Variation in Leaf Functional Traits Is Attributable to Species

HOW DO LEAF FUNCTIONAL TRAITS VARY ACROSS

ECOLOGICAL SCALES?

Julie Messier

Biology Department McGill University, Montréal, Québec, Canada

February 2009

A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Masters of Science

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- ACKNOWLEDGEMENTS -

First and above all, I would like to give my dearest thanks to my supervisors, Brian J. McGill and Martin J. Lechowicz, whose constant support, guidance and encouragements have been indispensable. Thanks to the National Science and Engineering Research Council (NSERC), the Smithsonian Tropical Research Institute, an aid-in-research grant from the Neo program of McGill University and the Organisme Québec-Amérique pour la Jeunesse for funding this research. I am also grateful to Ricardo Cossio for help with field work and to Andy Gonzalez, Omar Lopez, Joe Wright, Mirna Samaniego, Reinaldo Uriola and Jose Barahona for advice and invaluable technical help along the bumpy road of graduate studies. I would also like to thank Mélisanne Loiselle-Gascon, Vanessa Ward, Jaclyn Paterson and Kyle Simpson, for technical help in processing samples. I would like to give special thanks to two beloved close friends (Maryse & Gerardo), to wonderful lab mates (Volker, Richard, Peter & Sergio) and to my family (Danielle, Mario & Simon). You have supported me through hurdles and tribulations and fueled my motivation all along this winding road. Without you, I would not have made it through these challenging two years.

- CONTRIBUTION OF AUTHORS -

JM & BJM conceived the study question and sampling design; JM carried out field work and data collection with guidance from BJM & MJL; BJM conceived the data analysis and carried it out with JM; BJM, JM and MJL collaborated on interpreting the data and writing chapter 1 of the manuscript: “How do leaf functional traits vary across ecological scales?”; JM wrote the remainder of the thesis with guidance from BJM and MJL.

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- TABLE OF CONTENTS -

ACKNOWLEDGEMENTS ...... ii CONTRIBUTION OF AUTHORS ...... iii TABLE OF CONTENTS ...... iv ABSTRACT ...... vi RÉSUMÉ ...... vii LIST OF TABLES ...... viii LIST OF FIGURES ...... ix GENERAL INTRODUCTION ...... 1

 Traits at the Heart of Ecology & Evolution

 The Role of Functional Traits

 The Study of Functional Trait Variation

 Ecological Questions Addressed Through Trait Variation . A - Trait Plasticity . B - Correlations among traits . C - Functional Classifications . D - Trait Variation & Community Composition . E - Trait Variation & Environnemental Gradients . F - Trait Variation & Ecosystem Processes . G – Trait Variation & the Niche . Interdependence of Research Topics

 Integrating and Structuring Trait Variation: How Do Traits Vary Across Ecological Scales?

 Research Objectives & Hypotheses METHODS ...... 24

 The Traits

 The Study Design

 Study Site Details

 Trait Measurement Protocol

 Statistical Analyses CHAPTER 1 - How Do Traits Vary Across Ecological Scales? ...... 30

 Body of the Text

 Method Summary GENERAL CONCLUSION ...... 40

 Endorsing Contrasting Viewpoints

 Intraspecific Trait Variability as “Noise”

 Trait Based Environmental Filters

 Future Directions: Are Species Distinct Entities in Trait Space? REFERENCES ...... 44 TABLES & FIGURES ...... 51 APPENDICES ...... 58

 Appendix I. - Species List

 Appendix II. - Chapter 1. Supplementary Material

. Methods . R Code Used in Partitioning of Variance . Results of Variance Partitioning Without Species Level . Calculation of Confidence Intervals

- ABSTRACT–

Functional traits, measurements of adaptive aspects of the phenotype, are increasingly used for the study of plant community ecology. Despite their importance, we do not know which ecological scales contain the most variation in a given trait, which hampers assessment of the wider relevance of findings from studies conducted at only one scale. To address this deficiency, I studied the variance distribution of two key leaf functional traits (leaf mass per area - LMA and leaf dry matter content - LDMC) across six nested ecological scales (site, plot, species, tree, strata, leaf) in lowland tropical rainforests of Panama. Variance in both traits is uniformly distributed across all scales except the plot level, which shows virtually no variance despite high species turnover among plots. This contradicts the widely held belief that species-level variation predominates in organizing species distribution and abundance and indicates that communities regulate plant ensembles by filtering on leaf functional traits regardless of species.

- RÉSUMÉ -

Les traits fonctionnels, attributs indicatifs des aspects adaptatifs du phénotype, sont de plus en plus utilisés pour étudier l’écologie des communautés végétales. Malgré l’importance des traits fonctionnels, nous ne savons pas à quelle échelle écologique un trait donné varie le plus, ce qui nous empêche de mettre dans un contexte général les découvertes des études conduites à une seule échelle. Pour combler cette lacune, j’ai étudié dans les forêts tropicales humides tropical du Panama la distribution de la variance de deux traits fonctionnels foliaires clés (la masse par surface foliaire - LMA et le contenu foliaire en matière sèche - LDMC) à travers six échelles écologiques emboitées (le site, la parcelle, l’espèce, l’arbre, la strate et la feuille). La variance de ces deux traits est uniformément distribuée à travers toutes les échelles, sauf à l’échelle de la parcelle qui ne présente aucune variance malgré la forte différence de composition d’espèces entre les parcelles d’un même site. Ces résultats vont à l’encontre de la croyance populaire selon laquelle la variation interspécifique joue un rôle prédominant dans le contrôle de la distribution et l’abondance des espèces. Ils indiquent plutôt que les communautés régulent l’assemblage des végétaux en exerçant un filtre sur les traits fonctionnels, indépendamment de l’espèce.

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- LIST OF TABLES -

Table 1. Summary of the seven main research areas involving p.42 functional traits. Table 2. Possible expected outcomes of partitioning the variance in p.44 traits across ecological scales according to different research paradigms. Table 3. Descriptions of study sites p.44 Table 4. Variance partitioning of the full nested linear models on p.45 leaf mass per area (LMA) and leaf dry matter content (LDMC) across six nested ecological scales. Table A1. List of tree species sampled with number of leaves p.60 sampled per species and site. Table S1. Variance partitioning of an alternative nested linear p.67 model of LMA and LDMC across five nested ecological scales (Leaf, Strata, Tree, Plot and Site), leaving out the species scale.

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- LIST OF FIGURES -

Figure 1. Location of study sites. PNSL – Parque Natural San p.46 Lorenzo. BCI – Barro Colorado Island. PNM – Parque Natural Metropolitano. Map from Google Earth. Figure 2. Boxplot of LMA and LDMC values for the 17 most p.47 abundant species (species for which 30 or more leaves were measured). Figure 3. Histogram of values for LMA and LDMC by site and by p.48 plot. Solid histograms represent the sites: PNM (red), PNSL (black) & BCI (blue). Lines represent the histograms of individual plots for each site, colors coded accordingly. Figure S1. LMA & LDMC variance partitioning across ecological p.68 scales for the full model (with species level included) and the alternative model (without species level).

- GENERAL INTRODUCTION -

Traits at the Heart of Ecology & Evolution 5 The distribution of life on the planet is extremely patchy; most species are absent from almost everywhere (Begon et al. 1996). Different organisms live in different environments because each species is adapted to a specific set of environmental conditions. Understanding the match between an organism and its environment is at the core of the scientific study of both ecology and 10 evolution. Ecology, by definition, is the study of the relation between organisms and their environment. Evolution studies the process of speciation that occurs as a result of the differential survival and reproduction of individuals, which depends on how well traits are matched to the local environment. An organism’s interaction with and adaptation to the 15 environment are determined by the combination of traits that govern its survival and reproduction. Hence, an understanding of traits is at the very heart of studies in ecology and evolution.

The Role of Functional Traits 20 The great diversity of plants and the wide variety of plant habitats suggest the existence of a correspondingly high diversity of plants strategies and attributes underpinning fitness in diverse environments. To meet the variety of challenges posed by such an array of environments, different ways to meet the same basic requirements needed for survival and reproduction 25 have evolved in plants. These properties of a plant that determine its adaptations to and fitness in different environments are called functional traits. They reflect the ecological strategy of the plant and its relationships with

environmental factors. Hence Violle et al. (2007) define a functional trait as “…any trait which impacts fitness indirectly via its effects on growth, 30 reproduction and survival”. Comprehension of plant function and functional traits is then key to understand the patterns of plant adaptive specialization, which is in turn necessary to understand community assembly, community structure and the functioning of ecosystems. The study of plant function and functional traits is 35 central to many classical and current issues on which plant ecologists have been focusing their interest and efforts.

The Study of Functional Trait Variation An extensive body of research concerns the same general question: 40 “How do functional traits vary?” It is essential to study trait variation because this variation is the basis of biological diversity. Biodiversity has traditionally been expressed in terms of species diversity, although a mere species count is little informative. Because traits give an explicit description of species and of the way they relate to their environment, trait diversity is a more fundamental 45 reflection of natural diversity. Thus, functional trait diversity draws a more concrete and descriptive portrait of biodiversity (Diaz and Cabido 2001). Trait variation is usually studied not as an end in itself but as a tool used to address different ecological questions. Functional traits can be informative of many different ecological processes because trait variation is intrinsically 50 complex. First, traits vary at all ecological scales (among organs, trees, species, communities and ecosystems). Second, variation results from multiple interacting mechanisms that act at all of these scales. Third, variation in a given trait is dependent on the other plant traits because the set of functional traits that characterize a plant is structured by fundamental tradeoffs at the

55 organismal level. For all these reasons plant trait variation is multifaceted and each of the numerous facets of trait variation reflects different processes. Thus, while numerous studies ask “How do traits vary”, research focusing on different aspects of trait variation can address quite distinct ecological issues.

60 Ecological Questions Addressed Through Trait Variation. To concretely present the different types of ecological issues that can be addressed by studying functional trait variation, I will briefly introduce the seven main research areas concerned with different aspects of trait variation. These seven research areas are summarized in Table 1 and presented 65 here in order of the increasing ecological scale at which they are investigated. The patterns emerging from Table 1 are that 1- a large body of research is based on trait variation, 2- distinct ecological questions are addressed by focusing on different aspects of trait variation, and 3- these diverse research areas work with traits across a wide range of ecological scales. Appendix I 70 further summarizes the ecological issues addressed in these research areas and gives examples of specific questions and findings.

A - Trait Plasticity Phenotypic plasticity is the ability of an organism to modify its 75 phenotype in response to differences in environmental conditions (Pigliucci 2001). Phenotypic plasticity is central to the survival of sessile organisms such as plants because it is the main mechanism by which individual plants deal with spatial and temporal environmental heterogeneity. The ecological breadth of a species partly depends on the amount of phenotypic plasticity that 80 it can express. Plants are plastic for a wide range of traits, from physiological, morphological, structural to developmental (Sultan 2000). There is extensive

evidence not only that different species and populations differ widely in their plastic responses to the same environmental variable, but also that different traits show varied levels of plasticity (e.g. Valladares et al. 2000). Thus, the 85 degree of plasticity of a trait can be seen as a trait in itself. Important advances have been made in understanding the adaptive value and genetic basis of reactions norms, which characterize variation of phenotypic expression of a genotype across changing environments (Scheiner 1993; Pigliucci 2001), and in understanding the factors affecting the plastic response of plants (e.g. 90 Valladares et al. 2000; Takahashi et al. 2005; Rozendaal et al. 2006). While phenotypic plasticity is often adaptive, we have come to realize that it is in some instances maladaptive, such as in stressful environments experiencing highly unpredictable change (e.g. Valladares et al. 2002). The fact that all plants do not express maximal phenotypic plasticity despite the fact that it can 95 be very advantageous demonstrates the existence of costs and limitations to phenotypic plasticity. The limits to plasticity have traditionally been attributed to internal factors, such as genetic costs, lag-time in response and phenotypic integration. However, much work remains to be done on the external limits to plasticity, such as environmental predictability, herbivory and multiple 100 simultaneous stresses, as they have been shown to be of major importance. (DeWitt et al. 1998; Pigliucci 2005; Valladares et al. 2007). In addition, ecologists are starting to realize the importance of the effects of plasticity on ecological interactions at all levels (Miner et al. 2005), so that trait-mediated interaction is an aspect of phenotypic plasticity that is being increasingly 105 studied. While on one hand functional trait plasticity is a fundamental process ecologically, on the other the resulting intraspecific variation is a problem for studies working on a species basis and attempting to assign single traits values

to species (e.g.Roche et al. 2004). Thus trait plasticity needs to be taken into 110 account by studies concerned with other aspects of trait variation

B - Correlations among traits The study of trait correlations focuses on one of the major goal of plant ecology, namely to identify the dimensions of ecological variation among 115 species and to understand their basis. The sets of traits that covary across species are called ‘primary dimensions of variation’ or ‘ecological strategy axes’, and reflect fundamental strategies used by plants to invest their resources and to face environmental challenges. Certain sets of traits vary simultaneously because they are structurally or physiologically linked and because plants 120 adapt to the environment by concurrently adjusting multiple traits (Westoby et al. 2002; Reich et al. 2003). Beyond describing these primary dimensions of variation, research in this area aims to uncover how trait variation is constrained by trade-offs and biophysical limitations, to understand trait- environment interactions, and to interprets trait correlation in the context of 125 organ and whole-plant ecological strategy.

There is a large literature focusing on the description and understanding of primary dimensions of variation. While there is no consensus on the list of primary dimensions of variation, there are five ecological 130 strategy dimensions that are commonly found in the literature: 1- leaf investment dimension, 2 - plant size dimension, 3- seed mass-seed output dimension, 4-leaf size-twig size dimension (Corner’s Rule) and 5- wood properties dimension. Some papers discuss several ecological strategy axes at once (Grime et al. 1997; Westoby 1998; Westoby et al. 2002; Diaz et al. 2004; 135 Westoby and Wright 2006; Wright et al. 2007; Curtis and Ackerly 2008), but

most studies focus on them separately. The level of description and comprehension of these dimensions are at varied states. The leaf investment, plant size and seed mass dimensions have been studied for some time so that we have a good understanding of their associated traits and of the trade-offs 140 generating them. While Corner’s Rule has also been observed for some time, we do not have a full description of the set of correlated traits and the reasons for these trade-offs has not been elucidated. The wood properties axis is just now coming into focus(Chave et al. 2009).

LEAF INVESTMENT: The leaf investment dimension, commonly called 145 the leaf economic spectrum, is arguably the most extensively studied dimension. In a nutshell, this axis is a trade-off between the rate and duration of return on energetic investment in leaves. On the slow return on investment end of this continuum, leaves have a long lifespan (LL), low leaf nitrogen

content per mass (LNCmass), high leaf mass area (LMA), low leaf diffusive

150 conductance (GS) and low maximum photosynthetic capacity (Amax). These leaves photosynthesize at a low rate for a long time, so have a slow return on energetic investment. On the quick return on investment end of the

spectrum, leaves have the opposite set of trait values: a short LL, high LNCmass,

low LMA, high GS and high Amax. These leaves photosynthesize at a high rate 155 for a short period of time, hence have a quick return on energy investment. Leaf trait relations have been studied for many years (e.g. Reich et al. 1992; Reich et al. 1998; Ackerly and Reich 1999), but Reich et al. (1999, 1997) were the first to clearly show the convergence of these tradeoffs across species and biomes (Reich et al. 1997; Reich et al. 1999). Wright et al. (2004, 2005) later 160 confirmed that these trait relationships are universal across all plants of the world and coined the term “Leaf Economics Spectrum” to this now widely known dimension (Wright et al. 2004b; Wright et al. 2005a). The traits of the

leaf economic spectrum are also known to be correlated with ecological properties of higher levels, such as whole-plant and ecosystem properties. 165 LMA negatively correlates with whole-plant relative growth rate (RGR) and performance and with the annual aboveground production of a forest per unit canopy mass (Grime et al. 1997; Reich et al. 1997). Thus LMA and correlated leaf traits are key factors regulating plant photosynthesis, growth and productivity at scales from leaf to ecosystem. 170 There is robust evidence for the ubiquity of this axis, we have come to a good understanding of cause of variation of the traits, and good progress is being made in elucidating the influence of different factors on trait relationships (Meziane and Shipley 1999; Wright et al. 2004a; Santiago and Wright 2007; Poorter et al. 2009). The attention of researchers has now 175 turned to understanding the physiological and evolutionary basis behind this trade-off axis (Shipley et al. 2006a). PLANT SIZE: The plant size dimension reflects the growth strategy of plants. The components of this dimension are plant height, architecture and level of light requirement (Poorter et al. 2003; Diaz et al. 2004; King et al. 180 2005). Light demanding species grow fast to maintain their high position in the canopy, thereby producing a slender stem, low-density wood and a narrow and shallow crown to avoid mechanical failure and self-shading. Shade- tolerant species have the opposite characteristics. SEED MASS-NUMBER: The seed mass-seed output dimension 185 represents a trade-off between the numbers of propagules and their probability of survival in early life stages (Westoby et al. 2002). In species that produce a smaller number of larger seeds, each seed is more likely to survive the establishment phase under unfavorable conditions because of the extra provisions available in the seed. On the other hand, species producing small

190 seeds in large numbers are more likely to have some seeds land in a favorable environment where they will germinate and establish successfully. This is the only well known strategy axis corresponding to the regeneration phase. Regenerative traits generally are decoupled from vegetative traits (Leishman and Westoby 1992), suggesting that selection pressures during juvenile and 195 mature stages are independent. LEAF-TWIG SIZE: The leaf size-twig size dimension is an architectural axis also known as Corner’s Rule. Corner’s Rule, formulated in 1949 (Westoby et al. 2002) states that twig diameter increases with leaf area and inflorescence size and decreases with branch density in the crown. However, the adaptive 200 significance of these trade-offs still has not been elucidated. While it makes sense that twig size should increases with a branch’s total leaf area to allow for the amount of xylem required in the supporting stem to meet the evaporative requirements of the branch, it is not clear why twig dimensions should correlate with individual leaf area. Yet, these trade-offs have been confirmed 205 by many studies (Chazdon 1991; Ackerly and Donoghue 1998; Brouat et al. 1998; Cornelissen 1999; Wright et al. 2007). WOOD DENSITY-STRENGTH: The wood properties dimension is an axis in the making that has recently received much attention (Chave et al. 2009). Wood serves three primary roles: transporting water and nutrients 210 across the plant, offering structural support for leaves and providing storage (Curtis and Ackerly 2008). Certain trade-offs are expected to occur in wood traits because the optimization of these functions come into conflict. For example, vessels with large xylem allow for faster sap transport, but produce a mechanically weaker wood. However, expected trade-offs in traits 215 representing competing functions are not necessarily found (e.g. Anfodillo et

al. 2006). Although the actual sets of wood trait correlations are not currently known, we are in the process of elucidating them (Preston et al. 2006). To further complicate the matter, wood property traits do not correlate only with each other but also with traits constituting other primary 220 dimensions of variation, such as leaf properties (Falster 2006; Wright et al. 2006a; Ishida et al. 2008; Swenson and Enquist 2008), tree height (Anfodillo et al. 2006) or seed size (Falster 2006).

C - Functional Classifications 225 Following the natural human impulse for categorization, ecologists build plant groupings based on functional traits. Functional classifications are the most common grouping type and sort species into plant functional types (PFTs) based on similar ecosystem effects and/or environmental responses (Box 1996; Diaz and Cabido 1997; Lavorel et al. 1997; Lavorel and Garnier 2002). 230 The angle this research area takes to study trait variation is unique in that it focuses on trait similarities instead of trait differences. Ultimately, the purpose of functional classifications is to simplify the task of relating community composition to vegetation properties and processes; by being explicit about the role of the components of plant assemblages, functional classifications allows 235 us to draw an informative picture of community composition. One popular application based on plant functional classifications is to model the shifts in vegetation in response to global climate change. PFTs are often built by measuring many traits believed to reflect key functions on as many species as possible, and then running multivariate analyses to see what trait correlations 240 naturally emerge (e.g. Leishman and Westoby 1992; Boutin and Keddy 1993; Grime et al. 1997). Interestingly, PFTs frequently produce groups that reflect the major growth forms of plants. Hence, functional classifications are

important in understanding community and ecosystem function and in predicting vegetation response to environmental factors and global climatic 245 change. Some other functional trait-based organization schemes pursue goals different from that of PFTs. For example, schemes have been built to allow comparison of plant species based on their strategies. The famous CSR plant functional type scheme is one of the first strategy schemes to have been 250 described (Grime 1974). The idea behind the CSR system is that plants have to adapt to three qualitatively distinct challenges: Competition (C), Stress (S) and Disturbance (R). Competition is any phenomena by which neighbors attempt While there is much ink flowing on the topic of strategy schemes, they remain in to use the same resources. Stress is any phenomena that limit primary 255 production. Disturbance is any phenomena that damages vegetation. Because there is a three-way trade-off in the capacity of a plant to adapt to these challenges, plants can be classified in a triangular space according to their capacity to deal with each of the three constraints. The classification, originally developed for herbs, has later been extended to apply to plants in 260 general (Grime et al. 1997) and a method was developed to assign plants to CSR functional types according to seven easily measurable attributes (Hodgson et al. 1999). As an alternative to the CSR system, Westoby (1998) suggested the Leaf-Height-Seed (LHS) strategy scheme. This system places species in a 3- dimensional space defined by the primary dimensions of variation discussed 265 earlier, that is, leaf investment strategy, height strategy and seed mass-seed output strategy. It is argued that the benefit of the LHS scheme over the CSR scheme is in its potential for worldwide comparison (Westoby 1998), but the conceptual realm of science and have yet to be applied.

270 D - Trait Variation & Community Composition One of the oldest challenges of community ecology has been to link species composition to the environment. A generally accepted concept is that environmental conditions sort out which species survive based on their traits. Here, the aspect of trait variation considered is how community trait 275 composition is controlled by abiotic factors that determine the range of allowed functional trait values. This environmental filter framework seeks to predict which species from the regional pool will occur in a community by regarding the environment as a series of filters acting on traits (Keddy 1992; Diaz et al. 1999; Shipley et al. 2006a). These filters operate sequentially at a 280 hierarchy of scales and preclude the survival of species with incompatible sets of traits. Research on environmental filtering is meaningful for studies of community assembly and species distribution. The idea that the environment sorts or filters species based on traits is not new (e.g. Janzen 1985), but the environmental filter concept was only 285 formalized by Keddy in 1992 (Keddy 1992). Discussions of how different environmental filters operate at different scales are found in the literature. For example, Ackerly & Cornwell (2007) looked at the effect of soil moisture availability on trait composition, while Diaz et al. (1998) and Box (1996) studied climate as a determinant of vegetation at regional and global scales. 290 Here again, while there is a lot of theoretical discussion of the concept, few empirical studies have demonstrated the existence of environmental filtering. The first quantitative method of trait-based community assembly was proposed by Shipley et al. (2006b). In a nutshell, they first measure the traits values of species from the regional pool and the community-aggregated trait 295 values, then establish the different species mixture scenarios that respect the

constraints posed by the community-aggregated values, and finally predict community composition by selecting the scenario that maximizes entropy. Work by Marks and Lechowicz brought insight onto the type of traits subject to environmental filtering. Firstly, from a model of tree seedling 300 growth and survival including 34 functional traits, they showed that in a given environment similar fitness could be achieved by what they call “alternative design”, or different combinations of trait values (Marks and Lechowicz 2006). Secondly, by distinguishing trait variation due to these “alternative designs” from trait variation due to environmental differences, Marks (2007) found that 305 highly integrative traits (traits measured at the level of the whole plant), and not organ-level traits (traits characterizing the different plant organs, such as leaves and roots), are affected by environmental gradients. With these tools in hand, we should better be able to detect the presence of environmental filters in the environment. 310 E - Trait Variation & Environmental Gradients An important aspect of functional trait variation is the link between traits and environmental gradients. By definition plants are adapted to and interact with the environment via their functional traits. Therefore, 315 determining what traits change and how they change in response to environmental gradients is at the root of understanding the match between organisms and their environment. The trait-environment interaction is intricate: a trait response might not be the same when measured at community-level or species-level, the environment affects not only the mean 320 trait value of a community but also the amount of trait variation, and the trait correlations change with gradients (Fonseca et al. 2000; Ackerly et al. 2002; Roche et al. 2004). Understanding how traits respond to abiotic factors is

relevant for any trait study directly or indirectly taking into account the environmental, thus for a large part of ecological research. 325 Gradients in many aspects of environment can influence the traits found, such as drainage, site exposure, past disturbance history, annual precipitation, seasonality of precipitation, altitude and temperature (Diaz et al. 1998; Mabry et al. 2000; Santiago and Mulkey 2005; Bhaskar et al. 2007). While many studies have examined how mean trait values change with 330 environmental gradients, few explore how trait variability changes across gradients. One of the few studies to have done so is that of Roche et al. (2004), who found that of nine leaf traits, LDMC was the trait showing the less intraspecific variability across a climatic gradient. It is important to realize that measuring traits at different levels will 335 reveal different patterns of trait variation across environmental gradients. As a case in point, Ackerly et al. (2002) show that the species-level and plot-level response of some leaf traits to gradients of insolation do not agree. They conclude that shifts in community-level mean trait values in response to environmental change reflect 1- changes in the identity of the constituent 340 species and 2- the response of individual species to the abiotic gradient and to their new biotic reality. Likewise, it is important to disentangle intra- from inter-community trait dynamics. To this end, Ackerly and Cornwell (2007) compared trait variation within- and between- plots for parcels sampled along a gradient of soil moisture availability. They found that while plot-mean trait 345 values for 5 different traits are correlated across the gradient, these same traits are generally not correlated among species within a plot. These two studies remind us that like most ecological patterns, trait-environment interaction are scale dependent, with different mechanisms at work at different scales.

In addition to its effects on individual traits, climate can influence trait 350 relationships. Fonseca et al. (2000) found that for plot-level mean trait values, the leaf width-SLA decreased in concert across increasing precipitation and phosphorus gradients. Wright et al. (2005b) found that as sites got warmer,

drier and more irradiated, LL–LMA and Amass -Nmass relationships became less positive. However, to put these effects in perspective, Wright et al. (2004b) 355 concluded based on an extensive database including species from around the world that, all things being considered, the influence of climate on trait coordination is quite modest. Finally, note that we should be cautious not to wrongly infer causality from trait-environment correlations. For example, Van der Veken (2007) 360 found that traits related to colonization ability at local scales are associated with variation in large-scale geographical range characteristics. That is, the distribution of some species may be limited by colonization capacity rather than by a match between the species traits and climatic factors.

365 F - Trait Variation & Ecosystem Processes Another important aspect of trait variation is the role of plant traits for ecosystem properties and processes (Diaz et al. 1999; Lavorel and Garnier 2002). Terrestrial plants are responsible for or involved in most fundamental

ecosystem processes such as nitrogen and CO2 cycles. Many traits have clear 370 linkages to ecosystem processes. For example, LNC to a large degree is the measurement of the concentration of the photosynthetic enzyme Rubisco, the main photosynthetic enzyme. LMA is strongly correlated with structural aspects of leaves such as cellulose and so is inversely correlated with decay rates of leaves. To the extent that diversity influences ecosystem function (e.g. 375 Loreau et al. 2002), Diaz and Cabido (2001) argue that functional diversity

(diversity of traits) will be a better predictor of ecosystem function than the more indirect measures of biodiversity such as species richness. With the desire to predict how change in vegetation as a response to global change will lead to changes in important terrestrial ecosystem functions 380 Lavorel et al. (2002) proposed a framework linking response traits (traits that define vegetation response to the environment) and effect traits (traits that determine the effect on ecosystem processes). A first test of this framework suggests that there are commonalities in traits that determine response to resource availability and effect on biogeochemical cycling. Uncovering traits 385 that are both response and effect traits allows connecting environmental changes to changes in ecosystem functioning, but these first results suggest that the link is often not straightforward.

G - Traits Variation & the Niche 390 There is growing interest in exploring how evolutionary lability of traits (interspecific trait variability within a phylogeny) is associated with the different components of the niche. Here, α- β- and γ-level traits are associated to the α- β- and γ- components of the niche and the relative lability of these traits is used to explain niche evolution and community phylogenetic 395 structure (Silvertown et al. 2001; Ackerly et al. 2006). This may lead to understanding of community assemblage and evolutionary processes. From Whittaker’s widely accepted idea that diversity is hierarchically structured in space from alpha (α – within habitat) to beta (β – among habitats) to gamma (γ – among regions) components, it follows that ecological niches 400 are also hierarchically structured in α, β and γ components. Thus, α-traits define within-habitat niche components, β-traits determine habitat affinity and γ-traits define the specie’s range. While an important part of a species’

current niche is determined by convergent adaptation to its current habitat, there is also has a component of legacy from their ancestors. The tendency to 405 retain ancestral niche characteristics is called niche conservatism. There is an ongoing debate on the relative degree of conservatism (or conversely, lability) of the α, β and γ niche. To address this question, ecologists compare the degree of interspecific variability of traits associated with the different niche components. 410 Two opposing hypotheses regarding the evolutionary lability of α-, β- and γ- traits are being debated and both find elements of support. On one hand, it is suggested that traits defining the α-niche are labile (they vary freely between congeneric species) whereas traits defining the β- and γ- niches are conservative (trait values tend to be retained through evolution so that they 415 are conserved within phylogenies) (Silvertown et al. 2001; Silvertown et al. 2006a). This point of view is based on the observation that some communities have higher frequency of congeners than would be expected from random community assembly (e.g. Webb 2000). Because most coexistence theories require species to differ sufficiently from one another to coexist, these 420 observations entail that habitat affinity, reflected by β-traits, must be common at least across the genus. Thus, α-traits must have differentiated near the tips of the phylogenetic tree for the congeneric species to differ sufficiently to coexist. One the other hand, an opposing hypothesis states that α-niche should 425 be conserved and β-niche labile. This point of view is based on the observation that in some communities, closely related species co-occur less frequently than expected from chance (Ackerly et al. 2006; Silvertown et al. 2006b). Interestingly, this is the opposite observation from those on which the

previous theory is based. The rationale is that β- niche has to be labile for close 430 relatives to occupy different habitats. I believe that some clarification is needed to put everybody on the same page and working together. Amongst other things, we need to 1) specify the context (taxonomic group, environmental conditions, scale, etc) in which each hypothesis is supported, 2) to be explicit about how much trait variation is 435 considered to be conserve and labile 3) to define how phylogenetically close species have to be for them to be considered “closely related” (e.g. congeneric? confamilial?) and 4) to test which traits reflect the α-niche and β-niche. This latter question might prove central in resolving the debate since, for example, change in LMA has been studied both within habitats to describe the alpha- 440 niche (Ackerly et al. 2006) and between habitats to describe the beta-niche (Ackerly 2004). Related questions are also been pursued. For example: is there a relationship between ecological and evolutionary distance (Prinzing et al. 2001; Silvertown et al. 2001; Silvertown et al. 2006b) ? What are the relative 445 roles of adaptation and ecological sorting processes in shaping present communities (Ackerly 2003; 2004; Silvertown 2004) ? What are the ecological and evolutionary processes responsible for the observed phylogenetic structures of communities? How does phylogenetic diversity within community affect functional diversity (Prinzing et al. 2008)? Does 450 phylogenetic conservatism of reproductive traits structure the distribution of these traits within and between forest communities (Chazdon et al. 2003) ? This hodgepodge of closely related yet unstructured questions makes it challenging to detect advances in this area. We are in great need of terminology clarification and of a comprehensive synthesis of the state of

455 knowledge of this field in order to bring together effective future research efforts.

Interdependence of Research Topics Although the preceding research areas are often viewed as separate 460 subjects, they in fact are all linked to one another. First, all study the same issue (trait variation) though from different angles. Second, these different research approaches often rely on each other; many areas need to make assumptions that are in fact central issues under study in other areas. Third, numerous studies deal with questions at the interface of two or more of the 465 trait-related research areas. Here are some examples of how the research areas described above can lean heavily on each other or even overlap: - Environmental filters (D) are often detected by looking at changes in community composition across environmental gradients (E) (see Lavorel and Garnier 2002).

470 - Different functional groups (C) can be defined based on the position of the species on primary dimensions of variation (B) (see Grime et al. 1997). - Studies that relate functional traits to ecosystem processes (F) often use plant functional types (C) to describe the components 475 of communities instead of using an exhaustive list of the constituent traits (see Diaz and Cabido 1997; Diaz et al. 2003).

- Exploring how trait correlations (B) change across environmental gradients (E) informs the constraints and trade- offs underlying these correlations– there are many good 480 examples: (there are many good examples: Fonseca et al. 2000; Wright et al. 2004b; Wright et al. 2005a; Wright et al. 2005b)

- The debate on the relative lability of α- and β- traits (G) implicitly assumes that environmental filtering on traits (D) is responsible for community composition because the 485 observations behind the rationale for the two opposing hypotheses are based on the over- or under- dispersion of congeneric species (see Ackerly 2003; 2004). - Some of the trait response to climatic gradients (E) is due to phenotypic plasticity (A) (see Lusk et al. 2008). 490 These examples highlight the interdependence of the different research areas. It may be convenient to think of trait variation as compartmentalized subjects, but the different facets of trait variation are in fact extensively linked in a tight web of relationships. Trait variation is one integral element even if it has been studied by dissecting it piece by piece. We are thus missing the most 495 important perspective into functional trait diversity: the integrated perspective.

Integrating and Structuring Trait Variation: How Do Traits Vary Across Ecological Scales? 500 The key to understanding trait variation as a whole is to be able to link and structure the different aspects of trait variation. Different approaches use traits across all ecological scales but we do not know how trait variation is structured across these scales. Building a bridge spanning the full range of relevant ecological scales will allow us to connect the different components of 505 trait variation. In addition, this knowledge will allow us to put into broader context the findings of individual studies and to understand the scope and limitations of their conclusions. We have been defining and comparing communities and species in terms of their functional traits without knowing

whether or not there is a lot of trait variation at these levels in comparison to 510 other scales. For example, do key functional traits vary more within or between communities, or within or between species within a community? Knowledge of cross-scale patterns of trait variation will also inform us of the relative importance of different mechanisms responsible for trait variation. Functional trait variation has been studied individually at all scales, from 515 within-individual e.g. (e.g. Valladares et al. 2000; Takahashi et al. 2005; Rozendaal et al. 2006) to among ecosystems e.g. (e.g. Reich et al. 1998; Reich et al. 1999). A few studies have also compared variation between two levels (Ackerly et al. 2002; Wright et al. 2004b; Ackerly and Cornwell 2007), but no one study has spanned all scales with consistent methods, thus making it 520 impossible to compare the amounts of variation among scales. Yet this knowledge is fundamental in order to link the different aspects of functional trait variation and to come to an integrated understanding of why and how traits vary.

525 Research Objectives & Hypotheses To address this shortcoming, this study aims to determine how the variance of two key traits characterizing leaf function is distributed across six nested ecological scales in tropical lowland rainforests of Panama. We have chosen to work with leaf traits because they are among the best studied and 530 understood traits, because they vary across all scales (from within trees to among biomes) and because data collection on leaf traits is relatively straightforward. The two studied traits are leaf mass per area (LMA; g/m2) and leaf dry matter content (LDMC; mg/mg). The six scales are: 1- among leaves within a tree stratum, 2- between the strata of a tree, 3- among trees of the

535 same species, 4- among species in a plot, 5- among plots in a site and 6- among sites. Following different paradigms commonly found in ecology, different variance distribution patterns can be hypothesized. Table 2 gives the partitioning of variance across scales expected according to different paradigms 540 organizing research in various subdisciplines within ecology. Researchers in a given subdiscipline are rarely explicit about the assumed variance partitioning and there are no models that make explicit predictions across these diverse ecological scales, so these are approximate and somewhat overstated expressions of alternative viewpoints to illustrate the relevance of actually 545 determining the scaling of trait variance in nature. “Environmental filtering” assumes that traits are fixed attributes of species and states that different sites will have different mixtures of species to provide the optimal trait average for the environmental conditions found at that site; thus variation at the site level is assumed to be driven by climate 550 and/or edaphic conditions that then drive species composition. Variation is thus expected to be found among sites and species and plots within a site (experiencing similar environments) should show no variation in traits. “Species cohesion” focuses on the species as the outcome of an adaptive evolutionary history with tradeoffs realized among species. Variation between 555 plots and sites are simply expected to be an increasing function of the dissimilarity in species composition between those plots or sites (i.e. no differences in species composition means no trait variation) as indicated by “f(dissim)” in Table 2. “Genetic variation” and “Phenotypic plasticity” acknowledge 560 differences among species, but emphasize variation among individuals either for reasons of fixed genetic variation or plasticity arising in genotype-

environment interactions. The “genetic variation” view emphasizes genetically fixed variation both among individuals of a species and among species, whereas the “phenotypic plasticity” view also acknowledges that important variation 565 can be found between different parts of a plant in addition to that present within and among individuals and species. Among plot and among site variances then are again simply an outcome of differences in species composition “f(dissim)”. Finally, “budgeting” focuses on how plants, being constrained by finite 570 resources, have to allocate limited resources among various functions (growth, reproduction, defence and maintenance) and organs (eg. leaves, trunk, fine and coarse roots etc.). This viewpoint emphasizes differences between species having different resource allocation schemes, between individuals that grow under different limiting factors, and within individuals investing resources to 575 the most profitable plant structures.

Individual scientists will implicitly adopt a viewpoint on trait variation that corresponds to their specific research focus. Here is a rough generalization of which viewpoint matches each of the general research areas discussed 580 earlier. Clearly, research focusing on trait plasticity (A) will tend to adopt the “phenotypic plasticity” viewpoint, sometimes emphasizing variation among individuals (plasticity) and other times variation within single plants over time (developmental plasticity, acclimation). Studies that explore trait correlations and primary dimensions of variation (B), that build functional classification (C) 585 and that explore the trait lability of different niche components (G) mostly take the “species cohesion” standpoint, but also sometimes espouse the “phenotypic plasticity” view. This depends on whether the study unit is the individual plant or the species and whether the mechanisms invoked for the

correlations is adaptive evolution or biophysical constraints. Studies concerned 590 with trait based community assembly (D) and the role of traits in ecosystem processes (F) tend to take the “environmental filtering” view of trait variation. Finally research concerned with response of traits to environmental gradients (E) can take the “environmental filtering” or “phenotypic plasticity” view depending on whether they consider that species turnover across gradients or 595 trait plasticity is responsible for the change in traits across gradients.

- METHODS -

The Traits 600 While other traits are also highly relevant to plant ecology, leaf traits are those for which we have the most knowledge at the multiple scales at which this study is conducted and also are among the easiest on which to collect data. Thus, leaf traits are the most appropriate starting point to study variance partitioning across ecological scales. The two chosen leaf traits have 605 standardized sampling protocols (Garnier et al. 2001; Cornelissen et al. 2003), are part of the leaf economic spectrum (Wright et al. 2004b) and have been identified as amongst the most important functional traits in plant ecology (e.g. Hodgson et al. 1999; Weiher et al. 1999; Westoby et al. 2002; Westoby and Wright 2006). The leaf economic spectrum has consistently been shown to 610 characterize the dominant trade-offs in foliar design and to reflect a plant’s fundamental energy investment strategy in its production capacity. Hence the leaf traits that are most important to work with are the ones that best characterize variation in foliar function. The first trait, leaf mass per unit area (LMA, g/cm2) is also commonly 615 expressed as its inverse: specific leaf area (SLA, cm2/g). At the leaf level, this trait is closely correlated in all species to mass-based maximum photosynthesis

(Amax), nitrogen content per mass (LNCmass), leaf lifespan (LL). LMA values thus

reflect the leaf energy-investment strategy of the plant. High LMA leaves, being thick and/or dense leaves, are durable and have low maximum 620 photosynthesis. Low LMA leaves, having a high leaf area displayed per unit mass invested (and hence efficient light capture) have a high maximum photosynthetic capacity, but are poorly defended and short lived. At higher

levels, LMA is correlated with relative growth rate of a plant (RGR) and with aboveground production. 625 The second trait, leaf dry matter content (LDMC – the ratio of leaf dry mass to its water-saturated mass) was not originally included by Reich et al. (1999, 1997) in early studies of correlations among foliar traits nor did Wright et al. (2004) include LDMC in the original presentation of the leaf economic spectrum. However, evidence has been accumulating that LDMC is a central 630 part of variation in leaf function (Diaz et al. 2004). This trait is particularly important because it is responsible for the correlations between the other foliar traits composing the leaf economic spectrum (Shipley et al 2006a). The leaf’s dry mass reflects the mass of the structural tissues of the leaf (dry matter) and the leaf’s fresh mass includes as well the mass of the metabolically active 635 cell components (cell sap). Thus LDMC, the ratio of the two, reflects leaf structure at the cellular level and the fundamental constraint of investing resources into structural tissues versus into the liquid phase metabolic processes that occur in the cell’s membrane and cytoplasm (Shipley et al. 2006a). 640 The Study Design To capture among site variation, I sampled three sites in lowland tropical forests of Panama from permanent plots managed by the Smithsonian Tropical Research Institute (STRI; Figure 1). The three sites, which are located 645 along a precipitation gradient across the Isthmus of Panama are: a 1 ha permanent plot in Parque Nacional Metropolitano (PNM) on the Pacific Coast, a 50 ha permanent plot on Barro Colorado Island (BCI) located on the Panama Canal and a 6 ha permanent plot in Parque Nacional San Lorenzo (PNSL) located on the Atlantic coast (Smithsonian Tropical Research Institute 2007).

650 In these old-growth forest plots, all the trees with a diameter > 10cm at breast height (dbh; 1.3m) have been identified and mapped by the Smithsonian Tropical Research Institute (STRI). The sites have been chosen to follow a precipitation gradient in order to assess the relative importance of climate on trait variability; site moisture balance and other indices of water availability 655 are climate measures known to have a large influence on leaf traits (Reich et al. 1999; Wright et al. 2001; Wright et al. 2004b; Wright et al. 2005b). To measure among plot variation, 20 m x 20 m plots located at least 60 m apart were sampled at each site: four in PNM, eight in BCI and eight in PNSL. To capture among species and among trees variation, I sampled all trees (with dbh 660 >10cm) of all species within each of these 20 plots. To capture the variation between and within strata within a tree canopy, leaves were collected per individual tree: 3 in the shade and 3 in the sun. To control for temporal variation in traits that occurs between seasons and years, all data was collected during the 2007 rainy season (September to December). In addition, only the 665 youngest fully expanded leaves were collected to avoid trait variation due to ontogeny. Twigs were collected from the gondola of a canopy crane for all plots in PNM and for four plots at PNSL. In the remaining plots, twigs were collected using a pole pruner and a shotgun.

670 Study Site Details Major natural disturbance or anthropogenic disturbances have not affected Parque Natural San Lorenzo (PNSL) and Barro Colorado Island (BCI) for over 200 years and Parque Natural Metropolitano (PNM) for 80 years. All study plots are located in mature forest. The three sites follow the precipitation 675 gradient (Figure 1; Table 3). The PNM site, located on the Pacific Coast of Panama, is the driest site with 1500 mm of rain per year and a dry season of

129 days. The BCI site, on an island located on the Panama Canal, receives 2600 mm of precipitation per year and has a dry season lasting 118 days. The PNSL site, found near the Caribbean coast, is the rainiest of the three sites 680 with 3000 mm of rain per year and a dry season lasting only 102 days. In terms of soil composition, the PNM and BCI sites are on volcanic parent material whereas the PNSL site lies on sedimentary parent material. The 1 ha permanent plot at PNM is located at 60 m of elevation, the 50 ha permanent plot at BCI is located at 140 m of elevation and the 6ha permanent plot at 685 PNSL is also found at an elevation of 140 m. Species composition changes rapidly across the precipitation gradient. While species richness usually increases with rainfall in the tropics (Gentry 1988), these three sites slightly deviate from this general pattern because BCI has higher average species richness per hectare than PNSL (see Table 3). However, tree density does 690 increases with increasing precipitation.

Trait Measurement Protocol The measurements of the three leaf traits generally follow the methods described in Cornelissen et al. (2003). Some changes from this protocol were 695 necessary in order to detect variation at each of the six scales of variation in this study. The differences between our protocol and that suggested in the handbook are: 1- that we sampled all species present in the plot and not only the most abundant ones; 2- we sampled all trees with a dbh >10cm in the plot, instead of only the most healthy trees; 3- we sampled from both the sun and 700 shade strata, instead of only sun leaves; and 4- leaves were fully rehydrated on the harvested branches before their fresh weight was measured. Extra precautions were taken to make sure all leaves were fully rehydrated before measuring the fresh weight. Because trees are likely in different hydration

state in the field, it is crucial to get a water-saturated LMA and LDMC values 705 to have standardized trait values that are comparable among trees (Garnier et al. 2001). In the field, the most accessible branches were cut to an average length of 50 cm. Each cut end of the twig was then immediately put in a test- tube filled with water. The twig-tube interface was sealed with duct tape and the branch placed in a sealed plastic bag. At the end of the field day, the base 710 of each twig was re-cut under water and left immersed overnight to rehydrate in an air-tight plastic bag to saturate ambient air with humidity. Rehydration took place in the dark at ambient temperature, for 12 to 20 hours. The following day, three of the youngest, fully-grown and developed leaves were selected on each twig. Leaf area and fresh weight were measured after 715 detaching the leaf at the base of the petiole, or leaflet at the intersection with the rachis, and gently rubbing them dry to remove surface moisture and dirt. Area was measured ± 0.01cm2 with a LI-3050C area meter (Li-Cor, Lincoln, NE). Precautions were taken to accurately measure the whole surface area of the leaf blade and petiole: regularly calibrating the meter, cleaning the belt 720 surface and cutting the leaves when they were curved or larger than the belt width. Fresh weight was measured to ± 0.001 gr. After a 48 to 96 hours in paper envelopes in a drying oven at 60°C, the dry weight of the leaves was measured to ± 0.001 gr. Palm fronds required some modification of this protocol. For palm species, the rachis of the palm frond was cut at about 50 cm 725 below the tip and placed in the tube with water by analogy to the cut twig end; subsequent steps followed the protocol for leaves. The three replicate “leaflets” thus came from the same frond.

730

Statistical Analyses Data normalized by log10 transformations were fitted by a general linear model where the sampling scales were nested one into another (i.e. nested ANOVA with random effects). A variance components analysis was 735 performed on this full model using the ‘lme’ and ‘varcomp’ functions of R, version 2.6.1 See supplemental material for details. (R Development Core Team 2007) which use a restricted maximum likelihood (REML) method. These analyses in R were cross-checked with Matlab code using a traditional Type I sum-of-squares (Gower 1963; The MathWorks Inc 2007). Since the 740 results were very similar, we report only the results from R/REML. These estimation methods are robust to unbalanced datasets and produce unbiased estimates. Differences between plots may be due to differences in environment or differences in species composition, with the latter likely to be high in tropical 745 forests due to the high species diversity. To assess the effect of this, we calculated the mean Sorensen’s similarity indexes between plots within each site, as well as the Sorensen’s similarity index between sites. To verify that variation in foliar traits at the species was not confounded with the variation at the plot level, we also built an alternative model which left out the species 750 level sampling (See Annex II).

- CHAPTER 1 -

How Do Traits Vary Across Ecological Scales? 755 Julie Messier1, Brian J. McGill1,2 and Martin J. Lechowicz1

1 Biology Department, McGill University, 1205 Dr Penfield Avenue, Montréal H3A1B1 Canada 760 2 University of Arizona, School of Natural Resources, 311 East 4th St., Tucson, AZ 8572 USA

N.B. This chapter is here presented as submitted to Nature.

765

Functional traits, measurements of adaptive aspects of the phenotype (Violle et al. 2007), are increasingly seen as the basis for rigorous, quantitative predictions of distribution and abundance in plant community ecology (McGill et al. 2006). Despite this increasing importance, we do not know which 770 ecological scales contain the most variation in a given trait, hampering assessment of the wider relevance of findings from studies conducted at only one scale. To address this deficiency, we partitioned the variance in two key traits reflecting leaf function (Leaf mass per area - LMA and leaf dry matter content - LDMC) across six nested ecological scales (site, plot, species, tree, 775 strata, leaf) in lowland tropical rainforests of Panama. We find that variance in both traits is uniformly distributed across all scales except the plot level, which shows virtually no variance despite high species turnover among plots. This contradicts the widely held belief that species-level variation is predominant in organizing species distribution and abundance and instead indicates that 780 communities loosely regulate species ensembles by filtering on leaf functional traits regardless of species. These findings bring substantial support to the idea that community assembly occurs on a trait basis (Janzen 1985; Lavorel and Garnier 2002) and suggest that by considering intra-specific variation we could be gaining important additional insights. 785 As their horizons broadened beyond Europe, Victorian naturalists were astonished at the enormous variation found among organisms. This variation not only inspired Darwin’s theory of natural selection, but raised questions about the causes and consequences of such variation. These issues remain 790 central to ecology and evolution to this day. In addition to the variation across continents and between species that so impressed the Victorian naturalists, we now know there is natural variation at all spatiotemporal and organizational

scales. We can find variation within a single organism (Niinemets and Valladares 2004), within a species (Valladares et al. 2000; Takahashi et al. 2005; 795 McGill et al. 2006; Rozendaal et al. 2006), among species (Wright et al. 2001; Westoby et al. 2002) and among communities (Ackerly et al. 2002; Wright et al. 2004b; Rozendaal et al. 2006; Ackerly and Cornwell 2007). Yet, no one study has spanned all scales using consistent methods, making it impossible to compare the amounts of variation among levels. To address this, we present a 800 standardized and integrative study that allows us to see how Nature’s variation is spread across ecological scales. Specifically, we explore two leaf functional traits in trees (Reich et al. 1999; Wright et al. 2004b; Shipley et al. 2006a). Functional traits are the characteristics of an organism that reflect its adaptation to, and performance 805 in, different environments. Since functional traits can indicate how an individual relates and responds to its environment, they offer a powerful approach to address ecological questions (McGill et al. 2006). This is especially true for highly diverse systems such as tropical forests where using species as the working unit often becomes overwhelming; functional traits can offer 810 more useful and perhaps more meaningful insights into community composition and ecosystem function (Lavorel and Garnier 2002; McGill et al. 2006; Westoby and Wright 2006). As ecologists increasingly pursue a functional trait approach, many advances have been made including the discovery of a fundamental tradeoff in leaf architecture (Reich et al. 1999; 815 Wright et al. 2004b) and a trait-based theory predicting species diversity and abundance in plant communities (R Development Core Team 2007). In addition, ecologists studying functional traits have increasingly recognized that both the patterns and implications of trait variation may depend on scale (Shipley et al. 2006b; Silvertown et al. 2006a; Ackerly and Cornwell 2007).

820 Despite these advances and the increasing amount of work on functional traits, we are still missing a fundamental piece of information about functional traits: how do traits vary across ecological scales? For example, a 2004 paper by Wright et al. (Wright et al. 2004b) found that although mean trait values change across communities, there is also a large amount of trait 825 variation within communities. At present we do not know the answer to basic questions such as whether traits vary more within or among communities, or within or among species within a community. Identifying which scales have the most variation in traits is an important check on assumptions that underlie many existing theories (e.g. that variation is greater among than within 830 species). A priori expectations have rarely been explicitly stated but implicit assumptions show a range of possible outcomes (Table 2). Furthermore, identifying scales that account for a large percentage of total variance will help to focus research efforts on the patterns and processes at scales with the most variance (McGill 2008). The objective of this study then is to measure how the 835 variance of two key leaf functional traits - leaf mass area (LMA) and leaf dry matter content (LDMC) - are distributed across six hierarchically structured ecological scales - leaf, tree, strata, species, plot & site - in lowland tropical forests of Panama. We analyzed this data using a nested, random trait, unbalanced ANOVA fit by both Type I sum of squares and maximum 840 likelihood.

The partitioning of variance in LMA and LDMC reveal fairly balanced distributions of variance across five of the six ecological scales (Table 4). However, the plot scale stands out as explaining a very small fraction (close to 845 0%) of the total variance. The results for the two traits seems similar, except that the scale at which most variation occurs is different for the two traits: the

sites scale accounts for 30% of total variance for LMA and the species scale for 34% of total variance in LDMC. Between plots in a site, the mean Sorensen’s index (a measure of β- 850 diversity) is 0.33 for PNM, 0.13 for BCI and 0.23 for PNSL: in each site only a small percentage of species co-occur between neighbouring plots. Between sites, Sorensen’s indexes are 0.02 between PNM and BCI, 0.26 between BCI and PNSL and 0.00 between PNM and PNSL. Variance partitioning of the alternative model with species removed 855 results in almost the same pattern of variance distribution found in the full model, except that in the alternative model the variance component at the tree level increased to subsume the variance component at the species level in the full model (Table S1 & Figure S3).

860 There are two main elements of interest in these results: 1) the distribution of variance across most of the six nested ecological scales is fairly even; and 2) the plot level is responsible for only a minute percentage of the total variance. The relatively uniform distribution of the variance between five of the 865 six ecological scales contradicts the prevailing belief that most of the variation in leaf functional traits is attributable to species. In fact, it suggests that processes at all five scales are equally important. For LMA, the intra-specific fraction of total variance is 53%, much greater than the inter-specific fraction of 17%. For LDMC, the intra-specific fraction of total variance is 50% 870 compared to inter-specific fraction of 34%. High levels of plasticity in these traits have been described in the past (Valladares et al. 2000) and indeed are often controlled for in between species comparisons (Wright et al. 2004b; Shipley et al. 2006b), but this is the first study designed to compare variation

across more than two scales. This raises the question of whether it is desirable 875 to assign mean functional trait values to different species if these traits show more variation within than among species (Figures 2 and 3). Increasingly, ecologists are appreciating that intra-specific variation can have consequences for community structure (Bolnick et al. 2003; Clark et al. 2004; Clark 2005) and these results encourage pursuing that possibility. We suggest that the 880 amount of variation around the mean trait value of a species is at least as meaningful and important as that mean value. In turn best practices would then be to report both mean trait value and standard deviation, in order to explicitly account for variation. We also encourage scientists distinguish the mean trait value for species and the maximum trait values. While it is 885 legitimate to control for variation within a tree by measuring only sun leaves,

that trait should be referred to as ‘traitsun’ to clearly distinguish it from a

“traitmean” expressed throughout the plant canopy. The second important finding is that the plot level accounts for essentially none of the total variance in LDMC and LMA. Figure 3 illustrates 890 that the plots at a site not only share a common mean trait value but also a similar spread of trait values around the mean. Similar results for the variance partitioning in the species-removed model (Table S1 and Figure S1) show that this is not an artefact of significant overlap in species composition between plots, which potentially could have caused the species and plot levels to be 895 confounded. This is further confirmed by the relatively low Sorenson similarities between plots within any one site. The negligible amount of variation in these traits between neighbouring plots despite their different species composition suggests an environmental filter operating on the distribution of trait values within the site; the mean value of a trait among sites 900 varies but the mean and the variance of the trait are fixed among plots within

a site. This environmental filter, possibly attributable to edaphic or climatic conditions, suggests that any community at a given site must have a similar distribution of trait values. If true, then the presence of an individual tree within a local community is controlled via a filter operating on functional 905 traits rather than on species identity per se. This filter on functional trait values will secondarily act as a coarse filter on species assemblage because each species has a substantial but finite degree of plasticity in its expression of trait values. We emphasize that many different combinations of species and individuals can produce the distribution of trait values found at a given site. 910 The fact that the pattern of variance partitioning across scales is largely consistent between LMA and LDMC suggests that both traits are fundamentally reflecting the same underlying processes that govern position of individuals along the leaf economic spectrum. In other words, the filter hypothesized above appears to be acting on all the elements of the leaf 915 economic spectrum. The idea that species assemblage of a local community should be determined by environmental filters on trait has previously been suggested (Janzen 1985; Keddy 1992; Weiher and Keddy 1995; Diaz et al. 1998; Lavorel and Garnier 2002; Shipley et al. 2006b) and in a few cases demonstrated (Shipley et al. 2006b), but our results are unique in: a) showing 920 that species identity is not critical to the filter, b) showing that not just the community mean trait values are acted upon but that the entire distribution is locally conserved, and c) explicitly comparing variation between plots within a site to variation between sites and between species to benchmark the strength of the filter. Perhaps the most surprising aspect of these results is that 925 environmental filtering can be so strong on the overall local distribution of a trait yet influence species composition so little because variation occurs within species as well.

These findings are true for LDMC and LMA, both traits that represent production capacity, one of the principal axes of life-history strategy in plants 930 (Wright et al. 2004b). These are both highly integrative traits that are affected by many interacting environmental factors such as light exposure, seasonal climate, drought and frost events, herbivory and disease, and mechanical resistance. We would not expect a similar analysis done on less integrative traits to give comparable results. Moreover, we might not find the same 935 pattern of variance components across environmental scales in ecosystems with different patterns of alpha and beta diversity and environmental variation. Finally, we should note that although the variance partitioning across scales is broadly similar between the two traits, LMA has a higher site level 940 (Wright et al. 2004b) variance than LDMC (29% vs 16% respectively) and lower species level variance (35% vs 21%, respectively). This suggests that LMA is more strongly influenced by water availability and other environmental variables whereas LDMC is a trait fixed deeper in the evolutionary history of a species. 945 From a practical perspective, the possible existence of a leaf trait filter on community assembly offer many exciting possibilities. It would make possible functional trait approaches to monitor community changes through time for sucessional, conservation or restoration purposes or to design forest management practices aiming to respect the functional integrity of the system. 950 From a theoretical perspective, if it is true that the distribution of the traits indicative of the position on the leaf economic spectrum are strongly regulated at a site, then it would be important to explore other major ecological strategy dimensions reflecting fundamental trade offs and their associated functional traits. We suspect these axes of variation would act as

955 further environmental filters, together precisely controlling community assembly and perhaps species composition if species differentiate themselves from one another along these other ecological strategy dimensions. Our results suggest that ecologists should pay more attention to traits and less to species to understand how communities are assembled (Lavorel and Garnier 960 2002).

Method Summary We assessed variation in LMA and LDMC across six nested ecological scales: site, plot, species, tree, strata and leaf. These are the scales most 965 commonly studied by ecologists and the two traits are among the most important in plants (Reich et al. 1999; Weiher et al. 1999; Wright et al. 2004b; Shipley et al. 2006a). Both LMA and LDMC are key traits in the leaf economic spectrum and have the advantage of well-established sampling protocols with low error variance (Cornelissen et al. 2003; Wright et al. 2006b). 970 We studied three sites with old-growth, lowland tropical forests located along the precipitation gradient across the Isthmus of Panama. We sampled four or eight 20 m x 20 m plots at each site, six leaves from all trees of all species in each plot, and three leaves from each of the sun and shade strata in each tree. The determinations of LMA and LDMC mostly follow Cornelissen et 975 al. (2003). Data normalized by log10 transformations were fitted by a general linear model where the scales were nested one into another. A variance components analysis was performed on this full model in R (R Development Core Team 2007) (See supplemental material for details) and cross-checked 980 with Matlab code (Gower 1963; The MathWorks Inc 2007). Since the results were very similar, we report only the results from R/REML. We calculated the mean Sorensen’s indices between plots for each site as well as between sites. To verify that variation in foliar traits at the species was not confounded with the variation at the plot level, we also built an alternative model which left out 985 the species level.

- GENERAL CONCLUSION -

990 Endorsing Contrasting Viewpoints This study shows that there is a significant trait variation at all ecological scales but one. The fact that our results do not fall in line with any of the principal paradigms governing trait-based ecology (Table 2) demonstrates just how surprising and instructive these findings are. 995 Investigation of this question was dearly needed and it now is clear that much more investigation and consideration of scaling issues is needed in the diverse research areas working studying functional trait variation. From the five paradigms presented in Table 2, we can produce the variance distribution pattern found in this study by combining elements from 1000 the “environmental filtering”, “phenotypic plasticity” and “budgeting” viewpoints. The lack of variation at the plot level can only be the result of environmental filtering on traits. The relative homogeneity of variance at the lower levels resembles the “phenotypic plasticity” and “budgeting” assumptions. Our results diverge from this viewpoint at the level of leaves 1005 within a stratum. The variance partitioning analysis pools in the lowest level variation due to differences among leaves in a strata and variation due to measurement error. However, it is unlikely that 10% (for LMA) and 15% (for LDMC) of the variation in trait values were due to measurement error. The “budgeting” paradigm is the only view that accredits this lower level. 1010 Intraspecific Trait Variability as “Noise” Intraspecific trait variation is the “pink elephant in the living room”: an obvious truth that goes unaddressed. Usually, this variation is implicitly

considered as a problem to work around and even plainly presented as such at 1015 times (Wilson et al. 1999; Roche et al. 2004). Our results clearly show that by disregarding intraspecific variation we might be throwing away the baby with the bathwater; the fact that there is more intra- than inter-specific variation in the two examined traits emphasizes that intraspecific variation should not be perceived as merely noise hindering the detection of a clear signal but on the 1020 contrary that it holds significant information. The variation occurring at each scale reflects different ecological mechanisms affecting traits and impacting plant-environmental interactions. Within a species, trait variation can be due to intraspecific genetic variability, to plasticity, ontogeny, water availability at time of leaf expansion, and to biophysical constraints. Future studies need to 1025 explicitly take into account this very large intraspecific variation. This can easily be done in at least two ways. First, it is important to recognize that we are often working with only subsets of the trait values presented by an individual, species or community, and to clearly define what we subsample. Very different interpretations can be drawn from sampling 1030 different subsets of trait values. For example, if researchers work only with sun leaves to minimize intraspecific variation, the trait values should be

referred to as “traitmax” or “traitsun” to avoid confusion with “traitmean”. Second, trait variation should be reported along with mean trait value (for example, as standard deviation) because patterns of variation hold meaningful information. 1035 Trait Based Environmental Filters The lack of variance at the plot level despite differences in species composition indicates the existence of a filter on the distribution of trait values within the site. In other words, any community at a given site must have a 1040 similar trait value distribution. This finding has two important implications.

First, the filter is operating on functional traits rather than on species identity per se. It is remarkable that filtering can be so strong (on the overall distribution of a trait) yet influence species composition so little (since variation occurs within species as well). This finding implies that future 1045 research will be much more successful in studying trait-environment relationships than species-environment relationships, as has been extensively done in the past. Second, filtering does not occur on the plot’s mean value, but on the distribution of values. For a given combination of trait values to be regulated, the environmental filter invoked here cannot be the traditional 1050 blind yes/no mechanism usually invoked. Some possible alternatives are that there is a feedback between the current trait composition and the filter or that the filter detected here is some emergent property of various filters jointly acting at different scales.

1055 Future Directions: Are Species Distinct Entities In Trait Space? Discovering that there is more intra- than inter-specific trait variation and that environmental filters act not on species but on traits questions whether the species is the relevant working unit for questions addressed through functional traits. While species are reproductively independent units, 1060 they might not be distinct units in terms of functional traits. The fact that species are not distinct from one another on one dimension of trait variation (i.e. on the leaf economic spectrum) raises the question of whether they segregate from one another in multidimensional trait space. To verify this, we first need to clarify what the other primary 1065 dimensions of variation are. The LHS dimensions are a good start (Westoby 1998), but more dimensions are probably needed to capture the variety of challenges faced by plants for their survival and reproduction and fully portray

the different facets of ecological behaviour. In addition to allow us to test whether species are distinct entities in trait space, a more thorough description 1070 of primary dimensions of variation and their constituent traits would allow us to verify if further environmental filters exist on those dimensions and if they collectively fine tune community composition. Above all, these findings strongly advise ecologists to pay more heed to traits and less to species to understand the interaction of plants with their environment (Lavorel and 1075 Garnier 2002).

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1360 - TABLES AND FIGURES -

Table 1. Summary of the seven main research areas involving functional traits.

Ecological Facet of Trait Research Area Question Studied Example of Papers Scale Variation A- Trait Plasticity Individuals & Trait variation due to Studies how much and why traits (Via et al. 1995; Species phenotypic plasticity vary within and among individuals Valladares et al. 2000; of a species in response to local Miner et al. 2005; environment Rozendaal et al. 2006) B- Correlations Species Co-variation among Seeks to identify primary (Reich et al. 1997; among traits sets of traits dimensions of variation among traits Westoby et al. 2002; that define trade-offs underpinning Wright et al. 2004b; different ecological strategies for Shipley et al. 2006a) survival and reproduction across environments C- Functional Species Trait similarity among Creates trait-based species grouping (Box 1996; Diaz and Classifications species reflecting similar ecosystem effect Cabido 1997; Lavorel or environmental response. et al. 1997) D- Traits Variation & Species & Trait composition and Focuses on the role of traits in (Keddy 1992; Diaz et Community Communities structure of ecological sorting (or environmental al. 1998; Shipley et al. Composition communities filtering) across environments. 2006a)

E- Trait Variation & Species & Traits variation across Focuses on which and how much (Fonseca et al. 2000; Environnemental Communities environmental traits vary with specific Ackerly et al. 2002; Gradients gradients environmental gradients Ackerly and Cornwell 2007) F- Trait Variation & Ecosystem Traits composition Studies how trait composition and (Diaz and Cabido 2001; Ecosystem and ecosystems functional diversity (measured as Lavorel and Garnier Processes structure functional types) control ecosystem 2002) processes. G- Trait Variation & Taxon Trait lability Studies niche structure by focusing (Silvertown et al. 2001; the Niche on how trait variation within a Ackerly 2003) phylogeny evolves.

Table 2. Anticipated outcomes of partitioning the variance in traits across 1365 ecological scales according to different research paradigms.

Ecological Scale Environmental Species Genetic Phenotypic Budgeting Filtering Cohesion Variation Plasticity Among sites Dominant f(dissim) f(dissim) f(dissim) f(dissim) Among plots within sites Negligible f(dissim) f(dissim) f(dissim) f(dissim) Among species within Dominant Dominant Dominant Dominant Dominant plots Among trees within Negligible Negligible Dominant Dominant Dominant species Among canopy strata Negligible Negligible Negligible Dominant Dominant within tree Among leaves within Negligible Negligible Negligible Negligible Dominant canopy stratum

Table 3. Description of study sites. 1 – STRI (Smithsonian Tropical Research Institute 2007), 2- Santiago 2005 (Santiago and Mulkey 2005), 3- Santiago 2004 (Santiago et al. 2004), 4- Condit 2004 (Condit et al. 1370 2004), 5- Leigh et al. 2004 (Leigh et al. 2004), * - the mean interval during which potential evapotranspiration (PET) exceeds rainfall, ** - species with stems ≥10 cm in diameter

Site Coordinates Mean Mean Parent Elevation Species Tree Annual Length of Material (m) Richness Density Precipitation Dry Season (#/ha) ** (#/ha) ** (mm) (days) *

PNM 8°59'N, 1850 1 129 2 Volcanic 2 60 3 36 3 318 3 79°33'W 1 BCI 9°10'N, 2620 1 118 4 Volcanic 5 140 5 91 5 429 5 79°51'W 1 PNSL 9°17'N, 3020 1 102 2 Sedimentary 2 140 3 87 3 659 3 79°58'W 1

1375 Table 4. Variance partitioning of the full nested linear models on leaf mass per area (LMA) and leaf dry matter content (LDMC) across six nested

ecological scales. All data were log10 transformed prior to analysis. n = 1965 leaves. Square brackets represent the 95% confidence intervals, 1380 which were calculated by bootstrapping (500 runs with 1965 randomly sampled data points with replacement). (* See Comment 1 in Supplemental Material)

Ecological % Variance of Trait & 95% C.I. Scale Log LMA Log LDMC Leaf & Error 10 [7:9] * 15 [9:15] * Strata 16 [16:21] 16 [12:26] Tree 22 [19:28] 17 [12:30] Species 21 [16:26] 35 [25:43] Plot 0 [0:0] 0 [0:0] Site 30 [27:32] 16 [13:19]

PNM

BCI

PNSL

1385 Figure 1. Location of Study Sites. PNSL – Parque Natural San Lorenzo. BCI – Barro Colorado Island. PNM – Parque Natural Metropolitano. Map from Google Earth.

Distribution of LMA for 17 most abundant species 250

225

200

175

150

125

100 LMA (g/m2) 75

25 Species 0

Virola sebifera BrosimumMarila utile laxiflora Castilla elastica

Tapirira Lueheaguianensis seemannii TovomitaSocratea longifolia exorrhiza Manilkara bidentata Trichilia tuberculata Oenocarpus maporaProtium panamense Faramea occidentalis Anacardium excelsum Lacmellea panamensisPerebea xanthochyma

Tabernaemontana arborea Distribution of LDMC for 17 most abundant species 0,7

0,6

0,5

0,4 LDMC (g/g) LDMC 0,3

0,2

Species 0,1

Virola sebifera BrosimumMarila utile laxiflora Castilla elastica

Figure 2. Boxplot of LMA and LDMC values for the 17 most abundant species (species for which 30 or more leaves were measured).

1395

1400

1405

1410

1415

Figure 3. Histogram of values for LMA and LDMC by sites and by plots. Solid histograms represent the sites: PNM (red), PNSL (black) & BCI 1420 (blue). Lines represent the histograms of individual plots for each site, colors coded accordingly.

- APPENDICES –

1425 Appendix I – Species List :

Table A1. – List of tree species sampled with number of leaves sampled per species and site. Species identification and phylogeny follows Croat (1978).

Leaf Species Family PNM BCI PNLS Total Alibertia edulis Rubiaceae 6 6 Alseis blackiana Rubiaceae 18 18 Anacardium excelsum Anacardiaceae 36 36 Andira inermis Fabaceae 12 12 Apeiba membranacea Tiliaceae 4 6 10 Aspidosperma cruentum Apocynaceae 18 18 Aspidosperma spruceanum Apocynaceae 12 6 18 Astrocaryum standleyanum Arecaceae 6 6 Astronium graveolens Anacardiaceae 12 12 Beilschmiedia pendula Lauracaeae 6 6 Brosimum alicastrum Moraceae 12 12 Brosimum guianensis Moraceae 6 6 Brosimum utile Moraceae 66 66 Carapa guianensis Meliaceae 6 6 Cassipourea elliptica Rhizophoraceae 6 12 18 Castilla elastica Moraceae 96 96 Cecropia insignis Cecropiaceae 4 4 Cecropia obtusifolia Cecropiaceae 9 9 Cespedesia spathulata Ochnaceae 12 12 Chamguava schippii Myrtaceae 6 6 Chimarrhis parviflora Rubiaceae 6 6 Chrysophyllum argenteum Sapotaceae 6 6 Cinnamomum triplinerve Lauraceae 12 12 Cordia alliodora Boraginaceae 18 18 Cordia bicolor Boraginaceae 18 18 Croton billbergianus Euphorbiaceae 24 24

Cupania scrobiculata Sapindaceae 18 18 Dendropanax arboreus Araliaceae 12 12 Desmopsis panamensis Annonaceae 6 6 Diospyros artanthifolia Ebenaceae 6 6 Dussia sp.1 Fabaceae 6 6 Eugenia coloradoensis Myrtaceae 6 6 Eugenia nesiotica Myrtaceae 12 12 Eugenia oerstediana Myrtaceae 12 12 Faramea occidentalis Rubiaceae 102 102 Ficus insipida Moraceae 6 6 Ficus maxima Moraceae 6 6 Garcinia intermedia Clusiaceae 6 6 Garcinia madruno Clusiaceae 12 12 Guapira standleyana Nyctaginaceae 6 6 Guarea guidonia Meliaceae 6 6 Guatteria dumetorum Annonaceae 6 18 24 Guazuma ulmifolia Sterculiaceae 6 6 Guettarda foliacea Rubiaceae 6 6 Hamelia axillaris Rubiaceae 6 6 Hasseltia floribunda Flacourtiaceae 6 6 Heisteria acuminata Olacaceae 6 6 Heisteria concinna Olacaceae 6 6 Herrania purpurea Sterculiaceae 6 6 Hieronyma alchorneoides Phyllantaceae 6 6 Hirtella triandra Chrysobalanaceae 12 12 Humiriastrum diguense Humiriaceae 6 6 Hybanthus prunifolius Violaceae 12 12 Inga nobilis Fabaceae 6 6 Inga pezizifera Fabaceae 12 12 Inga sapindoides Fabaceae 12 12 Jacaranda copaia Bignoniaceae 12 12 24 Lacistema aggregatum Lacistemataceae 6 6 Lacmellea panamensis Apocynaceae 12 18 30 Lindackeria laurina Flacourtiaceae 6 6 Luehea seemannii Tiliaceae 42 12 54 Manilkara bidentata Sapotaceae 30 30 Maquira guianensis Moraceae 6 6 12 Maranthes panamensis Chrysobalanaceae 12 12 Marila laxiflora Clusiaceae 66 66

Matayba apetala Sapindaceae 6 6 Miconia elata Melastomataceae 6 6 Miconia ligulata Melastomataceae 12 12 Miconia minutiflora Melastomataceae 6 6 12 Miconia sp.3 Melastomataceae 6 6 Mortoniodendron Tiliaceae anisophyllum 6 6 NA (unknown) NA (unknown) 24 24 Nectandra purpurea Lauraceae 18 18 Ochroma pyramidale Bombacaceae 6 6 Ocotea cernua Lauraceae 6 6 Ocotea dendrodaphne Lauraceae 6 6 Ocotea ira Lauraceae 6 6 Oenocarpus mapora Arecaceae 18 24 42 Ormosia coccinea Fabaceae 6 6 Palicourea guianensis Rubiaceae 3 3 Perebea xanthochyma Moraceae 6 30 36 Picramnia latifolia Simaroubaceae 6 6 Piper reticulatum Piperaceae 6 6 Pittoniotis trichantha Rubiaceae 6 6 Platypodium elegans Fabaceae 6 6 Poulsenia armata Moraceae 24 24 Pourouma bicolor Cecropiaceae 6 12 18 Pouteria reticulata Sapotaceae 6 6 Protium costaricense Burseraceae 12 12 Protium panamense Burseraceae 6 42 48 Protium tenuifolium Burseraceae 12 12 Psychotria horizontalis Rubiaceae 6 6 Pterocarpus rohrii Fabaceae 12 12 Quararibea asterolepis Bombacaceae 6 6 Randia armata Rubiaceae 6 6 Simarouba amara Simaroubaceae 18 18 Sloanea meianthera Elaeocarpaceae 6 6 Sloanea terniflora Elaeocarpaceae 6 6 Socratea exorrhiza Arecaceae 6 72 78 Spondias mombin Anacardiaceae 18 18 Symphonia globulifera Clusiaceae 6 6 Tabebuia guayacan Bignoniaceae 6 6 Tabernaemontana arborea Apocynaceae 42 42

Tachigali versicolor Fabaceae 6 12 18 Tapirira guianensis Anacardiaceae 48 48 Terminalia oblonga Combretaceae 6 6 Theobroma bernoullii Sterculiaceae 24 24 Tovomita longifolia Clusiaceae 66 66 Trattinnickia aspera Burseraceae 6 6 12 Trichilia poeppigii Meliaceae 6 6 Trichilia tuberculata Meliaceae 36 36 Turpinia occidentalis Staphyleaceae 6 6 Unonopsis panamensis Annonaceae 6 6 Unonopsis pittieri Annonaceae 12 12 Virola elongata Myristicaceae 12 12 Virola multiflora Myristicaceae 6 6 Virola sebifera Myristicaceae 6 30 36 Virola surinamensis Myristicaceae 6 6 Vochysia ferruginea Vochysiaceae 24 24 Xylopia macrantha Annonaceae 12 12 Zanthoxylum acuminatum Rutaceae 6 6 Site Totals and Grand Total 282 752 918 1952 1430

Appendix II – Chapter 1 Supplementary Material

Methods 1435 Traits and Scales We assessed variation in LMA and LDMC across six hierarchical ecological scales: 1- between leaves within a canopy stratum, 2- between sun and shade strata within trees 3- among trees within a species, 4- among species within a plot, 5- among plots within a site and 5- among sites. These are the 1440 scales most commonly studied by ecologists and the two traits are among the most important in plants (Reich et al. 1999; Weiher et al. 1999; Wright et al. 2004b; Shipley et al. 2006a). The ratio of a leaf’s mass per unit area (LMA: g/m2) is part of the leaf economic spectrum and closely correlated to photosynthetic capacity, nitrogen content per mass and leaf lifespan (Wright 1445 et al. 2004b). The ratio of a leaf’s dry mass to its water-saturated mass (LDMC: g/g) governs correlations among the traits in the leaf economic spectrum ; LDMC reflects the fundamental tradeoff in investing resources in structural tissues versus liquid phase processes (Shipley et al. 2006a).

1450 Data Collection To capture trait variation among sites, three old-growth forests located along the precipitation gradient across the Isthmus of Panama were sampled: Parque Nacional Metropolitano (PNM: ~1800 mm/year), Barro Colorado Island (BCI: ~2600mm/year), and Parque Nacional San Lorenzo (PNSL: 1455 ~3000mm/year)(Smithsonian Tropical Research Institute 2007). Water availability has a large influence on leaf functional traits (Reich et al. 1999; Wright et al. 2004b). To measure variation among plots within a site, we sampled 20 m x 20 m plots located 60 m apart at each site: four in the 1 ha

PNM permanent plot, eight in the 50 ha BCI permanent plot and eight in the 6 1460 ha PNSL permanent plot. To assess variation among species within a plot and among trees within a species, we sampled six leaves from all trees of all species for individuals with a diameter at breast height (dbh) greater than 10cm. To capture the variation between strata within a tree, we collected three leaves from each of the sun and shade strata when a tree had leaves both in the sun 1465 and shade. Samples were collected using a canopy crane in the four PNM plots and in four of the PNSL plots and using a shotgun in the other plots. We sampled a total of three sites, 20 plots, 120 species, 328 trees and 1952 leaves. The measurements of the two leaf traits follow standard protocols (Cornelissen et al. 2003). 1470 Data Analysis

Data normalized by log10 transformations were fitted by a general linear model where the scales were nested one into another (i.e. nested ANOVA with random effects). A variance components analysis was performed on this 1475 full model using the ‘lme’ and ‘varcomp’ functions of R, version 2.6.1 (See supplemental material for details) (R Development Core Team 2007) which use a restricted maximum likelihood (REML) method. These analyses in R were cross-checked with Matlab code using a traditional Type I sum-of- squares (Gower 1963). Since the results were very similar, we report only the 1480 results from R/REML. Differences between plots may be due to differences in environment or differences in species composition, with the latter likely to be high in tropical forests due to the high species diversity. To assess the effect of this, we calculated the mean Sorensen’s indexes between plots for each site. To verify that variation in foliar traits at the species was not confounded with the

1485 variation at the plot level, we also built an alternative model which left out the species level.

R Code Used in Partitioning of Variance

varcomp.LMA<-varcomp(lme(log(LMA)~1, random= ~1|Sitio/Parcela/Especie/Arbol/Strata, 1490 data=d,na.action=na.omit),1)

The preceding code was used from the command line in R for the full model on LMA, and with the obvious minor changes for LDMC, to calculate the variance partitioning of the traits across the six nested ecological scales. 1495 The model is a nested general linear model with random nested effects. The ‘ape’ and ‘nlme’ libraries are necessary to use the ‘varcomp’ and ‘lme’ commands.

Results of Variance Partitioning Without Species Level

1500 varcomp2.LMA<-varcomp(lme(log(LMA)~1, random=~1|Sitio/Parcela/Arbol/Strata, data=d, na.action=na.omit),1)

The preceeding code was used to partition variance in R for the 1505 alternative model removing the species level to avoid possible confounding of species and plot:

Table S1. – Variance partitioning of an alternative nested linear model of LMA and LDMC across five ecological scales (Leaf, Strata, Tree, Plot and 1510 Site), leaving out the species scale. All data were log10 transformed prior to analysis. n = 1965 leaves. Square brackets represent the 95% confidence intervals, which were calculated by bootstrapping (500 runs with 1965 randomly sampled data points with replacement).

Ecological % Variance of Trait & 95% C.I. Scale Log LMA Log LDMC Leaf & Error 11 [7:10] * 15 [9:15] Strata 17 [17:24] 16 [12:25] Tree 43 [38:45] 47 [42:56] Plot 0 [0:0] 2 [1:4] Site 29 [27:32] 18 [16:21] 1515 * See Calculation of Confidence Interval comment below

LMA & LDMC variance partitioning across five (alternative model) and six (full model) nested ecological scales 100 10 % 11 % 15 % 15 % Site 90 Plot Species 80 16 % 17 % 16 % 16 % Tree 70 Strata 22 % Leaf & Error 60 17 % 43 % 50 47 % 40 21 % % Variance 35 % 30 6e-7 % 4e-7 %

20 2 % 1e-6 % 30% 29 % 10 16 % 18 % 0 LMA- full LMA-alternative LDMC-full LDMC-alternative Trait & Model Figure S1. – LMA & LDMC variance partitioning across ecological scales for 1520 the full model (with species level included) and the alternative model (without species level).

Calculation of Confidence Intervals

In Table 3 and Table S1, the leaf & error level shows a bootstrap confidence interval that does not include the estimated variance. This is 1525 because random sampling with replacement will typically duplicate leaves and not sample the full variance found in the 3 leaves measured in the experimental design used in estimating the variance component, resulting in a bias at this level.

1530