Cinnamomum camphora growing on a university campus in
central Japan, leaves on the better insolated southern side of the canopy had a halflife
of about 1 year compared to almost 2 years on the north side. Osada et al. (2001)
studied leaf longevity over more than 3 years at different heights in Dipterocarpus
sublamellatus, Elateriospermum tapos, and Xanthophyllum stipitatum – trees all
more than 30 m tall growing in a mature tropical rainforest. They found that leaf
longevity consistently is shortest in the sunlit upper canopy of individual trees.
Similar results were obtained for 15 tree species in a tropical forest that differ in
maximum height (Meinzer 2003), suggesting that tree maturity rather than just tree
height determines the pattern of leaf longevity with the tree canopy. Miyaji et al.
(1997) studied leaf longevity in 3-m-tall cacao trees (Theobroma cacao) growing
under shelter trees in a tropical plantation. Leaf longevity changed depending on the
timing of leaf emergence and level in the canopy (Fig. 7.5). Longevity of upper
leaves ranged from 120 to 200 days, the middle layer from 180 to 250 days, and the
lower layer from 280 to 370 days; leaf longevity of bearing-age cacao trees was
longer in the more-shaded, lower canopy. There may be a size-dependent shift in the
degree of branch autonomy such that in the transition from saplings to trees a greater
degree of branch autonomy ensues as apical control shifts from the sapling apex to
individual branches in the tree crown. In this vein, we can rephrase our overall summary
of the relationship between insolation and leaf longevity. When the autonomous
unit organizing shoot growth is wholly shaded (an individual plant or major
branch), then leaf longevity becomes longer; conversely, when a shoot is only a
poorly insolated part of a larger autonomous unit, then its leaf longevity is shortened
relative to the sunlit part of the autonomous system controlling shoot growth.
85

86 7 Endogenous Influences on Leaf Longevity
Apparent no. of living leaves on 100 branches
800
600
400
200
0
600
400
200
0
600
400
200
0
UL
ML
LL
Jul. Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June Jul. Aug.Sept. Oct. Nov. Dec.
1983 1984
Fig. 7.5 Leaf survivorship curves in the upper (UL), middle (ML), and lower (LL) layers of the
canopy in 7-year-old Theobroma cacao in a Brazilian plantation under a canopy of shelter trees.
Different symbols represent cohorts of leaves emerging at different times. All leaves in the upper
layer had fallen by November 1984, while some leaves still remained in the middle and lower
layers. (From Miyaji et al. 1997)

Chapter 8
Exogenous Influences on Leaf Longevity
Early spring ice storm, Ithaca, New York
The normal value of leaf longevity for a species reflects functional relationships at
the foliar and whole-plant level, but longevity can be both prolonged and shortened
by environmental conditions. From first principles, leaf longevity is expected to
increase in environments where critical resources are scarce. This generalization is
rooted in a cost–benefit analysis of leaf longevity arguing that the nature of leaves
in resource-limited environments imposes a long payback period on the cost of
their construction (Chabot and Hicks 1982; Kikuzawa 1991). In this view, selection
pressure is expected to act to prolong leaf longevity in light-, water-, or nutrientlimited
environments. This expectation is consistent with observations among species
and plants in differing resource environments, but not within individual plants.
The expectation applies to conditions of resource limitation, not stress conditions
that near or exceed the limits to a species’ survival and reproduction. Stress events
such as deep drought, unseasonal frost and freezing, lengthy flooding, salinity, air
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_8, © Springer 2011
87

88 8 Exogenous Influences on Leaf Longevity
pollutants, and attack by herbivores or pathogens each impose qualitatively different
challenges to leaf function (Kozlowski and Pallardy 2002), which we also address
in this chapter.
Box 8.1 Succession
Vegetation is inherently dynamic: plants grow and interact with one another
while responding to changing environmental conditions. Occasionally these
dynamics in a plant community are punctuated by more disruptive events that
destroy some part of the plant community. Succession refers to the sequence
in which plants colonize and develop in an area after such a disturbance. A
successional sequence can be initiated by disturbances at large spatial scales
such as volcanic eruption, windstorms, fire, flooding, and landslides or at
small spatial scales by simply the death of a single tree. In the case of a big
volcanic eruption such as that of Krakatau in 1883, all the vegetation on this
isolated oceanic island was killed by a thick layer of ash and the succession
began on barren land. Even in this extreme case plants and animals dispersed
to the island within several decades, and more than 200 species were recorded
on Krakatau only 50 years after the eruption. Succession typically is initiated
less dramatically and involves colonization from nearby undisturbed areas.
Because stochastic factors play a large role in dispersal and colonization, we
cannot forecast precisely the course of succession, but we can recognize species
that during early versus late stages of succession have characteristic suites
of features. Early successional plant species produce abundant small seeds,
have a high growth rate with low stem density, high maximum photosynthetic
rates, and short leaf longevity. Late successional plant species produce fewer
but large seeds, have low growth rates with high stem density, low maximum
photosynthetic rates, and long leaf longevity.
Insolation and Leaf Longevity
Diverse lines of evidence among and within species support the generalization
that leaf longevity is relatively short in sunny compared to shaded environments.
Early successional species are widely observed to have shorter leaf longevity
than late successional species (Kikuzawa 1978, 1982, 1983, 1988; Koike 1988),
which is consistent with the greater insolation typical of sites after disturbance.
Similarly, in the understory of both tropical forests (Reich et al. 1991, 2004) and
mature temperate forests (Kikuzawa 1984, 1988, 1989; Lei and Koike 1998),
species typically have long-lived leaves, some surviving more than a single season.
If a species occurs in both sun and shade, leaf longevity is long in the
shaded environment (Kikuzawa 1989; Sterck 1999; Reich et al. 2004).
For example, in a Southeast Asian tropical forest, leaf survivorship of the

Insolation and Leaf Longevity
Leaf survival ratio
1
0.8
0.6
Gap
Understory
0.4
0
0 5 10 15 20 25 30 35 40
Leaf age (months)
Fig. 8.1 Survivorship of Elateriospermum tapos (Euphorbiaceae) leaves on the forest floor and
in canopy gaps. (From Osada et al. 2003)
shade-tolerant tree Elateriospermum tapos was greater in the understory than in
canopy gaps (Osada et al. 2003; Fig. 8.1). Kai et al. (1991) reported similar
observations for the semideciduous shrub Ligustrum obtusifolium and then
experimentally confirmed the role of insolation in affecting leaf longevity. They
subjected cloned plants growing in a nursery to 7%, 20%, and 100% full sun; in
100% sunlight, almost all leaves were shed before mid-December, whereas in
the shaded plots some leaves remained until the next autumn. The evergreen
shrub Daphniphyllum macropodum normally retains leaves 4–5 years in the
understory of deciduous broad-leaved forests but only 2 years in canopy gaps; a
similar trend is observed in the low-growing evergreen Pachysandra terminalis
(Kikuzawa 1989). Finally, leaf survivorship in the evergreen shrub Rhododendron
maximum decreased for plants growing in the understory of more-open forests
from 5 years under an evergreen canopy, to 4 years under a deciduous canopy,
and only 3 years in canopy gaps (Nilsen 1986; Fig. 8.2). Because canopy gaps
arise suddenly, existing leaves on understory species can be subjected abruptly
to substantially greater insolation; understory species with fairly long-lived
leaves should be more tolerant of high insolation after gap formation than those
with relatively short-lived leaves (Lovelock et al. 1998). Lovelock et al. tested
this expectation by assessing the degree of photoinhibition in 12 tree species
from tropical rainforest, finding that species with long-lived leaves (more than
3.5 years) were more tolerant of abrupt increases in light than species with shortlived
leaves (less than 2 years).
89

90 8 Exogenous Influences on Leaf Longevity
Fig. 8.2 Leaf survivorship
curves for Rhododendron
maximum in different light
regimes: O canopy gap, D
deciduous broad-leaved forest
floor, E evergreen forest floor.
(From Nilsen 1986)
Relative Leaf Number
Aridity and Leaf Longevity
100
90
80
70
60
50
40
30
20
10
0
On the assumption that leaf longevity is governed by the time required to pay back
the costs of leaf construction, we can generally expect sublethal levels of water
shortage to be associated with longer-lived leaves. There is a variety of experimental
and observational evidence supporting this point of view at the level of individual
species, but interspecific comparisons of the relationships between water
availability and leaf longevity are not straightforward.
The contrast between the deciduous and the evergreen habit illustrates the ambiguities
of the relationship between water availability and leaf longevity. The vegetation
of regions prone to water shortage can include both drought-deciduous species
that drop their leaves at the onset of a dry season and evergreen species that retain
leaves through the dry season. Drought-deciduous plants usually have higher maximum
photosynthetic rates than evergreen plants (Comstock and Ehleringer 1986;
Ackerly 2004), which is consistent with the general relationship between leaf
longevity and photosynthetic rate. On the other hand, the co-occurrence of species
with different foliar habits indicates that leaf longevity is only part of a larger syndrome
of adaptive alternatives to water shortage. A study comparing species with
short-lived versus long-lived leaves in the understory of a seasonal tropical forest
in Panama illustrates this point (Tobin et al. 1999). Species with long-lived leaves
had deeper root systems than species with short-lived leaves and thus could avoid
drought conditions during the dry season. We cannot expect a simple pattern of leaf
longevity in relationship to water stress across species, but if the cost–benefit
perspective on leaf longevity (Chabot and Hicks 1982; Kikuzawa 1991) is valid,
then it must apply within species.
1
O
D
Rhododendron
maximum
(1983)
2 3 4 5 6 7 8 9
YEARS
E
10

Aridity and Leaf Longevity
There is good intraspecific evidence for prolonged leaf longevity in response
to aridity. For example, Encelia farinosa is a drought-tolerant shrub distributed
along a precipitation gradient in Arizona and California. Its leaves become more
tomentose under drier conditions, decreasing rates of transpiration but also
increasing the cost of leaf construction as well as reducing photosynthetic capacity.
Hence, the payback period on leaf construction is extended and leaves survive
longer in the drier regions (Sandquist and Ehleringer 1998). Similar results were
found when the effect of drought on leaf longevity was investigated experimentally
in Cryptantha flava, a desert shrub in Utah (Casper et al. 2001). Leaf
longevity was compared between plants receiving half versus all natural precipitation.
Stomatal conductance and photosynthetic rates were lower in the plants
receiving less precipitation, and as expected leaf longevity became longer:
leaves present at the initial census persisted 49.2 days in the dry plot versus 22.6
days in the control (Fig. 8.3). Similar trends occur in the dioecious shrub
Pistacia lentiscus in southern Spain where precipitation ranges from 350 to
1,000 mm year −1 in a Mediterranean climate regime (Jonasson et al. 1997). Leaf
longevity in male plants of P. lentiscus was shorter under more-arid conditions;
the relationship in female plants was the same, but it was not statistically significant
because of confounding effects from variation in fruit production (Jonasson
et al. 1997). A severe drought extended leaf longevity in five species of deciduous
trees in a Swiss forest, mostly because of later leaffall (Leuzinger et al.
2005). In general, we can expect drought to extend leaf longevity within species
but not always among species.
Number of leaves
12
10
8
6
4
2
0 140 145
Julian date
Drought
Control
150 155 160 165 170 175
Fig. 8.3 Effect of drought treatment on leaf longevity in the desert plant Cryptantha flava. Leaf
longevity was prolonged by drought. (From Casper et al. 2001)
91

92 8 Exogenous Influences on Leaf Longevity
Nutrients and Leaf Longevity
The decrease in leaf longevity with higher levels of foliar nitrogen content is a
well-established interspecific relationship (Field and Mooney 1986; Field
1991; Reich et al. 1991, 1992, 1994; Wright et al. 2004; Poorter and Bongers
2006), but this negative relationship may or may not apply within species or
among species at a site. Observational and experimental evidence for the effect
of fertility on leaf longevity in general shows that for a given species leaf longevity
will be shorter at more fertile sites. For example, leaf longevities of
Picea abies, P. jezoensis, and P. glehnii were longer on nutrient-poor serpentine
soil compared to more fertile brown forest soil (Kayama et al. 2002).
Fertilization of the prostrate tundra evergreen shrub Ledum palustre var. decumbens
increased leaf turnover (Shaver 1981). Fertilization of Pseudotsuga menziesii
var. glauca and Abies grandis, coniferous trees of the Pacific Northwest
in North America, reduced leaf longevity by about one-fourth (Balster and
Marshall 2000). In the Hawaiian tree Metrosideros polymorpha, leaf longevity
varies between 2 and 5 years and is longer on more infertile sites; fertilization
decreases longevity on fertile sites but not at the infertile sites where longevity
is already long (Herbert and Fownes 1999; Cordell et al. 2001). In this tree species
longevity decreased as leaf nitrogen content increased across sites (Herbert
and Fownes 1999). In Larrea tridentata, an evergreen desert shrub, fertilization
shortened leaf longevity, and the effect was enhanced by irrigation (Lajtha and
Whitford 1989; Fig. 8.4). A 100-fold increase in nutrient availability decreased
leaf longevity of the perennial floating-leaved aquatic plant, Hydrocharis
morus-ranae var. asiatica, from 15–20 to 10–15 days (Tsuchiya 1989); lower
levels of fertilization did not significantly alter leaf longevity (Tsuchiya 1989;
Tsuchiya and Iwakuma 1993).
Box 8.2 Density Dependence
A density dependence in population regulation occurs whenever differences
in either birth rate or death rate result in lowering of the population
growth rate as the density of the population increases. If the density dependence
is driven by changes in the death rate of individuals, we speak of
density-dependent mortality factors. In general a population is considered
to be regulated at some equilibrium density by density-dependent factors
such as the reduction of birth rate resulting from short supply of food,
increase in death rate from overcrowding, and similar regulatory responses.
Without some sort of density-dependent factors, population numbers could
not be regulated.

Nutrients and Leaf Longevity
Box 8.3 Growth Rate Hypothesis
Short, cool growing seasons (f) are a disadvantage for plant growth. To overcome
and compensate for this disadvantage, the growth rate hypothesis
(GRH) predicts that natural selection will favor rapid growth in response to
increases in tissue nutrient concentration, especially phosphorus (P) because
of its critical role in the P-rich ribosomes required for protein synthesis (Elser
et al. 2000; Kerkhoff et al. 2005).
% Leaves Remaining
100
75
50
25
100
75
50
25
100
75
50
25
unfertilized
fertilized
Unwatered
6 mm/wk
25 mm/mo
M J J A S O N D J F M A M J J A S O
Fig. 8.4 Joint effects of fertilization and irrigation on desert evergreen plants. Open symbols,
leaves emerging in spring; closed symbols, leaves emerging in autumn; wk weeks, mo months.
(From Lajtha and Whitford 1989)
93

94 8 Exogenous Influences on Leaf Longevity
Effects of Environmental Stress on Leaf Longevity
The form and function of each species comprise an evolved functional design suited
to a particular range of environmental conditions. In an environment within the
limits of their evolved capacity, plant species generally can respond effectively to
resource limitations, including through adjustments in leaf longevity of the category
discussed earlier in this chapter. Environmental stress arises when conditions
fall near or beyond the limits of a functional design, near the point where function
can no longer be sustained. In terms of foliar function and questions of impact on
leaf longevity, a stress might arise from any biotic or abiotic factor that incapacitates
a leaf to the point where its production potential no longer will yield a net
return on the resources invested in constructing and maintaining the leaf. In this
context, expectations rooted in a cost–benefit analysis of foliar function often must
be founded on analysis of carbon investments and gain at the whole-plant level, not
just single leaves in isolation. We illustrate this perspective with a brief discussion
of some important biotic and abiotic stressors.
Biotic Stressors: Herbivory and Disease
An herbivorous caterpillar, Actias selene gnoma
The original framework of Chabot and Hicks (1982) for cost–benefit analyses of
foliar function included a term for leaf loss caused by herbivory or disease, but

Biotic Stressors: Herbivory and Disease
incorporating these effects into a comprehensive model of leaf longevity is not
straightforward. First, there are two elements to foliar defense against herbivores
and disease: constitutive and induced defenses (Karban and Baldwin 1997).
Chabot and Hicks (1982), as well as subsequent cost–benefit models for leaf
longevity in this vein (Kikuzawa 1991, 1995a,b; Kikuzawa and Ackerly 1999),
have assessed a constitutive cost at the time of leaf construction, which cannot
account for the cost of induced defensive responses to herbivore or pathogen
attack. Second, the efficacy of an induced plant response is highly contingent
on the ecology of the interaction between plant and attacker. For example, early
abscission of gall-infested leaves can act as the density-dependent mortality factor
for the gall-forming insects (Sunose and Yukawa 1979; Yukawa and Tsuda
1986), thus reducing the risk of attack for uninfested or future leaves. This sort
of selective shortening of leaf longevity is illustrated by the response of Populus
attacked by the gall-forming aphid (Pemphigus betae); nearly 90% of freshly
fallen green leaves were gall infested, compared to less than 10% of the leaves
still attached to the trees (Williams and Whitham 1986). On the other hand,
infection of Populus by a rust fungus such as Melampsora medusae can result
in anything from complete to only slight leaf loss (Newcombe and Chastagner
1993). Third, accounting the marginal value of a leaf at the time of attack
requires assessing the return on initial investments to that point in time, the
potential future return from the leaf in light of the cost and potential efficacy of
any induced defenses, and integrating these costs and benefits at the wholeplant
level. An effective model for the response of leaf longevity to herbivore
or pathogen attack thus must scale up from the leaf to whole-plant level to
address the underlying question of tolerance versus defense (Nunez-Farfan
et al. 2007) as strategies for plant response to herbivory and disease.
Box 8.4 Mangroves
Many tree species in five different plant families have evolved the capacity to
grow in intertidal swamps along the ocean shoreline in tropical and subtropical
regions. These trees, which are commonly referred to as mangroves, have
converged to distinctive morphological and physiological adaptations to survive
the stress associated with the twice-daily tidal alternation of saltwater
versus freshwater around their roots. Mangroves are usually evergreen
because their leaves are important for maintaining the metabolic and physical
processes involved in salt exclusion and maintenance of stable tissue water
potentials. Although mangroves have the evergreen leaf habit, the longevities
of their individual leaves in fact are not very long, usually only 6–12 months
(Gill and Tomlinson 1971), or sometimes up to 24 months (Tomlinson
1986).
95

96 8 Exogenous Influences on Leaf Longevity
Abiotic Stressors: Ozone and Natural Oxidants
Pollutants arising from anthropogenic sources fall outside the realm of specific
adaptive responses but nonetheless can elicit generalized stress response mechanisms
that have an evolutionary basis. Ozone provides a good example of this sort
of preadaptation. Foliar responses to tropospheric ozone from anthropogenic
sources are essentially the same as responses to UV radiation, drought, high temperatures,
or other natural sources of oxidative stress (Bussotti 2008). In general,
longer-lived leaves are more resistant to all oxidative stress whether natural or
anthropogenic in origin. In terms of leaf longevity, the impact of ozone-induced
oxidative stress, or probably most other anthropogenic pollutants as well, depends
on a dose–response relationship. At lower doses in which tissue-level repair mechanisms
confer sufficient resilience to maintain photosynthetic functions, leaf longevity
should be extended to recover the initial leaf construction costs as well as the
subsequent repair costs associated with the stress. At some higher dose, however,
we can expect the leaf to be abandoned and recovery of investments shifted to
shorter-lived leaves with higher production potential. Bussotti (2008) lends support
to these suppositions, which invite further study.
Abiotic Stressors: Salinity
The impact of salinity on leaf longevity has this same sort of dose–response dependency,
at least so long as the species have at least some degree of salinity tolerance
and do not simply die on exposure to saline conditions. Mangroves, a plant functional
group tolerant of levels of salinity in their tidewater habitats that would be
fatal for most plants, illustrate the interspecific variation and dose–response dependence
in salinity effects on leaf longevity. Leaf longevities increase from only 0.36
years for Sonneratia alba and 0.65 years for Avicennia alba at the seaside on up to
2.66 years for Xylocarpus granatum at the upper edge of a mangrove swamp in
Thailand (Imai et al. 2009). The leaf half-life of Avicennia germinans is 160 days
in a Venezuelan mangrove swamp (Suarez 2003), but under experimental conditions
in the absence of salt the half-life rises to 425 days, dropping to 195 days at
170 mol m −3 NaCl and to 75 days at 940 mol m −3 NaCl (Suarez and Medina 2005).
Although Avicennia tolerates salt, it is clear that increasing salinity decreases leaf
longevity. From a simplistic cost–benefit analysis, one might expect instead that
increasing salinity would impair photosynthetic activity and extend leaf longevity
to pay back the costs of leaf construction. This apparent contradiction is resolved
at the whole-plant level because the leaves of Avicennia function not only in photosynthesis
but also in salt secretion (Suarez 2003; Suarez and Medina 2005).
Because the metabolic costs of salt excretion increase with leaf age and salinity,
there is a point at which carbon gains at the whole-plant level are better served by
shortening leaf longevity to take full advantage of the high foliar photosynthetic

Abiotic Stressors: Flooding
potential of young leaves before they are offset by increasing costs associated with
salt excretion. In this instance, the shift from leaf to whole-plant level in resolving
the stress arises not in limits to tissue repair but in competing foliar functions.
Abiotic Stressors: Flooding
Flooding can impair leaf function in terrestrial plants through two effects: submersion,
which cuts off access to atmospheric CO 2 , and anaerobic conditions in the root
zone that impair root function and reduce the transpirational stream to emergent
leaves (Mommer et al. 2006; Parolin 2009). Depending on their degree of flood
tolerance, species differ in the impact of flooding on leaf longevity (Terazawa and
Kikuzawa 1994). Alnus japonica, a flood-tolerant riparian species, responds to
flooding by developing adventitious roots near the surface and lenticels on the stem
for air exchange; leaf longevity is prolonged under relatively short or shallow flooding
conditions but shortened by deeper or long flooding. The response of leaf longevity
is reversed in the upland, flood-intolerant Betula platyphylla var. japonica
(Terazawa and Kikuzawa 1994). Similarly, in herbaceous species from wetlands,
submergence in water through which light can penetrate prolongs leaf longevity,
but in species from terrestrial habitats leaf longevity is shortened by submergence
(Mommer et al. 2006). Most trees species submerged by the muddy floodwaters of
the Amazon River immediately lose all their leaves, but others retain leaves
throughout floods that can persist up to 9 months of the year; in some cases the
retained leaves may actually carry on photosynthesis during submergence and in
others only resume aerial photosynthesis as the flood recedes (Parolin 2009). For
most of these Amazonian trees, flooding is an unfavorable period for photosynthesis,
more akin to winter or prolonged drought than to an abiotic stress in which a
dose–response relationship determines shifts in leaf longevity.
97

Chapter 9
Biogeography of Leaf Longevity
and Foliar Habit
Tropical montane forest on Mt. Kinabalu, Borneo
There is, apparently, no general restriction on variation in leaf longevity per se
along local and regional spatial gradients. Leaf longevity is only part of a suite of
foliar traits that act in concert to ensure effective photosynthetic function in a given
environmental regime (Wright et al. 2004; Shipley et al. 2006). Coordinated quantitative
variation among the set of foliar traits can underpin equivalently effective
photosynthetic function despite considerable variation in leaf longevity (Marks and
Lechowicz 2006). As a consequence, leaf longevity typically varies substantially
among species even in a single locality, a point made forcefully in earlier chapters
but worth reinforcing here with another example. A careful study of 100 species
representing four growth forms in the understory of a tropical montane forest
(Fig. 9.1) shows the high variability in leaf survivorship curves among co-occurring
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_9, © Springer 2011
99

100 9 Biogeography of Leaf Longevityand Foliar Habit
Probability of leaf survival
a
1.0
0.8
0.6
0.4
0.2
0.0
Trees
c
1.0
Climbers
d
0.8
0.6
0.4
0.2
species; survivorship, in turn, is consistently correlated with elements in the leaf
economic spectrum (Wright et al. 2004) as well as with aspects of foliar defense
such as condensed tannin content and leaf toughness (Shiodera et al. 2008). Ideally,
we might look for geographic pattern in the mean values of leaf longevity but the
comprehensive, community-based samples required to do so are too scarce. We turn
instead to the biogeography of foliar habit, which is rooted in leaf demography and
commands not only the long-standing interest of plant geographers but also the
attention of contemporary climate modelers.
Biogeography of Foliar Habit
b Herbs
Goniothalamus macrophyllus Diplazium cordifolium
Cyathea contaminans Alocasia macrorrhiza
Psychotria sp.
Piper arcuatum
Epiphytes
Months after leaf flushing
Asplenium cuneatum
Elaphoglossum sp.
0.0
0 5 10 15 20 25 30 0 5 10 15 20 25 30
Fig. 9.1 Leaf survivorship for 100 understory species co-occurring in a tropical montane forest
in Indonesia. Note that by exception the Alocasia plants that were censused grew along a riverside
opening less shaded than the other species. (a) Woody plants. (b) Herbaceous plants. (c) Climbing
plants and (d) Epipytic plants (From Shiodera et al. 2008)
As a prelude to this discussion, we should note that the geography of ecosystems
dominated by evergreen versus deciduous species has not been stable throughout
Earth’s history. There are long-term influences on the patterns we see today that
are set by both the evolution of the global environment and the phylogenetic
history of contemporary plants. Both long-term changes in the global environment
and the evolutionary diversification of the global flora have led to a temporally
shifting mosaic in global land cover over Earth’s history. Although there

Contemporary Distribution of Deciduous and Evergreen Habits
have long been evergreen broadleaf forests in tropical regions, the extensive
needle-leaved boreal forests that influenced the perspectives of nineteenth-century
phytogeographers did not exist until recently (Taggart and Cross 2009). During
most of the more than 400 million years since terrestrial plants first evolved in
the Silurian, the planet has been in a “greenhouse” mode characterized by
relatively warm climates worldwide that were only occasionally interrupted by
episodes of cooling and glaciation (Tabor and Poulsen 2008; DiMichele et al.
2009). During this time the deciduous habit arose, most likely as an adaptation to
seasonally dark and fire-prone polar environments (Brentnall et al. 2005; Royer
et al. 2003, 2005). By the Eocene, when the flora had close affinity with that of
today, the distribution of evergreen and deciduous vegetation differed substantially
from the present day. Broadleaf evergreen forests extended from equatorial
regions to latitudes as high as 60°, and deciduous conifer forests were found in
polar regions (Brentnall et al. 2005; Utescher and Mosbrugger 2007). This long
period of “greenhouse” conditions is in contrast to the “icehouse” conditions that
began and have persisted since the transition from the Eocene to Oligocene about
34 million years ago (MYA) when the planet became cooler and more subject to
cyclic glaciation than it had been during the late Paleozoic and earlier Cenozoic
(Coxall and Pearson 2007; Zachos et al. 2001). Although the contemporary phytogeography
of vegetation types (Melillo et al. 1993) has arisen in these “icehouse”
conditions, on uniformitarian principles a well-grounded theory of foliar
habit should predict both contemporary and paleo-distributions of evergreen and
deciduous habits.
Contemporary Distribution of Deciduous
and Evergreen Habits
Chabot and Hicks (1982) posed the question: What can account for the bimodal
distribution of evergreen forests along latitudinal gradients (Fig. 9.2), ecosystems
dominated by broadleaf evergreen species at low latitudes and needle-leaf evergreen
species at high latitudes (Melillo et al. 1993)? This query may, in fact, not be
the best question around which to develop a theory of foliar habit. The potential
problem is that despite the conceptual frameworks imposed by those interested in
classifying, modeling, and mapping the broad patterns of global vegetation, plant
communities only rarely, if at all, are composed entirely of evergreen or deciduous
species. The norm is co-occurrence of species with these contrasting foliar habits
within and among plant growth forms and vegetation strata, albeit in varying
proportions. For example, the low-growing woody species of the tundra are mostly
evergreen, but there are also many deciduous species of Salix. Boreal forests are
dominated by evergreen conifers, but deciduous species of Populus and Betula are
frequent on successional sites, Salix species widespread, and Fraxinus, Alnus, and
Ulmus not uncommon trees in rich, moist sites. There is no shortage of deciduous
herbs and shrubs in the boreal forest understory. The transition from boreal forests
101

102 9 Biogeography of Leaf Longevityand Foliar Habit
Fig. 9.2 Global distribution of evergreen trees. (From Woodward et al. 2004)
south to temperate broad-leaved deciduous forests transits a “mixed-wood” zone
with evergreen and deciduous trees in more or less equal proportions. South of the
deciduous broad-leaved forests, broad-leaved forests of evergreen oaks predominate,
and further south give way to evergreen as well as deciduous forests in the subtropics
and tropics. Mesic tropical forests are basically evergreen, but in seasonally
dry regions tropical deciduous forests predominate. These and many other examples
spanning widely divergent spatial scales illustrate a pattern of evergreen predominance
at both ends of the latitudinal gradient in contemporary vegetation that,
although not inviolate, is common enough to demand general explanation. We
clearly need a theory addressing the fundamental basis for spatiotemporal variation
in foliar habit.
Theory for the Geography of Foliar Habit
Our premise is that a theoretical analysis of the geographic distribution of the evergreen
and deciduous habits should be based on a theory of leaf longevity. Evergreen
and deciduous habits are defined at the canopy level but set by the temporal
dynamics of leaf turnover and leaf longevity. Previous theories with their intellectual
heritage in the canopy-level perspectives of Monsi and Saeki (1953)
have not really tried to predict conditions favoring evergreen versus deciduous species.
Because this issue was an explicit motivation for the seminal review by
Chabot and Hicks (1982), at least some of the existing theory for leaf longevity

Theory for the Geography of Foliar Habit
(Kikuzawa 1991, 1995a,b, 1996) has offered predictions about the factors determining
foliar habit. The analysis by Kikuzawa (1991) recognizes the existence of sustained
periods in the annual cycle that can be unfavorable for photosynthetic activity,
and that hence would appear to compromise the raison d’etre for maintaining
leaves in these unfavorable seasons. These unfavorable periods may be set, for
example, by extreme cold, as in the winter of the temperate zone, or by droughts,
as in the aseasonal tropics. To address the existence of the deciduous versus evergreen
habits, Kikuzawa (1991, 1995a, 1996) adapted the basic theory shown by
(4.3) and Fig. 4.2 to seasonal environments. Photosynthesis during the favorable
period simply follows (4.3). If plants retain leaves during an unfavorable period, the
leaves do not yield photosynthetic gains and in fact incur maintenance costs (respiration)
during this period. Hence, (4.3) can be recast in the form:
f 1+
f t
∫ ∫ ∫
G= pt ()d t+ pt ()d t+ +
pt ()dt−
0 1 [] t
1 2
t
⎧⎪ ⎫⎪
⎨∫mt ()d t+ ∫ mt ()d t+ +
∫ mt ()dt⎬−c
⎪⎩ f 1 + f [] t + f ⎪⎭
103
(9.1)
where f is the fractional length of the favorable period within a year and t is the
Gaussian notation. This equation gives photosynthetic gain by subtracting maintenance
costs of leaves during the unfavorable periods from photosynthetic gains
during the favorable period. Note that the maintenance costs during favorable
periods are already subtracted from gross photosynthetic gain; thus, p(t) is net
gain, the outcome of this subtraction.
What then is the optimal replacement timing of leaves for individual plants in a
seasonal environment with a period unfavorable for photosynthetic production?
Much as in the aseasonal situation, the solution is obtained by finding t that
maximizes g = G/t, but now G is expressed by (9.1) and follows a zigzag curve
through time, increasing during summer and decreasing during winter (Fig. 9.3).
The optimal timing again obtains at the point when the line from the origin touches
the zigzag curve. An analytical solution is not readily available, but numerical
solutions can be found through appropriate simulations. If the optimal leaf longevity
under certain conditions exceeds the length of the favorable period, then the plant
is predicted to be evergreen. If the solution is for leaf longevity shorter than or equal
to the length of the favorable period, then the plant should be deciduous.
Simulations carried out for regions differing in length of favorable period yield predictions
for patterns of occurrence in evergreen and deciduous plant species (Kikuzawa
1991, 1995a, 1996). Where favorable period length (f) is equal to 1 year, all plants are
expected to be evergreen, because plants can carry out photosynthesis throughout a year
(Fig. 9.4). Even in such locations, however, there can be species whose leaf longevity
is shorter than 1 year. The evergreen habit combined with leaf longevity less than the
full favorable period suggests that a tree retains leaves throughout a year but with a high,
asynchronous turnover in individual leaves. When the favorable period length becomes

104 9 Biogeography of Leaf Longevityand Foliar Habit
Fig. 9.3 Schematic representations to show photosynthetic production under favorable periods
of different lengths. (a) Evergreen species have an advantage because of the low maintenance
costs during a short unfavorable period. (b) Shedding leaves during the unfavorable period is
advantageous in the longer favorable period. (c) Paying back construction costs within one short
favorable period. Solid increasing line indicates net gain during favorable periods. Broken
decreasing lines are maintenance costs during unfavorable periods. Broken increasing line from
the origin touches the curve at the point where the marginal gain is maximum
shorter than 1 year, the deciduous habit will appear. The percentages of deciduous
species increase and evergreen species decrease with the shortening of favorable
period. The percentage of evergreen species reached a minimum value at an intermediate
length of f (at around f = 0.5). When the favorable period length becomes
still shorter, the percentage of evergreen species increases again.
Various observations are consistent with this sort of interplay between the length
of the favorable period and the balance between evergreen and deciduous foliar
habits. Some tree species, such as Mallotus japonicus and Alnus japonica, are
deciduous in central Japan but are evergreen on Okinawa in southern Japan. Some
evergreen trees in Singapore such as Trema orientalis, Ficus elastica, and Duabanga
sonneratioides become deciduous north along the Malay Peninsula (Koriba
1948a,b). Almost all the trees in a riparian forest in Costa Rica were evergreen
compared to only about half in nearby upland forests (Frankie et al. 1974; Opler
et al. 1980). Similarly, Condit et al. (2000) reported that the percentage of the
deciduous tree species across the Isthmus of Panama increased from 14% on the
Atlantic Ocean side (annual precipitation, 2,839 mm) to 28% on Barro Colorado
Island (2,570 mm) and 41% on the Pacific Ocean side (2,060 mm). When the favorable
period length becomes still shorter, the percentage of evergreen species
increases again. Such shifts in the balance of deciduous and evergreen species can
occur even in the restricted growing season of arctic regions. Of 18 plant species

Theory for the Geography of Foliar Habit
Percentages of Leaf Habit
100
50
0
1.0
Fig. 9.4 Simulation of changes in the percentages of deciduous (open) and evergreen (shaded )
species at different length of favorable period (year) within a year (f). Dotted bar indicates the
evergreen habit but shorter leaf longevity than 1 year
whose foliar habit is evergreen or wintergreen on King Christian Island at 77°50¢N,
ten species were summergreen in Greenland at 72°50¢N. Similarly, seven evergreen
species whose leaf longevity is longer than 2 years on King Christian Island were
reported to be wintergreen with leaf longevity shorter than 2 years in Greenland
(Bell and Bliss 1977).
Because favorable period length often shortens from the Equator to higher
latitudes, the trend on length of the favorable period shown in Fig. 9.5 might also
be taken to reflect changes in percentages of deciduous and evergreen habits from
the Equator to the poles. This possibility is appealing because the bimodal distribution
of evergreenness on the latitudinal gradient has long puzzled ecologists (Chabot
and Hicks 1982), but is this a fair interpretation of the simulations? Taking winter
cold as an example of a constraint on photosynthetic production, there is some
intuitive basis for interpreting the simulations in this way (cf. Fig. 9.3). When the
unfavorable period is short, it can be advantageous to use overwintered leaves in
the next spring by paying maintenance costs in winter rather than shedding old
leaves at the end of summer and producing new leaves in spring (see Fig. 9.3a).
When winter becomes longer, the cumulative maintenance costs of maintaining
foliage overwinter increases, so that shedding leaves before the onset of the
unfavorable period and producing new leaves with high photosynthetic capacity in
the next season becomes advantageous (see Fig. 9.3b). When the unfavorable
period length becomes still longer, it may be difficult for the leaves to pay back
0.6
Length of Favorable Period (f)
(year)
Latitude
0.2
105

106 9 Biogeography of Leaf Longevityand Foliar Habit
Fig. 9.5 Relationships between favorable period length and leaf longevity in three alpine plant
species: deciduous Sieversia pentapetala (a), evergreen Phyllodoce aleutica (b), and evergreen
Rhododendron aureum (c). (From Kikuzawa and Kudo 1995)
their construction costs within the short favorable period, so extended leaf longevity
leads to an evergreen habit (see Fig. 9.3c). This rationale is supported by the
contrasting trends in the leaf longevity of evergreen versus deciduous species
reported by Wright et al. (2005a) along global temperature gradients associated
with length of the growing season. Leaf longevity of evergreen species decreased
with mean annual temperature whereas that of deciduous species increased. In
summary, deciduous plants are unable to retain their leaves over an unfavorable
period but do prolong leaf longevity when the favorable period lengthens.
Conversely, evergreen species have to prolong leaf longevity when the unfavorable
period lengthens to pay back the construction and maintenance costs of leaves
unproductive during the unfavorable period. Although these patterns are in accord
with intuitive arguments provided by Kikuzawa (1991, 1995a, 1996) to account for
the bimodal distribution of evergreen habit on latitude, the development of relevant
analytical theory is desirable.
In principle, these arguments should apply not only to interspecific behavior on
latitudinal gradients but also to variation in leaf longevity for species at local spatial
scales. We can illustrate and test the ideas using situations such as topographic
variation in the timing of spring snowmelt caused by differences in winter snow
depth on Mount Daisetsu in northern Japan. Kudo and Kikuzawa (Kudo 1992, 1996;

Theory for the Geography of Foliar Habit
Box 9.1 Ecosystem
The concept of ecosystems emerged early in the twentieth century as ecologists
began to grapple with the complex interactions defining the relationships
between the biota and the abiotic environment. The concept is appealing in its
generality, applying equally well from a pond to an ocean, or from a woodlot
to a forest biome, or for that matter to the planet as a whole. At the heart of the
ecosystem concept is the recognition that flows of energy and materials
through the system sustain the interactions among its biotic and abiotic
components. Ultimately all ecosystems depend either on the thermal energy
and material flows associated with deep-sea vents or, most commonly, on the
solar energy that is captured by photosynthetic organisms such as plants and
phytoplankton – the primary producers. Other organisms in ecosystems
function as consumers of primary producers or decomposers breaking down
organic matter. In contrast to the emphasis of evolutionary biology on the
diversity and adaptation of organisms, ecosystem science has focused more
on the overall structure and nature of the flows of materials and energy through
the system than on the particular organisms in the system. A contemporary
challenge in ecosystem science is to understand the relationship between
biodiversity and ecosystem function.
Kikuzawa and Kudo 1995) studied two evergreen species and a deciduous species
associated with these snowbed habitats in which snowmelt occurred from early
June through early August (see Fig. 9.5). In both evergreen species, leaf longevity
declined with a longer favorable period, whereas in the deciduous species leaf longevity
increased with the length of the favorable period. Leaf longevity of the
deciduous species is restricted by the length of the favorable period; thus, leaf longevity
necessarily is reduced in shorter favorable periods. In contrast, evergreen
species can prolong leaf longevity beyond winter, thus compensating for the
decrease in photosynthesis resulting from a shortened favorable period by
prolonging leaf longevity and exploiting subsequent snow-free periods. By
changing favorable period length in Kikuzawa’s (1991) model with other parameters
held constant, Kikuzawa and Kudo (1995) simulated this pattern of decreasing
versus increasing leaf longevity in evergreen and deciduous species, respectively,
with a longer snow-free period (Fig. 9.5). Because leaf longevity is only one
element in the suite of foliar traits affecting production potential, this snowbed
community also affords an example of how the deciduous species adjust to ensure
payback on leaf construction costs when the favorable period is short. Unable to
extend their leaf longevity, plants growing in places subject to shorter snow-free
periods instead increased their photosynthetic rates by increasing investment for
photosynthetic machinery and decreasing costs such as defense. In three deciduous
species in this snowbed habitat, leaf mass per area (LMA) decreased and foliar
107

108 9 Biogeography of Leaf Longevityand Foliar Habit
nitrogen increased as the favorable period length shortened across the sampled
microhabitats (Kudo 1996). Consistent with Kikuzawa’s cost–benefit analysis of
foliar carbon economy (Kikuzawa 1991, 1995a,b, 1996), there clearly is interplay
between leaf longevity and foliar habit that should figure in analyses of the
geography of foliar habit as well.
Modeling Foliar Habit in Relationship to Climate
The greatest current concern in predicting the distribution of foliar habit at a global
scale is in models for shifts in vegetation in response to climate change. These
dynamic global vegetation models (DGVMs) draw on the distinction between evergreen
and deciduous foliar habit to characterize future vegetation zones but are cast
at the scale of global zonation in broadly defined plant functional types (Woodward
et al. 2004; Sitch et al. 2008). The scale and the definition of DGVMs unfortunately
do not allow detailed attention to the relationships between leaf longevity and foliar
habit. One place where climate models of foliar habit, however, have explicitly
considered the role of leaf longevity is in analyses of the possible origin of the
deciduous habit in polar forests during warmer periods in earth history (Brentnall
et al. 2005). In an adaptation of the University of Sheffield’s conifer forest growth
model (Osborne and Beerling 2002), Brentnall and his colleagues (2005) use leaf
longevity as a key driver in analyses of variation in foliar habit along simulated
mid-Cretaceous climate gradients. Using wood from extant conifers, they calibrate
their model with a method relating the fine structure of wood anatomy to leaf longevity
(Falcon-Lang 2000a,b; Falcon-Lang and Cantrill 2001) and then test their
predictions against a broad sampling of fossil wood deposits. Their analyses demonstrate
the advantage of the deciduous habit in high-latitude conifers during the
mid-Cretaceous when the earth was warmer and polar regions were forested
(Fig. 9.6), a finding substantiated by experimental studies of extant conifers with
evergreen versus deciduous habits (Royer et al. 2005).
Fig. 9.6 Fractional cover of
deciduous conifers (open
circles) versus evergreen
conifers (closed circles) as a
function of latitude in simulations
that consider the role
of leaf longevity in affecting
survival, reproduction, and
competitive ability during the
mid-Cretaceous. (From
Brentnall et al. 2005)
Fractional cover
0.5
0.4
0
0.2
0.1
0
60
70 80 90
Latitude

Chapter 10
Ecosystem Perspectives on Leaf Longevity
A streamside forest in fall
K. Kikuzawa and M.J. Lechowicz, Ecology of Leaf Longevity,
Ecological Research Monographs, DOI 10.1007/978-4-431-53918-6_10, © Springer 2011
109

110 10 Ecosystem Perspectives on Leaf Longevity
As an integral part of the adaptive strategy for productivity at the level of individual
plants, leaf longevity should scale up to impact flows of energy and materials at the
ecosystem level. Consequently, leaf longevity and foliar habit consistently appear
in enumerations of traits relevant to ecosystem function (Weiher et al. 1999;
Lavorel and Garnier 2002; Cornelissen et al. 2003; Kleyer et al. 2008). The past
decade has seen a flood of papers discussing linkages between various traits and
ecosystem function: useful entry points to this literature include Lavorel and Garnier
(2002), Díaz et al. (2004), Wright et al. (2005b), Quetier et al. (2007), and Suding
and Goldstein (2008). Although leaf longevity and its foliar correlates clearly influence
ecosystem processes (Thomas and Sadras 2001; Wright et al. 2005b; Cornwell et al.
2008), scaling up the effects of leaf longevity at the level of individual plants or
species to the aggregate influence of species assembled in diverse communities
across the landscape is not at all straightforward (Suding et al. 2008). Zhang and
colleagues (Zhang et al. 2009) provide perhaps the best example of what is possible
if one is willing to invest the effort. They followed leaf longevity on individual
species in ten evergreen forests in eastern China for 5 years, calculating frequencyweighted
mean leaf longevity for each forest, which was negatively correlated
with mean annual temperature and positively correlated with mean annual precipitation.
Very few ecosystem studies focus to this degree on leaf longevity per se at the
level of individual species, or for that matter on any other species-specific traits.
Some models of forest productivity incorporate an impressive amount of detail on
individual species at the population level in the forest community (cf. Medvigy et al.
2009), but the focus typically remains on the forest as a whole, not the detailed
analysis of individual trees and species that in aggregate decide the functional
characteristics of the forest. It clearly is no easy task to assess how leaf longevity
and associated traits at the species level scale up to affect ecosystem function.
In any case, our goal in this closing chapter is not so much to discuss the influence
of leaf longevity and foliar habit on ecosystem function, but rather the obverse – to
highlight work in ecosystem ecology that may help improve theory for leaf longevity.
Through perspectives drawn from ecosystem function, we basically turn the discussion
of leaf longevity back to the Chabot and Hicks’ (1982) seminal review, with a
focus on better accounting the costs of foliar construction and defense in predicting
variation in leaf longevity. The relatively limited treatment of these factors in the
initial cost–benefit models for leaf longevity (Kikuzawa 1991, 1995a,b, 1996)
leaves room for improvement in our understanding of leaf longevity as a key factor
in foliar function.
Leaf Turnover and Leaf Longevity in the Ecosystem
The most direct connection of leaf longevity to ecosystem function is through leaf
turnover because this turnover rate essentially defines a storage term for materials
in the system as well as an indication of system productivity. Not surprisingly,
there is a positive correlation between leaf longevity and mean retention time of

Nutrient Resorption and Leaf Longevity
MRTB (number of growth seasons)
6
5
4
3
2
1
0
0
1 2 3 4 5 6
Leaf life span (number of growth seasons)
Fig. 10.1 Relationship between leaf longevity and mean retention time of biomass (MRTB) in
various ecosystems. (From Mediavilla and Escudero 2003b)
biomass in canopies (Fig. 10.1). The ratio of leaf biomass and leaf litter production
gives an estimate of mean retention time of leaves in the canopy, and these
data in turn can be used to estimate leaf longevity (cf. Chap. 3). More importantly,
the voluminous data gathered by ecosystem ecologists on the quality of fallen
foliage has considerable bearing on theory for leaf longevity. Foliar nitrogen in
particular is not only an important determinant of decomposition and nutrient
cycling at the ecosystem level (Cornwell et al. 2008) but also a critical correlate
of leaf longevity and photosynthetic function (Wright et al. 2004). Construction
of leaves requires investment of nitrogen that can only be acquired from the
environment at some carbon cost to the plant (Givnish 2002), which contributes
to the total carbon cost of leaf construction that under current theory (Kikuzawa
1991, 1995b, 1996) must be recouped over the lifetime of the leaf. If nitrogen and
also phosphorus can be resorbed from senescing leaves and recycled into new
leaves, then leaf construction costs may be less than if these foliar resources had
to be acquired de novo in the environment.
Nutrient Resorption and Leaf Longevity
Resorption of nutrients is a normal part of the leaf senescence process, but full
recovery of critical and often relatively scarce nutrients such as nitrogen and phosphorus
apparently is not possible. Killingbeck (2004) distinguishes potential
resorption and realized resorption. Potential resorption considers all biochemically
resorbable nutrients, those not so refractory as to be mobilizable only at exceedingly
high metabolic costs. Potential resorption is considered a fixed value specific
to each plant species and is thought to be phylogenetically dependent. In terms of
realized resorption, not all nutrients that potentially could be resorbed in fact will
be retranslocated from senescing leaves, so realized resorption typically is less than
111

112 10 Ecosystem Perspectives on Leaf Longevity
RESORPTION PROFICIENCY
BASED ON NUTRIENT CONCENTRATION IN
SENESCED LEAVES
COMPLETE
RESORPTION
INCOMPLETE
RESORPTION
< 0.7% N
I
N
T
E
R
M
E
> 1.0% N
< 0.05% P
DECIDUOUS SPP.
D
IATE
> 0.08% P
DECIDUOUS SPP.
< 0.04% P
> 0.05% P
EVERGREEN SPP. EVERGREEN SPP.
Fig. 10.2 Resorption proficiency of nitrogen (N) and phosphorus (P). Proficiency is the ratio of
mass of each nutrient to the leaf mass at leaffall. If the ratio is less than the values in the figure,
resorption is complete; it is considered incomplete if the ratio is greater. SPP., species. (From
Killingbeck 1996)
potential resorption. Killingbeck (1996) also distinguished resorption proficiency
from efficiency (Fig. 10.2). Efficiency is the percentage difference between nutrient
concentration per unit area of a green leaf immediately before shedding to the initial
concentration of the green leaf. Proficiency, simply the nutrient concentration
of fallen leaves, is directly relevant to biogeochemical cycling (Parton et al. 2007),
whereas efficiency is more directly relevant to foliar function and leaf longevity.
The value of efficiency varies greatly among species, but the value of proficiency
does not vary so much (Killingbeck 1996, 2004).
On average, a little less than half the nitrogen and a little more than half the
phosphorus in a leaf is resorbed (Eckstein et al. 1999; Kobe et al. 2005; Yuan and
Chen 2009), but in light of the huge range of interspecific variation in resorption
efficiency and the lack of any correlation between nitrogen resorption efficiency
(NRE) and phosphorus resorption efficiency (PRE) (Fig. 10.3), this fact provides
little or no insight into alternative modes of foliar function. The apparent lack of
correlation between NRE and PRE is somewhat misleading, however, because in
fact tropical species are more efficient in resorbing phosphorus and temperate and
boreal species are more efficient in resorbing nitrogen, which would appear to
reflect latitudinal differences in soil availability of nitrogen relative to phosphorus
(Yuan and Chen 2009; Fig. 10.4). The NRE also is lower and PRE higher on average

Nutrient Resorption and Leaf Longevity
Phosphorus Resorption Efficiency, %
100
90
80
70
60
50
40
30
20
10
0
0 10 20 30 40 50 60 70 80 90 100
Nitrogen Resorption Efficiency, %
Fig. 10.3 Resorption efficiencies for nitrogen (NRE, x-axis) and phosphorus (PRE, y-axis) collated
by Yuan and Chen (2009) for a wide range of tree and shrub species representing all growth forms
and regions
Phosphorus Resorption Efficiency, %
100
90
80
70
60
50
40
30
20
10
0
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Latitude
Nitrogen Resorption Efficiency, %
90
80
70
60
50
40
30
20
10
0
113
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70
Latitude
Fig. 10.4 Latitudinal trends of nitrogen resorption efficiency (NRE) and phosphorus resorption
efficiency (PRE) for tree and shrub species in Yuan and Chen (2009) for species that had both
measures of efficiency. Although overall there is no correlation between NRE and PRE for a
species, this is because mid-latitude similarities mask the inverse, negative relationships at high
and low latitudes

114 10 Ecosystem Perspectives on Leaf Longevity
Nitrogen Resorption Efficiency, %
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
1.0 10.0
Leaf longevity, months
100.0
Phosphorus Resorption Efficiency,
%
in evergreen compared to deciduous woody species (Yuan and Chen 2009), which
presumably has more to do with the functional ecology of the two foliar habits than
with site-dependent differences in nitrogen and phosphorus availability. Considering
the carbon costs of acquiring the nitrogen and phosphorus to construct a leaf
(Givnish 2002), we might expect that interspecific variation in NRE and PRE
would be related to leaf longevity. In principle, recovery of nitrogen or phosphorus
from senescing leaves might be less costly than acquiring these resources de novo
from the soil, and hence could reduce the total carbon cost of leaf construction.
What little evidence there is, however, suggests there is no relationship between
leaf longevity and either NRE or PRE (Fig. 10.5). Reich et al. (1992) did, however,
report a significant negative relationship between the absolute amount of resorbed
nitrogen and leaf longevity: the greater the amount of resorbed nitrogen, the shorter
the leaf longevity. This result and the broad range of resorption efficiencies across
species suggest that at least in some instances resorption may act to reduce the
effective cost of leaf construction and thus might act to reduce leaf longevity.
Resolving this possibility will require more studies of nitrogen and phosphorus
availability at sites where NRE and PRE are determined.
In this regard, it is noteworthy that longer-lived leaves generally have lower
nitrogen concentration (Wright et al. 2004) and hence decompose more slowly
(Parton et al. 2007). Because leaffall and decomposition comprise a critical pathway
connecting the production and decomposing functions of ecosystems (Thomas
and Sadras 2001), a positive feedback may generally exist between the availability
of soil resources and the frequency-weighted mean leaf longevity of ecosystems.
The quality and quantity of materials in fallen leaves will affect their fragmentation
and decomposition (Grime et al. 1996). If the decomposition rate of the fallen
leaves is rapid, organic matter can be decomposed to inorganic material quickly and
absorbed by plant roots. Able to absorb abundant nutrients, plants could then grow
vigorously, elongating shoots and shedding relatively short-lived leaves that are
subject to more rapid decomposition. Conversely, longer-lived leaves are difficult
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
1.0 10.0 100.0
leaf longevity, months
Fig. 10.5 There is no apparent relationship between leaf longevity and either NRE or PRE, although
the available data are sparse and not entirely consistent (Wright et al. 2004; Yuan and Chen 2009)

Photosynthetic Nitrogen Use Efficiency and Leaf Longevity
for litter invertebrates to consume, decompose slowly, and accumulate as a thick
litter layer that reduces nutrient availability and slows both plant growth rates and
the nutrient turnover rate in the ecosystem (Eckstein et al. 1999; Kikuzawa 2004).
In short, the turnover rate of leaves is correlated with the availability of nitrogen
and phosphorus in an ecosystem, and there is a positive feedback between turnover
and availability that can simplify modeling the carbon cost of nitrogen and phosphorus
acquisition in an improved theory for leaf longevity.
Photosynthetic Nitrogen Use Efficiency and Leaf Longevity
There is a potential benefit in nitrogen resorption, but also a potential cost in lost
photosynthetic capacity, so we can expect an environmentally dependent interplay
among these foliar characteristics as the leaf ages. Although there are positive correlations
among foliar nitrogen and phosphorus concentrations and photosynthetic
capacity (Wright et al. 2004), we also need to consider the degree to which NRE
and leaf longevity are conditional on photosynthetic nitrogen use efficiency (PNUE,
the photosynthetic rate per unit nitrogen). Comparable arguments also may apply
to phosphorus, but these are less well known because past work has emphasized
species from regions where soil nitrogen availability limits productivity.
Escudero and Mediavilla (2003) examined PNUE and nitrogen resorption in
nine evergreen tree species from a Mediterranean climate. Although nitrogen was
retranslocated immediately before leaffall, foliar nitrogen concentration was maintained
rather constant throughout almost all the life of the leaf before an abrupt
decline. But, because photosynthetic capacity decreased with leaf age, the PNUE
also decreased linearly with time. The shorter the leaf longevity, the greater the
rate of decrease in PNUE. A similar relationship between leaf longevity and the
decreasing rate of PNUE was observed in 23 Amazonian tree species (Reich et al.
1991), but no significant relationship was reported in a comparison of seven tree
species in an evergreen broadleaf forest in central Japan (Hikosaka and Hirose
2000). In the Amazon, tree species from various habitats were sampled, but the
Japanese species came from a single forest. Plant species in the same habitat tend
to have similar PNUE (Hirose and Werger 1994, 1995).
We can consider this trade-off at the whole-plant level as well, which could bear
on the control of resorption and longevity at the leaf level. Nitrogen use efficiency
of an individual plant (NUE) is the ratio of annual net production and annual nitrogen
absorption, which can be expressed as the product of nitrogen productivity (NP, the
annual net production per standing nitrogen mass of the plant) and mean residence
time of nitrogen (MRT) (Hirose 2002, 2003):
115
NUE = NP × MRT (10.1)
Nutrient use efficiency can be expressed by the ratio of the amount of litterfall to
the amount of nutrient in the litterfall (Vitousek 1982, 1984); that is to say, the
inverse of the nutrient concentration in the litter expresses the nutrient use efficiency.

116 10 Ecosystem Perspectives on Leaf Longevity
If we regress nutrient use efficiency of various forests against nutrient absorption
rate, we obtain a decreasing relationship between the two (Vitousek 1982). This
finding suggests that in nutrient-rich forests, nutrient use efficiency (NUE) is low
whereas in nutrient-poor forests the trees use nutrients efficiently. Fertilization
decreased the nutrient use efficiency, indicating this relationship is not merely the
outcome of autocorrelation. It is also suggested that if there is a lowest limit of
nutrient amount to achieve positive net production, the relationship is not a simple
decreasing function, but should be an optimum curve (Bridgham et al. 1995).
When nutrient is resorbed from senescing leaves, total CO 2 assimilation of the
canopy can be improved by the shedding of older leaves only when the increase
in photosynthesis from resorbed nitrogen (N) exceeds the photosynthesis of the
leaves lost. This condition is satisfied if the ratio (100 × PNUE in the old leaves/
PNUE in the young leaves) is less than the percentage of N recovered from
senescing leaves before abscission. In other words, the N of old leaf × efficiency of
old leaf should be less than the retranslocation ratio × N of old leaf × efficiency
of new leaf:
PNUE old / PNUEnew < r
(10.2)
Otherwise, retention of the old leaves would result in a higher total CO 2 assimilation
for the whole-leaf biomass. Accordingly, under N limitation, maximum leaf
longevity must be constrained by both the rate of decline in PNUE with leaf age
and the efficiency of N resorption; the balance between these factors will determine
a minimum relative PNUE for leaf retention. In agreement with the foregoing
expectations, instantaneous PNUE of the leaf cohorts in nine Mediterranean tree
species was usually above the predicted minimum PNUE for a leaf to be retained
(Escudero and Mediavilla 2003).
Defense of Leaves and Leaf Longevity
Consistent with a carbon-focused cost–benefit analysis of leaf longevity, PNUE
will also be adversely affected and leaf longevity altered if the photosynthetic
capacity of leaves is impaired during their lifetime. This impairment can occur not
simply because of aging of tissues but also because of either damage through
herbivore and pathogen attack or damage associated with abiotic factors such as
wind or falling branches. The characteristics of the leaf can modulate these risks to
at least some degree, and at some cost, the biotic risks through constitutive and
facultative defenses and the abiotic risks through investments in stronger foliar tissue.
Although Chabot and Hicks (1982) raised these points, the associated costs and
benefits have not been incorporated into a theory for leaf longevity. Nor will it be
easy to do so (see Agrawal (2006) for an entry into the tangled history of research
on plant defense), but there are at least two possibilities among existing theories of
defense for establishing links to theory for leaf longevity. Both, in turn, have links
to aspects of ecosystem ecology.

Defense of Leaves and Leaf Longevity
Fig. 10.6 Relationship between growth and investment for defense. Each curve represents a
hypothetical species; arrows indicate the maximum realized growth rate. The optimal defense
differs depending on the growth rate of the plant species. For plants that have a high potential
growth rate, it is advantageous to invest in growth by reducing the investments for defense, but
plants with a lower potential growth rate should invest for defense. (From Coley et al. 1985)
The first possibility is the resource availability theory for plant defense (Coley
et al. 1985; Agrawal 2006). This theory is predicated on the assumption that the
resource environment in which a plant grows will condition its defensive investments.
The theory is developed with reference to herbivores but in principle should
also apply to defense against pathogens. The logic of the predictions rests on the
following mathematical model (Coley et al. 1985) generating the series of growth
curves shown in Fig. 10.6:
a b
( 1 ) ( )
117
g = G −kD − H − mD
(10.3)
where g is the realized growth rate and G the potential growth rate, which represents
the rate without loss by herbivory or without any defense against herbivory. D is
investment for defense, H the potential herbivory, and k, m, a, and b are constants.
The first term of the right-hand side of (10.2) is the growth rate, indicating that the
potential growth rate (G) is reduced by the investment for defense (D). The second
term is the level of leaf damage from herbivory, suggesting potential herbivory (H)
is reduced by the defense. The subtraction of herbivory losses from growth gives

118 10 Ecosystem Perspectives on Leaf Longevity
the realized growth; note that the herbivore damage is not given by the percentage
but by the absolute difference of the two terms.
The resource availability theory predicts a close correlation between leaf longevity
and defense. Plant species in resource-rich, sunny environments should invest more
for growth rather than defense, replacing leaves quickly to avoid declines in aging
leaves and attain vigorous growth at the whole-plant level. Nitrogen-rich leaves
have this high growth potential and short longevity, on one hand, but also are attractive
resources for herbivores on the other (Mooney and Gulmon 1979). Leaves in a
resource-rich environment may incur more herbivore damage (Coley 1988; Coley
and Barone 1996) but can tolerate losses because of the high return on investment
possible in the resource-rich environment. On the other hand, plant species in
resource-poor environments will have lower photosynthetic potential and hence
longer-lived leaves. This situation places a premium on investments in defense
over growth. To summarize, the resource availability theory predicts: (a) a negative
correlation between growth and defense, and a positive correlation between growth
and amount of herbivory, and (b) a positive correlation between leaf longevity and
defense and a negative correlation between leaf longevity and growth. The theory
does not address interspecific variation in leaf longevity among co-occurring
species, but at least it has the potential to link theory for leaf longevity to environmental
gradients in resource availability that affect ecosystem productivity.
Timing of Leaf Emergence, Leaf Longevity,
and Leaf Defense
One of the earliest theories for plant defense, the apparency theory (Feeny 1970,
1976), also has bearing on theory for leaf longevity. Apparency theory made a
qualitative argument that if plants were more easily found by herbivores or pathogens
because of their abundance, stature, persistence, or some similar factor, then
they would be subject to more frequent and diverse attacks and should have a
quantitative defense founded on reducing foliage quality for the attacker by heavy
investments in tannins, fiber, and other constitutive defenses. Conversely, a less
apparent plant would escape generalist herbivores or opportunistic pathogens and
need only mount a less costly, qualitative defense against potential attackers specially
adapted to finding the plant despite its lack of apparency. The ideas are
simple but in some ways compelling and not without support (Agrawal 2006).
This apparency perspective on defense is interesting for theory of leaf longevity
because it might provide a link between leaf phenology and leaf longevity, and
leaf phenology in turn is being altered by global warming (Parmesan 2006).
Collating coherent data on the frequency and intensity of losses to herbivory and
disease at the community and ecosystem levels may allow probabilistic estimates
of leaf vulnerability that could be incorporated into an improved theory of leaf
longevity.

Linking Leaf Longevity and Ecosystem Function
Linking Leaf Longevity and Ecosystem Function
In summary and conclusion, there are two aspects of ecosystem studies that potentially
can inform a theory for leaf longevity. First, if knowledge of ecosystem function
lets us effectively quantify the carbon costs of acquiring nitrogen and
phosphorus across ecosystems, then we could more explicitly assess the costs and
benefits of acquiring these resources through resorption versus uptake from soil.
Second, if we could effectively quantify the age-dependent risks of leaves for
herbivore or disease damage across ecosystems, then we could better factor this
aspect into the accounting of the carbon costs of leaf construction. In principle, both
avenues hold promise, but in practice neither is likely to soon yield a firm quantitative
foundation for the test of a refined theory for leaf longevity. That realization,
however, does not preclude a priori refinement of the theories that qualitatively
explore these relationships at the leaf and whole-plant levels.
119

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139

Subject Index
A
Abscission, 21
Adaptive radiation, 59
Alkaloids, 55
Allometric relationships, 33
Anthocyanins, 21
Apparency
to herbivores, 55
theory, 118
Aquatic plants, 54
Autonomous unit, 85
B
Branch whorl, 31
Bud
apical, 9
burst, 9
hypsophyllary, 13
lateral, 9
naked, 13
scale, 9, 10, 13
scale development, 13
timing of budbreak, 14
C
Canopy
architecture, 2
ergodic hypothesis, 51
hollowing phenomenon, 82
photosynthesis model, 50
structure, 2
Coloring, 21
Construction cost, 6
leaf, 38, 42, 68
Cost-benefit ratio, 42
Crown, 2
hollowing, 82
D
Deciduous, 2
brevideciduous, 3
drought, 3, 90
habit, 101
incomplete deciduousness, 3
semideciduous, 3
Defense, 117
biological, 55
chemical, 55
constitutive, 95
induced, 95
induced chemical, 55
physical, 54
plant defenses, 54
Delayed greening, 49
Density dependence, 92
density-dependent mortality factor, 95
Depression effect, 17
Diel effect, 17
Disturbance, 88
Dynamic global vegetation models
(DGVMs), 108
E
Early, mid and late successional species, 15
Ecosystem, 107
level, 110
Efficiency, 112
Endogenous factors, 21
Even-aged cohort, 29
Evergreen
broad-leaved tree species, 13
habit, 101
plant species, 2, 59
semievergreen, 3, 59
Evolution, vii
Exogenous factors, 21
141

142 Subject Index
F
Fagus type, 9
Ferns, 59
Flooding, 97
G
Geographic distribution, 102
Geography of foliar habit, 102
Greenhouse, 101
greenhouse Earth, 8
Gross primary production (GPP), ix, 36, 37
Growth rate hypothesis, 93
H
Herbivore, 95
herbivory, 54, 117
satiate herbivores, 55
Heteronomous, 11
Heterophylly, 25
Heteroptosis, 3
Homonomous, 11
I
Icehouse, 101
Intrinsic rate of population growth, 48
Isometric relationship, 33
L
LAI
optimum leaf area index, 50
Leaf, 7
abscission, 24
construction cost, 38, 42, 68
decomposition rate of the fallen, 114
dry matter content LDMC, 68, 75
economic spectrum, 68
embryonic, 9
lamina, 9
mesophyllic leaves, 6
primordia, 9
Leaf emergence
duration of the period of, 27
flush type, 9
period, 62
simultaneous-type, 82
successive type, 82
Leaf exchanger, 2
Leaf expansion
duration of the period of, 14
full expansion, 14
Leaffall
duration of the period of, 27
period, 62
Leaf half-life, 29, 31
Leaf lifespan, 23
Leaf longevity, 23
cohort-based estimates of, 30
functional, 35, 36
potential, 43
theory for, 43, 76, 102, 116
Leaf mass per unit area (LMA), 42, 68
Leaf senescence, 21
senescence-associated genes, 21
Leaf survival curve, 25
l x curve, 30
survivorship curve, 29
Leaf traps, 34
Leaf turnover rate, 34
Litter traps, 34
M
Macrophylls, 8
Mangroves, 63, 95, 96
Marcesence, 24
Marginal gain, 43
Mean labor time, 17
Mean retention time of biomass in canopy,
110–111
Metamer, 8
Microphylls, 8
Modular unit, 8, 25
N
Natural selection, vii
Net ecosystem production (NEP), ix
Net primary production (NPP), ix, 33
Nitrogen
absolute amount of resorbed, 114
foliar nitrogen (content), 52, 68, 70, 72, 115
latitudinal trends of NRE, 113
mean residence time of nitrogen
MRT, 115
productivity NP, 115
resorption efficiency, 112
use efficiency of an individual plant
(NUE), 115
Nutrient turnover rate, 115
O
Overcast effect, 17
Ozone-induced oxidative stress, 96

Subject Index
P
Pathogen, 95
Phenolics, 55
Phenology, vii
foliar, 59
Phosphorus
latitudinal trends of PRE, 113
phosphorus resorption efficiency (PRE), 112
Photosnthesis
light-response curves, 15
Photosynthetic capacity, 14, 68
daily, 72
decline with leaf age, 19, 46
maximum photosynthetic
capacity, 16
Photosynthetic function
onset of full, 24
photoinhibition, 35
Photosynthetic nitrogen use efficiency
(PNUE), 76, 115
Photosynthetic rate
at leaf death, 48
midday depression of, 16
response to irradiance, 14
Plant canopy, 2
Plastochron interval, 78, 79
R
Relative growth rate, 79
Resorption of nutrient, 111
potential resorption, 111
realized resorption, 111
resorption proficiency, 112
Resource availability theory, 118
Rhyniophyta, 7
S
Salinity, 96
Sclerophylly, 4
sclerophylls, 6
Seasonality, viii
Seasonal climatic changes, vii
Shade
deeply shaded, 84
partially shaded, 84
self-shading, 46, 82
shading effect, 17
Shoot, 8
determinate shoot growth, 10, 77
embryonic, 11
indeterminate shoot growth, 10, 77
long shoot, 11, 83
preformed, 11
seedling shoot growth, 80
short shoot, 11, 83
Snow-free period, 35, 107
Specific leaf area (SLA), 69
Specific leaf weight (SLW), 69
Spring ephemeral, 3
Standing leaf biomass, 34
Static life table analyses, 31
Steady-state leaf numbers, 34
Succeeding-type, 10
Succession
early successional plant
species, 88
early successional species, 80
late successional plant species, 88
late successional species, 80
Summergreen, 2, 3, 59, 63
T
Terrestrial plants, 54
Trade-offs, 33
Turnover
in the canopy, 52
leaf, 25
optimal timing of, 44
U
Unfavorable season, 35
V
Value of a leaf, 49
W
Wintergreen, 2, 3, 59
Wood density, 80
143

Organism Index
A
Abies balsamea, 61
Abies firma, 60
Abies grandis, 92
Abies mariesii, 61
Abies veitchii, 14, 61
Acer mono, 20
Acer palmatum, 69, 70
Actias selene gnoma, 94
Actinidia deliciosa, 15
Adenostoma fasciculatum, 81
Aesculus flava, 63
Aesculus sylvatica, 3
Aesculus turbinata, 80
Akebia trifolia, 81
Alnus hirsuta, 9, 11, 20, 26, 63, 78, 82
Alnus japonica, 34, 41, 97, 104
Alnus sieboldiana, 17, 20, 82
Ambrosia trifida, 64
Annona spraguei, 15
Araucaria araucana, 31, 61
Archaeopteris, 8
Asplenium incisum, 59
Asplenium wrightii, 60
Athyrium brevifrons, 60
Athyrium otophorum, 60
Athyrium pycnosorum, 60
Athyrium wardii, 60
Avicennia alba, 96
Avicennia germinans, 63, 96
B
Betula grossa, 81
Betula platyphylla, 20, 80, 82, 97
Blechnum niponicum, 59, 60
Brasenia schreberi, 64
Brassica napus, 15
C
Camellia japonica, 35, 77
Carpinus caroliniana, 4
Carpinus cordata, 78
Carya cordiformis, 63
Castanopsis cuspidata, 13, 62
Castanopsis sieboldii, 15
Ceratopetalum apetalum, 78
Cercidiphyllum japonicum, 25
Cettia diphone, viii
Cinnamomum camphora, 85
Cinnamomum japonicum, 85
Cinnamomum sintoc, 63
Cleyera japonica, 13, 83
Cleyera ochnacea, 62
Cochlospermum fraseri, 36
Coffea arabica, 15
Coniogromme japonica var. fauriei, 60
Connarus panamensis, 15
Cornopteris decurrenti-alata, 60
Cryptantha flava, 91
Cryptocarya obliqua, 63
Cucumis sativus, 15
Cyathea arborea, 57
Cyathea furfuraca, 59
Cyathea hornei, 59
Cyathea pubescens, 59
Cyathea woodwardioides, 59
D
Daphne kamtschatica, 63
Daphniphyllum macropodum, 89
Dendrocnide excelsa, 78
Desmopsis panamensis, 15
Dipterocarpus sublamellatus, 85
Dipteronia, 11
Doryopteris lacera, 60
145

146 Organism Index
Doryopteris polylepis, 60
Doryphora sassafras, 78
Dryopteris crassirhizoma, 59
Dryopteris phegopteris, 60
Duabanga sonneratioides, 104
E
Elateriospermum tapos, 85, 89
Encelia farinosa, 91
Erythrophleum chlorostachys, 36
Eucalyptus miniata, 36
Eucalyptus tetrodonta, 36
Eurya japonica, 13, 62
F
Fagus crenata, 63, 78, 82
Ficus elastica, 104
Fragaria virginiana, 81
G
Glossopteris, 8
Glycine max, 64
H
Halimium atriplicifolium, 81
Helianthus tuberosus, 71
Heliocarpus appendiculatus, 20, 78
Homolanthus caloneurus, 63
Hydrocharis morus-ranae var.
asiatica, 92
I
Ilex aquifolium, 62
Illicium religiosum, 62
Inga edulis, 63
L
Laguncularia racemosa, 63
Larix decidua, 61
Larrea tridentata, 92
Ledum palustre var. decumbens, 92
Lepisorus ussuriensis, 59
Leptopteris wilkesiana, 59
Ligustrum obtusifolium, 89
Linum usitatissimum, 64
M
Machilus thunbergii, 13, 15, 62
Maesa japonica, 13
Magnolia obovata, 26
Mallotus japonicus, 104
Melampsora medusae, 95
Metrosideros polymorpha, 92
Microlepia marginata, 60
Morisonia americana, 15
Myriophyllum spicatum, 65
N
Nelumbo nucifera, 64, 65
Nothofagus moorei, 78
Nymphaea odorata, 64
Nymphaea tetragona, 64
O
Osmanthus chinensis, 32
Ouratea lucens, 15
P
Pachysandra terminalis, 89
Pemphigus betae, 95
Phaseolus vulgaris, 31
Phlomis fruticosa, 16
Phyllitis scolopendrium, 59
Phyllodoce aleutica, 106
Picea abies, 61, 92
Picea glehnii, 92
Picea jezoensis, 92
Picea mariana, 61
Pieris rapae, viii
Pinus contorta, 61
Pinus longaeva, 61
Pinus pumila, 15
Pinus resinosa, 61
Pinus sylvestris, 61
Pinus tabulaeformis, 30, 61
Pinus taeda, 61
Piper, 41, 42
Pistacia lentiscus, 91
Podocarpus nubigena, 61
Podocarpus saligna, 61
Polygonatum odoratum, 20
Polygonum sachalinensis, 20
Polypodium japonicum, 60
Polystichum retroso-paleoceum, 60

Organism Index
Polystichum tripteron, 59, 60
Populus maximowiczii, 69, 70
Potamogeton crispus, 65
Pseudotsuga menziesii var.
glauca, 92
Psychotria emetica, 63
Psychotria limonensis, 63
Pteridium aquilinum, 42
Pyrrosia tricuspis, 59
Q
Quercus acuta, 13
Quercus coccifera, 62
Quercus crispula, 63, 78, 82
Quercus glauca, 15
Quercus mongolica var.
grosseserrata, 26
Quercus myrsinaefolia, 62
Quercus rotundifolia, 62
Quercus rubra, 15
Quercus suber, 62
R
Rhizophora mangle, 63
Rhododendrom maximum, 89, 90
Rhododendron aureum, 106
Rumohr standishii, 60
S
Saxegothaea conspicua, 61
Scepteridium multifidum var. robustum, 60
Shorea robusta, 4
Sieversia pentapetala, 106
Sonneratia alba, 96
Symplocos prunifolia, 62
T
Terminalia ferdinandiana, 36
Theobroma cacao, 15, 85
Tilia japonica, 82, 84
Trema orientalis, 104
U
Ulmus davidiana, 11
W
Welwitschia, 14
X
Xanthium canadense, 64, 85
Xanthophyllum stipitatum, 85
Xylocarpus granatum, 96
Xylopia micrantha, 15
147