Rev Iss Web Jvs 12097 24-5 777..780

Journal of Vegetation Science September 2013 777 — 974 Issue 5 Volume 24 Journal of Vegetation Science Journal of Vegetation Science Volume 24 • Issue 5 • September 2013 • ISSN 1100-9233 Advances in plant community ecology Volume 24 Issue 5 September 2013 Contents

Special Feature: Functional Diversity

N.W.H. Mason & F. de Bello – Functional diversity: a tool for answering challenging ecological questions 777 S. Pavoine, A. Gasc, M.B. Bonsall & N.W.H. Mason – Correlations between phylogenetic and functional diversity: 781 mathematical artefacts or true ecological and evolutionary processes? N.W.H. Mason, F. de Bello, D. Mouillot, S. Pavoine & S. Dray – A guide for using functional diversity indices to reveal 794 changes in assembly processes along ecological gradients F. de Bello, C.P. Carmona, N.W.H. Mason, M.-T. Sebastià & J. Lepš – Which trait dissimilarity for functional diversity: 807 trait means or trait overlap? N.W.H. Mason & S. Pavoine – Does trait conservatism guarantee that indicators of phylogenetic community structure 820 will reveal niche-based assembly processes along stress gradients? E. Laliberté , D.A. Norton & D. Scott – Contrasting eff ects of productivity and disturbance on plant functional diversity 834 at local and metacommunity scales P. Gerhold, J.N. Price, K. Püssa, R. Kalamees, K. Aher, A. Kaasik & M. Pärtel – Functional and phylogenetic community 843 assembly linked to changes in species diversity in a long-term resource manipulation experiment L. Chalmandrier, T. Münkemüller, L. Gallien, F. de Bello, F. Mazel, S. Lavergne & W. Thuiller – A family of null models 853 to distinguish between environmental fi ltering and biotic interactions in functional diversity patterns R.J. Pakeman & A. Eastwood – Shifts in functional traits and functional diversity between vegetation and seed bank 865 M. Bernard-Verdier, O. Flores, M.-L. Navas & E. Garnier – Partitioning phylogenetic and functional diversity into alpha 877 and beta components along an environmental gradient in a Mediterranean rangeland M. Hejda & F. de Bello – Impact of plant invasions on functional diversity in the vegetation of Central Europe 890 Š. Janeček, F. de Bello, J. Horník, M. Bartoš, T. Černý, J. Doležal, M. Dvorský, K. Fajmon, P. Janečková, Š. Jiráská, 898 O. Mudrák & J. Klimešová – Eff ects of land-use changes on plant functional and taxonomic diversity along a productivity gradient in wet meadows T. Herben, Z. Nováková & J. Klimeš ová – Comparing functional diversity in traits and demography of Central 910 European vegetation C.M. Hulshof, C. Violle, M.J. Spasojevic, B. McGill, E. Damschen, S. Harrison & B.J. Enquist – Intra-specifi c and 921 inter-specifi c variation in specifi c leaf area reveal the importance of abiotic and biotic drivers of species diversity across elevation and latitude M. Carboni, A.T.R. Acosta & C. Ricotta – Are diff erences in functional diversity among plant communities on 932 Mediterranean coastal dunes driven by their phylogenetic history? S. Lavorel, J. Storkey, R.D. Bardgett, F. de Bello, M.P. Berg, X. Le Roux, M. Moretti, C. Mulder, R.J. Pakeman, 942 S. Dí az & R. Harrington – A novel framework for linking functional diversity of plants with other trophic levels for the quantifi cation of ecosystem services M. Moretti, F. de Bello, S. Ibanez, S. Fontana, G.B. Pezzatti, F. Dziock, C. Rixen & S. Lavorel – Linking traits between 949 plants and invertebrate herbivores to track functional eff ects of land-use changes V.D. Pillar, C.C. Blanco, S.C. Müller, E.E. Sosinski, F. Joner & L.D.S. Duarte – Functional redundancy and stability 963 in plant communities

Indexed and abstracted in: Absearch, Biological Abstracts, Elsevier BIOBASE/Current Awareness in Biological Sciences, Current Contents, Environmental Periodicals Bibliography, International Association for Vegetation Science ISI Web of Science

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jjvs_24_5_ic.inddvs_24_5_ic.indd 1 77/27/2013/27/2013 11:56:1311:56:13 AMAM Journal of Vegetation Science 24 (2013) 777–780 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Functional diversity: a tool for answering challenging ecological questions Norman W. H. Mason & Francesco de Bello (SPECIAL FEATURE EDITORS)

Keywords Abstract Beta diversity; Community assembly; Ecosystem function; Environmental filtering; Functional trait diversity provides a powerful means of addressing ecology’s per- Niche complementarity; Phylogenetic diversity sistent questions, through its dual role as an indicator of mechanisms driving differences in species composition between communities and as a predictor of Received 7 May 2013 ecosystem-level processes. Functional traits provide a means of testing mecha- Accepted 7 May 2013 nisms behind species turnover between communities because environmental Co-ordinating Editor: Bastow Wilson heterogeneity, competition and disturbance influence species fitness via their traits. Functional traits also provide a link between species and multiple ecosys- Mason, N.W.H. (corresponding author, tem-level processes, such as primary productivity, nutrient fluxes and resilience, [email protected]): Ecosystem since species influence these processes via their traits. This special issue demon- Processes, Landcare Research, Private Bag strates that functional diversity offers a practical means of investigating ecology’s 3127, Hamilton, New Zealand persistent questions. de Bello, F. ([email protected]): Institute of Botany, Academy of Sciences of the Czech Republic, Dukelska 135, 379 82, Trebon, Czech Republic and Department of Botany, University of South Bohemia, Na Zlate stoce 1, 370 05, Cesk e Budejovice, Czech Republic

Quantification of functional diversity and its Introduction components Many studies are emerging using functional traits and Understanding the diversity of functional diversity indices functional diversity (FD) to assess long-standing ecological questions. However, as pointed out by Cadotte et al. The quantification of FD requires a set of measureable (2011), the body of empirical evidence on the patterns concepts. Many definitions of FD have been provided by of FD is still rather small. This special issue presents a different authors. Most definitions agree that FD is the number of studies demonstrating how FD might help us diversity of the functional traits of the species in a commu- to solve some of the long-standing questions in ecology. nity. However, this general definition gives little specific In this Editorial, we outline how the special issue has guidance on how the concept of diversity should be advanced different aspects of FD research. These aspects applied to functional trait data. Mason et al. (2005) pro- include: (1) the quantification of FD; (2) the use of FD posed a definition of FD as the distribution in functional to reveal mechanisms driving community assembly and trait space of the species presence and abundance in a com- ecosystem functions; and (3) relationships between FD munity, including three components: (1) the amount of and other aspects of biodiversity. We devote a specific functional trait space filled by species in the community section to each of these areas, providing a background (functional richness); (2) the evenness of abundance distri- and synthesis of the different studies comprising this bution in filled trait space (functional evenness); and (3) special issue. We discuss some possible developments the degree to which the distribution of species abundances and uncertainties that still remain to be resolved in the maximizes divergence in functional traits (functional field. divergence).

The advantage of this definition is that it identifies dif- within-community diversity into within vs. between spe- ferent measurable components of FD. Often the plethora cies components. of available indices is bewildering for ecologists trying to Partitioning the total diversity of a region (gamma diver- decide how to measure FD, but some indices that appear to sity) into within- (alpha diversity) and between-commu- be complementary are actually very closely related mathe- nity (beta diversity; Whittaker 1975) components, is matically (Pavoine & Bonsall 2011). In this special issue, receiving increasing attention as a means of prioritizing two papers (Mason et al. 2013; Pavoine et al. 2013) show conservation efforts. Methods have emerged that permit that categorizing and decomposing FD indices according to direct comparison of the alpha and beta components of the above three FD components not only helps to simplify taxonomic, functional and PD (Bernard-Verdier et al. selection of indices, but also provides a framework for 2013; Carboni et al. 2013; Janecek et al. 2013). The FD interpreting variation in FD index values along ecological within a community (alpha diversity) can be further gradients. The power of mathematical decomposition is decomposed into between-species and within-species very well illustrated in Pavoine et al. in assessing evidence components. It is becoming evident that taking into for correlations between FD and phylogenetic diversity account this within-species FD may be essential to under- (PD) indices in field and simulated data. In particular, Pav- standing the patterns of species co-existence and of species oine et al. demonstrate that most existing indices are distributions (Hulshof et al. 2013). In particular, trait dis- affected by co-varying factors (usually species richness and similarity measures that take into account within-species evenness in species abundance), and that removal of the trait variability provide a more robust way to assess the effect of these co-varying factors often requires observed joint effects of environmental filtering and niche comple- index values to be expressed relative to an appropriate null mentarity on community assembly (de Bello et al. 2013). model. Community assembly processes Alpha, beta and gamma functional diversity The last few years have seen a rapid increase in the use of The definition of FD proposed in Mason et al. (2005) FD indices to reveal plant community assembly processes. focuses very much on trait diversity between species This is one of the most persistent questions in ecology and within communities (alpha diversity). However, recent one of the main themes of this special issue. In general, years have seen a proliferation of studies examining FD at evidence for niche complementarity (when trait dissimilar- a variety of different scales (e.g. Fig. 1) – within species, ity between co-occurring species increases fitness) is between communities (i.e. beta diversity), regional (i.e. assumed when observed FD is higher than that expected gamma diversity) and even global. This special issue spans under an appropriate null model. By contrast, evidence of all these scales, particularly decomposing regional diversity environmental filtering is assumed when observed FD is into within- and between-community components and, less than expected (Cornwell et al. 2006). In this special issue, Mason et al. (2013) use simple assembly models to provide some guidance on which indices, combined with which null models, can reliably detect changes in assembly Functionaldiversity(FD) processes across a wide range of ecological contexts. partitioning Comparison of results from several field studies within this special issue (Gerhold et al. 2013; Janecek et al. 2013; FD between communities Laliberte et al. 2013) reveal various possible shifts in FD along productivity and disturbance gradients. There is an obvious gap in the literature for an integrated study, incor- FD between species

Total FD within communities porating a large number of data sets and using standardized methods for calculating FD, to test whether assembly

FD within species processes shift in a consistent direction along ecological within communities gradients – particularly gradients of productivity and dis- REGION1 REGION2 turbance. Three studies in this special issue (Chalmandrier et al. Fig. 1. Different hierarchical levels at which functional diversity (FD) can 2013; Herben et al. 2013; Laliberte et al. 2013) demon- be studied. This hypothetical example shows the case of two regions with strate the importance of scale in using FD indices to reveal a different spatial partitioning of FD. Region 1, compared to Region 2 presents a higher total diversity but a smaller between-species FD within assembly processes. Laliberte et al. found increased envi- communities. Region 1 presents a smaller within-species FD within ronmental ﬁltering with increasing fertilization/productiv- communities. ity at the metacommunity scale, due to exclusion of short

Journal of Vegetation Science 778 Doi: 10.1111/jvs.12097 © 2013 International Association for Vegetation Science N.W.H. Mason & F. de Bello Functional diversity and slow-growing species. At local community scale, Links between functional and phylogenetic diversity where biotic interactions influence species relative abundances and co-occurrences, they found evidence for Phylogenetic diversity is increasingly used as a means of increasing niche complementarity with increasing produc- detecting niche-based community assembly processes. tivity. Chalmandrier et al. and Herben et al. show that the This obviously assumes that variation in the traits that spatial scale of study influences the definition of the pool influence assembly processes is phylogenetically con- of species on which null models are based, which strongly served. Another, often unacknowledged, assumption is determines conclusions on community assembly. that trait conservatism at the species pool level is retained A variety of novel uses for FD in the study of community during community assembly. Studies dealing with PD in assembly processes have been revealed in this special issue. this special issue generally suggest that PD indices are Hejda & de Bello (2013) used FD indices to show that inva- poorly suited to the study of plant community assembly sive plants often introduce novel trait combinations to processes. Gerhold et al. (2013) do show some congru- invaded plant communities. Pakeman & Eastwood (2013) ence between the responses of PD and FD to experimen- show contrasts in the relationship between FD and ecologi- tally manipulated productivity in meadow communities, cal gradients for vegetative vs seed bank communities. If but also found that PD and FD were generally poorly cor- future studies find a consistent disconnect between FD in related. Bernard-Verdier et al. (2013) and Pavoine et al. vegetation and seed banks, this might improve our under- (2013) demonstrate that PD very poorly captures varia- standing of how disturbance impacts FD in plant commu- tion in functional structure along gradients of soil resource nities. Hulshof et al. (2013) compare the contribution of availability and salinity. This is true even when there is intra- and inter-specific trait variation in FD. They reveal a strongly significant phylogenetic trait conservatism at the decrease in the relative contribution of intra-specific varia- species pool level. Pavoine et al. also show that when tion as species richness increases. These novel applications strong correlations between FD and PD are obtained, this of FD indices to explore questions around community is almost always attributable to co-varying factors such as assembly demonstrate that this is a young and expanding species richness and evenness in species abundances. field. Using simulated communities Mason & Pavoine (2013) show that PD indices are unable to recover known shifts in assembly processes along ecological gradients. They Ecosystem processes within and across trophic demonstrate that this is because turnover in phylogenetic levels composition and diversity along ecological gradients does Managing ecosystems to ensure the provision of multiple not capture functional turnover. In other words, the phy- ecosystem processes and services is a key challenge for logenetic signal in trait variation at the species pool level is theoretical and applied ecology. Recent syntheses and often not maintained during community assembly. empirical studies have highlighted that functional traits predict the effects of global changes on ecosystem services Conclusion better than species diversity per se (Cadotte et al. 2011). Therefore, functional traits and FD are receiving increas- Collectively, the studies within this special issue provide a ing attention as the main biotic component through good example of the potential power of functional diver- which different organisms and communities can influ- sity to answer ecological questions. They also reveal many ence ecosystems processes. In this special issue, three of the potential pitfalls involved in using functional diver- studies show how to incorporate FD, within and across sity indices as indicators of community assembly and eco- trophic levels, to improve understanding of variation in system function. Many ecological questions will remain ecosystem processes. Lavorel et al. (2013.) propose a new problematic for years to come and inspire, new generations framework for assessing multitrophic linkages via func- of ecologists to develop novel approaches in search of tional traits. Moretti et al. (2013) explore these multi- answers. The present special issue shows that functional trophic trait linkages and their effects on multiple diversity may lead to new perspectives on persistent ecolo- ecosystem processes focusing on the linkages between gical question. plant and grasshopper traits and their interactions on grassland biomass. These studies will help to test hypoth- Acknowledgements eses and identify knowledge and data gaps that limit our We thank all authors, editors and referees that, with their understanding of functional processes in ecosystems. passion and dedicated work, made this special issue in the Another study demonstrates the benefit of functional Journal of Vegetation Science possible. Our most special redundancy for stability and resilience of ecosystems after thanks go to Sally Sellwood, for her invaluable contribu- disturbance (Pillar et al. 2013).

Journal of Vegetation Science Doi: 10.1111/jvs.12097 © 2013 International Association for Vegetation Science 779 Functional diversity N.W.H. Mason & F. de Bello tion as editorial secretary. We also thank Robin Pakeman, Janecek, S., de Bello, F., Hornık, J., Bartos, M., Cern y, T., Valerio Pillar, Bastow Wilson and Meelis Partel€ for their Dolezal, J., Dvorsky, M., Fajmon, K., Janeckova, P., Jirask a, help in improving this editorial. S., Mudrak, O., & Klimesova, J. 2013. Effects of land-use changes on plant functional and taxonomic diversity along a References productivity gradient in wet meadows. Journal of Vegetation Science 24: 898–909. de Bello, F., Carmona, C.P., Mason, N.W.H., Sebastia, M-T., & Laliberte, E., Norton, D.A., & Scott, D. 2013. Contrasting effects Leps, J. 2013. Which trait dissimilarity for functional of productivity and disturbance on plant functional diversity diversity: trait means or trait overlap? Journal of Vegetation at local and metacommunity scales. Journal of Vegetation Sci- Science 24: 807–819. ence 24: 834–842. Bernard-Verdier, M., Flores, O., Navas, M-L., & Garnier, E. Lavorel, S., Storkey, J., Bardgett, R.D., de Bello, F., Berg, M.P., 2013. Partitioning phylogenetic and functional diversity into Le Roux, X., Moretti, M., Mulder, C., Pakeman, R.J., Dıaz, alpha and beta components along an environmental gradi- S., & Harrington, R. 2013. A novel framework for linking ent in a Mediterranean rangeland. Journal of Vegetation Science functional diversity of plants with other trophic levels for the 24: 877–889. quantification of ecosystem services. Journal of Vegetation Sci- Cadotte, M.W., Carscadden, K., & Mirotchnick, N. 2011. Beyond ence 24: 942–948. species: functional diversity and the maintenance of ecologi- Mason, N.W.H. & Pavoine, S. 2013. Does trait conservatism cal processes and services. Journal of Applied Ecology 48: guarantee that indicators of phylogenetic community struc- 1079–1087. ture will reveal niche-based assembly processes along stress Carboni, M., Acosta, A.T.R., & Ricotta, C. 2013. Are differences gradients? Journal of Vegetation Science 24: 820–833. in functional diversity among plant communities on Medi- Mason, N.W.H., Mouillot, D., Lee, W.G., & Wilson, J.B. 2005. terranean coastal dunes driven by their phylogenetic his- Functional richness, functional evenness and functional tory? Journal of Vegetation Science 24: 932–941. divergence: the primary components of functional diversity. Chalmandrier, L., Munkem€ uller,€ T., Gallien, L., de Bello, F., Ma- Oikos 111: 112–118. zel, F., Lavergne, S., & Thuiller, W. 2013. A family of null Mason, N.W.H., de Bello, F., Dray, S., Mouillot, D., & Pavo- models to distinguish between environmental filtering and ine, S. 2013. 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Journal of Vegetation Science 780 Doi: 10.1111/jvs.12097 © 2013 International Association for Vegetation Science Journal of Vegetation Science 24 (2013) 781–793 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Correlations between phylogenetic and functional diversity: mathematical artefacts or true ecological and evolutionary processes? Sandrine Pavoine, Amandine Gasc, Michael B. Bonsall & Norman W.H. Mason

Keywords Abstract Assembly processes; Biodiversity; Diversity indices; Environmental ﬁltering; Niche Questions: Is phylogenetic diversity (PD) an accurate surrogate for functional complementarity; Null model; Phylogenetic diversity (FD)? How are FD:PD correlations affected by the diversity index used, signal; Simpson index; Species richness; covarying factors and/or the strength of the phylogenetic signal in ecological Surrogates traits?

Abbreviations Location: Field study, coastal marsh plain Mekhada, Algeria, complemented by FD = Functional diversity; PD = Phylogenetic simulated data. diversity Methods: FD and PD indices might correlate simply because variation in species Received 31 March 2012 richness and evenness (referred to as co-factors) influences both FD and PD val- Accepted 4 January 2013 ues. We partition FD and PD indices into components influenced by species rich- Co-ordinating Editor: Andreas Prinzing ness, evenness and species’ (functional and phylogenetic) characteristics. When a simple partition was not found, comparison to null models was used to remove Pavoine, S. (corresponding author, the effects of co-factors. We examined correlations between ten FD and PD indi- [email protected]): Gasc, A. ([email protected]): ces, among which several were shown to be connected using our mathematical Museum national d’Histoire naturelle, partitioning approach and several were transformed by comparison with null Departement d’Ecologie et Gestion de la models to control for effects of co-factors. In doing this, FD values were calcu- biodiversite, UMR 7204 CNRS-UPMC, 61 rue lated using simulated trait values with varying phylogenetic signal. We then Buffon, 75005, Paris, France Pavoine, S. & Bonsall, M.B. (michael.bonsall selected a subset of complementary FD and PD indices in exploring the influence @zoo.ox.ac.uk): University of Oxford, of environmental variables on diversity across 75 plant assemblages in Mekh- Department of Zoology, Mathematical Ecology ada. Research Group, South Parks Road, Oxford, OX1 3PS, UK Results: Altogether, mathematical partitioning and the comparison to null Gasc, A.:Museum national d’Histoire models successfully removed the effects of co-factors when comparing FD and naturelle, Departement Systematique et PD. For all indices affected by species richness, FD:PD correlations approached 1, Evolution, UMR 7205 CNRS, 43 rue Buffon, irrespective of the trait evolution model used. In contrast, simulations showed 75005, Paris, France that FD:PD correlations measured with indices unaffected by co-factors Bonsall, M.B.: St. Peter’s College, New Inn decreased when the phylogenetic signal in traits decreased. Applied to plant Hall Street, Oxford, OX1 2DL, UK assemblages in Mekhada, complementary diversity indices showed that, despite Mason, N.W.H. (masonn@landcareresearch. co.nz): Landcare Research, Private Bag, 3127, significant (but moderate) FD:PD correlation, FD but not PD was significantly Hamilton, New Zealand correlated with the main stress gradient (salinity). Conclusions: From both our simulations and analysis of plant community diversity, PD was a poor surrogate for FD. In Mekhada, PD was also less correlated with environmental variables than FD. Species richness was found to be a better surrogate for FD than PD in identifying the ecological processes that dis- tribute species along the salinity gradient.

ecosystem function and prioritize areas for conservation. Introduction These studies generally assume that PD is a surrogate for Phylogenetic diversity (PD) is increasingly used to under- functional diversity (FD), with closely related species being stand community assembly processes, predict rates of more similar in their functional traits than distantly related

Journal of Vegetation Science Doi: 10.1111/jvs.12051 © 2013 International Association for Vegetation Science 781 Correlating phylogenetic with functional diversity S. Pavoine et al. ones (Webb et al. 2002; Blomberg et al. 2003). Disparities simply correlating PD with FD is thus not sufficient; it is between FD and PD have also been used to infer that phy- imperative to find an association between PD and some logeny captures more ecologically relevant functional vari- aspect of the ecological process (e.g. along an environmen- ation than the limited number of traits used to estimate FD tal gradient as in our case study) or processes affecting the (Cadotte et al. 2009). However, there has been little criti- community organization, similar to the association cal assessment of whether PD can really act as a general between FD and the same aspect of the ecological process surrogate for FD. Testing this requires appropriate indices (es). For example, several studies in meadow grassland for examining correlation and association between FD and communities have found that increased FD was associated PD. This study explores the relationships between FD and with higher rates of productivity (Petchey et al. 2004). At PD indices. Using real and simulated data, we test whether a broader scale, the correlation between FD of forest tree PD is likely to be a useful surrogate for FD in community assemblages and plant productivity was found to be higher ecological studies. In doing so we aim to identify which in less productive, boreal environments (due to niche com- diversity indices have the power to discriminate relation- plementarity effects) compared to productive, temperate ships between FD and PD, and are not confounded by co- environments, where niche complementarity effects are varying factors. weakened by highly productive species occurring in monospecific stands (Paquette & Messier 2011). As might be expected, the choice of which functional traits to IS PD likely to be a realistic surrogate for FD? include in an analysis is the most important aspect in mea- Our starting point, as with many studies, relies on the suring FD (Petchey & Gaston 2002). For example, PD has hypothesis that (unmeasured) traits are evolutionary con- been found to be a better indicator of ecosystem function served. As shown in Webb et al. (2002) and Kraft et al. (e.g. primary productivity; Flynn et al. 2009) than FD, (2007), patterns in PD depend on whether critical traits are simply as a result of the difficulties associated with finding conserved or convergent. For PD to be a generally useful relevant traits with which to measure FD (Cadotte et al. surrogate of FD, the relationship between FD and PD thus 2009). needs to be robust against phylogenetically independent trait variation. Mismatch between FD and PD may be Identifying non-redundant indices for testing FD and explained by lack of resolution in phylogenies but also by PD correlations phenotypic plasticity in traits of distant species (Kluge & Kessler 2011). For many functional aspects of diversity, in In this study, we focus on the mathematical explanation a range of different phylogenies, phylogenetic distances that affects the relationships between PD and FD. Biodiver- are able to explain a significant amount of variation in trait sity is a multifaceted concept, which no single index can values across taxa (Blomberg et al. 2003). However, varia- hope to quantify in an easily interpretable way (Pavoine & tion in traits across species is the outcome of both environ- Bonsall 2011). Measures of both FD and PD are affected by mental and historical factors (Freckleton & Jetz 2009), so how species are distributed in functional or phylogenetic that the strength of the phylogenetic signal in trait varia- space and also how abundances vary among species. When tion can differ markedly according to the specific traits and the objective of a study is to identify ecological mecha- the phylogeny under investigation (Blomberg et al. 2003). nisms that explain species’ occurrences and abundances, a Indeed, cases where phylogenetic and trait variation are guiding principle in selecting interpretable FD and PD indi- very tightly linked may well be the exception rather than ces is that each index should quantify a single, indepen- the rule (Blomberg et al. 2003). Both conserved and con- dent aspect of biodiversity (Mason et al. 2005). vergent traits have been found to be involved in ecological Interpreting correlations between FD and PD requires indi- processes, such as environmental filtering and competition ces that uphold this principle. (Emerson & Gillepsie 2008). There are many biological rea- The FD and PD indices depend on three metrics: (1) spe- sons to expect no relationships between PD and FD, or cies’ identities; (2) species’ abundances; and (3) species’ even a negative relationship (Emerson & Gillepsie 2008). distribution in functional or phylogenetic space. In this One obvious explanation is that species may evolve while study, our first aim is to provide a definitive means of persisting within an assemblage, thus decreasing the rela- assessing whether indices proposed to measure FD and PD tionships between FD and PD (Kluge & Kessler 2011). are truly independent of diversity measures that depend Overall, trait divergence (Prinzing et al. 2008) and adap- only on species’ identities and abundances, such as species tive diversification (Fukami et al. 2007) can decrease the richness and evenness. Our approach seeks to mathemati- link between FD and PD. cally partition diversity indices into their basic compo- As the ultimate objective for the use of PD is to elucidate nents. This partitioning provides a more definitive the ecological mechanisms that structure communities, approach to assessing whether an index is truly

Journal of Vegetation Science 782 Doi: 10.1111/jvs.12051 © 2013 International Association for Vegetation Science S. Pavoine et al. Correlating phylogenetic with functional diversity independent from, or shares some redundancies with, might arise simply due to some artefact (like variation in other indices. species richness), rather than a real ecological process.

Using null models to remove redundancy between Methods indices We have used real data to evaluate the relevance of statisti- Recently, conceptual arguments have been made for using cal approaches that compare FD and PD. When necessary, null models to explore (and hence remove) redundancy this real data set was compared with simulated data (see between FD or PD indices and existing diversity measures. FD and PD correlations in simulated data). The field study We have explored this approach for those indices for which is located at La Mafragh, Mekhada, (15 000 ha, 36°48′N, a simple mathematical partitioning has not been obtained. 008°00′E), a coastal marsh plain of low elevation (from 1 For example, for indices that are based only on species’ to 4 m a.s.l. for most of the area) in the east of Annaba in characteristics, comparison of observed FD or PD values to Algeria. This region is located in a sub-humid bioclimate those expected from null models that randomize species with warm winters (Emberger 1955). It is furrowed by riv- occurrences (Hardy 2008) have been used to control for the ers, and constitutes a basin filled by alluvial and colluvial trivial effects of species richness. Also, indices that are based deposits. The lowest parts are composed of large and small on species’ characteristics and abundances can be compared marshes. The study area was defined in de Belair (1981). It with a null model that randomizes the abundances across constitutes 10 000 ha within the coastal plain where 102 species within each community (Hardy 2008) to control for sites were defined on a regular grid (average size of the effect of the evenness of species’ abundances. sites = 100 ha). Five of these sites, with very high heterogeneity, and 22 other sites (predominantly located in the central marsh) with three species or less, were excluded Aims and objectives from our analyses, leaving 75 sites. The plant abundance Our broad aim is to understand the association between data were collected in 1979 based on three releves per site FD and PD in communities. The ultimate objectives of this (separated by 200 m). Details on the sampling design can work are twofold. First, we aim to provide a test of whether be found in de Belair (1981) and Pavoine et al. (2011). (and under what conditions) PD is correlated with FD. Sec- A total of 56 plant species was observed. Nine biological ond, we aim to identify a set of appropriate indices for test- traits are available for the recorded plant species: (1) life ing correlations between FD and PD in real plant cycle (perennial, annual, biennial and/or seasonal), (2) communities, according to the following criteria: pollination (autogamous, entomogamous and/or anemogamous), (3) median of the potential flowering period, (4) 1. Each index should quantify a single component of FD length of the flowering period, (5) maximum and (6) mini- or PD that is independent of species richness and evenness. mum height of adult individuals, (7) spikiness (absent, 2. Each index should provide good power to detect FD:PD sometimes present, always present), (8) succulent leaves correlations. (absent, sometimes present, always present), (9) hairy 3. Correlation levels between FD and PD should not be leaves (absent, sometimes present, always present). Life dependent on any mathematical artefact. cycle and flowering periods are important to determine We begin by decomposing FD and PD indices into sim- how plants can cope with harsh (xeric) seasonal condi- pler components to identify those that are truly indepen- tions. For instance, most dicots in La Mafragh were annual dent from species richness and evenness. We then use null or biennial, completing their life cycle before the onset of models to remove remaining redundancies between indi- the xeric season (Pavoine et al. 2011). Pollination mode ces and analyse whether this affects the strength of FD:PD may influence species occurrence along the main stress correlations. We simulate trait values to test whether the gradient if biotic pollen vectors are less abundant in high strength of the phylogenetic signal in trait variation affects stress, low productivity sites. Spikiness, succulent leaves FD:PD correlations. Finally, we examine FD:PD correla- and hairy leaves are morphological adaptations that aid tions across real plant assemblages in the Algerian coastal water conservation. Spikiness and hairy leaves also reflect marsh plain using a subset of identified non-overlapping solar radiation, which can be important for avoiding pho- FD and PD indices. Doing this, we intend to examine the toinhibition and photorespiration in high stress environ- potential for FD:PD correlations to reveal underlying eco- ments. Plant height is an indicator of light competition logical processes that structure species communities rather ability, and is usually associated with a K-selected (com- than simply providing a robust test of a range of different petitive) ecological strategy (Westoby et al. 2002). processes. This especially relates to the use of diversity par- A phylogenetic tree was established based on Webb & titioning measures to identify indices where correlations Donoghue (2005) for topology; further details are given in

Pavoine et al. (2011). As with any study in phylogenetic Tree size (TS) is the sum of all branch lengths in a phy- community ecology, our results might be affected by logeny (Faith 1992) or a functional dendrogram (Petchey uncertainties associated with the phylogenetic tree (which & Gaston 2002). FEve (Villeger et al. 2008) is an abun- is only an estimation of the unknown real tree). The phy- dance-weighted measure of functional regularity. If a sin- logenetic tree we utilize has been widely used in studies on gle quantitative trait is considered, FEve would have its phylogenetic community analyses (179 citations for Webb maximum value if all species’ values were regularly & Donoghue 2005 according to the ISI Web of Knowledge, spaced in the quantitative trait axis and if all species had August 2012). Using this phylogenetic tree we integrated similar abundances. We have also used this index when the most up-to-date estimates of branch lengths all species have equal abundances and named it FEvep. (Wikstrom€ et al. 2001; Hedges & Kumar 2009) (in contrast HED and EED (Cadotte et al. 2010) measure evenness in many previous studies on phylogenetic community diver- phylogenetic or functional distinctiveness (where distinc- sity have just used Wikstrom€ et al. 2001; or simply tiveness is high for species that are distant to all others in counted nodes ignoring branch lengths, and hence lack a phylogeny or a functional dendrogram). B (Shimatani comprehensive details on evolutionary history). The pedo- 2001) is a measure of the covariance between the abun- logical data were also collected in 1979 from each site (de dances of pairs of species and the functional or phyloge- Belair 1981). Eight soil variables were considered: Clay netic distances between them. We also examined four 2+ 1 (%), silt (%), sand (%), K2O(&), Mg (mEq 100 g ), indices that are very similar: FAD (Walker et al. 1999), Na+ (mEq 100 g1), K+ (mEq 100 g1), elevation (m). The which is the sum of pair-wise functional or phylogenetic whole data set is described in detail and available as Sup- distances between species; MFAD = FAD/(S 1) plementary Material in Pavoine et al. (2011). (Schmera et al. 2009), where S is the number of species; As we could not consider the myriad of diversity indices meanD, which is the mean of pair-wise functional or developed so far, we analysed the diversity of the 75 plant phylogenetic distances between species (Weiher et al. = assemblages with the richnessP (S number of species), the 1998); and QE (Rao 1982), which is the mean of pair- – ¼ S 2 Gini Simpson index (G 1 i¼1 pi ,wherepi is the rela- wise functional or phylogenetic distances between tive abundance of a species) and ten distinct diversity indi- species, where species are weighted by their relative ces that represented different aspects of PD and FD. These abundances. indices were originally developed to measure FD, PD or both. As shown in Pavoine & Bonsall (2011), all these indi- Using mathematical partitioning to identify redundancy ces of diversity can be adapted to describe either FD or PD. among diversity measures Several of the ten diversity indices are known to depend The indices on only a single aspect of diversity: meanD, which mea- The ten diversity indices we studied include information sures the functional/phylogenetic divergence among spe- on species richness, species’ abundances and characteris- cies; B, which measures the covariance between species’ tics. Although the indices are based on similar assump- functional/phylogenetic distances and abundances; EED, tions, they differ in which aspect of FD and PD they which measures evenness in species’ functional/phyloge- describe. Basically, when applied to FD each of these indi- netic distinctiveness. The other more integrative indices ces is deﬁned according to one of the following three crite- might depend on the effects of species number, evenness ria: functional distances among species (obtained through in the species abundance, species’ functional and phyloge- an extension of Gower distance; Gower 1971; Electronic netic characteristics and the interaction between these Appendix S1); a functional space (obtained by principal components. We have partitioned these more integrative coordinates analysis applied to functional distances; Gower diversity indices into simple indices, i.e. into indices that 1966); a functional dendrogram (obtained by an depend on a single aspect of diversity. We did this by iden- unweighted pair group method based on the arithmetic tifying, in the equations of these indices, terms that depend mean, UPGMA, algorithm applied to functional distances; only on species richness (and not on species’ abundances Petchey & Gaston 2002). Similarly, when applied to PD, or characteristics), terms that depend only on species even- each of the diversity indices is based on one of the follow- ness, terms that depend only on species’ characteristics and ing criteria: phylogenetic distances (sum of branch lengths terms associated with an interaction between species’ on the shortest path that connects two species on the phy- abundances and characteristics (if, for instance, some char- logenetic tree); a phylogenetic space (obtained by principal acteristics are common in more abundant species in a com- coordinates analysis applied to phylogenetic distances; munity while other characteristics are retained in rare Gower 1966); the phylogenetic tree. A complete descrip- species). Partitioning of indices reveals redundancy tion of the indices is given in Appendix S2. between indices.

accelerating rates of evolution (Blomberg et al. 2003). Using null models to provide complementary indices With the height of the phylogenetic tree standardized to We also evaluated the ability of null models (Gotelli & equal one, and the variance–covariance matrix of species’ Graves 1996) to remove the confounding effects of species trait values equal to r²C(h,g), where h is the value at the richness and evenness for estimating correlations of FD root node and g is the rate of trait change, we used the fol- and PD. This approach, based on a transformation of each lowing parameter values for the AC model: r ²=1, h = 0, diversity index, was applied on both simulated and real g = exp(2), g = exp(10), g = exp(20); phylogenetic data. A transformed index was deﬁned as standardized signal increases with g,wheng tends towards 0 the model effect size (SES) (Gotelli & McCabe 2002), such that tends towards BM (see Blomberg et al. 2003 for details).

SES = (obsX meanX)/SDX where the subscript (X) We also used completely random traits drawn from a stan- denotes the original diversity index, obsX is the observed dard normal distribution with mean 0 and variance 1. We value of the index in one of the 75 plant assemblages, and simulated species pools with nine quantitative traits based meanX and SDX are the mean and SD from an underlying on one of the evolutionary models (we chose to simulate null model. nine traits as our real data set contained nine traits). Spe- We generate two different null models. A pool of species cies abundances for species pools were derived from the was defined by the 56 plant species observed in the study real abundances of plants within the 75 plant assemblages area. For the first null model, random assemblages were from Mekhada. The real phylogenetic tree associated with determined with the same number and abundance of spe- plant assemblages from Mekhada was used to describe cies as that observed in the real assemblages. However, the evolutionary relationships between simulated species. identities of the species were replaced with the identity of For diversity indices that required functional distances species randomly drawn from the species pool (model 1p among species, we used the squared Euclidean metric in Hardy 2008). This null model supposes that the presence because, by definition, it is expected to vary linearly with and abundance of species within assemblages is indepen- phylogenetic distances under the Brownian motion model dent of their traits and phylogeny. The second null model (e.g. Harmon & Glor 2010). We simulated 1000 species applies only to diversity indices that include species’ abun- pools per evolutionary model for the raw diversity indices, dances. It assumes that species have random abundances but only 50 species pools under the SES transformations within an assemblage. Using each real assemblage, random (due to computational limits). Each SES transformation assemblages were obtained by permuting abundances was based on 200 theoretical values per null model. among present species (model 1s in Hardy 2008). SES1 will For each simulated data set, we applied all indices to define the SES transformation associated with the first null evaluate PD and simulated FD within the 75 plant assem- model and SES2 the SES transformation associated with blages; we then computed Spearman rank correlation the second null model. between FD and PD with each of the diversity indices. This For all indices that do not include species’ abundances, a non-parametric measure of correlation is more appropriate transformation by SES1 is expected to provide new indices than Pearson product-moment correlation as the high free from variations in species richness. For all other indi- number of simulations prevented us from analysing the ces (QE, B, FEve), the new indices obtained by SES1 and effects of distribution shape and Pearson correlation is sen- SES2 transformations are expected to depend on interac- sitive to extreme values. tions between abundances and species’ characteristics only. All indices and the transformations used are summa- The FD and PD correlations in real data rized in Table 1. We estimated the phylogenetic signal in functional trait variation with the Mantel test since it allows us to analyse The FD and PD correlations in simulated data the strength of the phylogenetic signal across all nine traits We analysed the impact of phylogenetic signal strength on simultaneously. Based on our mathematical partitioning the correlation between FD and PD. We assumed that the (see Using mathematical partitioning to identify redun- same index is used for measuring FD and PD (Appendix dancy among diversity measures) and simulations (see the S3). The FD:PD correlation analyses were done for the ten FD and PD correlations in simulated data), we selected a raw diversity indices, the indices transformed by SES1 and subset of four diversity indices in addition to species rich- the indices transformed by SES2. ness and evenness. The SES transformation was applied For this analysis, we simulated traits with various phylo- with 1000 theoretical values per null model and assem- genetic signals. To do this, we applied the Brownian blage. We analysed the impact of the choice of the diversity motion model (BM) and the accelerating (AC) model of index on the FD:PD Spearman correlation. We then analy- trait evolution to the phylogenetic tree with different sed the associations between FD, PD and environmental

Table 1. Summary of the indices used. Details on each index can be found in Appendix S2.

Short deﬁnitions Abbreviations References Dependence on

Species Species’ Associated richness abundances simpler indices1

Sum of differences among species FAD Walker et al. (1999) Yes No meanD Sum of differences among MFAD Schmera et al. (2009) Yes No meanD species divided by species richness Average difference between two species meanD Weiher et al. (1998) No No Covariance between species’ B Shimatani (2001) No Yes SES1B, SES2B characteristics and abundances Average difference between two QE Rao (1982) Yes Yes meanD, B, SES1B, individuals SES2B, SES1QE Sum of branch lengths on a TS Faith (1992); Yes No SES1TS2 phylogenetic, functional tree Petchey & Gaston (2002) Diversity in species distinctiveness HED Cadotte et al. (2010) Yes No EED, SES1EED Evenness in species distinctiveness EED Cadotte et al. (2010) No No SES1EED Evenness in differences among individuals FEve Villeger et al. (2008) No3 Yes SES1FEve2, SES2FEve2 Evenness in differences among species FEvep Villeger et al. (2008) No3 No SES1FEvep2 1By simple index we mean that unaffected by species richness and/or species’ abundances. 2As in the main text, SES1 means standardized effect size (SES) transformation by the first null model, and SES2 means SES transformation by the second null model. These transformations are expected to remove the dependence on species richness and to control the dependence on species evenness. 3The ranges of these indices do not depend on species richness; however, the overall impact of species richness in the values of these indices has not been determined by our study. We thus relied on null models to remove any potential effects of species richness. variables by linear additive regressions (nominal type I the estimated FD:PD correlations for both MFAD and FAD error a = 5%) to evaluate whether PD can be used as a can still approach 1 due to the effects of species richness in surrogate for FD. All calculations were completed in R (R MFAD and FAD. In fact, FD:PD correlations are mostly dri- Foundation for Statistical Computing, Vienna, AT, AU) ven by species richness if the coefficient of variation (CV) with the packages listed in Appendix S4. of species richness is much higher than the CV for functional and phylogenetic meanD (proof in Appendix S5-A). Results In this case, comparing functional and phylogenetic MFAD Mathematical partitioning or FAD is like comparing species richness with itself, which, obviously, is rather meaningless. The FD:PD correlations for FAD, MFAD and HED can arise The HED is also linked to EED by a function of species simply due to variation in species richness richness: HED = ln(S) EED. Partitioning HED thus shows it Mathematical analysis allows a better understanding of is influenced by species richness, and as for FAD and how species richness influences the correlation between MFAD, FD:PD correlations for HED approach 1 when vari- FD:PD for MFAD and FAD. Because MFAD = (S 1) ation in HED is mostly driven by variation in species rich- meanD, and FAD = S(S 1) meanD, where S is the species ness. richness within an assemblage, the correlations between FD:PD for MFAD and FAD depends on (1) the mean and The TS: a complex function of species richness variance of species richness within assemblages, (2) the mean and variance of meanD for functional traits and phy- The last index based on species’ presence/absence that we logeny, (3) the pair-wise covariances (normed by means) studied, TS, is known to be highly dependent on species between species richness, the phylogenetic meanD and the richness (Petchey & Gaston 2002). Several studies have functional meanD (proof in Appendix S5-A). When com- explored the link between TS and species richness by per- paring FD and PD with MFAD and FAD, it is thus difficult forming simulations with a regularly increasing number of to unravel whether the observed correlation is due to spe- species randomly drawn from a species pool. With these cies richness only or to similarities in the functional and simulations, Petchey & Gaston (2002) found that TS phylogenetic characteristics of the species. To answer this increases roughly linearly with species richness for trees question, MFAD and FAD have to be partitioned into spe- with many nodes close to the root; and that, in contrast, cies richness and meanD. TS first increases drastically and then saturates with Even if the pair-wise correlations between species rich- increasing species richness for trees with many nodes close ness, FD and PD (as measured by meanD) all equal zero, to tips.

When species are randomly drawn from a pool, the values is expected only when species richness is low. To connection between TS and species richness depends on understand how and why each component of diversity tree shape. Consider a pool of S species, a tree (functional drives the FD:PD correlation, one has to evaluate the dendrogram or phylogeny) composed of K branches effects of species richness, species evenness, meanD and B with lengths lk, k=1, …,K.Letdk be the number of tips on the strength of the correlation. that descend from branch k. On average, the expected value for TS in an assemblage of n species randomly drawn Interpreting FEve and FEvep from the species pool is (proof in Appendix S5-B): Null models are necessary to analyse FEve and FEvep as TSðÞn there is no simple way of extracting the contributions of " #, S XK X d X Sd S species richness, species’ abundances and functional or ¼ k k lk lk lk phylogenetic characteristics from the mathematical n k¼1 ksuchthat n ksuchthat n n dk n Sdk n expressions for these diversity indices. For FEve and FEvep a minimum spanning tree must be built but it is dependent TS is thus a function of polygons of S and n of various on which species are in the assemblages. Although the degrees, which depends on the shape of the tree consid- range (0,1) of these indices is not dependent on species ered. We need a null model to remove trivial effects of spe- richness, we used the SES transformations to verify cies richness. whether co-factors could affect their values.

The QE: a function of G, meanD and B indices The FD and PD correlations in simulated data For quadratic entropy (QE), Shimatani (2001) showed that Although EED is, in theory, not expected to be affected by = 9 + – QE meanD G B,whereG is the Gini Simpson species richness, we found in our simulations unexpected index, which depends on species richness (S) and species’ P moderate correlations between functional and phyloge- ¼ S 2 abundances (pi), G 1 i¼1 pi ; B is the covariance- netic EED even when traits were simulated independently like measure between the abundances of pairs of species of the phylogeny. We thus applied the SES1 transforma- ¼ (pipj) and the distances between them (dij) B 2 tion to EED. Under model 1, FD:PD correlations obtained P G with SES1HED and SES1EED are equal (Appendix S5-D). dij meanD pipj SSðÞ1 and meanD is the average i>j Henceforth, we considered only SES1EED. FD:PD correla- distance among species tions obtained with SES1FAD, SES1MFAD, SES1meanD and simply meanD are identical (Appendix S5-D). Hence- XS XS ¼ 1 forth, we only considered meanD. We applied the SES meanD ð Þ dij S S 1 i¼1 j¼1 transformations to all indices that depended on species’ abundances because they could all potentially be affected QE is thus dependent on the main effects of both species’ by the shape of distribution of species’ abundances within abundances (G) and species characteristics (meanD), and the species pool and the communities. SES1 was thus on the interactions between species’ abundances and char- applied to the following indices: B, EED, FEve, FEvep, QE acteristics (B). and TS. Under null model 2, SES2QE = SES2B (Appendix The FD:PD correlation for QE depends on the pair-wise S5-D), SES2 was thus applied to the following indices that covariance between G, functional and phylogenetic me- depend on species’ abundances: B,FEve. anD, and functional and phylogenetic B (Appendix S5-C). Indices confounded by species richness gave high corre- It can also be shown that, when there is no correlation lations even when the phylogenetic signal in trait data was between functional and phylogenetic distances between weak. Many of the indices (HED, FAD, MFAD, TS, QE and species, a FD:PD correlation for QE approaching 1 can only to a lesser extent EED and FEve) we have considered are occur due to the effects of species abundances (Appendix confounded and were found to be influenced by co-factors S5-C). To extend this further, G can also be decomposed (Fig. 1, Appendix S6). The indices B, FEvep and meanD into the species richness (number of species, S) and an seemed unaffected by co-factors. Given that when using index of species evenness independent of species richness: null models, correlations are strongly influenced by the X strength of the phylogenetic signal, null models correctly S S E ¼ 1 p2 remove the influence of the co-factors on the FD:PD corre- S 1 i¼1 i lation. However, for many indices FD:PD correlations were Given that the component of QE dependent on species moderate or low even when traits followed a Brownian richness is (S 1)/S, the impact of species richness on its model of evolution (i.e. when the phylogenetic signal was

Fig. 1. Bar plots of the average Spearman correlation between FD and PD obtained when simulating traits according to the Brownian model (Blomberg et al. 2003), AC model (with varying values for parameter g), and random normal distribution. “SES1” is written before the name of indices transformed by method SES1 based on null model 1, and “SES2” before the name of indices transformed by method SES2 based on null model 2 (see Methods). strong) and strongly decreased with increasing accelerating (FEvep, df=73, P = 0.202), 0.398 (B, P < 0.001), 0.465 rate of evolution in the model AC (Fig. 1). All indices (SES1TS, P < 0.001), to 0.526 (meanD, P < 0.001). transformed by null model 2, where abundances are Despite these correlations, linear additive regression permuted among species within assemblages, were associ- revealed that FD was more correlated with environmental ated with low FD:PD correlations even when traits fol- variables than PD. For FD, we found that all indices except lowed a Brownian model of evolution (Fig. 1). This might B were significantly correlated with the environmental be due to a lack of abundance variability in our data set variables: SES1TS (r²=0.410, P < 0.001), FEvep (r²=0.373, (phytosociological estimations). P < 0.001), meanD (r²=0.250, P = 0.011), B (r²=0.030, Based on this analysis, we selected the following indices, P = 0.978). In contrast, for PD, none of the indices were which collectively quantify each of the primary compo- significantly correlated with the environmental variables: nents, richness, evenness and divergence, as identified by SES1TS (r²=0.184, P = 0.082), meanD (r²=0.169, Mason et al. (2005), to analyse our real plant community P = 0.122), B (r²=0.159, P = 0.153), FEvep (r²=0.011, diversity data set: P = 0.373). Species richness (log-transformed, r²=0.218, P = 0.030), but not species evenness (r²=0.179, P = 0.093), 1. SES1TS and meanD, which both describe the size of was correlated with the environmental gradient. Overall, functional or phylogenetic space occupied by the species of in our case study, PD was thus a poor surrogate for FD. an assemblage (i.e. functional or phylogenetic richness), Species richness was a better surrogate. Log-transformed but SES1TS is more adapted to the tree-shape structure of species richness was correlated with FD (correlations, phylogenetic data and meanD is more adapted to the anal- df = 73, FEvep, r = 0.314, P = 0.006; meanD, r = 0.413, ysis of a table of functional traits which does not have an P < 0.001; SES1TS, r = 0.437, P < 0.001), but not with PD intrinsic tree structure. (FEvep, r = 0.006, P = 0.958; meanD, r = 0.222, 2. FEvep, which describes how regularly spaced species’ P = 0.056; SES1TS, r = 0.100, P = 0.392). A complemen- traits or phylogenetic positions are in a multivariate space tary analysis demonstrated that FD diversity indices (i.e. a special case of functional or phylogenetic evenness SES1TS, FEvep and meanD increased with increasing ele- where all species have the same abundance); vation and with decreasing salinity (Appendix S7). 3. B, which determines whether the most functionally or phylogenetically distant species have the highest abundances (i.e. functional or phylogenetic divergence). Discussion

Species richness and evenness completed the list of Throughout this paper we searched for interpretable FD diversity indices. and PD indices that could help tease apart the factors inﬂu- encing the correlations between FD, PD and environmental gradients. All indices confounded by species richness The FD and PD correlations in real data provided high FD:PD correlation even for traits that We found a signiﬁcant phylogenetic signal in the func- evolved independently of the phylogeny. Mathematical tional distances among species (Mantel test, r = 0.310, partitioning and the comparison to null models led to a set P = 0.001, 1000 permutations). FD:PD correlations of indices independent of species richness and where the depended on the index selected and varied from 0.149 effects of species’ abundances are controlled. We selected

Journal of Vegetation Science 788 Doi: 10.1111/jvs.12051 © 2013 International Association for Vegetation Science S. Pavoine et al. Correlating phylogenetic with functional diversity four of these indices and applied them to the plant com- signal. Further, phylogenetically distant species often co- munity structure on the Mekhada coastal marsh plain. occurred in the most stressful conditions, with salt-tolerant Below we discuss the results of our case study; highlight monocots (i.e. Juncaceae and Cyperaceae) and halophyte di- the problems our analyses exposed in comparing FD and cots (i.e. Amaranthaceae) both common in highly saline PD; discuss the ability of our partitioning and null models environments (Pavoine et al. 2011). The fact that PD is a to avoid these problems; and conclude by considering the poor surrogate of FD, when unbiased measures or null implications of our results for the usefulness of PD as a sur- models are used, is not due to a lack of quality of the phylo- rogate for FD. genetic tree. It is due to a convergence in traits between dicots (Amaranthaceae) and monocots (Juncaceae, Cyperaceae). The separation between these two clades is acknowledged Salinity gradient in Mekhada marsh plain in all current phylogenies. The lack of association between In Mekhada, the main environmental gradient is a salinity PD and the salinity gradient is thus, at least partly, due to gradient, with low elevation areas being on average more similarities in the traits of distant clades with different bi- saline (Pavoine et al. 2011). We analysed whether environ- ogeographic origins. Both the salinity gradient and the mental variables are similarly correlated with both FD and phylogeny are associated with trait values but are indepen- PD. Plant abundances were not influenced by functional dent of one another. traits or phylogeny. However, for indices based on species occurrences, FD significantly decreased with increasing Issues raised when comparing FD and PD salinity. This was found even though the functional traits available for this data set are expected to be less directly A simple error when comparing FD and PD would be to affected by the environmental gradient than unavailable think that there is only a single way to measure diversity. physiological traits (Pavoine et al. 2011). We found moder- In our simulations, we obtained very different FD:PD cor- ate FD:PD correlations; and while relationships between PD relations according to the diversity index used. This was and the primary stress gradient (salinity) in our study area partly due to co-factors affecting diversity values and also were in the same direction as for FD, they were non-signifi- partly due to the infinite possible ways of measuring diver- cant. Even species richness was a better surrogate for the sity even with the same data and the same objective. connections between FD and the salinity gradient. Over the last 30 years, a huge range of diversity indices The correlation between FD and species richness is an has been developed (Schweiger et al. 2008; Pavoine & ecological result rather than a mathematical artefact. Plots Bonsall 2011). When analysing their data, ecologists are with low species richness in Mekhada are located in thus often confronted with the difficult choice of which flooded, salty areas where only species with particular trait indices are best for their circumstances (data, objective combinations can establish (low meanD and SES1TS val- etc.; Ricotta 2005). A typical reaction is to apply the most ues, indicating reduced functional richness). In contrast, widely used index. However, different disciplines seem to areas with higher species richness are located at higher ele- be biased towards using different indices. One can thus be vations where silt and sand add to clay to provide greater tempted to measure FD with the most widely used index heterogeneity of soil types and where more species with in functional community ecology, and to measure PD with diverse traits can establish (high FEvep values, which may the most widely used index in phylogenetic community be associated with increased influence of niche comple- ecology. However, there should be good biological argu- mentarity). In the flooded, salty areas, all but the salt-toler- ments for doing this, since results produced using two dif- ant and halophyte species are filtered out, which causes ferent indices to measure FD and PD are hardly functional clustering in plant assemblages (where co-exist- interpretable (Appendix S3). The first basic rule when ing species have similar traits). In these locations, species comparing FD and PD is thus to use the same index of were perennial, often with spiky structures and glabrous diversity for the two aspects. We followed this rule leaves, and they were mostly anemogamous. Indeed the throughout our study. most salty areas are likely to be unfavourable habitats for In this choice for an index of diversity, it should be kept pollinators due to the high level of disturbance through in mind that interpreting diversity indices is difficult when regular flooding (Pavoine et al. 2011). More generally, on different aspects influence diversity. Simple mathematical a stress gradient, functional diversity and species richness partitioning of diversity indices allowed us to separate the can be correlated if only a few species with similar traits effects of species richness, species’ abundances and func- can establish in the harshest areas (following the “physio- tional or phylogenetic characteristics. Knowing these rela- logical tolerance” hypothesis of Mason et al. 2008). tive effects is important as, for instance, we showed that Patterns of PD were found to be less clear, which can be multiplying meanD by the species richness (MFAD) or the explained by the moderate, instead of strong, phylogenetic squared species richness (FAD) considerably increases FD:

PD correlations. A common error when measuring FD and choose (Hardy 2008). To avoid unnecessary complications, PD is to assume that we are only measuring the functional the SES approach should thus be used exclusively when and phylogenetic characteristics of the species. However, mathematical partitioning is not possible. For instance, the relative influence of species richness and/or species’ mathematical partitioning revealed that the complemen- abundances on FD and PD in comparison with species’ tary index meanD could be extracted from both FAD and functional and phylogenetic characteristics depends on the QE. It also showed that the SES1 approach is useless with index used. Choosing an index of FD and PD is thus adopt- FAD, MFAD and meanD, since it provides similar results to ing a very particular view of biodiversity. those observed using meanD. We have seen that disentangling effects of species’ characteristics, species’ abundances and species richness, Removing co-factors to disentangle ecological through partitioning or using null models, is essential mechanisms when correlating FD and PD to avoid misleading and exag- The FD:PD correlations should reflect the degree to which gerated correlations due to co-factors (species richness and phylogenetic relatedness corresponds with functional simi- evenness) that are independent of species’ characteristics. larity. The fact that indices confounded by species richness Even if only FD or only PD is considered, this approach to give high FD:PD correlations even when the phylogenetic disentangling multiple contributions to assessment of signal in trait data is weak is undesirable when the aim is diversity might be also very informative. For example, to reveal assembly mechanisms. Consequently, we require Walker et al. (1999) analysed graminoid species in a lightly methods to remove these co-factors when analysing the grazed site and in a heavily grazed site in an Australian correlations between FD and PD and when analysing the rangeland. They found that within the lightly grazed grass- correlations between either FD or PD and environmental land, dominant species were functionally more dissimilar factors (e.g. Mouillot et al. 2011). to one another, and functionally similar species more sepa- An often-reported strategy to remove the influence of rated in abundance rank, than expected on the basis of co-factors consists of regressing FD (and/or PD) on the co- average ecological distances in the community (index me- factors and working on functional (or phylogenetic) resid- anD). They found that this functional redundancy uals. However, our results (especially for index TS) high- between minor and dominant species led to increased light that we need to know how FD and PD are linked via resilience in biomass production, with minor species co-factors before deciding how to remove these co-factors. increasing in abundance when abundance of the domi- To analyse variation in TS relative to species richness, some nant species was reduced by intense grazing. This study studies have, for instance, used species richness and/or its shows that removing the influence of covarying factors quadratic term as an explanatory variable in a linear from measures of FD or PD can aid in revealing ecological regression and have analysed the residuals of the regres- processes. More generally, Table 2 in Pavoine & Bonsall sion. The equation that links TS with species richness (2011) demonstrated how comparing patterns in FD/PD showed that TS does not have a linear relationship with and patterns in species richness or evenness can help to species richness or its quadratic term. tease apart mechanisms of community assembly. We found that at least one index is, by definition of its mathematical formula, unaffected by co-factors: meanD. The PD is a poor surrogate for FD We can be confident that any observed correlation between this index and a co-factor in field or experimental A critical ecological question when comparing FD with PD studies would be due to ecological mechanisms rather than is whether similarities between phylogenetic distances and to a mathematical artefact. In contrast, measures of diver- functional distances among species translate into similari- sity based on indices such as MFAD, FAD, HED, TS and QE ties between the FD and PD of species assemblages. Using confound several components of diversity. Mathematical simulations, we found that FD:PD correlations are moder- partitioning is sufficient to decompose MFAD, FAD, HED ate or low, depending on the diversity index used, even and QE into simpler indices, necessary to decipher the with the Brownian model of trait evolution. The Brownian mechanisms that lead to observed diversity patterns. How- model assumes a correlation close to 1 between the ever, for other indices such as TS, null models are required. squared Euclidean functional distances and time-scaled In addition, any index using species’ abundances might be phylogenetic distances used here among species. A signifi- affected by the shape of the distribution of abundance cant phylogenetic signal in functions is thus not sufficient (skewed vs. even). Null models can also help to control for for ensuring high FD:PD correlation. Recent simulation these sorts of issue. studies corroborate this and have shown that this can affect The use of null models (SES approach), however, adds a conclusions about ecological processes. Mason et al. critical methodological decision – which null model to (2013) found that FD indices gave high power to detect

Journal of Vegetation Science 790 Doi: 10.1111/jvs.12051 © 2013 International Association for Vegetation Science S. Pavoine et al. Correlating phylogenetic with functional diversity changes in assembly processes along a hypothetical stress questions that were not solved by previous analyses that gradient, while PD indices gave only weak power for the focused on a single aspect of biodiversity. same assembly model (Mason & Pavoine 2013). Kraft et al. Analysing the relative contributions and pair-wise cor- (2007) found that the power of PD to detect community relations of species richness, species’ abundances and char- patterns is higher with environmental filtering, larger spe- acteristics allows identification of the mechanisms cies pool sizes and higher number of traits considered, and controlling species occurrences and abundances. This lower with competition. When communities were struc- approach that details FD and PD patterns can be usefully tured by competition, the power to detect phylogenetic applied at local scales to analyse community and ecosys- community structure was found to be high only if traits tem processes, and also at broader scales, to explore, for were more conserved than expected according to a Brown- example, variation in FD and PD along altitudinal and lati- ian model. Kraft et al. (2007) also found that this power tudinal gradients (Tallents et al. 2005). Finally, simple cal- depends on the index of PD used. The power of detection culation of correlation between FD and PD can be of phylogenetic community structure thus definitely complemented by methods that analyse the detailed func- depends on the evolution of traits, the diversity index used tional and phylogenetic compositions of assemblages in a and the ecological processes underpinning community more descriptive way (Pavoine et al. 2011). These analyses composition. can identify which traits and which clades are responsible A phylogenetic signal in traits at the species pool level for the levels of FD and PD and further our understanding is not sufficient to ensure a FD:PD correlation, as illus- of how ecological communities are structured and func- trated by our case study and simulations. Concrete exam- tion. ples are also given elsewhere, e.g. in Pillar & Duarte (2010). They measure a phylogenetic signal at the species Acknowledgements pool level and a phylogenetic signal at the metacommunity level. The phylogenetic signal at the species pool level The authors thank the co-ordinating editor, Andreas Prin- corresponds simply to the degree of evolutionary conser- zing, the referee, Oliver Schweiger, and an anonymous vatism of trait differentiation in the species pool. 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Phy- divergence: the primary components of functional diversity. logenetic diversity of forest trees in the Usambara mountains Oikos 111: 112–118. of Tanzania: correlations with altitude. Botanical Journal of Mason, N.W.H. & Pavoine, S. 2013. Does trait conservatism the Linnean Society 149: 217–228. guarantee that indicators of phylogenetic community Villeger, S., Mason, N.W.H. & Mouillot, D. 2008. New mul- structure will reveal niche-based assembly processes tidimensional functional diversity indices for a multifac- along stress gradients?. Journal of Vegetation Science 24: 820 eted framework in functional ecology. Ecology 89: 2290– –833. 2301. Mouillot, D., Albouy, C., Guilhaumon, F., Ben Rais Lasram, F., Walker, B., Kinzig, A. & Langridge, J. 1999. Plant attribute diver- Coll, M., Devictor, V., Meynard, C.N., Pauly, D., Tomasini, sity, resilience, and ecosystem function: the nature and

signiﬁcance of dominant and minor species. Ecosystems 2: Supporting Information 95–113. Webb, C.O., Ackerly, D.D., McPeek, M.A. & Donoghue, M.J. Additional supporting information may be found in the 2002. Phylogenies and community ecology. Annual Review of online version of this article: Ecology and Systematics 33: 475–505. Webb, C.O. & Donoghue, M.J. 2005. Phylomatic: tree assembly Appendix S1. Calculation of functional distances for applied phylogenetics. Molecular Ecology Notes 5: 181–183. among species. Weiher, E., Clarke, G.D.P. & Keddy, P.A. 1998. Community Appendix S2. Complete description of the diversity assembly rules, morphological dispersion, and the coexis- indices. tence of plant species. Oikos 81: 309–322. Appendix S3. Using different indices for measuring Westoby, M., Falster, D.S., Moles, A.T., Vesk, P.A. & Wright, I.J. FD and PD increases the risk of misinterpretation. 2002. Plant ecological strategies: some leading dimensions of Appendix S4. List of R packages used. variation between species. Annual Review of Ecology and Sys- Appendix S5. Mathematical details. – tematics 33: 125 159. Appendix S6. Average Spearman correlation € Wikstrom, N., Savolainen, V. & Chase, M.W. 2001. Evolution of between FD and PD obtained with trait simulations. the angiosperms: calibrating the family tree. Proceedings of the Appendix S7. Correlations between environmental Royal Society of London Series B – Biological Sciences 268: 2211– variables and diversity indices on the marsh plain Mekhada. 2220.

Journal of Vegetation Science Doi: 10.1111/jvs.12051 © 2013 International Association for Vegetation Science 793 Journal of Vegetation Science 24 (2013) 794–806 SPECIAL FEATURE: FUNCTIONAL DIVERSITY A guide for using functional diversity indices to reveal changes in assembly processes along ecological gradients Norman W.H. Mason, Francesco de Bello, David Mouillot, Sandrine Pavoine & Ste´ phane Dray

Keywords Abstract Co-existence; Environmental filtering; Functional divergence; Functional evenness; Question: Which functional diversity indices have the power to reveal changes in Functional richness; Functional trait; Limiting community assembly processes along abiotic stress gradients? Is their power affected similarity; Niche complementarity; Null models; by stochastic processes and variations in species richness along stress gradients? Species richness Methods: We used a simple community assembly model to explore the power Nomenclature of functional diversity indices across a wide range of ecological contexts. The NZ Plant Names Database http:// model assumes that with declining stress the influence of niche complementar- nzflora.landcareresearch.co.nz/ (accessed 5 ity on species fitness increases while that of environmental filtering decreases. August 2010) We separately incorporated two trait-independent stochastic processes – mass Received 29 March 2012 and priority effects – in simulating species occurrences and abundances along a Accepted 24 September 2012 hypothetical stress gradient. We ran simulations where species richness was Co-ordinating Editor: Martin Zobel constant along the gradient, or increased, decreased or varied randomly with declining stress. We compared observed values for two indices of functional richness – total functional dendrogram length (FD) and convex hull volume (FRic) – Mason, N.W.H. (corresponding author, [email protected]): Landcare with a matrix-swap null model (yielding indices SESFD and SESFRic) to remove Research, Private Bag 3127, Hamilton 3240, any trivial effects of species richness. We also compared two indices that mea- New Zealand sure both functional richness and functional divergence – Rao quadratic entropy de Bello, F. ([email protected]): Institute of (Rao) and functional dispersion (FDis) – with a null model that randomizes Botany, Academy of Sciences of the Czech abundances across species but within communities. This converts them to pure Republic, Dukelska´ 135, CZ-379 82 Trˇebonˇ , measures of functional divergence (SESRao and SESFDis). Czech Republic Mouillot, D. ([email protected]): Results: When mass effects operated, only SESRao and SESFDis gave reason- Laboratoire ECOSYM, UMR 5119 CNRS-UM2- able power, irrespective of how species richness varied along the stress gradient. IRD-IFREMER, Place Euge` ne Bataillon cc 93, FD, FRic, Rao and FDis had low power when species richness was constant, and 34095 Montpellier, France variation in species richness greatly influenced their power. SESFRic and SESFD Pavoine, S. ([email protected]): De´ partement d’Ecologie et Gestion de la biodiversite´ ,UMR were unaffected by variation in species richness. When priority effects operated, 7204 CNRS-UPMC, Muse´ um national d’Histoire FRic, SESFRic, Rao and FDis had good power and were unaffected by variation naturelle, 61 rue Buffon, 75005 Paris, France in species richness. Variation in species richness greatly affected FD and SESFD. University of Oxford, Department of Zoology, SESRao and SESFDis had low power in the priority effects model but were unaf- Mathematical Ecology Research Group, South fected by variation in species richness. Parks Road, Oxford OX1 3PS, UK Dray, S. ([email protected]): Conclusions: Our results demonstrate that a reliable test for changes in assem- Laboratoire BBE-CNRS-UMR-5558, Univ. bly processes along stress gradients requires functional diversity indices measur- C. Bernard – Lyon I 43, Bd du 11 Novembre ing either functional richness or functional divergence. We recommend using 1918, 69622 Villeurbanne Cedex, France SESFRic as a measure of functional richness and either SESRao or SESFDis (which are very closely related mathematically) as a measure of functional divergence. Used together, these indices of functional richness and functional divergence provide good power to test for increasing niche complementarity with declining stress across a broad range of ecological contexts.

allowing subordinate species to evade competition with Introduction superior competitors (Mason et al. 2011b). This should Functional diversity indices have the potential to reveal cause multi-species communities where light competition community assembly processes (Mason et al. 2008b, is intense to have greater functional diversity than those 2012). Vegetation changes along gradients of environmen- where light is not limiting (Mouchet et al. 2010) for traits tal stress are amongst the most widely observed patterns in linked to spatial or temporal differentiation in resource use plant ecology (e.g. Richardson et al. 2004; Peltzer et al. and acquisition. There is recent field evidence for this in 2010). Documenting how community assembly processes plant communities (Mason et al. 2012). change along these gradients is crucial for understanding Where size-symmetric below-ground competition dom- the drivers of observed vegetation change. Functional inates, species with similar niches are more likely to co- diversity is not a single, non-divisible concept, but rather is exist, since small differences in competitive ability do not composed of multiple components (Mason et al. 2005), have disproportionate effects on the outcome of competi- with a large variety of functional diversity indices having tion (Rajaniemi 2003). Co-existence between functionally been proposed to measure these components (Schleuter similar species also results from slower growth rates in et al. 2010; Pavoine et al. 2013). Plant ecologists clearly stressed communities, which reduce the pace of competi- need guidance on which indices to use. Recent theoretical tion after disturbance (following the dynamic equilibrium and practical advances in the measurement of functional theory of Huston (1979) and supported by experimental diversity have led to an increase in the use of functional evidence; Rajaniemi 2003; Wardle & Zackrisson 2005). diversity indices to detect changes in community assembly Consequently, selective pressure for niche differentiation processes along ecological gradients (Mason et al. 2011a,b; between co-occurring species is less intense in stressed Pakeman 2011; Spasojevic & Suding 2012). However, communities. Rather, fitness is enhanced by traits that these studies are usually restricted to a single ecological maximize acquisition and retention of limiting below- context, and so provide little guidance to ecologists on ground resources (e.g. Richardson et al. 2005; Lambers which indices to use. Theoretical models allow us to assess et al. 2008; Holdaway et al. 2011). the power of functional diversity indices to detect changes This is consistent with environmental filtering (i.e. when in assembly processes along gradients across a wide range local fitness is enhanced by possession of traits similar to a of contexts. This study uses simple theoretical models that locally ‘optimal’ trait value; Mouillot et al. 2007; Mason et al. embody clearly defined assembly processes to test which 2011b). The prevalence of environmental filtering in stressed functional diversity indices most reliably reveal changes in communities should cause decreased functional diversity these processes along gradients of abiotic stress. (Mouchet et al. 2010). For some stresses, such as nitrogen limitation, fitness could be enhanced by niche differentiation via facilitation (e.g. Walker & Chapin 1986). The hypothesis Why should functional diversity change along stress that functional diversity should decline with stress assumes gradients? facilitation effects will be minor compared with the influence Stress gradients often represent a shift from below-ground of environmental filtering in stressed communities and is competition for soil nutrients and water to above-ground basedonfieldevidencefromplant communities occurring competition for light with declining stress (e.g. Coomes & along a soil phosphorus gradient (Mason et al. 2012). We Grubb 2000). Competition for light is size-asymmetric (i.e. recognize that factors other than below-ground resource lim- larger individuals are disproportionately advantaged) itation may impose stress on plant communities. We refer to whereas below-ground competition is size-symmetric ‘stress gradients’ in this study, for the sake of brevity. (Schwinning & Weiner 1998; Berntson & Wayne 2000; Cahill & Casper 2000). Competition for light provides more Using functional diversity indices to detect trait-based competitive species with an increasing advantage as they assembly processes outperform (i.e. become taller than) less competitive ones (Grime 1973a,b, 2001; Huston & DeAngelis 1987). Niche Mason et al. (2005) identified three primary components differentiation is required for species to co-exist when of functional diversity – functional richness, functional competition is size-asymmetric, since without niche differ- evenness and functional divergence. Each component pro- entiation even small differences in fitness lead to exclusion vides independent information on the distribution of spe- of all but the fittest species (Aikio 2004; Kohyama & Tak- cies in functional trait space, and a separate index is ada 2009). Thus, niche differences (i.e. spatial or temporal required to quantify each component (Mouchet et al. differentiation in resource use and acquisition) between 2010). Of the three components, functional richness and co-occurring species should increasingly enhance local functional divergence (or indices that combine them) have fitness as light competition becomes more intense, by most often been linked to community assembly processes

(Mouchet et al. 2010; Mason et al. 2012; Spasojevic & strong influence on community assembly. Mass effects Suding 2012) or ecosystem functioning (Petchey et al. occur when source–sink metapopulation dynamics allow 2004; Mouillot et al. 2011). species to occur at low abundance in communities where The various indices of functional richness aim to mea- they are unable to maintain a viable population (Kunin sure the volume of niche space occupied by the species 1998; Leibold et al. 2004). This produces a pattern where within a community (Mouchet et al. 2010). Functional environmental heterogeneity and spatial distance both richness should increase when niche complementarity have significant, independent effects on species turnover enhances probabilities of species occurrence (Mason et al. (Cottenie 2005). Potential for mass effects is highest in 2012). Functional divergence measures the degree to highly heterogeneous landscapes where communities with which the abundance of a community is distributed different abiotic conditions are separated by very small spa- toward the extremities of occupied trait space (Mouchet tial distances (Kunin 1998). Mass effects disrupt the rela- et al. 2010). Functional divergence should increase when tionship between traits and occurrence probabilities of niche complementarity enhances species’ relative abun- species, making it difficult for functional richness indices to dances (Mason et al. 2012). Various indices measure both detect changes in assembly processes (Mason et al. 2013). functional richness and divergence (Mouchet et al. 2010) – Priority effects occur when assembly order influences FDvar (Mason et al. 2003), Rao quadratic entropy the outcome of inter-specific competition (e.g. Ejrnæs (de Bello et al. 2010) and functional dispersion (Laliberte et al. 2006). They generally produce patterns where com- & Legendre 2010). They should increase when niche communities in similar environments exhibit persistent differ- plementarity enhances either, or both, species’ occurrence ences in species composition (Fukami et al. 2005; Ejrnæs probabilities and abundances. et al. 2006), and seem to be most evident during succes- An important property of functional diversity indices is sion (e.g. Fukami et al. 2005), especially when competi- that they be independent of existing diversity measures tion for light has a strong influence on community (Mason et al. 2005; Pavoine et al. 2013). Functional rich- composition during succession (e.g. Ejrnæs et al. 2006). ness increases monotonically with species richness. This Priority effects disrupt the relationship between species’ means that observed values of functional richness can traits and both occurrences and abundances, potentially increase in the absence of any change in assembly pro- making it difficult for both functional richness and func- cesses, simply due to corresponding increases in species tional divergence indices to detect changes in assembly richness (Mason et al. 2008b). Comparing observed values processes. for functional richness with those expected from matrix- Reliable detection of changes in trait-based assembly swap null models that randomize species’ occurrences processes along stress gradients requires a set of functional (Manly & Sanderson 2002) can remove any trivial effects diversity indices that are robust against trait-independent of species richness (Mason et al. 2011a; Richardson et al. stochastic assembly processes. Specifically, we need indices 2012). This is important for ensuring that changes in func- that can detect the influence of trait-based processes on tional richness do not lead to spurious conclusions about either occurrence probabilities or abundances, since trait- changes in assembly processes. independent processes might disrupt the link between spe- Recent studies have used a null model that randomizes cies’ traits and either occurrence probability or abundance. abundances across species but within communities to test for trait-based assembly processes in biological communi- Aims and objectives ties (Mason et al. 2008a). Comparison of observed values for indices that measure both functional richness and We aim to explore the power of functional diversity indices divergence with expected values from this null model to detect shifts in assembly processes along gradients under effectively converts these indices into pure measures of a wide range of ecological contexts. To achieve this aim we functional divergence (Mason et al. 2012). This is advanta- sought to develop a simple assembly model that varies trait geous, since there is only one existing measure of func- convergence in communities along a hypothetical stress tional divergence (FDiv; Villeger et al. 2008), and it seems gradient, by altering the relative influence of niche com- to have only moderate power to detect assembly processes plementarity and environmental filtering on species fit- (Mouchet et al. 2010). ness. This model simulates trait values for multiple species pools. It quantifies the effects of environmental filtering and niche complementarity on species fitness, using Functional diversity, assembly processes and trait- explicitly defined functions. It varies the relative influence independent stochasticity of these assembly processes along the gradient, with niche Stochastic processes that act independently of species complementarity becoming more influential as stress traits, particularly mass and priority effects, may have a declines. It incorporates two forms of stochasticity – mass

and priority effects – to test whether indices are robust ensures FitnessEFi is bound between 0 and 100 when | | against trait-independent processes. It also tests whether Ti Topt RangeT. We aimed to avoid negative values variation in species richness affects the power of indices to for fitness since we used fitness to scale species’ abun- detect changes in assembly processes along the stress gradi- dances (see below) and it is not logical for species to be ent. Our ultimate aim is to select a set of functional diver- assigned negative abundances. We used a quadratic func- sity indices that will provide a reliable test of the tion as a simple means of modelling a non-linear decline in hypothesis that niche complementarity, and hence func- fitness with increasing distance of the species’ trait value tional diversity, will increase with declining abiotic stress. from the optimal trait value. To do this we use the following selection criteria: We model the effect of niche complementarity on fitness as the competitive effect on potential colonizers of 1. Each index must have reasonable power to detect the species that are already ‘present’ in the community. In the increasing influence of niche complementarity with basic model, species’ local fitness determines their selection declining stress (henceforth power, for brevity) when order. We assume that the species closest to the locally either mass effects or priority effects influence commu- optimal trait value has the highest fitness. Since it colo- nity assembly. nizes first, we assume its fitness is unaffected by competi- 2. Power must be high across a range of species richness tion from species that colonize subsequently. Competition levels. affects the fitness of all other species. We calculate compe- 3. Variation in species richness along the stress gradient tition as a declining cubic function of the distance between must not affect the power of any index. species in trait space: 4. Collectively, the indices must provide reasonable power across all the contexts examined. ¼ ð j jÞ3 ð Þ Cij b RangeT Ti Tj 2

Methods where Cij is the competitive effect of present species j on colonizing species i,andb is a scaling factor that ensures | | We begin by outlining the basic assembly model. We then Cij is bound between 0 and 1 when Ti Topt RangeT. detail how priority and mass effects are built in to incorpo- We used a cubic function since it is a simple non-linear rate stochasticity. Finally, we list the FD indices we use and function and gives a steeper decline in the competitive explain the two null models examined. effect of co-occurring species with increasing trait dissimilarity than does the quadratic function. This in turn provided greater variation in trait convergence along the Basic assembly model stress gradient. The model context is one where the relative influence of The overall effect of competition from the species pres- niche complementarity and environmental filtering ent on the fitness of potential colonizers is estimated as the change along a stress gradient, with the influence of envi- sum of Cij values weighted by the fitness of the species ronmental filtering being greatest at the most stressed sites. present: The aim of our study was to examine the ability of func- "# tional diversity indices to test the hypothesis that the influ- XS ¼ ; ð Þ ence of niche complementarity on community assembly FitnessNCi 100 min Cij Fitnessj 100 3 ¼ will increase relative to that of environmental filtering j 1 with declining abiotic stress. Consequently, we designed where Fitnessj is the overall fitness of present species j (see our model to produce communities of increasing trait below for calculation of overall fitness). When modelled in divergence with declining stress by varying the relative this way, competitive effects are additive and the intensity influence of environmental filtering and niche comple- of competition exerted by a species is proportional to its fit- mentarity on species fitness. We model the effect of envi- ness. A potential colonizing species will experience intense ronmental filtering on fitness as a declining quadratic competition if it has similar traits to a species that is already function of the difference between a species’ trait value present and has high fitness. Overall fitness is calculated as and the locally optimal trait value for a given site: a combination of environmental filtering and niche com- ¼ ð j jÞ2 ð Þ plementarity effects on fitness: FitnessEFi a RangeT Ti Topt 1

Fitnessi ¼ c FitnessEFi þ d FitnessNCi ð4Þ where: RangeT is the range of values for trait T in the species pool, Ti is the trait value for species i, Topt is the locally where c + d = 1, so that ﬁtness is always bound between 0 optimal trait value for the site and a is a scaling factor that and 100.

In the basic model, species are selected in order of their abundance value, abundance is largely determined by ﬁtness. The ﬁrst species selected is the one with the highest having complementary traits to species that are already

FitnessEFi (i.e. with a trait value closest to the local opti- present. mum), and its fitness is assumed to be unaffected by competition (i.e. Fitness = 100). Fitness for all remaining NCi Generating regional species pools and locally optimal species is then calculated including both Fitness and the EFi trait values influence of competition from the first species (i.e. as measured by Cij 9 Fitnessj in equation 3, with species j being Values for a single trait were simulated for 130 indigenous the first colonizing species). The species with the highest New Zealand herbaceous and woody angiosperm, gymno- fitness is then selected as the second colonizer. The process sperm and fern species following a Brownian motion is repeated until the number of selected species equals the model of evolution with mean of 0 and SD of 1 (Blomberg desired local species richness. Fitness of the selected species et al. 2003). These species form the pool of potential colo- is then scaled to abundance so that the most abundant spe- nizers for our simulated communities. We chose to exam- cies has 103 times the abundance of the least abundant ine only a single trait, since identifying ecological species, and differences in abundance between species are mechanisms driving variation in functional diversity often proportional to their differences in fitness. This degree of requires each trait to be analysed separately (Mason et al. variation in abundances is typical in plant communities. At 2011b). We used Phylomatic (http://www.phylodiversity. high levels of d, colonization order has increasing influence net/phylomatic/) to construct a hypothesized phylogenetic on fitness, since late-arriving species will, on average, have tree for our species, and used the New Zealand Plant Phy- lower FitnessNCi than early colonizers. logeny Database (http://plantphylogeny.landcareresearch. As demonstrated by de Bello et al. (2011), Albert et al. co.nz/WebForms/Home.aspx) to manually graft species (2012), Laliberte & Legendre (2010) and de Bello et al. that were not available in Phylomatic. Branch lengths (2012), calculating functional diversity for multiple traits were computed following Grafen (1989) using the ‘com- can introduce a variety of complications, and necessitates pute.brlen’ in R package ‘ape’ (Paradis et al. 2004; R Foun- complex methodological decisions. To avoid such compli- dation for Statistical Computing, Vienna, AT, Australia). cations, the model uses a single trait to determine both There may be some potential for characteristics of our phy- niche complementarity and environmental filtering effects logeny or the model of trait evolution to alter results. How- on fitness. There is a good precedent for this based on field ever, it seems that aspects like phylogenetic tree shape studies showing that single traits may simultaneously generally have only subtle effects on the likelihood of influence species’ occurrences along stress gradients as detecting assembly processes (Kraft et al. 2007). Also, a well as influencing competitive interactions between spe- recent study using the same simulation model as used here cies at the local community level (Cornwell & Ackerly has shown that the strength of trait conservatism used in 2009; Mason et al. 2012). However, we recognize that spe- generating species pools via trait evolution models does cies’ responses to environmental heterogeneity and inter- not affect the relationship between species turnover and specific competition may be influenced by different traits functional traits along environmental gradients (Mason & in many instances (Chesson 2000). Pavoine 2012). Locally optimal trait values for each site were spaced evenly along the central 60% of the gradient of total traits Varying assembly processes along the gradient present in the species pool. The intention here was to Values for c and d vary along the hypothetical stress gradi- ensure that functional diversity values at the extremes of ent. We simulated species’ abundances for ten points the stress gradient were not affected by limitations in the (termed sites henceforth) along the stress gradient. At the trait values present in the species pool. There is a risk that most stressed site, c = 0.91 and d = 0.09, so that 91% of functional diversity values might be artificially constrained overall fitness is determined by environmental filtering if the optimal trait values at the ends of the stress gradient and 9% is determined by niche complementarity. Moving are too close to the edge of the trait space occupied by the from the most stressed to least stressed end of the gradient, species pool. Altering locally optimal trait values is consis- we decreased c and increased d by intervals of 0.09, so that tent with multiple observations that the trait values of at the least stressed site c = 0.1 and d = 0.9, with 10% of locally dominant species vary monotonically along stress overall fitness due to environmental filtering and 90% due gradients (Peltzer et al. 2010). to niche complementarity. In this way, fitness, and hence Under this basic model, species’ abundances are a abundance, is largely determined by proximity to the deterministic function of their traits and the traits of locally optimal trait value at the most stressed site. At the co-occurring species. This is quite unrealistic, since least stressed site, for all but the species with the highest stochastic processes that act independently of traits also

Journal of Vegetation Science 798 Doi: 10.1111/jvs.12013 © 2013 International Association for Vegetation Science N.W.H. Mason et al. Functional diversity and stress influence abundance in plant communities. Consequently, fitness is strongest. This is consistent with evidence that we do not use the basic model to test whether functional priority effects are stronger in high productivity environ- diversity indices can detect changes in assembly processes. ments where size-asymmetric competition for light is more Rather, we employ two extensions that incorporate differ- intense (Ejrnæs et al. 2006). This model tests whether ent types of stochasticity. functional diversity indices can detect changes in trait- based assembly process in communities where priority effects disrupt the effect of these processes on both occur- Mass effects model rences and abundances. This is a simple extension of the basic model where, for each site, the species present are randomly selected from Functional diversity indices and null models the species pool (e.g. if we wanted a community of ten species, we would randomly select ten species from the regio- We examined two indices of functional richness – the FD nal species pool). The basic model then calculates the of Petchey & Gaston (2002) and the FRic of Villeger et al. fitness and abundance of the selected species. Under this (2008), which is derived from the convex hull volume of model, species’ occurrences are completely unrelated to Cornwell et al. (2006). For a single trait, FRic is simply the their traits, but abundance is a deterministic function of range of trait values spanned by the species present. To the traits of the species present. This is an extreme form of ensure values for each simulated community for both mass effects. This model tests whether functional diversity these measures were bound between 0 and 1, they were indices can detect changes in trait-based assembly pro- expressed as a proportion of the values for the entire spe- cesses in communities where these processes have weak cies pool. We used FDiv as a measure of functional diver- effects on species’ occurrences (i.e. neither biotic nor abi- gence. For functional evenness (i.e. evenness in the otic filtering excludes species from local communities), but distribution of abundance in functional trait space; Mason a strong effect on species’ abundances. The assumption et al. 2005) we used the FEve of Villeger et al. (2008). We that mass effects are not influenced by the traits linked to examined two mathematically quite similar indices that environmental filtering and niche complementarity is per- are a combination of both functional evenness and func- haps an oversimplification (Mu¨ nkemu¨ ller et al. 2012). tional divergence – FDis (Laliberte & Legendre 2010) and However, seed production and dispersal are the traits most Rao quadratic entropy (referred to as Rao henceforth; Rao likely to drive mass effects, since high propagule pressure is 1982; Pavoine & Doledec 2005; de Bello et al. 2010). the key factor in maintaining sink populations (Mouquet Although there is some debate about the appropriate dis- et al. 2004). Further, there is evidence that traits linked to similarity measure to use in calculating Rao (de Bello et al. regenerative strategy vary independently of those linked 2012), we use Gower distance, as recommended by Pavo- to resource use and acquisition (Grime et al. 1997; Dıáz ine et al. (2009). et al. 2004), so this assumption may be more or less We compared FRic and FD values relative to random realistic. expectation under a matrix-swap null model (Manly & Sanderson 2002) using the standardized effect size (SES) of Gotelli & McCabe (2002). These indices are termed SES- Priority effects model FRic and SESFD henceforth. FDis and Rao were compared This model randomizes the order of species’ colonization, with values obtained when abundances were randomized whereas species’ traits determine colonization order in the across species but within plots (after Mason et al. 2008a). basic model. In this model, colonization order affects the These indices, termed SESFDis and SESRao, are pure mea- degree of competition experienced by each species, with sures of functional divergence, since the functional rich- early-colonizing species, on average, experiencing less nesses of the observed and randomized communities are intense competition. Species fitness is calculated as in the always identical. We also compared FDiv to this null basic model, but competitive effects on fitness are based on model. We used 104 randomizations in all null-model this randomized colonization order. The desired number of analyses, to ensure accurate estimates of SES values. We species is then selected based on species fitness (so when did not randomize abundances between communities generating communities of ten species, the ten fittest spe- because it is unclear how SES values derived in this way cies are selected). Since colonization order may have a relate to the three primary components of functional strong influence on the level of competition a species expe- diversity. Observed FEve was not compared with either riences, the priority effects model incorporates stochasticity null model since it is independent of species richness, and in the relationship between traits and both species’ occur- it is unclear how SES values generated when randomizing rences and abundances. This stochasticity is highest in the species’ abundances within communities should be least stressed sites where the influence of competition on interpreted.

Under the mass effects model, functional richness indi- influence of niche complementarity on fitness (as indices (FD, FRic, SESFD and SESFRic) should have no cated by the coefficient d). We tested whether the power of power to detect changes in assembly process, since spe- indices was affected by variation in species richness along cies’ occurrences are unrelated to their traits. Similarly, the gradient, by simulating communities of varying species indices that are partially influenced by functional rich- richness. To examine power with increasing species richness (FDis and Rao) might also have low power in the ness, communities containing five species were simulated mass effects model. However, indices of functional diver- for the two most stressed sites (i.e. sites with the lowest d gence (FDiv, SESFDis and SESRao) should have good values). Communities with ten species were simulated for power since they are only influenced by how abundance the two next most stressed sites, and so on until communi- is distributed in occupied trait space. Under the priority ties with 25 species were simulated for the two least effects model, the effect of traits on species’ occurrences stressed sites (i.e. the two sites with the highest d values). is also disrupted, but much less severely than is the mass This provides an indication of power when species richness effects model, so functional richness indices should have increases with the influence of niche complementarity (d). good power to detect changes in assembly processes. Pri- We repeated this process in reverse to estimate power ority effects also disrupt the relationship between traits when species richness declined with d. We also examined and relative abundances, which might influence the power when species richness varied randomly with d. power of functional divergence indices, and indices that Where significant negative correlations were obtained, are partially influenced by functional divergence (FDis these were recorded as ‘negative power’, so that power and Rao). was bound between 1and1.

Simulation framework Results The same simulation framework was applied to the mass For the priority effects models, FRic, FDis, Rao and SES- effects and priority effects models. In each case, it began FRic had power of >0.95 for all levels of species richness with the generation of trait values for the species pool. (Fig. 1a). Thus, these indices had at least a 95% chance Then communities were assembled for each of the ten sites of detecting an increase in niche complementarity with occurring along the hypothetical stress gradient. Func- decreasing stress. FD and SESFD had power of >0.9 for tional diversity values were calculated for each of the sites, the two lowest levels of local species richness (i.e. five along with Pearson correlation and Spearman rank corre- and ten species), but not the three highest (i.e. 15, 20 lation coefficients between each functional diversity index and 25 species). All other indices had low power in and the influence of niche complementarity on fitness (as comparison. indicated by the coefficient d). Significant positive correla- For the mass effects model, SESFDis and SESRao had tions (i.e. two-tailed P < 0.05) between functional diver- power of 0.8 at all levels of local species richness sity indices and d indicate that they are able to detect the (Fig. 1b). The power of FDis and Rao increased with spe- increasing influence of niche complementarity on commu- cies richness, being ~0.8 for the two highest levels of local nity assembly with declining stress. Both these correlation species richness (i.e. 20 and 25 species). All other indices coefficients gave similar power for all indices, so we only had very low power. present the results for Pearson r here. We also examined Variation in species richness along the stress gradient log-linear, quadratic and exponential relationships did not markedly affect the power of FRic, FDis, Rao, SES- between functional diversity indices and d. In no instance FRic, SESRao and SESFDis in the priority effects model did these relationships yield greater power (according to (Fig. 2a). In contrast, power for FD was much higher when Akaike’s information criterion; Burnham & Anderson species richness increased and much lower when it 2002) than Pearson r. decreased with declining stress. Power for SESFD showed We generated trait values for 100 hypothetical regional the opposite pattern, being much higher when species species pools (i.e. 100 separate simulations of trait values richness decreased and much lower when it increased with for the 130 species in our phylogenetic tree), and simulated declining stress. communities at five levels of local species richness: 5, 10, For the mass effects model, the power of SESFD, SES- 15, 20 and 25. For example, with a local species richness FRic, SESRao and SESFDis was unaffected by variation in level of 5, each of the ten simulated local communities species richness along the stress gradient (Fig. 2b). How- (one community for each point along the stress gradient), ever, variation in species richness strongly affected the contains five species. For each species richness level, we power of FD, FRic, FDis and Rao. In the case of FD and recorded the proportion of significant correlations (termed FRic, power was negative when species richness declined power henceforth) obtained between each index and the with declining stress.

5 10 15 20 25 Increasing Random Decreasing (a) 1 (a) 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 FDis Rao FDiv SESFD SESFRic SESFDis SESRao SESFDiv FD FRic FEve

Power 0 –0.2 FD FRic FEve FDis Rao FDiv SESFD SESFRic SESFDis SESRao SESFDiv Power –0.2 –0.4 –0.4 –0.6 –0.6 –0.8 –0.8 –1 –1 1 5 10 15 20 25 (b) Increasing Random Decreasing 0.8 (b) 1 0.6 0.8 0.4 0.6 0.2 0.4 0 0.2 FD FRic FEve FDis Rao FDiv SESFD SESFRic SESFDis SESRao SESFDiv

Power 0

–0.2 FD FRic FEve FDis Rao FDiv SESFD SESFRic SESFDis SESRao SESFDiv –0.4 Power –0.2 –0.6 –0.4 –0.8 –0.6 –1 –0.8 –1 Fig. 1. Power of functional diversity indices to detect the increasing inﬂuence of niche complementarity with declining stress when: (a)priority Fig. 2. Power of functional diversity indices to detect the increasing effects disrupt the relationship between species traits and abundances, inﬂuence of niche complementarity with declining stress with species and (b) mass effects disrupt the relationship between species traits and richness increasing, decreasing or varying randomly as stress declines. In occurrences. Numbers associated with different types of bar shading (a) priority effects disrupt the relationship between species’ traits and indicate species richness of local communities. FD is the sum dendrogram abundances, while in (b) mass effects disrupt the relationship between branch length, FRic is convex hull volume, FEve is functional evenness, species’ traits and occurrences. FD is the sum dendrogram branch length, FDis is functional dispersion, Rao is quadratic entropy and FDiv is FRic is convex hull volume, FEve is functional evenness, FDis is functional functional divergence. SESFD and SESFRic are, respectively, observed FD dispersion, Rao is quadratic entropy and FDiv is functional divergence. and FRic expressed relative to a matrix-swap null model. SESRao, SESFDis SESFD and SESFRic are, respectively, observed FD and FRic expressed and SESFDiv are, respectively, observed Rao, FDis and FDiv expressed relative to a matrix-swap null model. SESRao, SESFDis and SESFDiv are, relative to a null model randomizing abundances across species, but respectively, observed Rao, FDis and FDiv expressed relative to a null within communities. model randomizing abundances across species, but within communities.

context-dependent. Thus, any study examining changes in Discussion assembly processes along gradients should employ several, We are aware of only one simulation study examining the complementary functional diversity indices. They demon- power of a broad range of functional diversity indices to strate that different types of stochasticity have contrasting detect community assembly processes (Mouchet et al. effects on the power of functional diversity to detect 2010). Their study did not consider how null models and changes in assembly processes, with our two assembly variation in species richness affected power. Nor did it models giving highly divergent results. They show that sto- determine which indices maintained power in the face of chastic processes alter the dependence of indices on species trait-independent, stochastic processes. Similarly, we are richness. They also reveal that we need to compare only aware of a single study using simulated data to test if observed functional diversity values to null models (using null models altered the conclusions drawn from functional standardized effect size, SES) to ensure that we draw the diversity patterns (de Bello 2012). However, that study right conclusions about assembly processes. considered a much narrower range of contexts than we Below we discuss what our results for functional diver- have. sity patterns tell us about shifts in assembly processes in Our results extend these existing studies in several key our two models. We speciﬁcally outline when and how ways. They show that the power of functional diversity null models improve our ability to detect shifts in assembly indices to detect changes in assembly processes is highly processes along gradients. We ﬁnish by suggesting a set of

Journal of Vegetation Science Doi: 10.1111/jvs.12013 © 2013 International Association for Vegetation Science 801 Functional diversity and stress N.W.H. Mason et al. functional diversity indices that should be appropriate for a decreases in the latter. However, when expressing wide range of ecological contexts. In doing this we provide observed values for these indices relative to those expected some guidance about what presence or lack of significant from a matrix-swap null model (i.e. SESFD and SESFRic), variation in these indices along stress gradients may reveal we would conclude, correctly, that the influence of niche about community assembly processes. complementarity on occurrences does not change with stress, irrespective of how species richness varies along the gradient. The power of FDis and Rao was also greatly Null models, stochasticity and power to detect shifting affected by variation in species richness, being much assembly processes higher when species richness increased with declining In the mass effects model, SESRao and SESFDis were the stress, which would affect conclusions about assembly pro- only measures to give reasonable power ( 0.8) for all lev- cesses. This is due to their sensitivity to functional richness, els of species richness. SESRao and SESFDis values were which is itself influenced by species richness, but not partially contingent on the random selection of species assembly processes in this model. Power of SESRao and from the species pool. This explains why we obtained only SESFDis was unaffected by variation in species richness moderate power. This result for SESRao and SESFDis along the gradient. These results show that using null reveals the increasing influence of niche complementarity models to generate SES values helped remove the influ- on species’ abundances with declining stress. That SESRao ence of species richness on functional diversity. This is and SESFDis had greater power than observed Rao and important to prevent drawing spurious conclusions about FDis shows that use of an appropriate null model to obtain assembly processes when species richness varies greatly standardized effect size (SES) improved our ability to between communities. detect changes in assembly processes along the stress gradi- In the priority effects model, variation in species rich- ent. FD, FRic, SESFD and SESFRic had power of ~0 for all ness along the stress gradient did not affect the power of levels of species richness. This is to be expected since trait- FRic, FDis and Rao. Thus, there was no evidence that vari- based assembly processes do not influence species’ occur- ation in species richness would lead to spurious conclu- rence probabilities under this model, so that functional sions under this model. Consequently, there was no need richness varies at random along the stress gradient. to use null models to remove the influence of species In the priority effects model, FRic and SESFRic reliably richness. detected an increase in functional richness with declining stress for all levels of species richness. FDis and Rao had Recommended indices and interpretation of significant higher power than SESFDis and SESRao, especially at low- correlations est levels of species richness. FDis and Rao are sensitive to changes in functional richness, but SESFDis and SESRao Our results permit us to identify a set of indices that give are not. This suggests that the power of FDis and Rao was reasonable power to detect changes in assembly processes largely due to increases in functional richness with declin- across the range of contexts examined. They also allow us ing stress. These results reveal the increasing influence of to suggest interpretations for presence or lack of significant niche complementarity on species’ occurrences (via correlations between these indices and stress gradients. increases in functional richness) with declining stress. The The recommended indices and interpretations of correla- comparatively low power of SESFDis and SESRao also tions between them and stress gradients are summarized indicates that priority effects disrupted the influence of in Table 1. trait-based assembly processes on species’ abundances. Our results indicate that when priority effects influence These results also show that the null models we used to species composition, but mass effects are weak, SESFRic produce SES values provided no increase in power under will have good power to detect increasing niche comple- the priority effects model. mentarity in the priority effects model. SESFRic was also robust against variation in species richness in both our assembly models, and thus satisfies all our criteria. When Null models, stochasticity and dependence of power on using multiple traits, SESFD might also be an appropriate species richness index. Its sensitivity to variation in species richness may In the mass effects model, FD and FRic led to completely have been partly due to saturation of observed FD values different conclusions according to whether species richness at high species richness, which itself could arise from our increased or decreased with declining stress. Using these using only a single trait. Here we have presented a basic indices we would conclude, incorrectly, that the influence matrix-swap null model where swapping of species’ occur- of niche complementarity on species’ occurrences rences between any pair of sites is permitted. Instances increases with declining stress in the former case and where sites are separated by large geographical distances

Table 1. Recommended indices and interpretations of signiﬁcant correlations between indices and stress gradients.

SESFRic SESRao/SESFDis Interpretation

+ NS Increasing influence of NC on occurrences, but not abundances, with declining stress NS + Increasing influence of NC on abundances, but not occurrences, with declining stress ++ Increasing influence of NC on both occurrences and abundances, with declining stress NS NS No change in influence of NC on either occurrences or abundances, with declining stress SESFRic is the standardized effect size for the FRic of Villeger et al. (2008) obtained using a matrix-swap null model to randomize occurrences. SESRaoand SESFDis are standardized effect size for (Rao) quadratic entropy and functional dispersion (FDis) obtained using a null model randomizing abundances across species but within communities. NC refers to niche complementarity. + indicates a significant positive correlation between functional diversity and declining stress. SESRao/SESFDis indicates that these indices are interchangeable due to their similar behaviour. may require application of spatial constraints (e.g. Rox- We do not recommend directly using observed Rao and burgh & Matsuki 1999) so that the null model incorporates FDis values, for three reasons. First, variation in species dispersal limitation effects (e.g. as proposed by Mason et al. richness strongly affected the power of both indices in the (2007)). However, in testing for changes in assembly pro- mass effects model. Second, it is difficult to interpret corre- cesses along ecological gradients we advise against using lations between these indices and stress gradients since environmentally constrained matrix-swap null models they measure both functional richness and functional (e.g. Peres-Neto et al. 2001). This is because environmen- divergence. This means we cannot use them to disentangle tal constraints on species’ occurrences are relevant in test- the effects of niche complementarity on occurrences from ing for shifts in assembly processes along gradients, and its effects on abundances. Finally, they provide no benefit thus should not be included in null models randomizing for detecting changes in assembly processes if appropriate species’ occurrences. In making these suggestions, we are indices of functional richness and functional divergence none-the-less aware that null models randomizing species’ are available. We have identified SESFRic as an appropri- occurrences have for decades been the subject of intense ate index of functional richness and SESRao or SESFDis as debate (e.g. Connor & Simberloff 1979; Diamond & Gilpin appropriate measures of functional divergence. However, 1982; Harvey et al. 1983; Gotelli 2000; Manly & Sanderson it is possible that Rao and FDis values, calculated using 2002; Hardy 2008). In the end, the appropriate null model occurrences rather than proportional abundances and always depends on the question and the context in which compared to a matrix-swap null model, could also reveal it is posed. the effects of assembly processes on species’ occurrences. SESRao or SESFDis should both have good power to In no instance did FEve provide good power to detect detect changes in assembly processes when mass effects changes in assembly processes. Previous studies have have a strong influence in species’ occurrences. Mathe- found little evidence for change in functional evenness in matically, these indices are very similar and consequently plant communities along ecological gradients (e.g. Mason can be used interchangeably. They were the only indices et al. 2012). It is possible that limited evidence for func- giving reasonable power to detect the increased influence tional evenness as an indicator of local assembly processes of niche complementarity on abundances in the mass could be due to limitations of FEeve (see Pavoine & Bonsall effects model. Either of these indices is required to avoid 2010; Appendix A). It is also possible that variation in func- spurious acceptance of the null hypothesis of no change in tional evenness is simply not associated with changes in trait-based assembly processes when mass effects strongly assembly processes. We require indices of functional even- influence species’ occurrences. While SESRao and SESFDis ness that more closely approximate the concept (as pre- had low power in the priority effects model, they were sented by Mason et al. (2005)) before we can verify this. robust against variation in species richness along the stress We recognize that the modelling approach we have gradient. Thus, variation in species richness will not cause employed is highly simplified, and may not fully reflect these indices to yield spurious conclusions when stochastic processes that occur in real plant communities. We also processes disrupt the relationship between species’ traits recognize that many processes, other than mass and prior- and abundances. The other index of functional divergence ity effects, might prevent relationships between functional we used, FDiv, had low power across all of the contexts we diversity from emerging. Our intention was to build a examined. Pavoine & Bonsall (2010) showed several model where changes in trait-based assembly processes are instances where FDiv values did not vary between com- clearly defined, and to use this model in selecting indices munities that did actually have different functional diver- for testing the hypothesis that these processes change gence (see their Appendix A), which may explain the low along stress gradients. Our study has successfully done this. power we observed for this index. However, it would indeed be interesting to test whether

Journal of Vegetation Science Doi: 10.1111/jvs.12013 © 2013 International Association for Vegetation Science 803 Functional diversity and stress N.W.H. Mason et al. different types of assembly models give similar results. Our References work could most fruitfully be extended using two quite different approaches. First, individual-based models would Aikio, S. 2004. Competitive asymmetry, foraging area size and provide useful insights since competitive interactions occur coexistence of annuals. Oikos 104: 51–58. between individuals (e.g. Huston & Smith 1987). This Albert, C.H., de Bello, F., Boulangeat, I., Pellet, G., Lavorel, S. & Thu- might provide a more realistic examination of how niche iller, W. 2012. On the importance of intraspecific variability for complementarity and environmental filtering influence the quantification of functional diversity. Oikos 121: 116–126. the distribution of species and abundance of local commu- de Bello, F. 2012. 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Journal of Vegetation Science 806 Doi: 10.1111/jvs.12013 © 2013 International Association for Vegetation Science Journal of Vegetation Science 24 (2013) 807–819 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Which trait dissimilarity for functional diversity: trait means or trait overlap? Francesco de Bello, Carlos P. Carmona, Norman W. H. Mason, Maria-Teresa Sebastia` & Jan Lepsˇ

Keywords Abstract Biodiversity; Community assembly; Environmental filtering; Functional traits; Question: Many functional diversity indices require the calculation of func- Intraspecific trait variability; Niche tional trait dissimilarities between species. However, very little is known about complementarity; Rao how the dissimilarity measure used might affect conclusions about ecological processes drawn from functional diversity. Received 30 March 2012 Accepted 25 September 2012 Methods: We simulated real applications of functional diversity, to illustrate Co-ordinating Editor: Martin Zobel the key properties of the two most common families of dissimilarity measures: (1) ‘Gower’ distance, using only ‘mean trait’ value per species and then stan- dardizing each trait, e.g. relative to its range; (2) ‘trait overlap’ between species, de Bello, F. (corresponding author, [email protected]): Institute of Botany, which takes into account within-species trait variability. We then examine how Academy of Sciences of the Czech Republic, these approaches could affect conclusions about ecological processes commonly Dukelska´ 135, CZ-379 82, Trˇebonˇ ,Czech assessed with functional diversity. We also propose a new R function (‘trova’, Republic and i.e. TRait OVerlAp) which performs computations to estimate species trait Department of Botany, Faculty of Sciences, dissimilarity with different types of data. University of South Bohemia, Na Zlate Stoce 1, CZ-370 05, Cˇ eske´ Budeˇ jovice, Czech Republic Results: The trait overlap approach generally produces a less context-depen- Carmona, C.P. ([email protected]): dent measure of functional dissimilarity. For example, the results are less depen- Terrestrial Ecology Group, Department of dent on the transformation of trait data (often required in empirical datasets) Ecology, Autonomous University of Madrid, and on the particular pool of species considered (i.e. trait range, regularity and 28049, Madrid, Spain presence of outliers). The results therefore could be more easily compared across Mason, N.W. H. ([email protected]): Landcare studies and biomes. Further, trait overlap more reliably reproduces patterns Research NZ Ltd, Private Bag 3127, Hamilton, expected when niche differentiation structures communities. The Gower New Zealand approach, on the contrary, more reliably detects environmental filtering effects. Sebastia`, M.-T. ([email protected]): Forestry and Technology Centre of Catalonia, Conclusion: The two approaches imply different conceptions of how species E-28240, Solsona, Spain dissimilarities relate to niche differentiation. Trait overlap is suitable for testing Lepsˇ,J.([email protected]): Department of the effect of species interactions on functional diversity within local communi- Botany, Faculty of Sciences, University of ties, especially when relatively small differences in species traits are linked to dif- South Bohemia, Na Zlate´ Stoce 1, CZ-370 05, ferent resource acquisition. Gower is better suited to detecting changes in ˇ Ceske´ Budeˇ jovice, Czech Republic and functional diversity along environmental gradients, as greater differences in trait Institute of Entomology, Biology Centre, values reflect increased niche differentiation. Combining trait overlap and Gow- Academy of Sciences of the Czech Republic, CZ-370 05, Cˇ eske´ Budeˇ jovice, Czech Republic er approaches may provide a novel way to assess the joint effects of environmental filtering and niche complementarity on community assembly. We suggest that attention should be given not only to the index of functional diversity considered but also whether the dissimilarity used is appropriate for the study context.

phylogenetic diversity, many indices have been proposed Introduction to estimate functional diversity, each showing different Functional diversity, the extent of trait differences between properties (Mouchet et al. 2010; Schleuter et al. 2010; species, is a key component of biodiversity (Petchey & Pavoine & Bonsall 2011). A common requirement Gaston 2002; Mason et al. 2005). As for taxonomical and for many functional diversity indices is estimating ‘trait

Journal of Vegetation Science Doi: 10.1111/jvs.12008 © 2012 International Association for Vegetation Science 807 Gower vs overlap for functional diversity F. de Bello et al. differences’ between species (Fig. 1). This is generally the popular packages to compute functional diversity. resolved by computing a pairwise species trait dissimilarity Gower distance is quite useful for combining different matrix (Pavoine et al. 2009; Laliberte & Legendre 2010), types of traits (e.g. quantitative, qualitative, semi-quantita- i.e. a matrix which contains the trait dissimilarity for each tive), it can deal with missing trait values, and the impor- pair of species in a dataset. This matrix can be based on sin- tance of individual traits can be weighted differently gle traits or on the combination of multiple traits (Botta- according to their importance (Pavoine et al. 2009; Lali- Dukat 2005; Lepsˇ et al. 2006; Mason et al. 2011). While berte & Legendre 2010). several studies have explored the behaviour of the existing As mentioned, for a given trait the Gower approach con- functional diversity indices (Villeger et al. 2008; Mouchet siders only a single mean trait value per species. However, et al. 2010; Schleuter et al. 2010), quite surprisingly very it is increasingly recognized that species present a consider- little is known about how the methods used to compute able amount of intraspecific trait variability, both within the trait dissimilarity matrix affect functional diversity val- and across communities (de Bello et al. 2010b; Hulshof & ues. In particular, it remains unclear how different meth- Swenson 2010; Violle et al. 2012). Using only a single ods for calculating dissimilarity might influence mean trait value for species, as with the Gower approach, conclusions about ecological processes drawn from func- obviously ignores this intraspecific trait variability (de tional diversity (see Mason & de Bello this issue, for the set Bello et al. 2010a; Lepsˇ et al. 2011; Albert et al. 2012). of ecological processes most commonly assessed with func- Another potential problem of the Gower approach is that tional diversity). different traits vary on different orders of magnitude. For To our knowledge, there are two main families of example the range of variation in height and seed mass approaches for estimating this trait dissimilarity matrix. described in Cornelissen et al. (2003) is 10À1 to 102 mand For illustration, let us consider here the case of a single 10À3 to 107 mg respectively. Transformations are, there- quantitative trait (we discuss below also cases combining fore, often required to account for lack of normality of trait different types of traits). The first family of approaches was values (Westoby 1998). It is unclear what is the effect of described by Botta-Dukat (2005) and Pavoine et al. (2009) trait range variations and potential transformations on and it is based on the popular Gower distance (Gower describing trait dissimilarity patterns. 1971). This approach, which we will call ‘Gower’ for sim- The second family of approaches to compute trait dis- plicity, considers only a single mean trait value per species similarity, which we call ‘Overlap’, has been proposed by to estimate trait dissimilarity. For quantitative traits, dis- different authors, although with different algorithms (Mac similarity is simply computed as the difference in mean Arthur & Levins 1967; Mouillot et al. 2005b; Lepsˇ et al. trait values between species. To allow comparisons across 2006; Mason et al. 2008, 2011; Geange et al. 2011). For a different traits, this difference is standardized between 0 given quantitative trait, the approach estimates the overlap and 1 for each trait. This standardization can be obtained in trait distribution between species, i.e. considering intra- in different ways, but mostly the trait difference between specific trait values (Fig. 1). The dissimilarity is commonly each species is divided by the spread of trait values existing computed as one minus the overlap in trait distribution in the dataset. The original Gower distance (Gower 1971) (given that the area of the density curve of trait distribu- divides, for example, the trait differences by the trait range tion is equal to one). The approach was designed to esti- (Botta-Dukat 2005). This approach is now used in most of mate overlap in resource use and acquisition between

Fig. 1. The trait dissimilarity between species, normally expressed by a triangular or symmetrical matrix, is essential to compute most functional diversity indices. Depending on the data available two families of approaches exist to compute it. The ﬁrst basically expresses differences between species’ mean trait values and standardizes trait values based on trait range (or similar). The second expresses trait overlap between the curves of trait distribution for each pair of species (**Here estimated with mean and standard deviation of trait values, but other calculations are possible, see text).

Journal of Vegetation Science 808 Doi: 10.1111/jvs.12008 © 2012 International Association for Vegetation Science F. de Bello et al. Gower vs overlap for functional diversity species, thus providing a means of testing for the effects of illustrate the general behaviour of these methods for sev- interspecific competition on community structure (Mac eral key applications of functional diversity. We claim that Arthur & Levins 1967; Mason et al. 2011). There are gen- no approach is better a priori but that users should be erally two ways to compute trait overlap. The first assumes aware of their potential advantages and pitfalls. Finally, we trait values to have a normal distribution around the trait considered only one trait but we discuss the implication of mean (Mac Arthur & Levins 1967; Lepsˇ et al. 2006). For our tests for combining multiple traits together. this approach is it necessary to estimate, for each species, the mean and standard deviation for each trait to build Test 1: Do Gower and overlap produce analogous normal trait distribution curves (Fig. 1). The second dissimilarity values? approach does not assume that trait density curves follow a Approach normal distribution. Rather, it uses kernel density estimators to build trait density curve, which requires no assump- The first test is a simple illustration of the expected distri- tions about curve shape (Mouillot et al. 2005b; Geange bution of dissimilarity values produced by the Gower and et al. 2011; Mason et al. 2011). The second is certainly Overlap approaches. This shows what kind of values are more realistic but probably requires more field measure- generally expected with both methods. We simulated ments. Our experience suggests these two methods give quantitative trait values for 20 species (Fig. 2, left panel). similar results, particularly relative to the Gower approach, We first defined the mean trait value for each species, with and therefore we do not compare them here. mean values increasing across species. For illustration pur- This study aims to comparing the two existing families poses we only show the case of mean values increasing on of approaches (i.e. ‘Gower’ vs ‘Overlap’) to compute trait a logarithmic scales, but similar patterns were observed dissimilarity. We develop several tests, mimicking com- when considering log and non-log scales (Test 2). Then we mon applications of functional diversity, to assess and dis- randomly created 20 individuals per species. For each spe- cuss the main properties and applications of these two cies we randomly selected 20 trait values from a normal approaches. For the different tests described below we fol- distribution, with the standard deviation (SD) proportional low a common approach using simulated trait values for a to trait mean, so that all the species have similar coefficient set of hypothetical species. In the following sections, we of variation (CV, i.e. SD divided by trait mean). The first outline each test (summarized in separate figures; i.e. increase of SD values with increased mean is a common Figs 2–7) and the implications of the results obtained with pattern in many traits (de Bello et al. 2010b). The CV was each test. Then we discuss more generally the applications fixed at 26%, a value within the range of values most often of these approaches and the biological implications of our found for many traits (Cornelissen et al. 2003). For results findings. We selected a number of tests that, we believe, with other values of CV see Appendix S1. After producing

Fig. 2. Left panel: data simulated with 20 species having log-increasing mean trait values and coefﬁcient of variation around the mean of ca. 26%. Central and right panels: typical histograms of trait dissimilarity between species obtained using Gower distance vs trait overlap (central and right panel respectively). Most values are close to zero with trait mean and close to one with trait overlap. For two species i and j (black circles in the left panel) the corresponding dissimilarity is shown for both methods (central and right panel respectively).

Journal of Vegetation Science Doi: 10.1111/jvs.12008 © 2012 International Association for Vegetation Science 809 Gower vs overlap for functional diversity F. de Bello et al. trait values for the 20 individuals per species, and log- Results transforming these values, we computed a new mean and SD of trait values per each species. Given that the individ- For the Overlap approach, transformation of trait data had ual values were generated randomly, the final values of a minimal effect on dissimilarity values (Fig. 3). By con- CV were not exactly 26% but very close to that quantity. trast, for the Gower approach, dissimilarity values were It should be noted also that the trait values within species, greatly affected by transformation of the trait data. We which were normally distributed before log-transforma- expect that they will vary more when the original range of tion, are slightly right-skewed (i.e. there are more small trait values is greater, because the effect of log-transforma- values and few large ones compared to the mean, Fig. 1; tion on raw trait data is greater when the trait range con- however, this skewness is negligible with a CV of 26%). sidered is greater. Comparing Gower vs Overlap values Then we computed trait dissimilarity between species (Fig. 3 lower panels, with and without log-transformation) either using the Gower approach or the Overlap approach shows in which conditions the two approaches are compa- (based on mean and SD). rable. Only after log-transformation and at low levels of dissimilarity (<0.2 in this specific case for the Gower approach) there is a correlation between the distances cal- Results culated with the Gower and Overlap methods. With log- The trait dissimilarity values obtained with the Gower dis- transformation, there seems to be a threshold above which tance were much lower than those obtained with the values with the Overlap approach tend to 1 and the corre- Overlap approach (i.e. the distribution was skewed lation is disrupted. Without transformation of trait data the towards smaller values; Fig. 2). The patterns observed in two approaches provide rather incomparable dissimilarity this example were also observed in all other examples in values. this paper and, most importantly, even across most of the real datasets for which we have made this comparison (not Test 3: How do the range of trait values in a pool of shown for simplicity). With the Gower approach there are species influence dissimilarity? many more values close to zero and only one single pair of Approach species has dissimilarity equal to 1 (i.e. the combination of species with highest and lowest trait values in the dataset). Very often researchers compare functional diversity values With Overlap, many species have trait dissimilarity equal, across different vegetation types or before/after producing or approaching, to one. Since overlap tends to 0 when spe- some experimental modification (Freschet et al. 2011; cies have sufficiently different trait means and small SD, Mason & de Bello 2013). This implies comparing func- dissimilarity will be close to one. An increase in SD values tional diversity across pools of species with, for example, will result in higher overlap and, therefore, generally different trait ranges. We expected that the results, and lower dissimilarity (Appendix S1). consequent biological interpretations, of these comparisons could indeed depend on the way species dissimilarities Test 2: How much do trait transformations affect are estimated. To show this we simulated four scenarios, dissimilarity? each with a pool of 65 species but with different trait Approach ranges (‘XL’, ‘L’, ‘M’, ‘S’). The trait mean across all 65 species was the same across all four scenarios. Within each One common problem when using traits to compute func- scenario, species trait mean values were randomly gener- tional diversity is transformation of trait values. Transfor- ated with a normal distribution around this fixed value mation alters the relative distances between species in trait (i.e. the same in all the four scenarios). The difference space, i.e. by magnifying distances between some species across the four scenarios was that the normal distribution pairs and ‘shrinking’ distances between others and their of species trait values around this fixed mean was set from effect will depend on the original spread of trait values. It is larger to smaller (i.e. (‘XL’, ‘L’, ‘M’, ‘S’ scenarios). We unclear to which extent this will affect the results. There- assumed here that the SD of the trait values within species fore, we followed the same approach as for the Test 1, but was constant for all species and scenarios (as in the case of expanding the test to 65 species. The trait values were either log-transformed data in test 1). We did this because we log-transformed (as for Test 1) or not before the calculation wanted to ensure that species with a given difference in of trait mean and SD. It should be noted that transforming trait means would have the same trait dissimilarity, irre- trait values increases here also the evenness of species distri- spective of their trait mean, to allow comparisons across bution along the trait gradient. Using raw or log-trans- different trait ranges. We simulated community samples formed data, therefore, also mimics the case of considering (20 communities per scenario), by randomly selecting 12– a more, or less, even trait distribution in a dataset. 15 species from the whole pool of 65 species (Fig. 4). The

Fig. 3. The dissimilarity with the Gower distance (left-above panel) is much more variable, compared to trait overlap (right-above panel) with and without transformation of the data (here log-transformation). Comparing trait dissimilarities based on trait overlap and Gower distance (lower panels) show some correlation between the methods only at lower values of dissimilarity and only with log-transformed data. assumption of a reduction in species richness of around i.e. species relative abundance being equal to 1/species 80% from the species pool to the community level was richness). As for many existing indices of functional diver- based on field observations (Pa¨rtel et al. 1996). Following sity, the trait dissimilarity is an essential parameter for the this first step, we then added a new species to each of the calculation of the Rao index (Pavoine & Bonsall 2011). For four scenarios. This species was intended to be an outlier this and the following tests, we also performed calculations increasing the existing trait range, and was assigned a using another functional diversity index, the one by Pet- mean trait value equal to 1.5 times the maximum value in chey & Gaston (2002). This index is defined as the total the trait range for each scenario (Fig. 4). We then recalcu- branch length in a trait dendrogram connecting all species, lated trait dissimilarity between species (either with Gower and was chosen because it should provide very different or with Overlap). results from the Rao index. We then computed the Rao index of functional diversity (Rao 1982; Botta-Duka´t 2005; Lepsˇ et al. 2006) for each Results and implications sample, using the matrices of dissimilarity between species computed using either the Gower approach or the Overlap This test shows two important patterns, both observed approach. We used the Rao index expressed in terms of with the Rao index (Fig. 4) and with the index of Petchey equivalent numbers (de Bello et al. 2010a) but we also dis- & Gaston (2002); Appendix S2). First, Overlap seems better cuss results in the context of other indices (see Appendix able to detect differences between XL, L, M and S scenar- S2 and Discussion). The Rao index is a key index of func- ios, with functional diversity decreasing from XL to S. This tional diversity, which reflects the property of various simi- confirms that Gower distance comparisons across data sets lar functional diversity indices (Pavoine & Bonsall 2011). should be done carefully as it depends on the pool of spe- It expresses the sum of trait dissimilarities between each cies used for its standardization. For example, consider pair of species in a sample weighted by species relative comparing functional diversity of grasslands and forests abundance (in all our examples with functional diversity, with height as a trait. The range of trait values will be we assume equal abundance of species in a community, clearly lower in grasslands and, intuitively, the functional

Fig. 4. Functional diversity computed with Gower (left panel) or trait overlap (right panel), depending on the range of trait values considered (from largest ‘XL’, to smallest ‘S’) with and without the inclusion of a new species outside this range. The scheme above the results shows the simulation approach, with 65 species equally spaced on a trait range. A total of 20 plots per range were built by adding 12–15 species randomly to each plot. The outlier species was set as having 1.5 times the trait value of the species with highest trait value in a given range. diversity should be lower. However if the trait standardiza- tive result arises because range-standardised distances tion is done for each vegetation type separately the values between pre-existing species decrease when the observed of functional diversity obtained with the Gower distance range of trait values increases. Consequently, outliers could be artificially similar across vegetation types (Fig. 4). strongly affect the mean dissimilarity values obtained with With the Overlap approach this problem does not seem to the Gower approach. It also implies that studies considering exist or, more generally, the range of trait values consid- repeated measures of species composition should carefully ered should influence much less the results of functional consider how to compute the dissimilarity with Gower diversity. As for Test 2, this test shows the context-depen- across repeated measures, if they do not want to artificially dence of dissimilarities values obtained using the Gower affect the variation in functional diversity. Most ecologists, approach, because the values depend on the standardiza- including our selves (Hejda & de Bello 2013), would avoid tion of trait differences. As we show with Test 2, standard- these problems by considering both invaded and non izations are not necessary with Overlap. invaded conditions together when estimating the observed Second, with the Gower approach the addition of an out- range of trait values. lier (or any new species in the dataset having sufficiently different traits from existing species) can produce some Test 4: How much do dissimilarity estimations counterintuitive results of functional diversity. Consider influence the tests on species niche differentiation the case of repeatedly measured of plots invaded by an alien and coexistence? species having extreme trait values relative to existing spe- Approach cies. Assuming, for simplicity, that the existing species remained, this should intuitively increase functional diver- One increasingly common application of functional diversity in the plots. The Overlap approach shows this pattern, sity is the study of niche differentiation within communi- while the opposite is found with Gower. This counterintui- ties (i.e. differences in alpha niches; Silvertown et al.

2006). It is expected that higher functional diversity should tional diversity and the Petchey & Gaston (2002) index, indicate communities with higher divergence between either using Gower or Overlap to determine species dissim- species in terms of traits, consistent with the hypothesis of ilarity. Finally, we also assessed the relationship between limiting similarity, i.e. in order to coexist species with dif- species richness and functional diversity, both with Rao ferent traits occupy different niches within a community and Petchey and Gaston indices. In this case, out of the pool (Mac Arthur & Levins 1967). Niche differentiation could of 65 species we randomly selected 1000 communities with arise, for a given trait, if species are evenly distributed in richness varying from 3 to 40 species. trait space (i.e. functional evenness Mason et al. 2005 or functional regularity Mouillot et al. 2005b). Results and implications Ideally, if we need an index of functional diversity to provide a general indication of niche differentiation Using the Rao index with the Gower approach, the com- between species, it should take higher values in communi- munities in the random assembly scenario wrongly ties with an even trait distribution (all else being equal). showed the same functional diversity as the community While similar tests have been applied to many indices of having even-spacing of trait values. By contrast, with the functional diversity (Mouchet et al. 2010), we further Overlap approach, functional diversity was higher when tested if this expectation was met using either Gower or trait values were evenly distributed. The reason for these Overlap. Again, as for test 4, we considered for this test the patterns is that, as we discussed above (Test 1), the highest Rao index although we discuss further the implications for values of dissimilarities with the Gower index are only considering other functional diversity indices (Appendix obtained between the species with most different trait val- S2 and below). We considered the pool of 65 species simu- ues. The same problem, on the other hand, does not apply lated for Test 2. Then we assembled communities either to the Petchey and Gaston index (Appendix S2). However, random or with even-spacing of trait values. With the ran- it is well known that this index is very strongly inﬂuenced dom scenario, species were randomly selected across the 65 by the number of species (which was constrained here). species. With the even-spacing scenario we ﬁxed a minimal Therefore we claim that, with the Gower approach, vari- distance between species with closest trait values (Fig. 5). ous indices of functional diversity similar to Rao (Pavoine This minimal distance was set, with the Gower approach, & Bonsall 2011) will show the highest values only when as to be between 3/65 and 5/65 (in order to produce 13–21 species having maximal and minimal trait values in the species). We then computed both the Rao index of func- species pool are included the community (i.e. functional

Fig. 5. Functional diversity (with the Rao index) patterns in communities assembled randomly or with even spacing in trait values (20 communities per scenario, each containing 13–21 species out of the 65 in the species pool). Random communities were assembled with species sorted randomly from the species pool. Even spaced communities were assembled by excluding all species too close on a trait gradient. Functional diversity, expressed here as the Rao index, should be higher with even spacing scenario reﬂecting higher niche differentiation.

Journal of Vegetation Science Doi: 10.1111/jvs.12008 © 2012 International Association for Vegetation Science 813 Gower vs overlap for functional diversity F. de Bello et al. diversity will depend, at least partially, on the range of trait tering along ecological gradients (Freschet et al. 2011; values). This example also serves to highlight the problem Mason et al. 2013). Environmental filtering implies that of interpreting a general index of functional diversity like variation across sites in their environmental conditions Rao. We currently lack pure indices of functional evenness causes different trait values to be selected for (i.e. the traits that meet all necessary criteria (Mason et al. 2005; Mouil- conferring greatest fitness vary according to local environ- lot et al. 2005a). For example, the most commonly used mental conditions; Go¨tzenberger et al. 2012). This gives index of functional evenness (Villeger et al. 2008) is rise to the often-observed pattern where species occurring affected by the trait range considered (lower trait range at a given site are more similar in their traits than species having higher chances to detect even-spacing). Other indi- from different sites (Dıáz et al. 1998; Go¨tzenberger et al. ces could be considered, after validation (Kraft & Ackerly 2012). In addition to this, it has long been theorised that 2010; Thuiller et al. 2010b). Consequently, it remains dif- environmental filtering influences species occurrences and ficult to isolate functional evenness from other functional abundances more strongly in stressed environments (Wei- diversity components, but the Rao index using trait Over- her & Keddy 1995; Mason et al. 2008; Pakeman 2011). lap could provide an interesting measure of functional This should lead to lower functional diversity at stressed vs evenness. benign environments (Mason et al. 2008; Carmona et al. Another pattern that we detected is a changing relation- 2012; Munkemuller et al. 2012). ship between species richness and functional diversity. We tested the behaviour of Rao functional diversity Using both the Rao index (Fig. 6) and the Petchey & computed with Gower and Overlap to detect these Gaston index (Appendix S2), we detected more linear pat- expected patterns. We defined a pool of 300 species having terns between species richness and functional diversity mean trait values regularly spaced between 1 and 300. We with overlap. Most of the dissimilarity values are higher then simulated 300 plots having a different mean trait (i.e. many values are close to 1) with overlap and species value (i.e. the mean of species trait values) across an envi- will be considered functionally different. This means that ronmental gradient. The mean trait value of the plots was with overlap there are greater chances to get a positive set to decrease with the environmental gradient. To simu- correlation between species richness and Rao compared to late environmental filtering, within each plot only a the Gower approach. reduced range of trait values, compared to the whole pool of 300 species, was allowed around the plot mean. We then Test 5: How much do dissimilarity estimations considered two scenarios where the range of trait values influence the tests on environmental filtering? around the plot mean was set either (1) to decrease with Approach the gradient (and therefore with the site trait mean) or (2) increase (Fig. 6). This simulates a case where the impor- Another increasingly common application of functional tance of environmental filtering either increases or diversity is to quantify the influence of environmental fil- decreases along the environmental gradient. Thus, the

Fig. 6. Relationship between species richness and functional diversity (with the Rao index) in 1000 communities randomly assembly out of the 65 in the species pool used in previous tests. See Appendix S2 for results on other functional diversity indices.

Journal of Vegetation Science 814 Doi: 10.1111/jvs.12008 © 2012 International Association for Vegetation Science F. de Bello et al. Gower vs overlap for functional diversity range of trait values allowed within a plot was lower at one These results suggest that even a simple study assessing extreme of the trait gradient (8% of the trait range of whole changes in functional diversity across an environmental pool of species) compared to the other (40%). Our idea was gradient should carefully consider various details of trait to simulate a large environment gradient, say an altitudinal dissimilarity computation before deriving conclusions gradient, in which environmental filtering was increasing, about environmental filtering. Users could also consider, or decreasing, towards one extreme of the gradient. For before calculations, whether the trait ranges in communi- each plot, a fixed number of species (15) were randomly ties increase with community trait means and whether selected from within the range of trait values allowed. data transformation is really required. We then computed trait dissimilarity (either with Gower or Overlap) and functional diversity values (again with the Discussion Rao index). For the Gower approach we used either raw or log-transformed trait data (this creates more ‘compacted’ In this study we show that the method used to compute trait values with increasing trait mean). For the Overlap species trait dissimilarity can have profound consequences approach we either considered the SD of trait values for for the detection of expected ecological patterns assessed each species to increase with trait mean (i.e. SD = 30% of using functional diversity. We claim that the two trait mean) or was considered as fixed (i.e. for all species it approaches considered (Gower vs Overlap), when applied was fixed to 10, so that species having a trait difference of carefully, are both viable but one should be aware of the 30 would have a dissimilarity around 0.85). assumptions, potential applications and pitfalls (Table 1). We also claim that, for a given trait, the Overlap approach Results and implications is (1) generally less context dependent for distance-based measures of functional diversity and (2) takes into account Both the Gower and Overlap approaches generally pro- intraspecific trait variability of species, which is most often duced the expected pattern of decreasing functional diver- neglected in functional ecology (Albert et al. 2012; Violle sity toward one end of the environmental gradient. et al. 2012). However in some specific conditions these patterns were We also claim that the two approaches (Gower vs Over- not properly revealed and users should be aware of possi- lap) imply rather different conceptions of the linkage ble existing limitations. The problem, in this case, seems between species dissimilarity and niche differentiation slightly more serious with the Overlap approach and when between species and are therefore suited to different appli- both (1) the SD of species trait values increases with species cations. The Overlap approach was created mainly to trait means and (2) when this occurs in combination with understand differences in terms of niche within a commu- community samples where the range of trait values nity, i.e. what kind of resources are used by coexisting spe- decreases with plot trait mean (for example when vegeta- cies and if species compete or not for the same resource tion with lower species has also a lower range of height (Mac Arthur & Levins 1967; Mouillot et al. 2005b; Lepsˇ values, which is a rather likely scenario; de Bello et al. et al. 2006). This implies that traits, and trait differences, 2012). In this case, species with greater trait values and are used to infer the ‘alpha’ niche of species (Silvertown greater SD will occur in communities with higher trait et al. 2006) and it suggests that trait overlap could help range. However, because of increased SD for a given dis- focussing particularly on niche differentiation within com- tance between species means, overlap will be greater than munities. As such Overlap could help depicting all biotic at the other extreme of the gradient (where species have mechanisms driving the coexistence of species, such as the lower SD in plots within a lower plot trait range). In this effect of competition is increasing, or even decreasing, the case the Gower approach, which is really proportional to functional differentiation between species (Mayfield & differences in trait means, should better discriminate Levine 2010; Mason et al. 2011). changes in FD across a gradient. The main risk with the It should be noted, that the Overlap approach generally Gower approach occurs in the same scenario (i.e. commu- assumes that even small differences between species may nities with higher trait mean having higher trait range), suggest that they occupy a different niche. This might be and when already normally distributed trait values are reasonable in some occasions but not in others. It might be unnecessarily log-transformed. In this case, again, func- mostly appropriate when small differences in trait values tional diversity does not vary along the environmental gra- imply differences in the mode of resource acquisition, or dient as expected. This is because when higher trait ranges the types of resources used. This applies, for instance, food correspond with higher species mean trait values log-trans- type for animals in the original concept of niche overlap formation shrinks distances between species in communi- (May & Mac Arthur 1972). Indeed our examples suggest ties with high range values more than in communities that species could be functionally more different than with low range values. assumed using the Gower approach. However, these

Fig. 7. Simulations to show the application of Gower vs Overlap approaches in studying environmental ‘filtering’ effects on functional diversity (see Test 5). Out of a pool of 300 species we assembled 300 plots having each 15 species across an environmental gradient, e.g. an altitudinal gradient. At low altitudes we simulated plots as having higher trait values and either higher trait range (scenario 1) or lower (scenario 2). For the Gower approach the trait data,which were normally distributed, were either log-transformed or not. For the Overlap approach we simulated cases where the standard deviation would increase with trait mean or would be similar for all species. assumptions might not be reasonable when competition and C could be, in both cases, close to 1. In this case the between species increases progressively with differences in Gower approach could be preferred, as greater differences trait values, as for example in the case of size asymmetrical in trait means reflect greater differences in competitive competition. Consider three herbaceous species, species A, ability. More generally, comparing results from the two B and C with heights of 10, 50 100 cm respectively, and a approaches may provide a novel way to differentiate fixed SD for height of 5 cm. In this case let us assume that between different types of competitive impacts on species the tallest species (C) is a superior competitor for light to distribution in trait space. species (B) and B is a superior competitor to the shortest It could be argued that Gower approach might be prefer- species (A). With the Overlap approach discussed here, able when dealing with niche differences across environ- however, the dissimilarity between species A and B and A mental conditions. In this case niche differentiation is

Table 1. Summary of the tests conducted and the main conclusions obtained.

Tests Gower Overlap

1. Dissimilarity Lower values, many values approaching zero (distribution Higher values, many values approaching one (distribution values skewed towards lower values) skewed towards one) 2. Trait values Dissimilarity largely dependent on the transformation of Dissimilarity largely independent on the transformation of transformations data (stronger effect when the differences between data species has more order of magnitudes) 3. Range of trait Dissimilarity largely dependent on the pool of species Dissimilarity is largely independent on the pool of species values considered and on the inclusion of outliers, or new considered species in the pool 4. Testing niche differences Even trait spacing is not always easily detected Even trait spacing is more easily detected within communities Species and functional diversity are less correlated Species and functional diversity are more correlated 5. Testing environmental The response of functional diversity to environmental The response of functional diversity to environmental filtering gradients is detected but care is needed gradients is detected but care is needed Conclusion The results are context dependent The results are less context dependent The pool of species considered in the calculation should be The approach can be used to compare across different carefully defined pool of species more easily Preferable for testing environmental filtering on large Preferable for testing niche differentiation and niche environmental gradients overlap within communities

expressed as the beta niche of species, i.e. differentiating dissimilarity matrices; see Botta-Duka´t 2005; Lepsˇ et al. species with a different optima between communities across 2006 and Pavoine et al. 2009). The new R function pro- environmental gradients (Weiher & Keddy 1995; Silver- vided in the supplementary material include all these town et al. 2006). Although even the Overlap approach options, for both the Gower and Overlap approach (for the produced similarly trustable results, in this case it might be Overlap approach both normal and kernel trait distribu- easier to use only trait means because the data are avail- tions are allowed). It should be noted that combining trait able in many existing databases (Kleyer et al. 2008; dissimilarities from multiple traits with the Gower and Klimesˇova´ & de Bello 2009). The approach probably Overlap have, however, a different logic. With Gower, the requires fulfilling fewer requirements in the type of data relative weight of individual traits will be affected by available (see Test 5) and less data collection effort. We spread and distribution of trait value within each trait suggest, anyway, that the species trait mean values used (de Bello et al. 2010b). For example, the relative weight of for calculations should be measured, for each species, in height and specific leaf area (SLA) on functional diversity different environmental condition (Lepsˇ et al. 2011). This values in meadows will depend on whether some forests requires specific field sampling campaigns or simulations, species are in the data set (or if the whole range of trait as discussed by Albert et al. (2012), in order to account for values existing is used for the standardization). If forest changing trait mean values of species along the considered species are included, the effect of height will be down- gradient. When using the Gower approach, however, we weighted in the meadows – because by including forests, recommend special care if using, or not, log-transforma- the range of heights will increase by more than an order of tions. We suggest particularly to log-transform trait values magnitude, whereas no such change will happen for SLA. if SD of species is expected to increase along with trait Therefore, in meadows, species differences in height will mean (as in height). Following of this recommendation have practically no influence on functional diversity calcu- ensures that FD values calculated with the Gower lated using two traits (i.e. height and SLA) if tree species approach behave similarly to those that would have been are used for Gower standardization (Appendix S3). Thus, it calculated using the Overlap approach. might appear that one community is more functionally In this study we show some key properties of the Gower diverse in terms of one trait, while these patterns depend and Overlap approaches based on single traits. Similar only on the mathematical properties of the approach recommendations should are valid when combining (de Bello et al. 2010b). Moreover, the influence of each multiple traits. Combining different traits together, both trait on functional diversity calculated with multiple traits with Gower and Overlap, in fact, implies combining trait will depend on which species are used for standardization. dissimilarities matrices for single traits. This can be done by This is not an issue when distances are calculated using the averaging the trait dissimilarity across all single traits, or Overlap approach. calculating Euclidean distance (i.e. summing the squares Finally, in this paper we dealt mostly with cases of com- of distances and then doing a square root of the result or all puting functional diversity within communities (alpha

Journal of Vegetation Science Doi: 10.1111/jvs.12008 © 2012 International Association for Vegetation Science 817 Gower vs overlap for functional diversity F. de Bello et al. diversity). It is well known that applying functional diver- Freschet, G.T., Dias, A.T.C., Ackerly, D.D., Aerts, R., van Bode- sity indices for measuring functional diversity among com- gom, P.M., Cornwell, W.K., Dong, M., Kurokawa, H., Liu, munities (i.e. beta diversity) further requires that a set G.F., Onipchenko, V.G., Ordonez, J.C., Peltzer, D.A., Rich- properties of trait dissimilarity are fulfilled (Ricotta 2005). ardson, S.J., Shidakov, II, Soudzilovskaia, N.A., Tao, J.P. & Here, we show that alpha functional diversity, which is Cornelissen, J.H.C. 2011. Global to community scale differ- essential for understanding patterns in niche differentia- ences in the prevalence of convergent over divergent leaf tion within and across communities, can be computed trait distributions in plant assemblages. Global Ecology and – with two main families of approaches (Gower and Over- Biogeography 20: 755 765. lap) and that in some cases one approach could be prove Geange, S.W., Piedger, S., Burns, K.C. & Shima, J.S. 2011. A unified analysis of niche overlap incorporating data of different more useful than another (Table 1). types. Methods in Ecology and Evolution 2: 175–184. Go¨tzenberger, L., de Bello, F., Brathen, K.A., Davison, J., Dubuis, Acknowledgements A., Guisan, A., Leps, J., Lindborg, R., Moora, M., Pa¨rtel, M., Pellissier, L., Pottier, J., Vittoz, P., Zobel, K. & Zobel, M. 2012. This research was supported by the Grant Agency of the Ecological assembly rules in plant communities-approaches, Czech Republic (GACR P505/12/1296 and P505/12/1390). patterns and prospects. Biological Reviews 87: 111–127. CPC was supported by a FPI scholarship (BES-2008- Gower, J.C. 1971. General coefficient of similarity and some of 009821). We thank the colleagues in Tomas Herben’s lab its properties. 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Journal of Vegetation Science Doi: 10.1111/jvs.12008 © 2012 International Association for Vegetation Science 819 Journal of Vegetation Science 24 (2013) 820–833 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Does trait conservatism guarantee that indicators of phylogenetic community structure will reveal niche-based assembly processes along stress gradients? Norman W.H. Mason & S. Pavoine

Keywords Abstract Biodiversity; Diversity indices; Environmental ﬁltering; Niche complementarity; Null model; Question: Is the strength of phylogenetic trait conservatism in the species pool Phylogenetic signal; Simpson index; Species reﬂected in the ability of a-andb-scale phylogenetic diversity indicators to richness; Surrogates; Trait evolution detect niche-based assembly processes along stress gradients?

Nomenclature Methods: We used a simple community assembly model to explore the power NZ Plant Names Database (http:// of indicators of phylogenetic community structure to detect assembly processes. nzflora.landcareresearch.co.nz/default.aspx) The model assumes that with declining stress the influence of niche complementarity on species fitness increases, while that of environmental filtering decreases. Received 29 March 2012 – Accepted 11 November 2012 We separately incorporated two trait-independent stochastic processes mass Co-ordinating Editor: Valerio Pillar and priority effects – in simulating species occurrences and abundances along a hypothetical stress gradient. We measured power for a-andb-scale indices of phylogenetic diversity (PD) as the proportion of simulations yielding a significant Mason, N.W.H. correlation between these indices and the community position along the stress ([email protected]): Landcare gradient. For a-scale indices, we ran simulations where species richness was con- Research, Private Bag 3127, Hamilton, 3240, New Zealand stant along the gradient, or either increased, decreased or varied randomly with Pavoine, S. ([email protected]): Museum declining stress. We used four models of trait evolution, differing in the degree of national d’Histoire naturelle, Departement phylogenetic trait conservatism, to examine how variation in the influence of d’Ecologie et Gestion de la Biodiversite, UMR phylogeny on trait variation impacted the power of PD indices. 7204 CNRS-UPMC, 61 rue Buffon, 75005, Paris, France and Results: None of the PD indices gave high power to detect assembly processes Department of Zoology, Mathematical Ecology for either the mass effects or priority effects model, even when trait variation Research Group, University of Oxford, South was strongly conserved. Those measures of a PD that gave moderate power in Parks Road, Oxford, OX1 3PS, UK the priority-effects model with strong trait conservatism were greatly affected by variation in species richness. Consequently, none of the indices examined met the necessary selection criteria. Additional analyses revealed that trait conservatism at the species pool level was poorly maintained at the meta-community scale, with phylogenetically-determined species composition having only moderate power to detect functionally-determined species turnover. This lack of a strong phylogenetic signal in trait variation at the meta-community scale is the most likely explanation for the poor ability of PD indices to detect assembly processes in our study. Conclusion: Phylogenetic diversity indices seem to have limited ability to detect trait-based assembly processes along ecological gradients, even when trait conservatism at the species pool level is strong. Consequently, PD is unlikely to provide a means of avoiding the tough decisions around which traits are most relevant for assembly processes. Rather, our findings emphasize the need to collect relevant functional trait data to understand the mechanisms controlling community assembly.

functional evenness and functional divergence. Each com- Introduction ponent provides independent information on the distribu- Phylogeny is increasingly proposed as a means of detecting tion of species and abundance in functional trait space, and niche-based processes of community assembly, especially a separate index is required to quantify each component for species pools where trait variation is phylogenetically (Mouchet et al. 2010). If we imagine phylogenies as conserved (Kraft et al. 2007; Kembel 2009; Swenson & describing the distribution of species relative to each other Enquist 2009). A key advantage of phylogeny over trait- in ‘phylogenetic space’, it is obvious that the concepts based approaches is that it does not require a decision on embodied by the three FD components can be used to which aspects of species function are important for com- define PD as: munity assembly. With trait-based approaches there is 1. Phylogenetic richness: the volume of phylogenetic always a risk that the functional traits measured will fail to space occupied by the species within a community. capture important aspects of function (Violle et al. 2007). 2. Phylogenetic evenness: evenness in the distribution of Many authors have recognized that a potential weakness abundance in phylogenetic space. of phylogenetic approaches to understanding community 3. Phylogenetic divergence: the degree to which the abun- assembly is that strong phylogenetic conservatism in trait dance of a community is distributed toward the extremities variation is far from universal (Blomberg et al. 2003; Cav- of occupied phylogenetic space (Mouchet et al. 2010). ender-Bares et al. 2009; Losos 2011; Pavoine et al. 2013). Indeed, confirmation of trait conservatism is viewed as In this way, we can apply existing theoretical advances necessary justification for studies using phylogeny to in the development of FD indices to obtain robust mea- understand niche-based processes in ecological communi- sures of a-scale PD. This could potentially greatly aid the ties (e.g. Webb et al. 2002; Srivastava et al. 2012). use of phylogeny to study niche-based assembly processes However, there are comparatively few studies testing along environmental gradients, since changes in a-scale whether trait conservatism at the species pool level is suffi- PD along a gradient may be indicative of changes in assem- cient to guarantee that indicators of phylogenetic commu- bly processes (see section ‘phylogenetic diversity, assembly nity structure (i.e. phylogenetic composition and diversity) processes and stress gradients’ below). can detect assembly processes at the community or metacommunity scale (but see Kraft et al. 2007; Kembel 2009). Testing the relationship between stress and In particular, it remains unclear whether indicators of phylogenetically determined b-diversity phylogenetic community structure are robust against the stochastic processes, such as dispersal (Kembel 2009), that For decades plant ecologists have demonstrated that turn- influence the assembly of real communities. This study over in functional composition (i.e. mean trait values at uses simple community assembly models, which incorpo- community level) follows a predictable pattern along stress rate stochasticity, to test whether a phylogenetic approach gradients (e.g. Grime 1974). Assuming that the traits influ- can reliably detect changes in assembly processes along a encing species turnover along stress gradients are phyloge- hypothetical stress gradient. netically conserved, we should also be able to retrieve a phylogenetic signal along stress gradients. However, measuring changes in phylogenetic composition is more com- Application of functional indices to measure a-scale plicated than for functional composition, because a phylogenetic diversity species’ position in phylogenetic space is defined by its Pavoine & Bonsall (2011) demonstrated that many indices relatedness to other species in the species pool. Recent of functional diversity (FD) can also be used as indices of methodological developments have provided a means of phylogenetic diversity (PD), if we replace functional testing for a phylogenetic signal in species turnover along dissimilarities between species with phylogenetic dissimi- environmental gradients (Pillar & Duarte 2010), using larities between species, functional dendrograms with phy- fuzzy set theory to estimate phylogenetically determined logenetic trees, or functional trait space with phylogenetic species composition. In essence, these methods provide a space. Phylogenies are a means of describing the distribu- test of whether differences in phylogenetic composition tion of species relative to each other in phylogenetic space. between communities are significantly correlated with dif- This is directly analogous to using traits to describe the ferences in environmental variables. A similar approach distribution of species in functional space, so that there is can reveal significant turnover in the composition of func- obvious potential for using existing FD indices as measures tional traits along gradients, so we can test whether results of PD, and vice versa. for phylogenetic composition match those for functional Mason et al. (2005) identified three primary composition. Within the framework presented in Pillar & components of functional diversity – functional richness, Duarte (2010) we can also test for phylogenetic signal at

Journal of Vegetation Science Doi: 10.1111/jvs.12033 © 2012 International Association for Vegetation Science 821 Phylogenetic diversity and assembly processes N.W.H. Mason & S. Pavoine the species pool level (i.e. correlation between pair-wise phylogenetic richness and divergence (Mouchet et al. functional and phylogenetic dissimilarities between 2010), including Rao quadratic entropy (Rao 1982; Pavo- species) and at the meta-community level (i.e. correlation ine et al. 2013) and multivariate dispersion (Laliberte & between pair-wise community dissimilarities in function- Legendre 2010). They should increase when niche com- ally and phylogenetically determined species composi- plementarity enhances either, or both, species’ occurrence tion). In this way, the framework of Pillar & Duarte (2010) probabilities and abundances. provides a means of testing whether phylogenetic signal in An important property of both phylogenetic and the species pool is sufficient to produce a phylogenetic functional diversity indices for understanding ecological signal in functional b-diversity and in species’ turnover processes is that they be independent of existing diversity along environmental gradients. measures (Pavoine et al. 2013). Observed values of functional or phylogenetic richness can increase in the absence of any change in assembly processes, simply due to corre- Phylogenentic diversity, assembly processes and stress sponding increases in species richness (Mouchet et al. gradients 2010; Pavoine et al. 2013). Comparing observed values of Stress gradients often represent a shift from size-symmetric phylogenetic richness to those expected from matrix-swap below-ground competition for soil nutrients and water to null models that randomize species’ occurrences (Manly & size-asymmetric above-ground competition for light with Sanderson 2002) can remove any trivial effects of species declining stress (Schwinning & Weiner 1998; Berntson & richness (Mason et al. 2011a; Richardson et al. 2012). Wayne 2000; Cahill & Casper 2000; Coomes & Grubb A null model that randomizes abundances across species 2000). Where size-symmetric below-ground competition but within communities (Mason et al. 2008b) can convert dominates, fitness is enhanced by traits that maximize indices that measure both phylogenetic richness and diver- acquisition and retention of limiting below-ground gence into pure measures of phylogenetic divergence resources (e.g. Richardson et al. 2005; Lambers et al. (Mason et al. 2012b). 2008; Holdaway et al. 2011). Niche differentiation is required for species to co-exist when competition is size- Aims and objectives asymmetric (Aikio 2004; Kohyama & Takada 2009). Thus, We use a simple assembly model to explore the power of niche differences between co-occurring species will indicators of phylogenetic community structure to detect increasingly enhance local fitness as light competition assembly processes along stress gradients under a wide becomes more intense (Mason et al. 2011b). This is con- range of ecological contexts. This model is described in sistent with an increase in the importance of niche detail by Mason et al. (2012a, 2013). It varies the relative complementarity and a decrease in the importance of envi- influence of environmental filtering and niche comple- ronmental filtering (Mason et al. 2008b) with declining mentarity on species fitness along a hypothetical stress gra- stress (see also Mason et al. 2012a,b; for more detailed dient, with niche complementarity becoming more arguments on this topic), and should cause multi-species influential as stress declines. It incorporates two forms of communities, where light competition is intense, to have stochasticity – mass and priority effects – to test whether higher FD (Mouchet et al. 2010; Mason et al. 2011b). indicators of phylogenetic community structure are robust Of the three FD components, functional richness and against trait-independent processes. It also tests whether functional divergence (or indices that combine them) have variation in species richness affects the power of a-scale PD most often been linked to community assembly processes indices to detect changes in assembly processes along the (Mouchet et al. 2010; Spasojevic & Suding 2011; Mason stress gradient. Our ultimate aim is to assess whether mea- et al. 2012b) or ecosystem functioning (Petchey et al. sures of a-andb-scale indicators of phylogenetic diversity 2004; Mouillot et al. 2011) in plant communities and sim- provide a reliable means of testing for niche-based assem- ulation studies. However, one study has found increased bly processes along stress gradients. In doing this, we also functional evenness with declining stress in fish communi- aim to select a set of a-scale PD indices that will provide a ties (Mason et al. 2008a). Assuming that PD is a surrogate reliable test of the hypothesis that niche complementarity for FD, we can use existing work on FD to predict how PD will increase with declining abiotic stress. components should change in response to changes in assembly processes (e.g. Kraft et al. 2007). Phylogenetic Our selection of PD indices used the following criteria: richness should increase when niche complementarity 1. Each index must have reasonable power to detect the enhances species’ occurrence probabilities (Mason et al. increasing influence of niche complementarity with 2012b). Phylogenetic divergence should increase when declining stress (henceforth, power, for brevity), when niche complementarity enhances species’ relative abun- either mass effects or priority effects influence community dances (Mason et al. 2012b). Several indices measure both assembly.

2. Power must be high across a range of species richness Zealand Plant Phylogeny Database (http://plantphy loge- levels. ny.landcareresearch.co.nz/WebForms/Home.aspx) to 3. Variation in species richness along the stress gradient manually graft species that were not available in Phylo- must not affect the power of any index. matic. Branch lengths were computed following Grafen 4. Collectively, the indices must provide reasonable power (1989) using ‘compute.brlen’ in R package ‘ape’ (Paradis across all the contexts examined. et al. 2004; R Foundation for Statistical Computing, Vienna, AT, US). Phylogenetic distances were defined as the sum of branch lengths in the smallest path that Methods connects two species on the phylogenetic tree (patristic Our assembly model and the rationale it is based on distances). Values for a single trait were simulated for the are described in detail in Mason et al. (2013), see also Data 130 indigenous New Zealand species. To do this, we S1. Consequently, we provide here only a brief description applied the Brownian motion (BM) and accelerating (AC) of the model, and its application in testing the power of PD model of trait evolution (Blomberg et al. 2003) to the phy- indices to detect changes in assembly processes. logenetic tree. With the height of the phylogenetic tree standardized to equal one and the variance–covariance matrix of species’ trait values equal to r²C(h,g), where h is Basic assembly model the value at the root node and g is the rate of trait change, The model context is one where the relative influence of we used the following parameter values for the AC model: niche complementarity and environmental filtering r²=1, h = 0, g = exp(À2), g = exp(À10), g = exp(À20). changes along a stress gradient, with the influence of envi- These models are termed AC 2, AC 10 and AC 20, respec- ronmental filtering being greatest at the most stressed sites. tively. Phylogenetic signal increases with g;wheng tends We model the effect of environmental filtering as a decline towards 0, the model tends towards BM (see Pavoine et al. in fitness with increasing difference between a species’ trait 2010 for details). value and the locally optimal trait value. We model the effect of niche complementarity on fitness as the competi- Mass effects model tive effect on potential colonizers of species that are already ‘present’ in the community. Fitness is estimated by com- This is a simple extension of the basic model where, for bining niche complementarity and environmental filtering each site, the desired number of species is randomly effects. Fitness is scaled to abundance so that the most selected from the species pool. The basic model then calcu- abundant species has 103 times the abundance of the least lates the fitness and abundance of the selected species. abundant species. This degree of variation in abundances is Under this model, species’ occurrences are completely typical in plant communities. unrelated to their traits, but abundance is a deterministic We simulated species’ abundances for ten points function of the traits of the species present. This is an (termed ‘sites’ henceforth) along the stress gradient. We extreme form of mass effects. This model tests whether PD chose to use a small number of sites since many studies of indices can detect changes in trait-based assembly vegetation change along stress gradients examine only a processes in communities where these processes have few independent sites (e.g. Mason et al. 2012b), due to dif- weak effects on species’ occurrences, but a strong effect on ficulties in finding suitable sites. At the most stressed site, species’ abundances. 90% of overall fitness is determined by environmental filtering and 10% is determined by niche complementarity. Priority effects model Moving from the most stressed to least stressed end of the gradient, we decreased the influence of environmental This model randomizes the order of species’ colonization, filtering and increased the influence of niche complemen- whereas species’ traits determine colonization order in the tarity by intervals of 10%, so that at the least stressed site basic model. In this model, colonization order affects the 10% of overall fitness was due to environmental filtering degree of competition experienced by each species, with and 90% due to niche complementarity. early-colonizing species, on average, experiencing less intense competition. Species fitness is calculated as in the basic model, but with competitive effects on fitness based Generating species pools on this randomized colonization order. The desired num- We used Phylomatic (http://phylodiversity.net/phylomat- ber of species is then selected based on species fitness (so ic/) to construct a hypothesized phylogenetic tree for 130 when generating communities of ten species, the ten fittest indigenous New Zealand herbaceous and woody angio- species are selected). Since colonization order may have a sperm, gymnosperm and fern species, and used the New strong influence on the level of competition a species

Journal of Vegetation Science Doi: 10.1111/jvs.12033 © 2012 International Association for Vegetation Science 823 Phylogenetic diversity and assembly processes N.W.H. Mason & S. Pavoine experiences, the priority effects model incorporates because it is unclear how SES values derived this way stochasticity in the relationship between traits and both relate to the three primary components of PD. Observed species’ occurrences and abundances. This model tests FEve was not compared to either null model since it whether PD indices can detect changes in trait-based appears to be independent of species richness (Mouchet assembly process in communities where priority effects et al. 2010), and it is unclear how SES values generated disrupt the effect of these processes on both occurrences when randomizing species’ abundances within communi- and abundances. ties should be interpreted.

Using FD indices to measure PD Testing for phylogenetic and functional signals in species turnover along stress gradients We selected indices that correspond to the FD components of Mason et al. (2005) to measure the PD components we We implemented the methods of Pillar & Duarte (2010) to identified in the Introduction. We examined one index of test whether phylogeny and function significantly influ- phylogenetic richness – phylogenetic branch length enced species turnover along the stress gradient. Specifi- (termed tree size, TS, henceforth) of Faith (1992). To cally, we used the correlation coefficient roPE (correlation ensure values for each simulated community for this mea- of differences between communities in phylogenetically sure was bound between 0 and 1, they were expressed as a determined species composition with distance along the proportion of the values for the entire species pool. We stress gradient) to test whether differences between com- decided not to use convex hull volume (the FRic of Villeger munities in phylogenetically determined species composi- et al. 2008) because this index requires reduction of tion were significantly correlated with distances between dimensionality, so that the phylogenetic space has fewer communities along the stress gradient. We used roTE dimensions than the species richness of the most species- (correlation of differences between communities in func- poor communities. This risks severe information loss when tionally determined species composition with distance using phylogenetic data. We used FEve as a measure of along the stress gradient) to test whether differences phylogenetic evenness (Villeger et al. 2008). We examined between communities in functionally determined species two indices that are a combination of both phylogenetic composition were significantly correlated with distances richness and phylogenetic divergence – FDis (Laliberte & between communities along the stress gradient. We used Legendre 2010) and Rao quadratic entropy (Pavoine & the coefficient roBF (correlation between matrices of inter- Doledec 2005; de Bello et al. 2010). TS was calculated specific functional and phylogenetic dissimilarities) to test directly from the phylogenetic tree. All other indices were for significant correlation in functional and phylogenetic calculated following principal coordinates analysis (PCoA) distances between pairs of species within the species pool. to convert phylogenetic distances between species to loca- Finally, we used roPT (correlation of differences between tions in Euclidean phylogenetic space (Diniz-Filho et al. communities in functionally and phylogenetically deter- 1998). See Pavoine & Bonsall (2011) for details on how mined species composition) to test whether differences each index was calculated. between communities in phylogenetically determined We compared TS values relative to random expectation species composition were significantly correlated with dif- under a matrix-swap null model that randomizes species ferences in functionally determined species composition. occurrences between sites while keeping the species rich- These correlation tests were achieved using the R imple- ness of sites and frequency of occurrence of species the mentation of Pillar & Duarte’s (2010) methods provided by same as in the observed data (Manly & Sanderson 2002) the SYNCSA package, using function syncsa (Debastiani & using the standardized effect size (SES) of Gotelli & McCa- Pillar 2012). be (2002). This index is termed SESTS henceforth. FDis and Rao were compared with values obtained when abun- The simulation framework dances were randomized across species but within plots (after Mason et al. 2008b). These indices, termed SESFDis The same simulation framework was applied to both the and SESRao, are pure measures of phylogenetic diver- mass effects and priority effects model, and for the Brown- gence, since the phylogenetic richnesses of the observed ian motion and accelerating evolution models studied. In and randomized communities are always identical. We each case, it began with the generation of trait values for used 104 randomizations in all null model analyses, to the species pool. Then communities were assembled for ensure accurate estimates of SES values. We did not use each of the ten sites occurring along the hypothetical stress FDiv (Villeger et al. 2008) as a measure of phylogenetic gradient. Alpha PD values were calculated for each of the divergence since it requires calculation of the convex hull. sites, and the Pearson r correlation coefficient between We did not randomize abundances between communities each PD index and the influence of niche complementarity

on fitness (d) was calculated. Significant positive correla- 1 5 BM tions between a PD indices and d indicate that they are able 0.8 10 0.6 15 to detect the increasing influence of niche complementar- 20 ity on community assembly, with declining stress. We also 0.4 25 0.2 examined log-linear, quadratic and exponential relation- 0 ships between PD indices and d. In no instance did these –0.2 relationships yield greater power than Pearson r. For each –0.4 SESTS SESRao simulation we also recorded the significance (at P < 0.05) –0.6 TS FEve FDis Rao SESFDis of the correlation coefficients proposed in Pillar & Duarte –0.8 –1 (2010). We generated trait values for 100 hypothetical regional 1 5 AC 2 species pools, and simulated communities at five levels of 0.8 10 0.6 15 species richness: 5, 10, 15, 20 and 25. For each species rich- 20 0.4 25 ness level, we recorded the proportion of significant corre- 0.2 lations obtained for each index (termed ‘power’ 0 henceforth). We tested whether the power of a PD indices –0.2 was affected by variation in species richness along the gra- –0.4 TS FEve FDis Rao SESTS SESFDis –0.6 SESRao dient, by simulating communities of varying species rich- –0.8 ness. To examine power with increasing species richness, –1 communities containing five species were simulated for AC 10 the two most stressed sites. Communities with ten species 1 5 were simulated for the two next most stressed sites, and so 0.8 10 0.6 15 on until communities with 25 species were simulated for 20 0.4 the two least stressed sites. This provides an indication of 25 0.2 power when species richness increases with the influence Power (proportion of significant results) 0 of niche complementarity (d). We repeated this process in –0.2 reverse to estimate power when species richness declined –0.4 with d. We also examined power when species richness –0.6 TS FEve FDis Rao SESTS SESFDis SESRao varied randomly with d. Where significant negative corre- –0.8 –1 lations were obtained, these were recorded as ‘negative AC 20 power’, so that ‘power’ was bound between À1and1. 1 5 0.8 10 Results 0.6 15 0.4 20 Alpha-scale phylogenetic diversity 0.2 25 0 In the mass effects model, none of the indices provided –0.2 high power to detect changes in assembly processes for any –0.4 of the evolution models studied (Fig. 1). SESRao and –0.6 TS FEve FDis Rao SESTS SESFDis SESRao –0.8 SESFDis performed best, but still only had power between –1 0.2 and 0.4 in the BM and AC 2 models of evolution. Power for all indices was negligible in the AC 10 and AC 20 Fig. 1. Power of phylogenetic diversity indices to detect the increasing models. influence of niche complementarity with declining stress when mass effects disrupt the relationship between species’ traits and occurrences. In the priority effects model, TS and SESTS had the Results are shown for four models of evolution: Brownian motion (BM), highest power (Fig. 2). However, with power between 0.6 and accelerating evolution with g = exp(À2), exp(À10) or exp(À20) (AC 2, and 0.8 in the BM model and between 0.5 and 0.7 in the AC 10 and AC 20, respectively). Numbers associated with different types AC 2 model, these indices do not provide a high degree of of bar shading indicate species richness of local communities. TS = the reliability in revealing changes in assembly processes. For sum dendrogram branch length, FEve = phylogenetic evenness, the AC 10 and AC 20 evolution models, the power of all FDis = phylogenetic dispersion and Rao = quadratic entropy. SESTS is indices was very low. observed TS expressed relative to a matrix swap null model. SESRao and SESFDis are, respectively, observed Rao and FDis expressed relative to a In the mass effects model, variation in species richness null model randomizing abundances across species, but within strongly influenced the power of TS, FDis and Rao (Fig. 3). communities. For these indices, power was higher when species richness increased with declining stress and lower when species

1 BM 5 20 richness decreased with declining stress. This effect was 10 25 0.8 obvious for all of the evolution models studied. 15 0.6 In the priority effects model, variation in species rich- 0.4 ness strongly inﬂuenced the power of TS, SESTS, FDis and 0.2 Rao (Fig. 4). For all of these indices except SESTS, power 0 was higher when species richness increased with declining –0.2 stress and lower when species richness decreased with –0.4 declining stress. The opposite effect was observed for

–0.6 TS FEve FDis Rao SESTS SESFDis SESRao –0.8 SESTS. The inﬂuence of species richness on power was evi- –1 dent for each of the evolution models studied.

AC 2 20 1 5 Phylogenetic and functional signals in species turnover 10 25 0.8 15 along stress gradients 0.6 0.4 For the mass effects model, correlations of differences 0.2 between functionally and phylogenetically determined 0 species composition and position along the stress gradient –0.2 (roTE and roPE, respectively) were generally quite low –0.4 (power <0.4; Fig. 5). This is perhaps not so surprising since

–0.6 TS FEve FDis Rao SESTS SESFDis SESRao the mass effects model, by randomly selecting the species –0.8 present in local communities, disrupts the relationship –1 between species’ occurrences and their traits. This stochas- AC 10 ticity in species occurrence may have disrupted the rela- 1 5 20 tionship between stress and both the functional and 0.8 10 25 phylogenetic composition of communities, leading to low 0.6 15 power for both roTE and roPE. 0.4 A more surprising result is that even for the Brownian 0.2 motion model (where trait variation is strongly Power (proportion of significant results) 0 conserved), functionally and phylogenetically determined –0.2 –0.4 species composition were signiﬁcantly correlated for fewer than 50% of simulations (i.e. power <0.5 for roPT; Fig. 5).

–0.6 TS FEve FDis Rao SESTS SESFDis SESRao –0.8 This is despite almost 100% of simulations yielding a –1 significant correlation for functional and phylogenetic dissimilarities between species (i.e. power ca. 1 for roBF). AC 20 1 5 20 This result suggests that the species pool-level phylogenetic 0.8 10 25 signal was poorly maintained at the meta-community 15 0.6 level, even when traits were strongly phylogenetically 0.4 conserved. Even for the evolution model with the weakest 0.2 phylogenetic signal (AC20) a non-negligible proportion of 0 runs (ca. 0.4) yielded a significant correlation for func- –0.2 tional and phylogenetic dissimilarities between species. –0.4 However, for this model a very small proportion of simula- < –0.6 TS FEve FDis Rao SESTS SESFDis SESRao tions ( 0.1) yielded a significant result for roPT. This –0.8 further emphasizes that phylogenetic signals in trait varia- –1 tion at the species pool level were not maintained at the meta-community level. Fig. 2. Power of phylogenetic diversity (PD) indices to detect the In the priority effects model, power for roTE was very increasing influence of niche complementarity with declining stress when high, with almost 100% of simulations yielding a signifi- priority effects disrupt the relationship between species’ traits and cant result (Fig. 6). This indicates that the functionally abundances. Numbers associated with different types of bar shading determined species composition of communities was indicate species richness of local communities. Results are shown for four models of evolution: Brownian motion (BM), and accelerating evolution almost always significantly correlated with their position with g = exp(À2), exp(À10) or exp(À20) (AC 2, AC 10 and AC 20, along the stress gradient. This result was consistent across respectively). Explanations for PD index codes are given in Fig. 1. all models of trait evolution (i.e. was independent of the

BM BM 1 1 Increasing Increasing 0.8 0.8 Random 0.6 Random 0.6 0.4 Decreasing 0.4 Decreasing 0.2 0.2 0 0 –0.2 –0.2 –0.4 –0.4 –0.6 Rao FEve FDis SESTS SESFDis SESRao –0.6 TS TS Rao SESFDis SESRao FEve FDis SESTS –0.8 –0.8 –1 –1

1 1 AC 2 AC 2 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 –0.2 –0.2 –0.4 –0.4 –0.6 –0.6 FEve Rao SESFDis FDis SESTS SESRao TS TS FDis SESTS SESFDis FEve Rao SESRao –0.8 –0.8 –1 –1

1 1 AC 10 AC 10 0.8 0.8 0.6 0.6 0.4 0.4 0.2

0.2 Power (proportion of significant results) Power (proportion of significant results) 0 0 –0.2 –0.2 –0.4 –0.4 –0.6 TS FEve FDis SESTS SESFDis SESRao –0.6 Rao TS FEve FDis Rao SESTS SESFDis SESRao –0.8 –0.8 –1 –1 1 1 AC 20 AC 20 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 –0.2 –0.2 –0.4 –0.4 –0.6 TS FDis Rao –0.6 FEve SESTS SESFDis SESRao

TS FEve FDis Rao SESTS SESFDis SESRao –0.8 –0.8 –1 –1 Fig. 4. Power of phylogenetic diversity (PD) indices with species richness Fig. 3. Power of phylogenetic diversity (PD) indices with species richness increasing, decreasing or varying randomly as stress declines when increasing, decreasing or varying randomly as stress declines when mass priority effects disrupt the relationship between species’ traits and effects disrupt the relationship between species’ traits and occurrences. abundances. Results are shown for four models of evolution: Brownian Results are shown for four models of evolution: Brownian motion (BM), motion (BM), and accelerating evolution with g = exp(À2), exp(À10) or = À À À and accelerating evolution with g exp( 2), exp( 10) or exp( 20) (AC 2, exp(À20) (AC 2, AC 10 and AC 20, respectively). Explanations for PD index AC 10 and AC 20, respectively). Explanations for PD index codes are given codes are given in Fig. 1. in Fig. 1.

Journal of Vegetation Science Doi: 10.1111/jvs.12033 © 2012 International Association for Vegetation Science 827 Phylogenetic diversity and assembly processes N.W.H. Mason & S. Pavoine level of trait conservatism). By contrast, power for roPE composition of communities was consistently significantly was moderate to low (<0.5), even for the Brownian correlated with their position on the stress gradient, phylo- motion model. This indicates that while the functional genetic composition was not. Results for roPT and roBF were the same as for the mass effects model, indicating 1 that, irrespective of the assembly model used, phylogenetic BM signals in trait variation at the species pool level were 0.8 poorly maintained at the meta-community level. 5 10 0.6 15 Discussion 20 0.4 25 Our results suggest that indicators of phylogenetic community structure do not provide a reliable means of detecting 0.2 changes in niche-based assembly processes or niche-based species turnover along gradients. Measures of a-level phy- 0 logenetic diversity (PD) did not reliably detect an increase roTE roPE roPT roBF in niche complementarity with declining stress. Measures 1 of b PD did not reliably detect functionally determined AC 2 changes in species composition along the stress gradient. 0.8 Our results also suggest that the weak phylogenetic signal 5 at the meta-community level may be responsible for the 10 low power of a-scale FD indicators to detect assembly 0.6 15 processes. 20 0.4 25 Consequently, non-significant results for correlations between stress gradients and a PD are not sufficient to reject the hypothesis that niche-based assembly processes

cant results) 0.2 fi change along stress gradients. Nor is a non-significant cor- b 0 relation between PD and stress (i.e. roPE) sufficient to roTE roPE roPT roBF reject the hypothesis that functionally determined species on of signi

Ɵ composition changes along stress gradients. 1 AC 10 The only other simulation study testing the power of PD indices to detect assembly processes that we are aware of 0.8 5 (Kraft et al. 2007) obtained equivocal results. Therefore, it 10 may be premature to declare that PD indices are of no use Power (propor 0.6 15 for revealing assembly processes, especially since our study 20 was not designed to provide a deﬁnitive general statement 0.4 25 on this question. Below we provide a synthesis of our results, with special reference to our selection criteria for a 0.2 PD indices. We then explore general explanations for the

0 roTE roPE roPT roBF Fig 5. Proportion of runs giving signiﬁcant results for the four matrix correlation coefﬁcients studied when mass effects disrupt the relationship 1 AC 20 between species’ traits and occurrences. Numbers associated with different types of bar shading indicate species richness of local 0.8 communities. The correlations studied were: roTE = correlation of 5 differences between communities in functionally determined species 10 composition with distance along the stress gradient; roPE = correlation of 0.6 15 differences between communities in phylogenetically determined species 20 composition with distance along the stress gradient; roPT = correlation of 0.4 25 differences between communities in functionally and phylogenetically determined species composition; roBF = correlation of functional and 0.2 phylogenetic dissimilarity between species. Results are shown for four models of evolution: Brownian motion (BM), and accelerating evolution 0 with g = exp(À2), exp(À10) or exp(À20) (AC 2, AC 10 and AC 20, roTE roPE roPT roBF respectively).

1 low power of the PD indices examined and consider BM whether the lack of power may be due to limitations in the 0.8 modelling framework used. We ﬁnish by discussing scenarios where phylogenetic data may have more potential 5 0.6 10 to reveal assembly processes, and by providing suggestions 15 for future modelling studies to investigate these scenarios. 0.4 20 25 0.2 Phylogenetic diversity indices do not satisfy criteria for detecting changes in assembly processes 0 roTE roPE roPT roBF None of the a PD indices examined satisﬁed our selection

1 criteria. They generally had very low power, and those that AC 2 had reasonable power in some contexts were strongly influ- 0.8 enced by variation in species richness. Consequently, none of these indices provides a reliable test of the hypothesis that 5 0.6 the influence of niche complementarity on community 10 15 assembly will increase with declining stress. Based on these 0.4 20 findings, we must advise against the use of PD indices to 25 reveal changes in niche-based assembly processes along 0.2 stress gradients. For the priority effects model, observed cant results)

ﬁ phylogenetic dendrogram branch length (TS) and TS com- 0 roTE roPE roPT roBF pared with a matrix swap null model (SESTS) both had some power to detect changes in assembly processes along

on of signi 1 Ɵ AC 10 the hypothetical stress gradients. This was true for all of the species richness levels studied in the two evolution models 0.8 where the phylogenetic inﬂuence on trait variation was

5 strongest (BM and AC 2). However, variation in species 0.6 Power (propor 10 richness strongly influenced the power of both TS and 15 SESTS in both the mass effects and priority effects model. 0.4 20 Consequently, using these indices to infer changes in 25 0.2 assembly processes along a gradient could lead to spurious conclusions if species richness also varies along the gradient. 0 This susceptibility to variation in species richness means roTE roPE roPT roBF that these PD indices are not suitable for revealing trait- based assembly processes, since a variety of trait-indepen- 1 AC 20 dent assembly processes might influence species richness. 0.8 None of the other indices provided much power in either the mass effects or priority effects model. Thus, none 5 0.6 of the indices examined satisfied all of our selection 10 15 criteria, so we cannot suggest their use to detect shifts in 0.4 20 assembly processes along stress gradients. By extension, 25 we also failed to find a set of indices that collectively pro- 0.2 vide power to detect shifts in assembly processes across all of the contexts studied. Based on these findings, it seems 0 roTE roPE roPT roBF that a PD indices are unlikely to provide a useful means for detecting changes in community assembly processes along Fig 6. Proportion of runs giving significant results for the four matrix stress gradients in ecological timescales. correlation coefficients studied when priority effects disrupt the relationship between species’ traits and occurrences. Numbers associated with different types of bar shading indicate species richness of local General problems – trait conservatism at species pool communities. Explanations of correlation coefficient codes are given in level not maintained during community assembly Fig. 5. Results are shown for four models of evolution: Brownian motion (BM), and accelerating evolution with g = exp(À2), exp(À10) or exp(À20) Pavoine et al. (2013) demonstrate that a PD is a poor (AC 2, AC 10 and AC 20, respectively). surrogate for a FD, since correlations between FD and PD

Journal of Vegetation Science Doi: 10.1111/jvs.12033 © 2012 International Association for Vegetation Science 829 Phylogenetic diversity and assembly processes N.W.H. Mason & S. Pavoine are generally not very strong, even when phylogeny munity assembly (e.g. see results for plant phylogenies in strongly determines inter-specific variation in trait values. Blomberg et al. 2003; appendix 6), although some studies This may limit the ability of PD indices to detect assembly have shown that strong trait conservatism can occur in processes that act via species’ traits. Using the same plants (Feild & Arens 2007; Donoghue 2008). modelling framework as we have used here, Mason et al. One potential limitation of our modelling approach is (2013) showed that a FD indices had very high power to that we consider only a single functional trait. Some sim- detect changes in trait-based assembly processes along the ulation studies (Kraft et al. 2007; Kembel 2009) have hypothetical stress gradient. Consequently, it seems that found that, in some instances, the power of PD indices the low power of a PD indices was largely due to their increased as the number of traits on which niche comple- inability to accurately capture variation in a FD. Our mentarity or environmental filtering acted increased. results, considered in light of Pavoine et al. (2013) and Thus, our use of a single trait may have limited the abil- Mason et al. (2013), suggest that species pool-level trait ity of PD to capture FD, since our functional trait space conservatism may generally be poorly maintained at the a has much lower dimensionality than the phylogenetic (i.e. local community) scale, although further work is space. However, Pavoine et al. (2013) used a large num- required to provide a definitive answer. If this is true, it is ber of traits and still found correlations between FD and possible that a-scale indicators of phylogenetic community PD to be relatively weak. Consequently, it seems unlikely structure may generally be poorly suited to detecting that including more trait dimensions in our modelling niche-based assembly processes in local communities. framework would have improved the power of PD indi- Our work extends the work of Pavoine et al. (2013) and ces markedly. Mason et al. (2013) to suggest that b-scale (i.e. meta-com- The shape of the phylogenetic tree we used, being quite munity) measures of phylogenetic community structure asymmetric, may also have limited the power of PD indices are poorly able to detect niche-based species turnover to detect assembly processes. However, Kraft et al. (2007) along stress gradients. We have shown that the low power found that tree topology had only a subtle effect on the of b PD indices is due to weak phylogenetic signal in trait power of PD indices. Consequently, it seems unlikely that variation at the meta-community level. If phylogenetic sig- the shape of our phylogenetic tree can provide the main nals at the meta-community level (b scale) were generally explanation for the low power of PD indices. More much weaker than those at the species-pool level (c scale), recently, evidence has emerged that poorly resolved phy- this would raise serious doubts about the overall ability of logenies with many soft polytomies (e.g. nodes joining phylogenetic approaches to detect niche-based assembly many species within a genus, with equal, arbitrary branch processes. However, it seems that studies examining the length) can artificially inflate trait conservatism at species ability of phylogenetic community structure to detect pool level (as measured using Blomberg et al.’s K statistic; assembly processes (including Kraft et al. 2007; Kembel Davies et al. 2011). Our tree does include several soft poly- 2009) have ignored this point. Consequently, it is impossi- tomies, so it is possible that we have over-estimated species ble to say whether this result represents a general pattern pool-level trait conservatism (though it is unclear whether or is due to some artefact in our modelling frame work. the measure of conservatism we used, roBF, is subject to this effect). This could partially explain the discrepancy we observed between species pool- and meta-community- Potential problems with the modelling framework level trait conservatism. It is unclear whether soft polyto- Other simulation studies (Kraft et al. 2007; Kembel 2009) mies may have also influenced variation in PD indices. We have found that, in some instances, PD may have good are unaware of any studies examining this question, and power to detect assembly processes. This raises the possibil- the influence of tree shape on measures of phylogenetic ity that the low power of PD indices that we have demon- community structure was beyond the scope of the present strated may be due to problems with our modelling study. framework. However, Kraft et al. (2007) did not include Another potential limitation to our treatment of the any trait-independent stochasticity in their trait-based phylogeny is that we assigned branch lengths after select- assembly models. Consequently, their simulations proba- ing our subset of species from the complete phylogeny of bly over-emphasize the role of traits (and hence phylog- vascular plants. This may have artificially constrained the eny) in assembly relative to real plant communities. phylogenetic space spanned by our species pool. An alter- Further, in both Kraft et al. (2007) and Kembel et al. native approach would be to ensure all our species were (2009), PD indices generally only had good power when inserted into a full phylogeny (e.g. like that provided by trait variation was extremely phylogenetically conserved. Angiosperm Phylogeny Group 2009) and then obtain our The level of phylogenetic conservatism that they used is phylogenetic distances directly from the full phylogeny. probably unrealistic for the plant traits that control com- However, since we used several strongly conserved models

Journal of Vegetation Science 830 Doi: 10.1111/jvs.12033 © 2012 International Association for Vegetation Science N.W.H. Mason & S. Pavoine Phylogenetic diversity and assembly processes of trait evolution, this should also have constrained varia- a poor shortcut for capturing functionally relevant trait tion in functional trait values. variation, there is no way of avoiding the hard work Our findings do not provide definitive evidence that required to demonstrate the significance of plant traits for indicators of phylogenetic community structure will gen- individual-level functions. However, there are many excit- erally have limited power to detect assembly processes. ing opportunities for phylogeny to advance community However, they do build on the work of previous authors ecological studies. In particular, there is great potential for demonstrating that caution must be exercised when inter- phylogeny to help us understand how the history of trait preting observed patterns of phylogenetic composition and evolution contributes to observed correlations between diversity. Indeed, there is growing evidence that rather stress gradients and measures of functional community than using phylogeny to derive surrogate measures of structure. functionally determined community structure, a more fruitful line of research might be to consider how phylogeny affects observed patterns in functional diversity and References composition (e.g. Safi et al. 2011; Srivastava et al. 2012). With respect to examining patterns along environmental Aikio, S. 2004. Competitive asymmetry, foraging area size and – gradients, it may be interesting to consider whether trait coexistence of annuals. Oikos 104: 51 58. Angiosperm Phylogeny Group 2009. An update of the Angio- evolution plays a major role in correlations between envi- sperm Phylogeny Group classification for the orders and ronment and functional diversity and composition. For families of flowering plants: APG III. Botanical Journal of the example, one could simulate traits for the species pool Linnean Society 161: 105–121. using models of varying trait conservatism and record the Berntson, G.M. & Wayne, P.M. 2000. Characterizing the size proportion of the simulated species pools yielding correla- dependence of resource acquisition within crowded plant tions as or more extreme than those observed. It would be populations. Ecology 81: 1072–1085. particularly interesting to compare observed correlations Blomberg, S.P., Garland, T. & Ives, A.R. 2003. Testing for phylo- with those for simulated species pools with similar levels of genetic signal in comparitive data: behavioural traits are trait conservatism (as measured by Blomberg et al.’s K or more labile. Evolution 57: 717–745. Pillar & Duarte’s roBF). If the observed correlations are Cahill, J.F. Jr & Casper, B.B. 2000. Investigating the relationship within the 95% confidence interval of the simulated between neighbor root biomass and belowground competi- species pools, then one might conclude that the role of trait tion: field evidence for symmetric competition belowground. evolution in generating the observed correlations cannot Oikos 90: 311–320. be ignored. This is just one of many exciting opportunities Cavender-Bares, J., Kozak, K.H., Fine, P.V.A. & Kembel, S.W. for using phylogeny to better understand how historical 2009. The merging of community ecology and phylogenetic processes influence observed patterns in plant communi- biology. Ecology Letters 12: 693–715. ties (Cavender-Bares et al. 2009). Coomes, D.A. & Grubb, P.J. 2000. Impacts of root competition in forests and woodlands: a theoretical framework and review of experiments. Ecological Monographs 70: 171–207. 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Journal of Vegetation Science Doi: 10.1111/jvs.12033 © 2012 International Association for Vegetation Science 833 Journal of Vegetation Science 24 (2013) 834–842 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Contrasting effects of productivity and disturbance on plant functional diversity at local and metacommunity scales Etienne Laliberte, David A. Norton & David Scott

Keywords Abstract Assembly rules; Disturbance; Environmental filtering; Functional diversity; Land-use change; Questions: Is trait convergence more intense when soil resource availability Limiting similarity; Null models and disturbance constrain productivity and limit above-ground competition? Do the effects of productivity and disturbance on functional diversity differ between Abbreviations the local and metacommunity scales? ANPP = above-ground net primary productivity; FDis = functional dispersion; Location: Semi-arid grasslands in New Zealand (43°59′ S, 170°27′ E). FRic = functional richness; NPP = net primary productivity Methods: We measured trait convergence and divergence in grasslands along gradients of primary productivity and disturbance at local (i.e. 1 m 9 1m)and Nomenclature metacommunity (8 m 9 50 m) scales, using long-term (27-yr) manipulations Moore & Edgar 1970; Healy & Edgar 1980; of soil resource availability and grazing intensity. We compared trait dispersion Allan 1982; Webb et al. 1988; Edgar & Connor metrics to those expected under different null models. 2000 Results: At the metacommunity scale, we found stronger trait convergence Received 2 November 2011 with increasing productivity and grazing intensity, where all short, slow- Accepted 14 December 2012 growing species were excluded from the potential species pool. However, Co-ordinating Editor: Norman Mason once this broad-scale filter on species pool was taken into account, we found that at the local scale, abundance-weighted functional dispersion of co-occur- Laliberte, E. (corresponding author, etienne. ring species was stronger than expected under our null model, thereby suggest- [email protected]): School of Plant Biology ing limiting similarity. Moreover, trait divergence became stronger at higher (M090), The University of Western Australia, 35 productivity and lower grazing intensity, where size-asymmetric competition Stirling Highway, Crawley, WA, 6009, Australia for light is likely to have been more intense. Norton, D.A. ([email protected]): School of Conclusions: At the metacommunity scale, environmental filtering led to Forestry, University of Canterbury, Private Bag species with particular traits being excluded from the species pool. In contrast, at 4800, Christchurch, 8013, New Zealand the local community scale where individuals interact, there was evidence of lim- Scott, D. ([email protected]): iting similarity. Our results suggest that environmental filtering and limiting 4 Murray Place, Lake Tekapo, New Zealand similarity are not mutually exclusive and jointly determine community structure, but can operate at different spatial scales.

(2) ‘limiting similarity’, whereby species that co-occur at a Introduction local scale differ significantly from each other in particular The assembly of plant communities is thought to be largely traits related to resource acquisition and/or use (Weiher & driven by two distinct processes: (1) ‘environmental filter- Keddy 1995; Grime 2006). The importance of these two ing’, whereby the pool of species that can potentially colo- distinct forces during community assembly is commonly nize a site (i.e. the potential species pool) is filtered in such inferred from patterns of trait dispersion (Gotzenberger€ a way that the plant species that can actually grow at that et al. 2012). Trait convergence is typically associated with site (i.e. the ‘realized’ species pool) are more functionally environmental filtering (Weiher & Keddy 1995; Dıaz et al. similar to each other than expected by chance, and 1998; Lebrija-Trejos et al. 2010), because species with

Journal of Vegetation Science 834 Doi: 10.1111/jvs.12044 © 2012 International Association for Vegetation Science E. Laliberteetal. Resource availability, grazing, trait dispersion traits that are poorly suited to local environmental condi- metacommunity-scale environmental filtering on poten- tions cannot establish or occur at very low abundance. On tial species pools is taken into account (de Bello et al. the other hand, trait divergence is thought to result from 2012). Testing these hypotheses requires an experimental local competition during community assembly (Weiher & design where both productivity and disturbance intensity Keddy 1995; Stubbs & Wilson 2004; Mason et al. 2011) are manipulated independently. In particular, it has been since classical competition theory states that there is a limit argued that trait-based community assembly is best studied to how similar co-occurring species can be (MacArthur & by constructing synthetic communities from a common Levins 1967). species pool, experimentally altering environmental condi- Environmental filtering and associated trait conver- tions and/or disturbance regimes, and following long-term gence is generally expected to become more intense where changes in species composition and trait distributions strong environmental constraints to plant establishment or (Grime 2002). growth are present (e.g. low productivity and/or high dis- In this study, we explore trait-based plant community turbance; Weiher & Keddy 1995). On the other hand, assembly in a 27-yr experiment conducted in New Zealand Grime (2006) predicted that higher productivity should grasslands, where a common pool of 25 plant species was lead to trait convergence due to competitive displacement sown into the resident vegetation within a 3-ha area in of poorer competitors, and that this should be most appar- 1982, after which soil resource availability and grazing ent for ‘core’ vegetative traits that strongly relate to growth intensity were experimentally manipulated (Scott 1999). rate and competitive ability, such as specific leaf area, leaf We evaluate plant trait dispersion patterns at both the dry matter content, leaf nutrient concentration or plant metacommunity scale (8 m 9 50 m) as well as the local height (Grime 2006). In contrast, higher disturbance (1 m 9 1 m) scale in order to distinguish environmental intensity should promote trait divergence by suppressing filtering effects on the potential species pool from local bio- competitive dominance (Grime 2006). Such divergence tic processes (de Bello et al. 2012). This unique long-term should be stronger for traits related to regeneration and experiment allowed us to explore in a controlled setting dispersal because disturbance increases the likelihood that how productivity and disturbance together influence pat- species with different regeneration niches could co-occur terns of trait convergence and divergence in plant commu- (Grubb 1977). An additional complexity is that some stud- nities. Specifically, we ask the following questions: ies have found evidence for convergence in vegetative 1 Is trait convergence at the metacommunity scale more traits and divergence in regenerative traits (Swenson & En- intense under strong abiotic regimes (e.g. low productivity quist 2009), whereas others found convergence for several and/or high disturbance)? traits under both higher productivity and disturbance 2 Is trait divergence at the local community scale more intensity (Pakeman et al. 2011), the latter of which does intense under environmentally benign conditions (e.g. not support predictions from Grime (2006). In addition, high productivity and/or low disturbance), where asym- Mason et al. (2012) found convergence of leaf traits with metric competition for light may become more important? decreasing soil fertility (i.e. total phosphorus concentration), while Spasojevic & Suding (2012) observed greater Methods plant functional diversity at both low and high soil Study area and site resource availability, although for different functional traits. Clearly, whether trait convergence and divergence The study was conducted on the AgResearch trial site at vary with productivity and disturbance in the way pre- Mount John, west of Lake Tekapo in the Mackenzie Basin dicted by Grime (2006) still remains unclear. of New Zealand’s South Island (43°59′S, 170°27′E, 820 m One possibility is that the effects of productivity on trait a.s.l.). Details about the climate, soils and settlement convergence or divergence depend on disturbance inten- history of the study area and study site are available sity, and vice versa, as predicted by some models of species elsewhere (Scott 1999). The dominant vegetation type co-existence (Huston 1979, 1994). For example, at low prior to human settlement in the area is likely to have been productivity, high disturbance intensity may lead to trait short-tussock grassland with a variable woody component convergence if only specific plant functional types can per- (McGlone 2001). Prior to the start of the experiment, vege- sist under such extreme conditions. Conversely, at high tation was a depleted native fescue tussock (F. novae-zelan- productivity, greater disturbance intensity may prevent diae) grassland dominated by the exotic mouse-ear trait convergence by suppressing competitive dominance hawkweed (Hieracium pilosella) and around 30–40 other and allowing the co-existence of functionally dissimilar plant species. This vegetation type is representative of large species. In addition, trait convergence and divergence areas of New Zealand rangelands (Wardle 1991). Detailed depend on how species pools are defined, such that local vegetation analyses are available elsewhere (Scott 2001, trait divergence may only become apparent once any 2007; Laliberte et al. 2010).

Experimental design adjusted based on residual bulk of the moderate grazing treatment (i.e. height of 1–2 cm). The grazing intensity The experiment is described in detail by Scott (1999) and treatment is relative (i.e. within each whole plot), and not is only summarized here. In 1982, 25 agricultural grass absolute, as the annual sheep grazing days achieved and legume pasture species were sown using a rotary hoe depended on the forage growth of the different fertilizer drill within a 3-ha area. The trial followed a split-plot treatments. This is a key component of the experimental design consisting of two spatial replications (blocks; ca. design that increases realism and relevance, since herbi- 1.5 ha each), each split into five whole plots (50 m vore density and production depends on primary produc- 9 50 m) receiving one of the following five nominal fer- tion in ecosystems (McNaughton et al. 1989). Grazing tilizer treatments: 0, 50, 100, 250 and 500 kgha1yr1 of occurs in the period November–May. sulphur-fortified superphosphate (i.e. a P/S fertilizer). Pre- vious studies from this study site showed that pasture Vegetation sampling growth responded to the addition of P and S, and that irri- gation of fertilized plots strongly increased production Sampling of all vascular plant species present within each (Scott 2000). A recent study (Laliberte et al. 2012b) 8m9 50-m plot was undertaken in November 2007 and showed that unfertilized plots were dominated by plants 2008 (Laliberte et al. 2010). Twenty 1 m 9 1-m quadrats with a conservative nutrient-use strategy, whereas plants were randomly positioned along two longitudinal tran- with leaf traits associated with rapid growth were more sects (ten quadrats per transect) in each plot. Transects abundant in fertilized plots. The fertilizer was applied were 3 m apart from each other and 2 m from the closest annually in early spring. The whole plots receiving fence. Cover (i.e. vertical projection of canopy, including 500 kgha1yr1 were also irrigated fortnightly from living and non-living components) of all vascular plant November to May of each year to represent the highest- species present in each 1 m 9 1-m quadrat was recorded intensity agriculture typical of the region. Fertilizer was using a seven point semi-quantitative scale (1, 0.1%; 2, applied each year for the first 19 yr of the experiment and 0.1–0.9%; 3, 1–5%; 4, 5–25%; 5, 25–50%; 6, 51–75%; 7, not applied since. 76–100%). Percentage cover per species per quadrat was Each whole plot was further split into six 8 m 9 50-m taken as the median of the percentage cover of the class. subplots (thereafter simply referred to as ‘plots’) corre- Cover values for 2007 and 2008 were averaged for each sponding to a two-way factorial design involving sheep species/quadrat combination. Although cover values are grazing intensity (lax, moderate and hard) and stocking not as reliable as biomass data for estimating species rela- type (mob vs. sustained). We consider this ‘plot’ (i.e. tive abundances, a previous study found that measures of 8m9 50 m) scale to represent a metacommunity since it functional diversity derived from cover values were simi- is larger than the scale at which individual plants interact lar to those derived from biomass data (Lavorel et al. and includes a set of similar local communities that are 2008). linked through dispersal (Leibold et al. 2004). In mob-grazing plots, a larger number of sheep (with Plant functional traits actual numbers depending on available feed-on-offer of the moderate plot) were introduced to plots for 3–4days, A previous principal components analysis (PCA) measured while sustained grazing plots received a lower number of from 14 plant functional traits measured on all plant sheep for a longer period (i.e. several weeks). However, species from our experiment showed that most of the trait only plots corresponding to the mob stocking type were variation (74%) between species was represented by the used in the present study as this made it possible to quan- first two PCA axes (Laliberte et al. 2012b). Leaf nitrogen tify the proportion of above-ground net primary produc- (N) concentration had the highest loading on the first PCA tion (ANPP) consumed (or destroyed) by sheep (Laliberte axis (Laliberte et al. 2012b), which is associated with the & Tylianakis 2012). Lax, moderate and hard grazing inten- fundamental leaf economics spectrum (Wright et al. sity levels corresponded to a ratio of 1:2:4 sheep grazing 2004). On the other hand, plant height had the strongest days, respectively, in the years 2–4 of the experiment, and loading on the second PCA axis, which we interpreted as a a ratio of 2:3:4 in subsequent years. Plots were always gradient of competitive ability for light resources (Laliberte grazed in groups of three, corresponding to the three mob- et al. 2012b). In this study, we restricted our analyses to stocked grazing intensity levels per whole plot. For each these two ecologically relevant traits, which together rep- grazing event (i.e. when vegetation had reached ca. 30 cm resented the most important axes of functional variation in height), sheep numbers were adjusted based on avail- among our species. Details on trait measurements are able feed-on-offer of the moderate grazing treatment. The available in previous studies (Laliberte & Tylianakis 2012; duration of grazing was the same for all three plots but was Laliberte et al. 2012b).

Productivity and grazing intensity indicate trait divergence, while negative values suggest trait convergence or environmental filtering (Ingram & We measured total (i.e. above- and below-ground) net Shurin 2009). Finally, we used linear mixed models to primary productivity (NPP) over an 18-mo period for each test for the interactive effects of productivity and grazing plot. Grazing intensity was measured as the proportion of intensity on functional richness and FRicdev,withran- ANPP consumed (or destroyed) by sheep over the 18-mo dom intercepts per block and whole plot in order to take sampling period (Laliberte & Tylianakis 2012; Laliberte into account the split-plot nature of the experiment. In et al. 2012a). Details of these measurements are available these linear mixed models, NPP and grazing intensity elsewhere (Laliberte & Tylianakis 2012; Laliberteetal. were first log-transformed and centred. Log transforma- 2012a). tion was used to linearize relationships, whereas centring was used so that unstandardized coefficients for a predic- Statistical analyses tor represented its average effect on the response variable when the conditioning variable (i.e. the other predictor Following Mason et al. (2005), we measured different fac- involved in the interaction) is held at its mean value ets of functional diversity to focus on different community (Aiken & West 1991). assembly processes at different scales. First, we focused on functional richness to detect environmental filtering at the Local-scale interactions and functional dispersion metacommunity scale. Second, we used a functional divergence measure to detect the influence of biotic interactions To explore whether fine-scale interactions between plants on trait dispersion patterns at local scales. (e.g. resource competition) could lead to trait divergence within local communities, we used the vegetation cover data at the 1 m 9 1-m scale, the scale at which most indi- Community-scale environmental filtering and functional richness vidual plants in the quadrat are expected to interact below- We measured functional richness (FRic; Villeger et al. and/or above-ground. We measured abundance-weighted 2008; Laliberte & Legendre 2010) for each 8 m 9 50-m functional dispersion for all 1 m 9 1-m quadrats and plot in order to quantify the strength of trait-based envi- averaged it over the 20 quadrats at the metacommunity ronmental filtering at the metacommunity scale (Cornwell (8 m 9 50-m plot) level. Functional dispersion is the et al. 2006), and to determine whether the strength or weighted average distance of individual species to the cen- direction of environmental filtering could depend on pro- troid of all species within a community, where weights are ductivity and/or grazing intensity. Leaf [N] and plant species relative abundances, and is the multivariate ana- height were first log-transformed to reduce the importance logue of the weighted mean absolute deviation (Laliberte of species with large trait values. & Legendre 2010). FDis is related to Rao’s quadratic We used a null model approach to test whether the entropy (Botta-Dukat 2005; Laliberte & Legendre 2010), observed functional richness in each plot was lower or which is the multivariate analogue of the weighted vari- higher than expected if metacommunity assembly was ance, but FDis is by construction less sensitive than Rao’s not influenced by species traits (i.e. leaf [N] and plant Q to species with extreme trait values. height). To do so, we created 999 random species compo- To test for trait divergence, we used a different null sition matrices where species richness per plot (i.e. row) model than that described above for functional richness. was preserved, and where column marginal frequencies First, and most importantly, we restricted the randomization were used as probabilities. The randomization was done between 1 m 9 1-m quadrats within each 8 m 9 50-m using the ‘commsimulator’ function (‘r1′ algorithm) in plot, in order to detect local-scale assembly processes the vegan R package (R Foundation for Statistical Com- independent of any filtering effect on species pools (Mason puting, Vienna, AT). We then computed standardized et al. 2011; de Bello et al. 2012). To do so, we kept species deviations from the null expectation (FRicdev)foreach abundance matrices for each plot (i.e. 20 quadrats as rows) plot: constant but randomized rows within the species 9 trait matrix (Stubbs & Wilson 2004), using 999 randomizations. l ¼ FRicobs null We then computed standardized deviations from the null FRicdev r null expectation (FDisdev) using the same approach described

above for FRicdev, and also used linear mixed models to test where FRicobs is the observed functional richness for a for the interactive effects of productivity and grazing inten- 9 l metacommunity (i.e. 8 m 50 m plot), null is the mean sity on FDisdev. Again, variables were log-transformed to of the null distribution of functional richness values, and remove the inﬂuence of large values and to linearize rnull is its standard deviation. Positive FRicdev values relationships, and predictors were centred.

Results Table 1. Results of linear mixed-effect models for (a) functional richness (FRic) and (b) FRicdev at the metacommunity (i.e. 8 m 9 50-m plot) scale. Community-scale environmental ﬁltering and functional b = unstandardized regression coefﬁcient; r = standard error; richness df = degrees of freedom; Prod = productivity.

At the metacommunity (i.e. 8 m 9 50-m plot) scale, there brdf t P was a highly significant (P 0.001) decrease in func- (a) tional richness with increasing productivity (Fig. 1a, Intercept 5.3374 0.3018 17 17.6824 <0.0001 Table 1a). Patterns of functional richness were not simply Productivity 5.3648 1.3544 17 3.9611 0.0010 Grazing intensity 1.5353 2.0144 17 0.7622 0.4564 driven by changes in species richness, since FRicdev across 9 all plots was significantly lower than zero (lower tail t-test, Prod Grazing 1.9752 7.3483 17 0.2688 0.7913 (b) P 0.0001; Fig. 1b). Importantly, FRic decreased with dev Intercept 0.1129 0.0274 17 4.1153 0.0007 = greater productivity (P 0.003; Table 1b), and the rela- Productivity 0.4800 0.1382 17 3.4728 0.0029 tionship between FRicdev and productivity depended on Grazing intensity 0.2812 0.2128 17 1.3215 0.2039 grazing intensity (productivity 9 grazing intensity interac- Prod 9 Grazing 2.0925 0.7095 17 2.9491 0.0090 tion, P = 0.009; Table 1b), such that reductions in FRicdev with higher productivity became more important under higher grazing intensity (Fig. 1b). The reduction in func- Discussion tional richness under greater productivity was clearly due to the loss of species with low height and/or lower leaf [N] Our study found evidence for both environmental filtering at high productivity, which interestingly were all native (trait convergence) and limiting similarity (trait diver- species (Fig. 2). gence), but at different spatial scales. At the metacommunity scale, plant species that encompassed almost the entire spectrum of functional variation from the potential Local-scale interactions and functional dispersion species pool were present at low productivity. Conversely, At the local (i.e. 1 m 9 1-m quadrat) scale, there were no with higher productivity, functional richness decreased significant (P > 0.29; Table 2a) changes in abundance- and was much smaller than expected under a null model, weighted functional dispersion with productivity or graz- indicating that metacommunity membership was ing intensity. However, by comparing the observed func- restricted to a non-random subset of ecological strategies tional dispersion to the within-metacommunity null relative to those found across the entire species pool. Still, expectation, we found that FDisdev was significantly higher once this metacommunity-scale environmental filtering than zero (t-test, P 0.0001; Fig. 3) across all plots, sug- was taken into account, we found evidence for limiting gesting that trait divergence was predominant at the local similarity at the local scale (where individual plants are scale. Importantly, FDisdev became significantly (P expected to interact), in that abundance-weighted func- 0.02; Table 2b) more positive with greater productivity tional dispersion was higher than expected under random (Fig. 3a) and lower grazing intensity (Fig. 3b), suggesting community assembly. Importantly, at the local scale we stronger local trait divergence where competition for light found stronger-than-expected trait divergence at higher is thought to be most intense. productivity and at lower grazing intensity, conditions

(a) (b)

Fig. 1. Changes in (a) functional richness (FRic) and (b) standardized deviations from the null model (FRicdev) with net primary productivity (NPP). Fert = fertilizer level. Graz = grazing intensity.

Fig. 2. Functional richness under increasing productivity (columns, from left to right) and grazing intensity (rows, from top to bottom). Each panel represents an individual 8 m 9 50-m plot (i.e. ‘metacommunity’). In each panel, all species present in each plot are plotted in a two-dimensional niche space deﬁned by leaf [N] and plant height. Axes are on logarithmic scales. Black circles indicate native species, while grey triangles indicate exoticspecies.

Table 2. Results of linear mixed-effect models for (a) functional dispersion competition on patterns of trait convergence (at the meta- 9 (FDis) and (b) FDisdev at the local community (i.e. 1 m 1-m quadrat) community scale) and divergence (at the local scale) b = r = scale. unstandardized regression coefﬁcient; standard error; become stronger at higher productivity, presumably due to = = df degrees of freedom; Prod productivity. shifts from size-symmetric below-ground competition to brdf t P asymmetric above-ground competition (Aerts 1999). (a) The strong trait convergence at higher productivity at Intercept 0.1813 0.0130 17 13.9872 <0.0001 the metacommunity scale was due to the disappearance of Productivity 0.0550 0.0500 17 1.1009 0.2863 (1) all short, slow-growing (i.e. low leaf [N]) native species; Grazing intensity 0.0408 0.0769 17 0.5314 0.6020 (2) Hieracium pilosella, the short exotic species with moder- Prod 9 Grazing 0.1145 0.2563 17 0.4470 0.6605 ately high leaf [N] that dominated all plots prior to the start (b) of the experiment (Scott 2007; Laliberte et al. 2012b); and Intercept 0.3930 0.0699 17 5.6185 <0.0001 (3) Festuca novae-zelandiae and Poa colensoi, two relatively Productivity 0.8754 0.3287 17 2.6629 0.0164 Grazing intensity 1.3156 0.4963 17 2.6505 0.0168 tall native tussock grass species with traits associated with Prod 9 Grazing 1.6251 1.7458 17 0.9309 0.3649 slow growth rates (Laliberte et al. 2012b). In trait-based community assembly analyses, a restricted range (or restricted multivariate volume) in functional attributes has which are expected to lead to stronger above-ground com- generally been attributed to abiotic environmental ﬁlter- petition between plants (Huston 1994; Grime 2002; Mason ing, whereby unusually stressful abiotic conditions only et al. 2011). Overall, our results show that the effects of allow species with evolved tolerances to these conditions

(a) (b)

Fig. 3. Changes in abundance-weighted functional dispersion (FDis) and standardized deviations from the null model (FDisdev)with(a) net primary productivity (NPP) and (b) grazing intensity. Fert = fertilizer level. to persist in the community (Van der Valk 1981; Weiher & faster growth rates (Craine & Lee 2003). This may reflect Keddy 1995; Weiher et al. 1998; Cornwell et al. 2006; the particular evolutionary history of New Zealand grass- Kraft et al. 2008; Cornwell & Ackerly 2009; Ingram & Shu- lands, which prior to human settlement were confined to rin 2009). However, in our study, we suggest that such sites with marginal environmental conditions (McGlone trait convergence resulted from strong competition for 2001). light and/or soil resources from faster-growing species at In contrast to trait convergence found at the metacom- high productivity, which over the >27 yr duration of the munity scale under greater productivity, we observed trait experiment led to the competitive exclusion of plant spe- divergence at the local scale. This local trait divergence cies with inherently slower growth, particularly those of became stronger at higher biomass and lower grazing short stature. Stronger trait convergence with higher pro- intensity, perhaps due to stronger competition for light ductivity is consistent with predictions from Grime (2006), (Mason et al. 2012). Trait divergence in plant communities showing that environmental filtering is not always due to has generally been interpreted as evidence for a dominant changes in abiotic conditions but can also result from role of competitive interactions in structuring local assem- inter-specific interactions. However, contrary to our blages (Weiher & Keddy 1995; Weiher et al. 1998; Stubbs hypothesis, higher grazing intensity strengthened the & Wilson 2004), whereby competition for resources entails environmental filtering at higher productivity, which limiting similarity with respect to strategies of resource could be due to negative effects of herbivores, such as acquisition and use (MacArthur & Levins 1967). Our find- trampling (Olff & Ritchie 1998). ing that trait divergence was higher at higher productivity As productivity and grazing intensity increased, we is consistent with the view that competition intensity found that environmental filtering was strongest for native increases with productivity (Grime 2002), even though we species. Native species in this study were functionally dis- found evidence for limiting similarity across all plots, tinct from exotic species, with exotic species exhibiting a regardless of productivity. In addition, our study showed rapid growth trait syndrome, while the reverse was true stronger evidence for limiting similarity under lower graz- for natives. These results are consistent with those from a ing intensity, where competition for light is also expected recent global meta-analysis comparing leaf traits among to be higher because grazing can suppress competitive co-occurring exotic and native species, where exotic dominance (Grime 1973; Laliberte et al. 2012a). Our species were positioned further along the acquisition–con- results are consistent with those of Mason et al. (2011), servation axis towards a faster growth strategy (Leishman who found lower niche overlap in more productive grass- et al. 2007). The difference in leaf traits between native lands and in the absence of mowing. and exotic species observed in our study may be partly Our results suggest that environmental filtering and explained by the fact that all sown species at the start of limiting similarity are both important in determining com- the experiment were exotic species that had been selected munity structure, but operate differently. Environmental based on their potential suitability as pasture species, of filtering shapes species pools by excluding species that which high intrinsic growth rate is a key characteristic. have poor local fitness. In our study, the stronger filtering Other studies that compared co-occurring herbaceous at high productivity and grazing intensity likely resulted native and exotic species in New Zealand grasslands also from competitive exclusion and negative effects associated found that exotic species had faster intrinsic growth rates with grazing over the >27-yr duration of the experiment. compared with native ones (Scott 1970; King & Wilson On the other hand, limiting similarity acts at a local scale 2006) or possessed leaf and root attributes associated with and allows species to co-exist by limiting niche overlap

(Mason et al. 2011), thus reducing inter-specific competi- Cornwell, W.K., Schwilk, D.W. & Ackerly, D.D. 2006. A trait- tion relative to intra-specific competition (Chesson 2000). based test for habitat filtering: convex hull volume. Ecology An important finding of our study was that both the 87: 1465–1471. strength of environmental filtering and limiting similarity Craine, J.M. & Lee, W.G. 2003. Covariation in leaf and root traits were not mutually exclusive, but depended on spatial scale for native and non-native grasses along an altitudinal gradi- – as well as productivity and disturbance. Further work ent in New Zealand. Oecologia 134: 471 478. along productivity and disturbance gradients in other Dıaz, S., Cabido, M. & Casanoves, F. 1998. Plant functional traits non-grassland systems is required to evaluate the generality and environmental filters at a regional scale. Journal of Vege- – of this finding. tation Science 9: 113 122. Edgar, E. & Connor, H.E. 2000. Flora of New Zealand, Vol. V. Maa- naki Whenua Press, Lincoln, NZ. € Acknowledgements Gotzenberger, L., de Bello, F., Brathen, K.A., Davison, J., Dubuis, A., Guisan, A., Leps, J., Lindborg, R., Moora, M., Partel,€ M., We wish to thank P. Fortier for help with fieldwork, Pellissier, L., Pottier, J., Vittoz, P., Zobel, K. & Zobel, M. 2012. L. Kirk, A. Leckie, N. Pink and J. M. Tylianakis for Ecological assembly rules in plant communities—approaches, logistical and academic support, and A. Simpson for patterns and prospects. Biological Reviews 87: 111–127. the use of stock. We also thank R. K. Didham, N. Grime, J.P. 1973. Competitive exclusion in herbaceous vegeta- Gross,H.Lambers,W.GLee,N.W.H.Mason,M.M. tion. Nature 242: 344–347. Mayfield, B. Shipley, E. Weiher, J. A. Wells and anon- Grime, J.P. 2002. Plant strategies, vegetation processes, and ecosystem ymous reviewers for providing insightful comments on properties, 2nd ed. John Wiley & Sons, Chichester, UK. previous versions of the manuscript. Field research was Grime, J.P. 2006. Trait convergence and trait divergence in her- funded by the Miss E. L. Hellaby Indigenous Grassland baceous plant communities: mechanisms and consequences. – Research Trust and the School of Forestry, University Journal of Vegetation Science 17: 255 260. of Canterbury. E. L. was supported by the University Grubb, P.J. 1977. The maintenance of species-richness in plant of Western Australia, the University of Canterbury, the communities: the importance of the regeneration niche. Bio- 52: 107–145. Fonds queb ecois de recherche sur la nature et les tech- logical Reviews Healy, A.J. & Edgar, E. 1980. Flora of New Zealand, Vol. III: adven- nologies (FQRNT), Education New Zealand, and the tive cyperaceous, petalous & spathaceous monocotyledons.Botany Australian Research Council (ARC). Division, Department of Scientific and Industrial Research, Lincoln, NZ. 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Journal of Vegetation Science 842 Doi: 10.1111/jvs.12044 © 2012 International Association for Vegetation Science Journal of Vegetation Science 24 (2013) 843–852 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Functional and phylogenetic community assembly linked to changes in species diversity in a long-term resource manipulation experiment Pille Gerhold, Jodi N. Price, Kersti Pussa,€ Rein Kalamees, Kaia Aher, Ants Kaasik & Meelis Partel€

Keywords Abstract Fertilizer addition; Functional diversity; Habitat filtering; Limiting similarity; Phylogenetic Question: There are contrasting opinions about how communities assemble diversity; Productivity; Resources; Sucrose along a productivity gradient, particularly in relation to competitive interactions. addition; Trait convergence; Trait divergence; One view is that functionally similar, and closely related species, are less likely Weaker competitor exclusion to co-exist (limiting similarity). Alternatively, competitive exclusion may act on dissimilar species because species bearing traits associated with low competitive Abbreviations C = control treatment; F = fertilizer addition ability are excluded (weaker competitor exclusion). We ask if patterns of treatment; FD = functional diversity; HF functional and phylogenetic diversity are related to changes in species diversity = habitat filtering; LS = limiting similarity; PD in response to fertility manipulations. = phylogenetic diversity; S = sucrose addition treatment; WCE = weaker competitor exclusion Location: Species-rich mesophytic grassland in Estonia. Methods: The grassland has been manipulated from 2002 to 2011 to increase Nomenclature (fertilizer addition) and decrease productivity (sucrose addition) in small-scale Ku¨ hn et al. 2004 (50 cm 9 50 cm) plots. We linked annual increases and decreases in species Received 5 April 2012 diversity to changes in functional and phylogenetic diversity. We used abun- Accepted 9 January 2013 dance-weighted mean pair-wise functional or phylogenetic distance of all possi- Co-ordinating Editor: Francesco de Bello ble species pairs. Results: We found convergence in four traits (plant height, leaf distribution, lat- Gerhold, P. (corresponding author, pille. eral spread, type of reproduction) and a decrease in mean functional and phylo- [email protected]), Price, J.N. genetic diversity, in support of weaker competitor exclusion or habitat filtering. € ([email protected]), Pussa, K. There was less support for limiting similarity, with divergence found for two ([email protected]), Kalamees, R. traits associated with decreasing species diversity (leaf distribution in the sucrose ([email protected]), Aher, K. ([email protected]), Kaasik, A. treatment and lateral spread in the fertilizer treatment). € ([email protected]) & Partel, M. Conclusions: Our results support the view that competition can lead to the ([email protected]): Department of Botany, exclusion of weaker competitors, rather than increasing functional and phyloge- Institute of Ecology and Earth Sciences, University of Tartu, Lai 40, 51005, Tartu, Estonia netic diversity, as expected from the principle of limiting similarity. However, Gerhold, P. : Department of Experimental multiple assembly processes, which are generally seen as mutually exclusive, Plant Ecology, Institute for Water and Wetland are operating simultaneously, albeit on different traits and at different stages of Research, Radboud University Nijmegen, PO community assembly. Box 9010, 6500, GL Nijmegen, The Netherlands Price, J.N.: School of Plant Biology, The University of Western Australia, WA 6009, Australia

ecological research, and remain largely unresolved (e.g. Introduction Keddy 1992; Weiher & Keddy 1995; Chesson 2000; Ack- The mechanisms driving non-random assembly patterns erly 2003; Gotzenberger€ et al. 2012; Price et al. 2012). in plant communities have long been of interest in Community assembly is often thought to result from

Journal of Vegetation Science Doi: 10.1111/jvs.12052 © 2013 International Association for Vegetation Science 843 Functional diversity in species loss and gain P. Gerhold et al. abiotic and biotic filters, with habitat filtering (abiotic) (2012), using a new methodology which included the increasing similarity among species, and biotic filters dark diversity component of the species pool (Partel€ et al. (mostly competition) preventing co-existing species from 2011), found evidence for both limiting similarity and being too similar (limiting similarity; MacArthur & Levins weaker competitor exclusion, depending on the traits 1967). Based on these traditional co-existence theories it and the community type considered. is often assumed that functional diversity should be low Conflicting results about the increase or decrease in in regions of strong abiotic stress (abiotic filters causing functional and phylogenetic diversity are also likely due to convergence), and high in regions where competitive intrinsic differences among community types (i.e. grass- interactions are stronger (biotic filters causing divergence; lands vs forests), and along environmental gradients (de Weiher & Keddy 1995). The idea that competition should Bello et al. 2012). The importance of competition differs leave an ‘imprint’ on patterns in communities can be along productivity gradients, but few studies have exam- traced back to Darwin, who noted that species of the ined functional and phylogenetic diversity along environ- same genus tend to be more similar, and hence should mental gradients (but see Pakeman et al. 2011; Mason compete more intensely, than more distantly related spe- et al. 2012; Spasojevic & Suding 2012), and as such, these cies (competition relatedness hypothesis; Darwin 1859). hypotheses remain largely untested. Weiher et al. (1998) Both limiting similarity and the competition relatedness found increased functional diversity with higher levels of hypothesis are based on the same mechanism and, to soil phosphate, in support of limiting similarity. Similarly, date, both have limited empirical support, as evidence for Mason et al. (2012) found divergence at high fertility and habitat filtering or random patterns has often been found convergence at low fertility in rain forest communities (see recent meta-analyses of Cahill et al. 2008; Gotzen-€ along a long-term soil chronosequence. Spasojevic & Sud- berger et al. 2012; Price & Partel€ 2013). Phylogenetic ing (2012) found support for multiple assembly processes, relatedness can be used as a surrogate for functional simi- with abiotic filtering occurring at the low end of a resource larity, although this approach has been criticized (e.g. gradient, producing convergence of some traits, but they from this issue Bernard-Verdier et al. 2013; Pavoine et al. also found increased functional diversity in other traits 2013), it can be useful if trait data are not available and if related to below-ground resource competition. At the the observed traits are phylogenetically conserved (Prin- resource-rich end of the gradient, they found increased zing et al. 2001; Kraft et al. 2007). functional diversity for height and leaf area, in support of The principle of limiting similarity is widely accepted limiting similarity or facilitation. Pakeman et al. (2011) in community assembly theory, but other mechanisms examined functional diversity along productivity and dis- can influence assembly in competitive communities (Hil- turbance gradients, and found for most traits that func- leRisLambers et al. 2012). For example, co-existence tional diversity declined with increased productivity and among species may be due to ‘weaker competitor exclu- disturbance, inconsistent with limiting similarity. The sion’, whereby functionally similar species co-exist in a authors suggest that this could be due to either habitat fil- sort of balanced competition (increased similarity) and tering or weaker competitor exclusion. weaker competitors are excluded (Chesson 2000; May- Experimental approaches can provide greater insight field & Levine 2010; Kunstler et al. 2012). One of the into mechanisms driving community assembly. It is well reasons this mechanism has been overlooked is partly established that increasing productivity (e.g. via fertiliza- methodological, as increased similarity among co-existing tion) in temperate grasslands decreases species richness, by species is usually attributed to habitat filtering, and many increasing biomass and light competition (e.g. Leps 1999; existing methods fail to separate these processes (de Bello Liira & Zobel 2000; Silvertown et al. 2006; Price & Morgan et al. 2012). These hypotheses are commonly tested 2007; Gross et al. 2009; Hautier et al. 2009). Fewer using null models to compare the trait dissimilarity and/ studies, however, have examined the effect of decreasing or phylogenetic relatedness among co-occurring species productivity on species richness, but opposite effects are to a model expected if communities were assembled at expected to occur, with less competition and decreased random (Weiher & Keddy 1995; Gotelli 2000; de Bello biomass resulting in an increase in species richness 2012; Gotzenberger€ et al. 2012). Null model tests gener- (Blumenthal et al. 2003; Perry et al. 2010). While resource ally assign one of three mechanisms to the observed pat- manipulation experiments are commonly performed, less terns: either patterns do not differ from random is known about the relationship between productivity and (attributed to stochastic or dispersal-based assembly), functional and phylogenetic diversity. Recently, Cadotte & traits/relatedness are more similar/closely related than Strauss (2011) examined phylogenetic patterns of natural expected (attributed to habitat filters), or species are more colonization and extinction in experimentally assembled dissimilar/distantly related than expected (attributed to communities over 2 yr. They found that successful biotic filters; Weiher & Keddy 1995). de Bello et al. colonists tended to be either closely or distantly related to

Journal of Vegetation Science 844 Doi: 10.1111/jvs.12052 © 2013 International Association for Vegetation Science P. Gerhold et al. Functional diversity in species loss and gain community residents, whereas extinctions did not exhibit any strong relatedness patterns. – + We used a long-term resource manipulation experi- (a) LS (c) LS ment to determine patterns of trait diversity and phyloge- Persistent netic relatedness in relation to changes in species species diversity (annual turnover). In a previous study at this Lost experimental site, Liira et al. (2012) found that fertilizer 0 species addition increased biomass and decreased species rich- + – Change in FD Change (b) WCE/HF (d) WCE/HF Gained ness. After 10 yr of manipulations, sucrose addition species decreased biomass, but no changes in species richness were detected. The authors suggest this is likely due to dispersal limitation, as species adapted to less fertile conditions are not available in the vicinity (Partel€ et al. 0 2000; Partel€ & Zobel 2007). The ‘missing’ species can also Change in species diversity experience a delay in colonization, and establish only Fig. 1. Hypotheses on the mechanisms of decreased (a, b) and increased afteranevenlongertime(>10 yr; Liira et al. 2012). We (c, d) species diversity in regard to change in functional diversity (FD, expand on this study by examining community assembly calculated as mean pair-wise distance between all species) in a patterns in relation to traits and phylogeny. Liira et al. community. Positive (+)andnegative(À) relationships are indicated, both (2012) examined absolute changes in species richness, for a decrease and increase of diversity. (a) Limiting similarity (LS). Negative relationship. Functionally similar species out-compete each and we examine the relationship between annual other, leading to trait divergence. (b) Weaker competitor exclusion or changes in species diversity (increases and decreases), habitat filtering (WCE/HF). Positive relationship. Species with similar trait functional diversity (hereafter FD) and phylogenetic values advantageous in competition will be favoured, leading to trait diversity (hereafter PD). The reduction found in species convergence. Alternatively, the habitat favours functionally similar species richness in fertilized plots in the previous paper was a with specific adaptations to the new conditions. (c) Limiting similarity (LS). mean trend, but all plots were characterized by inter- Positive relationship. Functionally dissimilar species are favoured due to annual species turnover. In stable communities, species limiting similarity, leading to trait divergence. (d) Weaker competitor exclusion or habitat filtering (WCE/HF). Negative relationship. Species with turnover might be due to random community processes similar trait values advantageous in competition will be favoured, leading or climatic variability across years, but in our case, turn- to trait convergence. Alternatively, habitat favours functionally similar over should be at least partly a deterministic response to species. the experimental treatments. We examined functional and phylogenetic diversity in that a decrease in species diversity is associated with a relation to changes in species diversity. We used multiple decrease in FD and PD. trait comparisons along an experimentally manipulated fertility gradient to distinguish between opposing forces Second, despite the fact that there was no mean increase governing community assembly. First, we hypothesize that in species richness in sucrose addition treatments com- decreased species diversity via increased productivity can pared to control plots (but higher richness and evenness result from different types of competition, which result in than in the fertilized treatment; Liira et al. 2012), there contrasting patterns of FD and PD. may be changes in FD and PD, prior to an increase in species diversity in the future. An increase in species diversity Limiting similarity (Fig. 1a). Competitive exclusion can be accompanied by an increase or decrease in FD and occurs between functionally similar and closely related PD, depending on the dominant assembly process. species, which leads to increased FD and PD (i.e. trait divergence and a phylogenetically over-dispersed com- Limiting similarity (Fig. 1c). Species increasing in abun- munity). In this case, we expect that a decrease in spe- dance are dissimilar and distantly related to resident species diversity is associated with an increase in FD and PD. cies due to increased competition between functionally Weaker competitor exclusion or habitat filtering similar and closely related species. Hence, we expect an (Fig. 1b). Closely related species with similar trait values increase in species diversity is associated with an advantageous in competition will be favoured, which increase in FD and PD. leads to decreased FD and PD (i.e. trait convergence and Weaker competitor exclusion or habitat filtering a phylogenetically clustered community). Alternatively, (Fig. 1d). Closely related species with similar trait values altered habitat conditions via changed productivity advantageous in competition will increase in abun- favour functionally similar species with specific adapta- dance, leading to trait convergence. Alternatively, tions to the habitat conditions. In this case, we expect altered environmental conditions favour functionally

similar species with specific adaptations to the habitat mean FD and PD in the experimental plots in our data set conditions. This leads to decreased FD and PD (i.e. trait (r = 0.113, P = 0.06; Appendix S1) demonstrating that PD convergence and a phylogenetically clustered commu- and FD are not varying in parallel. nity). In this case, we expect an increase in species diver- The phylogeny of species was based on the phylogeny sity is associated with a decrease in FD and PD. for higher plants of Central Europe (Durka & Michalski 2012). We used the phylogenetic tree pruned down to the species list across the plots (n = 58) as the reference list to Methods measure PD in single plots. The study area is part of a long-term resource manipula- We calculated both FD and PD using the species′ cover tion experiment examining the effect of fertilizer and in the plots and the dissimilarity measure – mean pair- sucrose addition on a mesophytic grassland community in wise distance of all possible species pairs (MPD; Pavoine & southeast Estonia. The grassland has been experimentally Bonsall 2011). For FD, MPD was computed from the Gow- manipulated from 2002 to 2011 to increase (fertilizer er distance (Gower 1971; modified by Podani 1999) addition) and decrease (sucrose addition; see Liira et al. between each pair of taxa in a sample in the means of trait 2012 for full details) productivity. The experimental design states, and weighted by species relative abundances. For consisted of three treatments: fertilizer (24 replicates), PD, MPD was computed from the distance matrix of the sucrose (24 replicates) and control (seven replicates), pruned phylogenetic tree as the average branch length which were applied on randomly located permanent plots. between each pair of taxa in the means of phylogenetic The location of the plots was determined using the distance, and weighted by species relative abundances. homogeneity principle to ensure that plots were of similar We used the program R and a modified version of the relative elevation and hence situated in similar moisture function ′mpd′ in the package picante (Kembel et al. conditions. Treatments were fully randomized. In all plots 2010; R Foundation for Statistical Computing, Vienna, (50 cm 9 50 cm), the presence of all vascular species was AT). The modification was necessary because the original ′ recorded, and the percentage cover estimated visually in mpd′ function in the picante package (v. 1.5-2) with the July (mid-summer) from 2002 to 2011. option ′abundance.weighted = TRUE′ included in the We linked annual changes in species diversity to MPD calculation of the diagonal in the dissimilarity matrix changes in functional diversity (FD), phylogenetic diver- between species (which is always 0). This creates a spuri- sity (PD) and mean trait values. We calculated species ous positive correlation with number of species (with a diversity using Simpson’s reciprocal index, 1/D (D = Σ [ni/ larger dissimilarity table, the relative importance of the 2 N] ), where ni is the cover of each species and N is the total diagonal is smaller). Our modified function (Appendix S2) cover in a plot. The diversity values were log-transformed did not use the diagonal with the weighted MPD, and is to obtain a normal distribution. We selected two groups of not intrinsically related to the number of species in the functional traits: (1) vegetative traits related to local com- dissimilarity matrix. munity processes (Dıaz et al. 1998; plant height, leaf distri- We analysed the relationship between annual changes bution, leaf area, lateral spread); (2) regenerative traits in species diversity, and changes in mean FD, PD and FD of related to colonization ability (Dıaz et al. 1998; type of single traits using a linear mixed model (generalized linear reproduction, start of flowering). The vegetative and mixed models using Markov chain Monte Carlo tech- regenerative traits are also associated with competitive niques: MCMC), taking into account the effects of treat- response (i.e. the ability of a species to tolerate competi- ment, year and experimental plot as random factors. All tion; sensu Goldberg & Landa 1991). We obtained plant analyses were performed using R. Models were fitted using trait values from the databases CLO-PLA (Klimesova&de the package MCMCglmm (Hadfield 2010), with 60 000 Bello 2009; leaf distribution, lateral spread), BiolFlor MCMC iterations for each analysis. The first 10 000 itera- (Kuhn€ et al. 2004; type of reproduction), Ecological Flora tions were discarded as burn-in and then the remaining of the British Isles (Fitter & Peat 1994; leaf area), Grime 50 000 were thinned ten times so that a final sample con- et al. (2007; start of flowering), and Flora of Estonia (1956 sisted of 5000 generated values from the posterior distribu- –1984; plant height). We standardized the absolute trait tion. We checked that thinning was sufficient to eliminate values between 0 and 1 across the species studied. the correlation between successive simulated values. We calculated two measures of FD: a mean FD for all six Visual inspection of the simulated values was carried out traits studied (simple mean values) and a FD for single to rule out convergence problems. traits. We also used PD as a surrogate for FD because PD We constructed two models for all analyses: we analysed may include some aspect of functional similarity that can- separately the increase and decrease in species diversity, in not be detected using the selected traits. We found a mar- order to allow for different scenarios in the two distinct ginally insignificant correlation between the change in cases (e.g. if we find a positive slope in the relationship

Journal of Vegetation Science 846 Doi: 10.1111/jvs.12052 © 2013 International Association for Vegetation Science P. Gerhold et al. Functional diversity in species loss and gain with decreasing species diversity, we are not restricting the 0.637 slope to be positive with increasing species diversity). 0.019 À À = We also analysed, using MCMCglmm (Hadfield 2010), = 0.811 0.334 6.049; 4.813) 0.056; 0.020) = the relationship between annual changes in species = À À ( ( Mean P Mean diversity and mean community trait values, in order to P determine if lower or higher trait values were preferen- tially included or excluded with changes in diversity. Addi- tionally, MCMCglmm was used to identify the change in abundance of single species in relation to the change in mean FD in plots, in order to detect the species most responsible for the observed changes in FD. 0.018 1.108 À reciprocal index; log-transformed) and annual = = 0.518 0.120 2.218; 4.538) 0.039; 0.006) = = À Results À ( ( Mean P Mean P We detected several differences in net species gain and loss, experimental plots. Treatments are: F, fertilizer addition; S, e in species diversity (see Fig. 1). Separate models were con- species diversity, FD and PD at the end vs. at the beginning on (WCE/HF), negative mean values support limiting similarity of the experiment in the treatments (Appendix S3). Annual changes in mean FD were negatively correlated to increased species diversity in the fertilized treatment (Table 1). Hence, an increase in species diversity 0.114) decreased FD. This supports the hypothesis of weaker 0.006) 3.319 0.027 À À À competitor exclusion or habitat filtering (WCE/HF; Figs 1, À = = 0.039 WCE/HF 2). Annual changes in PD were similar to changes in FD, 0.017 WCE/HF 6.575; 0.048; = = À À ( i.e. increased species diversity decreased PD in the fertil- ( Mean (b) Increase in species diversity P Mean P ized treatment, supporting the hypothesis of WCE/HF (Table 1, Figs 1, 2). When analysing FD of single traits, we found support for WCE/HF, as well as some support for limiting similarity (LS; Table 2). In the fertilized treatment, we found support for WCE/HF with decreasing species diversity in plant height, and with increasing species diversity in lateral spread and hip between annual changes in species diversity (calculated as Simpson’s 0.023 1.443 type of reproduction. For lateral spread, we also found some À = = 0.749 support for limiting similarity with decreasing species diver- 0.347 8.659; 11.128) 0.073; 0.027) ic diversity (with 95% confidence limits in brackets) in relation to chang = = À À died traits) and (II) in phylogenetic diversity (calculated as MPD) in the ( sity in fertilized plots. In the sucrose addition treatment, ( P Mean P Mean with increased species diversity we found a reduction in FD for leaf distribution supporting WCE/HF, whereas decreased e mean values support LS, negative mean values support WCE/HF (Fig. 1). species diversity supported LS (Table 2). Lost species were more likely tall (only in the sucrose addition treatment), with a rosette growth form (in both fertilized and sucrose addition treatments), small leaves (in fertilizer treatment) and with short lateral spread (in the 0.007 1.942 versity and change in phylogenetic diversity = fertilizer treatment). Lost species also reproduced more = 0.603 0.489 0.028; 0.037) 3.643; 8.004) = likely by seed (in both fertilized and sucrose addition treat- = À À ( ( Mean P Mean ments) and flowered earlier in year (in the fertilizer addi- P tion treatment). Increased species diversity changed the mean value of three traits (in the fertilizer treatment only); gained species more likely had small leaves, a rosette growth form and flowered earlier in year (Appendix S4). Species diversity decreased in the experimental plots mainly due to a significant decrease in the abundance of 0.013 Results of GLMM using Markov chain Monte Carlo techniques for the relations À species such as Carex hirta, Ranunculus acris, Stellaria graminea, 1.544 = = 0.240 0.394 0.038; 0.012) Veronica chamaedrys (in the fertilizer addition treatment) and 3.069; 5.784) = = À À ( ( Table 1. changes in (I) mean functional diversity (calculated as MPD over all six stu sucrose addition; C, control. Mean: mean changestructed in for functional (a) or a phylogenet decrease in species diversity, where positive mean values support the hypothesis of habitat filtering or weaker competitor exclusi (LS; see Fig. 1); and (b) an increase in species(a) diversity, Decrease where in positiv species diversity FSC F(I) SC Relationship between change in species diversityMean and change in mean functional diversity P (II) Relationship between change in species di Mean Knautia arvensis (in both the fertilizer and sucrose addition P

(a) (b) Change in FD Change in PD –10 0 5 10 –0.3 –0.1 0.1 0.3 –2 –1 0 1 2 –2 –1 0 1 2 Change in species diversity Change in species diversity

Fig. 2. Relationship between annual changes in (logarithmic) species diversity and annual changes in (a) mean functional diversity (FD) and (b)mean phylogenetic diversity (PD) in the fertilizer addition treatment from 2002 to 2011. Separate models were constructed for a decrease in species diversity (negative values on x-axes) and an increase in species diversity (positive values on x-axes). Using GLMM with Markov chain Monte Carlo techniques, the models were significant (a)atP = 0.0172 for an increase in species diversity, in support of weaker competitor exclusion (WCE/HF), and (b)atP = 0.0392 for an increase in species diversity, in support of WCE/HF (Table 1, Fig. 1). treatment; Appendix S5). On the other hand, the abun- (Grime 2006; Mayfield & Levine 2010). Both processes dance of Elytrigia repens increased significantly with decreas- likely interact, i.e. changed habitat conditions may favour ing species diversity in the fertilizer addition treatment. species adapted to these conditions and this is confounded Species diversity increased due to an increase in the abun- by increased competition (Grime 2006). Our results are dance of Carex hirta, Dactylis glomerata, Geranium pratense, consistent with Janecek et al. (2013), who found a Knautia arvensis, Poa angustifolia, Ranunculus acris, Ranunculus decrease in FD with fertilization in a wet meadow, and polyanthemos, Stellaria graminea and Vicia cracca (all in the fer- attributed this to increased competition for light. Similarly, tilizer addition treatment; Appendix S5). Pakeman et al. (2011) found reductions in FD with an increase in productivity for most traits along a natural productivity gradient. Mason et al. (2011) found the most Discussion abundant co-existing species in experimental grassland Patterns of FD and PD are increasingly being used to detect communities were more similar in traits that maximize the importance of abiotic and biotic processes governing resource acquisition. Convergence in plant height is con- community assembly (Kraft et al. 2007; Emerson & Gilles- sistent with Liira et al. (2012), who found in the same pie 2008; Kembel 2009; Swenson & Enquist 2009; Gotzen-€ experiment after 8 yr of resource manipulation that spe- berger et al. 2012; Kunstler et al. 2012; Mason et al. cies common in fertilized plots had a high growth form, 2012). A commonly held belief is that FD should increase probably due to reductions in light availability. along a gradient from high abiotic stress to more fertile, We found some support for limiting similarity, with competitive communities (Weiher & Keddy 1995). An decreases in species diversity as species became more alternative, but largely untested hypothesis, is that FD dissimilar in leaf distribution (sucrose addition treatment) should decrease with increased productivity as species and lateral spread (fertilized treatment). Divergence in leaf bearing traits associated with low competitive ability are distribution may be linked to the changed habitat condi- excluded (Chesson 2000; Mayfield & Levine 2010). The tions in the sucrose addition treatment altering environ- latter may be confounded with habitat filtering as changed mental conditions, and creating open niches in space and/ productivity can act as an abiotic filter, such that only or time. These open niches may favour the establishment species adapted to the altered conditions can persist (Grime of species adapted to the changed conditions, but some of 2006). We tested these opposing hypotheses using a long- the resident species also persist, resulting in co-existence of term resource manipulation experiment, and found dissimilar species. Divergence in lateral spread may be an several non-random patterns associated with a change in effect of different assembly processes governing the above- species diversity in response to the fertility manipulations. and below-ground community (Price et al. 2012). That is, We found more support for weaker competitor exclu- divergence may appear below-ground because of niche sion or habitat filtering hypothesis (WCE/HF) than for lim- differentiation – it might be advantageous that the roots of iting similarity (LS). WCE/HF indicates that closely related lateral branches possess different strategies to capture het- species with similar trait values advantageous in competi- erogeneously distributed resources (Partel€ et al. 2012), tion (or in altered habitat conditions) were favoured independent of above-ground processes (where mostly

Table 2. Results of GLMM using Markov chain Monte Carlo techniques for the relationships between annual changes in species diversity (calculated as Simpson’s reciprocal index; log-transformed) and © annual changes in functional diversity of single traits (calculated as MPD) in the experimental plots. Treatments are: F = fertilizer addition, S = sucrose addition, C = control. Mean = mean change in func- 03ItrainlAscainfrVgtto Science Vegetation for Association International 2013 tional diversity (with 95% conﬁdence limits in brackets) in relation to change in species diversity (see Fig. 1). Separate models were constructed for (a) a decrease in species diversity, where positive mean values support the hypothesis of habitat ﬁltering or weaker competitor exclusion (WCE/HF), negative mean values support limiting similarity (LS; see Fig. 1); and (b) an increase in species diversity, where positive mean values support LS, negative mean values support WCE/HF (Fig. 1)

Relationship between change in species diversity and change in functional diversity of single traits

(a) Decrease in species diversity (b) Increase in species diversity

FSCFSC

Vegetative traits Plant height Mean = 0.046 Mean = 0.010 Mean = 0.003 Mean = 0.002 Mean = À0.012 Mean = 0.004 (0.029; 0.063) (À0.017; 0.039) (À0.044; 0.052) (À0.050; 0.053) (À0.066; 0.038) (À0.055; 0.061) P = 0.002 WCE/HF P = 0.001 P = 0.908 P = 0.916 P = 0.600 P = 0.865 Leaf distribution Mean = À0.030 Mean = À0.075 Mean = À0.066 Mean = 0.010 Mean = À0.061 Mean = 0.003 (0 = non-rosette, (À0.075; 0.013) (À0.142; À0.010) (À0.188; 0.066) (À0.044; 0.063) (À0.118; À0.006) (À0.083; 0.094) 1 = rosette) P = 0.169 P = 0.030 LS P = 0.306 P = 0.665 P = 0.027 WCE/HF P = 0.938 Leaf area Mean = 0.008 Mean = À0.036 Mean = 0.032 Mean = À0.037 Mean = À0.026 Mean = 0.044 (À0.086; 0.088) (À0.136; 0.061) (À0.093; 0.150) (À0.081; 0.010) (À0.072; 0.018) (À0.035; 0.127) P = 0.784 P = 0.388 P = 0.571 P = 0.121 P = 0.260 P = 0.277 À Lateral spread (mÁyr 1 )Mean= À0.084 Mean = 0.012 Mean = À0.023 Mean = À0.059 Mean = 0.008 Mean = À0.072 (À0.123; À0.047) (À0.051; 0.076) (À0.135; 0.094) (À0.108; À0.013) (À0.041; 0.058) (À0.158; 0.008) P = 0.0004 LS P = 0.706 P = 0.681 P = 0.024 WCE/HF P = 0.718 P = 0.084 Regenerative traits Type of reproduction Mean = À0.074 Mean = 0.050 Mean = À0.079 Mean = À0.087 Mean = À0.032 Mean = À0.090

(0 = vegetatively, (À0.180; 0.015) (À0.067; 0.162) (À0.250; 0.065) (À0.161; À0.013) (À0.106; 0.042) (À0.216; 0.043) gain and loss species in diversity Functional 1 = by seed) P = 0.098 P = 0.340 P = 0.285 P = 0.023 WCE/HF P = 0.396 P = 0.174 Start of flowering Mean = 0.053 Mean = 0.058 Mean = 0.009 Mean = 0.006 Mean = 0.015 Mean = 0.040 (À0.014; 0.112) (À0.017; 0.130) (À0.092; 0.111) (À0.025; 0.037) (À0.020; 0.048) (À0.014; 0.089) P = 0.078 P = 0.125 P = 0.858 P = 0.666 P = 0.368 P = 0.117 849 Functional diversity in species loss and gain P. Gerhold et al. evidence for WCE/HF was found). Alternatively, diver- script, and especially for suggesting modification of the gence, i.e. limiting similarity, may be influenced by non- function ′mpd′ when weighted by species relative abun- resource-based competition through negative plant–soil dances. This study was funded by the Estonian Science feedbacks, i.e. soil biota decreases fitness and competitive Foundation (grants 8039, 8323, 8613, SF0180119), Euro- ability of plant species repeatedly occupying a particular pean Regional Development Fund (Center of Excellence site (also known as Janzen–Connell effect; Bever 2003; FIBIR), European Social Fund through MOBILITAS post- Kardol et al. 2007). The studies of Petermann et al. (2008) doctoral grant (MJD47) and the European Union 7th and Liu et al. (2012) suggest that negative plant–soil feed- framework project SCALES (FP7-226852). backs may function on the level of traits and/or phylogeny, leading to functionally divergent/phylogenetically over- dispersed plant communities. References Species that decreased in abundance in fertilized plots Ackerly, D.D. 2003. Community assembly, niche conservatism, had a rosette growth form, small leaves, short lateral and adaptive evolution in changing environments. 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Journal of Vegetation Science 852 Doi: 10.1111/jvs.12052 © 2013 International Association for Vegetation Science Journal of Vegetation Science 24 (2013) 853–864 SPECIAL FEATURE: FUNCTIONAL DIVERSITY A family of null models to distinguish between environmental ﬁltering and biotic interactions in functional diversity patterns L. Chalmandrier, T. Munkem€ uller,€ L. Gallien, F.de Bello, F. Mazel, S. Lavergne & W. Thuiller

Keywords Abstract Assembly rules; Biotic and abiotic filtering; Limiting similarity; Null models; Simulated Questions: Traditional null models used to reveal assembly processes from communities functional diversity patterns are not tailored for comparing different spatial and evolutionary scales. In this study, we present and explore a family of null models Nomenclature that can help disentangling assembly processes at their appropriate scales and Base de Donnees Nomenclaturales de la Flore thereby elucidate the ecological drivers of community assembly. de France (http://bbock.free.fr/botanica/ BDNFF.php). Location: French Alps. Received 3 April 2012 Methods: Our approach gradually constrains null models by: (1) filtering out Accepted 5 November 2012 species not able to survive in the regional conditions in order to reduce the spa- Co-ordinating Editor: Rasmus Ejrnæs tial scale, and (2) shuffling species only within lineages of different ages to reduce the evolutionary scale of the analysis. We first tested and validated this Chalmandrier, L. (corresponding author, loic. approach using simulated communities. We then applied it to study the func- [email protected]), Munkem€ uller,€ T. tional diversity patterns of the leaf–height–seed strategy of plant communities in ([email protected]), the French Alps. Gallien, L. ([email protected]), Mazel, F. (fl[email protected]), Lavergne, S. Results: Using simulations, we found that reducing the spatial scale correctly ([email protected]) & detected a signature of competition (functional divergence) even when environ- Thuiller, W. ([email protected]): mental filtering produced an overlaying signal of functional convergence. How- Laboratoire d’Ecologie Alpine, UMR CNRS ever, constraining the evolutionary scale did not change the identified 5553, Universite Joseph Fourier, BP 53, functional diversity patterns. In the case study of alpine plant communities, Grenoble Cedex 9, 38041, France investigating scale effects revealed that environmental filtering had a strong de Bello, F. ([email protected]): Institute of Botany, Academy of Sciences of the Czech influence at larger spatial and evolutionary scales and that neutral processes Republic, Trebon, CZ-379 82, Czech Republic were more important at smaller scales. In contrast to the simulation study results, decreasing the evolutionary scale tended to increase patterns of functional divergence. Conclusion: We argue that the traditional null model approach can only identify a single main process at a time and suggest to rather use a family of null models to disentangle intertwined assembly processes acting across spatial and evolutionary scales.

Studies intending to infer processes of community Introduction assembly from diversity patterns often focus on niche dis- The effect of biotic interactions on community structure similarities between co-existing species (Kraft et al. 2007; has been predominantly studied at small spatial scales (e.g. Munkem€ uller€ et al. 2012). The level of species niche Swenson et al. 2006), but new evidence suggests that this overlap in a community can be described via functional effect is also pervasive at large spatial scales (Gotelli et al. diversity indices using a set of functional traits, which 2010). However, it is often difficult to detect signatures of reflect species’ ecological characteristics (Lavorel & biotic interactions in large-scale diversity patterns due to Garnier 2002). Under strong environmental filtering, suc- the overriding selective effect of abiotic processes (Vamosi cessful species in a local habitat are more likely to share et al. 2009). similar trait values leading to functional convergence

Journal of Vegetation Science Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science 853 Null models to distinguish environmental ﬁltering and biotic interactions L. Chalmandrier et al.

(Petchey et al. 2007). Under strong competition, species tially important, we do not investigate the effect of spatial with overlapping niches are less likely to co-exist leading resolution (Vamosi et al. 2009) in this study. Often a to functional divergence (MacArthur & Levins 1967). study with a large spatial scale includes a broad range of Although theoretical predictions of functional diversity environmental conditions and thus a species pool with a patterns are straightforward when assembly processes are broad range of trait values (Willis et al. 2010). Large scales considered in isolation, ecologists face difficulties in eluci- thereby reinforce the detection of the effect of environ- dating the opposing effects that biotic and abiotic pro- mental gradients, while a small spatial scale is better sui- cesses have on patterns of functional diversity in ted to detect competition (Thuiller et al. 2010; Mouquet communities when these two processes interact with et al. 2012). The evolutionary scale is determined by the each other. age of the lineages considered (e.g. which can delimit genera or families). We expect that it is more likely to detect environmental filtering when the evolutionary scale is The traditional approach to studying community assembly rules large, because adaptations related to the bioclimatic niche The statistical approach to identify the signal of competi- tend to be conserved within old lineages (Crisp et al. tion vs. environmental filtering is based on the idea that 2009), which could mask the level of functional diver- communities assemble through a hierarchy of ecological gence expected between closely related competing spe- filters (Diamond 1975; Weiher & Keddy 1995). In the first cies. stage of this hierarchical approach, a ‘regional species pool’ Studies exploring community patterns of functional is defined as the set of species present in the region due to diversity at both varying evolutionary and spatial scales biogeographical and historical processes (Ricklefs 2004). are rare (e.g. Swenson et al. 2006). One interesting finding Successive environmental factors (e.g. climate, land use or is that not only a too large spatial scale but sometimes also soil) filter adapted species from this ‘regional pool’ into a a too small spatial scale can hinder the detection of biotic more convergent ‘local species pool’. In a second stage, interactions. This may happen because the species pool species from the local species pool are filtered by biotic misses species for which the environmental conditions are interactions to form the ‘observed communities’. When suitable but which are excluded by competition from the competition for resources predominates, we expect a pat- entire study. This ‘dark diversity’ (sensu Partel€ et al. 2011) tern of functional divergence in the observed community may be present in neighbouring areas or lie dormant in the relative to the local species pool. soil seed bank. Including such dark diversity in the local The detection of significant patterns relies on comparing species pool can be critical for detecting biotic interactions the observed functional diversity to the diversity expected (de Bello et al. 2012b). under a model of random assembly from a selected species Here, we explore the interacting effects of gradually pool. Often patterns of competition can only be identified changing spatial and evolutionary scales of the species when comparing observed communities to random assem- pool on patterns of functional diversity and inferred pro- blies from the local species pool, because the regional spe- cesses of community assembly. First, we present a simula- cies pool tends to be functionally too diverse. Therefore, tion study using virtual community data generated with a the identification of an appropriate local species pool has process-based model that allows fine-tuning of the relative been widely discussed but no consensus has yet been strengths of the different assembly processes present. Sec- reached (Partel€ et al. 2011). Here, we propose to further ond, we present a field case study using plant community constrain the traditional null model approach based on the plots in the French Alps. We assume the patterns of func- regional pool composed of all species observed in the study tional diversity in alpine plant communities will be domi- (de Bello 2012). Our suggested constraints on this regional nated by strong environmental filtering but biotic species pool take into account two important factors that interactions are also likely to operate, although quite are responsible for the differences between the regional rarely discerned in such systems (e.g. Spasojevic & Suding and the local species pool: the spatial and evolutionary 2012). The challenge is thus to remove the large-scale scales. environmental filtering effects from the diversity patterns in order to detect the influence of small-scale processes. Finally, we propose a family of null models to distinguish The effects of spatial and evolutionary scales the respective effects of environmental filtering and com- The spatial scale of a study can either relate to the extent petition by manipulating the spatial and evolutionary of the sampling area across which the species pool has scales in the statistical analyses, and test our proposed been constructed (e.g. habitat, region or continent) or to methodology with the virtual community data and the the resolution (i.e. plot size) of the study. Albeit poten- case study.

Journal of Vegetation Science 854 Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science L. Chalmandrier et al. Null models to distinguish environmental ﬁltering and biotic interactions

(five values; Table S1) 100 times, leading to a total of Methods 10 000 simulated communities with different assembly Data rules and different phylogenetic contexts. Simulation study – Model overview In a first step, we generated 10 000 independent species Field case study pools of 400 species by simulating phylogenies and trait Study system and site. The study site was the 25-km long evolution along these phylogenies, with rates of trait evo- Guisane Valley located in the centre of the French Alps lution (d) varying over evolutionary time (Pagel 1999). (ca. 260 km2;44.9° N, 6.5° E). The valley was character- Each species was characterized by a single trait that defined ized by contrasting climate conditions, with mean annual the species-specific niche optimum and a niche breadth temperatures ranging from 2.7 °Cto7.7°C. As in other that was equal for all species. The phylogenetic signal for valleys of the central Alps, the landscape is a mosaic of these traits, i.e. the trend for closely related species to be coniferous and deciduous forests, shrub heaths, sub-alpine more similar than distantly related species, was measured grasslands and alpine meadows. All these habitats were using Blomberg’s K (Blomberg et al. 2003). represented in our data set. For each species pool, a single community was initial- Community data and distribution data. We used two data- ized with 100 individuals randomly drawn from the spe- bases compiled by the Alpine National Botanic Conserva- cies pool. For each simulation step, 100 random tory. The data used to study community patterns were individuals were sequentially removed from the commu- from a phytosociological survey at the scale of the French nities and replaced by individuals from the species pool Alps, from which we extracted the 95 sites for the Guisane (asynchronous updating). The probability of an individual valley (Boulangeat et al. 2012b). Sites were representative from species i entering the community k,Pall,i,k, depended of the heterogeneity of the valley climate conditions on the specified assembly rules and their relative impor- (Albert et al. 2010). Herbaceous community plots were tance defined by the factors Benv (environmental filtering), surveyed by expert botanists in homogeneous vegetation 2 Bcomp (competition) and Babun (recruitment) (Table S1). with a size of 100 m on average. Smaller plots had a mini- 2 mum of 10 m and some forest plots were sampled up to 2 P ; ; ¼ exp B log P ; ; þ B all i k env env i k comp ð Þ 1000 m . The abundance estimates were based on an 1 – log Pcomp;i;k þ Babun log Pabun;i;k abundance dominance scale using cover classes (0.5, 3, 15, 37.5, 62.5 and 87.5%) and then normalized between 0 and 1 to obtain an estimate of the relative abundance of Penv,i,k modelled the environmental filter: the closer the species trait value (i.e. niche optimum of the species) was each species. Our community data set included 542 spe- to the environmental conditions of the community k,the cies. The second data set used to include dark diversity into our definition of species pools was a plant occurrences higher was its probability to enter. Pcomp,i,k modelled the competition filter: the closer the species trait value was to database (presence-only data) covering the French Alps (3 those of the individuals already present, the lower was its million occurrence points for 2748 species). probability of entering. In this way, competition between Functional traits. We used the functional trait database individuals was defined as symmetric. Note that as conspe- ANDROSACE (see Appendix 2 for details). We chose three cifics had the same trait values, intra-specific competition functional traits: specific leaf area, height and seed mass to was stronger than inter-specific competition. Pabun,i,k mod- describe species’ ecological strategies according to the leaf– elled the recruitment filter: the more abundant the species height–seed scheme (LHS; Westoby 1998). These traits are was in the community, the higher its probability of strongly related to the fundamental processes of plant life, entering. This term counteracted the high intra-specific i.e. dispersal, establishment and persistence (Weiher competition value generated by the competition filter (see et al.1999), and their combination has been proved to cap- Appendix 1 for details on how the three filters were ture well the existing variation in plant ecological strategies defined). (Lavergne et al. 2003). Specific leaf area (SLA, i.e. light

The factors Babun, Benv and Bcomp weighted the impor- intercepting area per leaf dry mass) reflects the trade-off tance of the three filters in community assembly. In the between resource acquisition and conservation. Height at special case of Benv and Bcomp equalling one, the equation maturity is related to competitive ability and avoidance of was comparable to a Lotka-Volterra equation with inter- environmental stress (Korner€ 2003). Seed mass strongly and intra-specific competition and a maximal growth rate influences dispersal and is related to establishment ability dependent on environmental suitability. (Pakeman et al. 2008). We calculated functional diversity We repeated each of the 100 combinations of the on the basis of these three traits assuming that they capture parameters Βenv (five values), Βcomp (four values) and d essential aspects of the niche. Given this assumption, func-

Journal of Vegetation Science Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science 855 Null models to distinguish environmental filtering and biotic interactions L. Chalmandrier et al. tional diversity of a community should be a good proxy for species being selected depended on the probability of it the amount of niche overlap in the community. In our occurring in the community given the environmental con- study case, the three traits described above presented a low ditions (‘suitability index’). In addition, we applied the to moderate phylogenetic signal (K ranging from 0.08 to ‘equiprobable randomization’ traditional approach (i.e. all 0.44, see Table S2) and were linked to the main environ- species have an equal probability of being selected; EQ-R). mental gradient in the study area (see Fig. S2). Due to Similarly, evolutionary scale of the null models could be missing data in the trait database, we excluded 169 species adapted by randomizing either ‘across all lineages’ (large characterized by less than two trait values. However, the evolutionary scale) or ‘within lineages’ (small evolutionary remaining species still accounted for more than 80% of the scale). We used the partial randomization scheme pro- total abundance of each community (Pakeman & Quested posed in Hardy & Senterre (2007). For a given age, we 2007). Finally, our data set represented a total of 95 com- defined the associated lineages across the phylogeny and munities and 373 species. only permuted the species within these lineages. This pro- Phylogeny. A genus-level phylogeny of alpine plants was cedure could be repeated for several ages, thus making it built using the workflow proposed in Roquet et al. (2013) possible to pinpoint shifting points of lineage age between with DNA sequences downloaded from Genbank (see convergent and divergent communities (‘intra-lineages Appendix 2 for details). The tips of the phylogenetic tree randomization’, IL-R, vs. ‘across-lineage randomization’, were resolved with polytomies to obtain a species-level AL-R, where the entire tree is randomized). We tested 19 phylogeny. age values regularly spaced along the tree. Note that IL-R could be easily combined with SB-R to study interacting Statistical analyses effects of reduced spatial and evolutionary scales on functional diversity patterns (Table 1). Functional diversity indices We analysed all simulated and real communities using these 40 different randomization schemes: one combining We usedP Rao’sP quadratic diversity (Rao 1982), expressed R EQ-R and AL-R (non-constrained null model), one com- as D ¼ dijfifj, with dij being a measure of the func- i j bining SB-R and AL-R, 19 combining EQ-R and IL-R, and tional distance between the species i and j,andfi being the 19 combining SB-R and IL-R. relative abundance of species i in the community (Ricotta Suitability indices. In order to perform SB-R, suitability 2005). indices were estimated for each species in each commu- For the simulated study, the functional distance nity. We defined suitability as a species’ probability of between species was calculated as the Euclidean distance occurring in the community given the environmental con- between their trait values. For the field study case, the ditions. For each simulated community k and each species three continuous traits were log-transformed to conform I, the suitability index was given as P (Equation 1, to normality and then standardized (i.e. centred and env,i,k Appendix 1). divided by their SD). The functional distance matrix was For the field data, we built a species distribution model calculated for each species pool based on the Euclidean dis- (Guisan & Thuiller 2005) in order to estimate species’ abi- tances. We applied the R-function quasieuclid to ensure the otic niches, and thereby their probability of occurrence Euclidean properties of our distance matrices despite the according to a set of climatic and topographic variables for missing data (package ade4; Pavoine & Doledec 2005; each species independently (see Appendix 2 for details). R Foundation for Statistical Computing, Vienna, AT). Based on the species distribution models, we extracted the probability of presence (suitability indices) for the 373 Null model algorithms plant species of the community data in each of the 95 com- Randomization schemes. The spatial scale of the null models munities. could be adapted by randomizing either ‘within the regio- Species pools. For the simulation study, the ‘true’ species nal species pool’ (large spatial scale) or ‘within the local pools were known and could be used directly for the differ- species pool’ (small spatial scale). We thus decreased the ent null models. For the field study, we constructed a spatial scale of the species pool by reducing it to the pool of ‘Reduced species pool’ (R-SP) from the species present in the species with similar environmental preferences to those 95 community plots. We further construct an ‘Extended spe- conditions that prevailed within each community (as cies pool’ (E-SP) by adding 350 supplementary species introduced by Peres-Neto et al. 2001). For each commu- (characterized by at least two trait values). These species nity, the randomization algorithm replaced each observed were known to be present in the Guisane valley according species with a species from the regional pool. In the ‘suit- to the plant occurrences database but were not present in ability-based randomization’ (SB-R), the probability of a our sampled community data set. The rationale behind this

Journal of Vegetation Science 856 Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science L. Chalmandrier et al. Null models to distinguish environmental filtering and biotic interactions strategy was to include potential dark diversity, i.e. species assembly (random diversity pattern), while SB-R wrongly able to both survive the environmental conditions of the indicated competition (significant divergence; Fig. 1, Guisane and disperse into the communities under study upper left corner). When both competition and environ- (Table 1). Based on the species distribution models men- mental filtering were strong (Benv = 2 and Bcomp = 10), tioned above, we extracted the probability of presence EQ-R was able to detect environmental filtering (signifi- (suitability indices) for these 350 plant species in each of cant convergence) but the additional use of SB-R also the 95 communities. allowed detection of competition (Fig. 1, lower right corner); only when applied together did the two randomization schemes successfully disentangled the interplay of Outputs of null models competition and environmental filtering. In the case of For each simulated community, we calculated the rank of moderate environmental filtering (Benv = 0.5), EQ-R and the observed diversity value in the distribution of 500 ran- SB-R successfully identified environmental filtering and domized values of each of the null models. High (low) rank competition if competition was also moderate (Bcomp = 1). values indicated higher (lower) than expected diversity When competition was stronger (Bcomp = 5), SB-R cor- under the null expectation. We chose a significance level rectly identified competition but environmental filtering of 5% (0.025 and 0.975 significant threshold). We then was too weak to be detected by EQ-R. studied the distribution of ranks across communities in relation to the parameters of the simulation model and the Influence of intra-lineage randomizations (evolutionary scale) null models. In the communities of the Guisane valley, the 40 differ- Overall, the intra-lineage randomizations (IL-R) did not ent randomization schemes for each of the two species better detect ecological processes than the across-lineage pools (R-SP, E-SP) resulted in a total of 80 null models. For randomizations (AL-R). The median of the distribution each null model, we used 1000 repetitions and reported of ranks was more or less constant regardless of the the ranks of observed values in the null distributions. We chosen age value for IL-R randomizations (Fig. 2, cut- controlled for the size of the sample space for the evolu- ting at the root corresponds to AL-R) for all ecological tionary constrained null model, i.e. the number of possible processes. random communities that could be generated by the null The phylogenetic signal of trait distribution in the phy- model. This was done for each evolutionary scale and each logeny only weakly influenced the outcome of IL-R and in community. an unexpected direction (Fig. 2). Even with a strong phy-

The number of possible random communities rij,fora logenetic signal, the IL-R randomization scheme did not community i and an evolutionary scale j defining lineages L substantially increase the rank values (Table 2). P P was calculated as: log rij ¼ logðÞnL! log nL;i;k! L k Field case study with nL the number of species in the lineage L and nL,i,k, the number of species of the lineage L in the abundance For the restricted species pool (R-SP), a decrease of the class k of the community i. spatial scale (i.e. use of SB-R, compared to EQ-R) only All analyses were carried out using the software R 2.14, slightly shifted the ranks towards less convergent func- with the following packages: ade4, adephylo, ape, geiger, tional diversity patterns (mean rank increase of 0.09; picante, spadicoR and randomForest. Fig. 3, top left), showing that environmental filtering was less pervasive at smaller spatial scale, but still over- Results whelming. This trend was consistent across communi- Simulation study ties (98% of the communities ranks increased; Table 2). For the sake of simplicity, we have only displayed the Influence of the suitability-based randomizations (spatial scale) outcome of one IL-R null model and chose an intermediate The null models built using both the traditional equi-prob- evolutionary scale in Fig. 3 (roughly corresponding to lin- able randomization (EQ-R) and the suitability-based ran- eages at the family or order taxonomic level). The reduc- domization (SB-R) correctly detected environmental tion of evolutionary scales tended to increase the ranks but filtering and competition processes when they acted in iso- provided more variable results between communities than lation (Fig. 1, upper right corner for competition, the reduction of spatial scale (Table 2). Finally, the com- Benv = 0 and Bcomp = 10; and lower left corner for bined reduction of spatial and evolutionary scales (using environmental filtering, Benv = 2 and Bcomp = 0). both SB-R and IL-R) most strongly increased the ranks When the communities were randomly assembled (mean rank increase of 0.16; Fig. 3). Fifteen out of the 18 (Benv = Bcomp = 0), EQ-R correctly detected neutral communities presenting a significant environmental filter-

Journal of Vegetation Science Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science 857 Null models to distinguish environmental ﬁltering and biotic interactions L. Chalmandrier et al.

Fig. 1. Comparison of the outcomes of the ‘equiprobable randomisation’ (EQ-R) and the ‘suitability-based randomisation’ (SB-R) null models for the simulated community data. Each subplot presents the distribution of the ranks in a violin plot (Hintze & Nelson 1998) generated for a specific combination of environmental filtering (Benv) and competition (Bcomp). Community assembly is random in the upper-left corner, driven by competition only in the upper-right corner (Bcomp > 0), driven by environmental filtering only in the lower-left corner (Benv > 0), and driven by the interplay of these processes in the lower-right corner). A rank value higher than 0.975 indicates a diversity value higher than expected under the null model, while a rank value lower than 0.025 indicate a diversity value lower than expected under the null model ing signal (convergence) at large spatial and evolutionary trend described above for a constraint on an intermediate scale (EQ-R: AL-R) presented no signal (neutral pattern) at evolutionary scale can be generalized: when the evolu- a small spatial and evolutionary scale (SB-R: IL-R). tionary scale was smaller, the environmental filtering was For the extended species pool, we observed the same less pervasive, although the proportion of communities trends: the ranks obtained using the combination of SB-R becoming significantly divergent remains negligible. This and IL-R increased strongly compared to the use of a non- was true whatever species pool was considered. Further- constrained null model (91% of the communities ranks more, we note that the choice of cutting age for IL-R heav- increased; Table 2), showing that environmental filtering ily impacted the sample space of the null model. At the was less pervasive at the small rather than the large spatial chosen intermediate and larger evolutionary scale, the and evolutionary scale. Moreover, 22 out of the 28 com- number of possible random communities remained largely munities with a significant environmental filtering signal superior to the number of used randomizations. However, at large spatial and evolutionary scale (EQ-R: AL-R) for smaller evolutionary scales, the sample space of the showed no signal at a small spatial and evolutionary scale null model decrease dramatically for some communities, (SB-R: IL-R). indicating lower power of the null model. With an increasingly smaller evolutionary scale, the out- Finally, regardless of the evolutionary and spatial scale come of the null models (either EQ-R: IC or IL-R: SB-R) considered, the communities appeared more convergent tended to detect a less convergent diversity pattern (i.e. the (i.e. ranks decreased; Fig. S3) when using the extended mean rank value increased; Fig. S4). This showed that the species pool (E-SP) as opposed to the reduced species pool

Journal of Vegetation Science 858 Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science L. Chalmandrier et al. Null models to distinguish environmental ﬁltering and biotic interactions

Fig. 2. Comparisons of the outcomes of the ‘intra-lineages randomisation’ (IL-R) as functions of the evolutionary scale. Each subplot contains the median of the distribution of ranks of communities generated for a specific combination of environmental filtering (Benv > 0) and competition (Bcomp > 0). A rank value close to ‘Root’ indicates a ‘close-to-root’ age value while an age value close to ‘Tips’ indicates a ‘close-to-tips’ age value. Specifically the randomizations at age ‘Root’ are ‘across-clades randomisation’ (AC-R), i.e. all tips are shuffled among each other. Closed (open) symbols indicates the coupling with EQ-R (SB-R); square (circle) symbols indicate the distribution of ranks for communities whose phylogeny was generated by a d parameter of 0.1 (10) and thus high (low) phylogenetic signal.

(R-SP); however, the differences between these species tency between the field and simulation studies about the pools were small. importance of evolutionary scaling likely resulted from the fact that in the simulation study species niches were fully Discussion known and described by the functional patterns. The evolutionary scaling did not add any further information. In Detecting biotic interactions: scale matters the field case study, the phylogenetic relationships were A primary result of our simulation study was that null likely to capture species’ niche dimensions not well repre- models constraining the spatial scale help in detecting bio- sented by the measured traits, such as the nitrogen fixing tic interactions, even if these were overlaid with strong ability of Fabaceae species. If measured traits do not fully environmental filtering. These constrained null models represent species’ niches, evolutionary constrained null reflected well the ‘local species pool’ (Zobel 1997). In con- models can be beneficial as they can buffer the lack of trast, reducing the evolutionary scale using intra-lineage information on niche-relevant species traits in the func- randomization did not improve the detection of biotic tional analysis (Carboni et al. 2013). interactions, even when the niche phylogenetic signal was high. The field case study provided complementary Interpretation of diversity patterns from field data insights. The combined use of constraints for the spatial (SB-R) and the evolutionary (IL-R) scale increased Alpine communities are highly constrained by steep cli- divergence in the functional diversity pattern and thus mate gradients (in particular temperature and radiation; identified a potential effect of competition. The inconsis- de Bello et al. 2012a). The challenge is thus to go beyond

Journal of Vegetation Science Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science 859 Null models to distinguish environmental ﬁltering and biotic interactions L. Chalmandrier et al.

Table 1. Overview of the different null models with null hypotheses associated with their tests.

Name Description Associated Hypothesis References

Randomization Large scale Across-lineages Species abundance values are shuffled All species in the phylogeny randomization (AL-R) across the entire phylogeny are functionally equivalent Reduction of Intra-lineages Species abundance values are shuffled Species within lineages are Hardy & Senterre 2007 evolutionary scale randomization (IL - R) within pre-defined lineages (defined by age) functionally equivalent Large scale Equi-probable Probability of being attributed to an All species of the regional randomization (EQ - R)* abundance value is equal for all species species pool are functionally equivalent Reduction of Suitability-based Probability of a species being attributed to Species of the local species Peres-Neto et al. 2001 spatial scale randomization (SB - R) an abundance value is proportional to the pool are functionally abiotic suitability of the site considered equivalent Species pools Reduced species Species pool composed of the species The species in the data set pool (R – SP) present in at least one of sites studied fully describe the species pool Geographical extended Species pool extended to species Dark diversity is missing and P€artel et al. 2011 species pool present in the study area according to needs to be included in (GE – SP) independent data the species pool *If evolutionary scales and spatial scales are independently varied, AL-R equals EQ-R. However, as they can be varied in combination (cf. Fig. 3, last column), we need to differentiate between AL-R and EQ-R.

Table 2. Summary of the change in ranks for the communities of the field case study when reducing spatial and evolutionary scales: at small spatial scale and large evolutionary scale (SB-R), at large spatial and small evolutionary scale (IL-R) and at small spatial and evolutionary scales (SB-R: IL-R). The reduced species pool (R-SP, first three rows) and the extended species pool (E-SP, last three rows). We used a 5% error rate to establish the significance threshold for switching ranks (0.025 and 0.975). For a graphical representation, see Fig 3.

Percentage of Mean increase SD of rank Number of communities Number of communities communities in rank increase switching from switching from increasing rank convergent to non-convergent to non-convergent convergent/total number of communities

R-SP EQ-R to SB-R 98% 0.09 0.06 9/18 0/77 EQ-R to IL-R 79% 0.08 0.13 7/18 1/77 EQ-R to SB-R: IL-R 89% 0.16 0.16 15/18 0/77 E-SP EQ-R to SB-R 100% 0.12 0.09 18/28 0/67 EQ-R to IL-R 80% 0.07 0.11 14/28 2/67 EQ-R to SB-R: IL-R 91% 0.19 0.18 22/28 1/67

environmental filtering to detect the additional influence further small-scale processes (e.g. micro-environmental of small-scale processes on community assembly. It was conditions not included in the suitability index or other thus logical to observe environmental filtering as the dom- biotic interactions favouring the co-existence of similar inant assembly process when an equi-probable null model species). Overall, we did not detect significant functional approach was applied (EQ-R). Reducing the spatial scale divergence in alpine plant communities, at any studied of the analysis by adding habitat suitability constraints evolutionary or spatial scale. This result may have several (SB-R) reduced functional convergence, indicating that explanations. the environmental factors considered in the suitability First, competitive interactions between plant species do index were originally driving the patterns of convergence. not necessarily lead to trait divergence (e.g. Laliberteetal. The functional convergence remaining might be due to 2013). Besides niche differentiation, the sharing of com-

Journal of Vegetation Science 860 Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science L. Chalmandrier et al. Null models to distinguish environmental ﬁltering and biotic interactions

Fig. 3. Comparison of the outcomes of the different constrained null models for the case study according to the scales of the analysis. Results at large spatial and evolutionary scales (EQ-R: AC-R) are compared against the results: at fine spatial scale and large evolutionary scale (SB-R, first column),atlarge spatial scales and small evolutionary (IL-R, second column), and at fine spatial and evolutionary scales (SB-R: IL-R, third column). The first row presents results for the reduced species pool (R-SP) and the second row presents results for the extended species pool (E-SP). The dotted lines represent the significance threshold of the rank values (0.025 and 0.975). The thick lines separate the communities whose ranks increased from these whose ranks decreased with the use of the constrained null model vs. the non-constrained null model. For a numerical summary, see Table 2 mon traits that enhance competitive ability can also lead ing that biotic interactions do not play an important role to the co-existence of species (Mayfield & Levine 2010). in the functional structuring of sub-arctic–alpine com- Second, our selection of key traits might not be appropri- munities (Mitchell et al. 2009; but see Spasojevic & Sud- ate to evaluate niche overlap. This is somehow unlikely, ing 2012). given that the use of these functional traits have been widely advocated for herbaceous ecosystems (Grime Detecting biotic interactions: (not) a matter of species 2006). However, we neglect the intra-specific trait vari- pool ability along the gradients of the Guisane valley (Albert et al. 2010). As competition is essentially an individual- In our field case study, extending the species pool did not level process, the use of aggregated species-level trait have a marked effect on the detection of competition. This values could mask the functional divergence between result suggests that the analysis was robust to the competing neighbours (Clark et al. 2011). Third, other inclusion of dark diversity. However, we cannot be sure biotic interactions and local ecological processes, such as that all of the dark diversity was included, as local compe- the removal of palatable species by grazers (de Bello et al. tition could have excluded species from the entire Guisane 2006) or local land use such as fertilization selecting for valley. species with high SLA (Quetier et al. 2007; Gerhold et al. Constraining species pools allows a reduction of the evo- 2013), might influence diversity patterns towards conver- lutionary scale but also increases the risk of Type II errors gence. as less random combinations of species can be drawn from Overall, we conclude that characterizing species by the species pool to construct the null expectation of the their position in the LHS plant ecology strategy scheme diversity pattern (Gotelli & Ulrich 2012). In our study, the mainly revealed the effect of environmental filtering at effect of this risk is striking when using IL-R, as the sample large spatial and evolutionary scales. Neutral processes space significantly decreases when the cutting age becomes and not niche-based competition seemed to drive com- very close to the tips (Fig. 4). When using IL-R, the sample munity assembly at small spatial and evolutionary scales. space should therefore be evaluated beforehand to evalu- These results are congruent with other studies suggest- ate the power of the null model.

Journal of Vegetation Science Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science 861 Null models to distinguish environmental ﬁltering and biotic interactions L. Chalmandrier et al.

Perspectives for diversity pattern analyses

Detecting the influence of biotic interactions in observed diversity patterns is a challenging task because of the pervasive environmental heterogeneity in large-scale ecological data sets (Cavender-Bares et al. 2009; Thuiller et al. 2010). Using a family of null models allows changing of the spatial and evolutionary scales of the analysis. Caution should however be taken: we showed the negative impact of flawed input data on the output of constrained null models and the importance of evaluating the sampling space when constraining null models. Finally, the combined interpretation of the different null model outcomes enables uncovering of fine-scale functional divergence patterns within large-scale convergence patterns.

Acknowledgements The research leading to these results received funding Fig. 4. Sample space of ‘intra-lineage randomization’ (IL-R) as a function from the European Research Council under the European of the evolutionary scale. Sample space was estimated as the number of Community’s Seventh Framework Programme FP7/2007– different random communities the IL-R null model can generate for an 2013, Grant Agreement no. 281 422 (TEEMBIO). TM was ‘observed’ community and at varying evolutionary scales. We displayed funded by the ANR-BiodivERsA project CONNECT (ANR- the median (diamond), maximum (square) and minimum (circle) over communities for each species pool (R-SP, ﬁlled symbols; E-SP, open 11-EBID-002), as part of the ERA-Net BiodivERsA 2010 symbols). The horizontal dotted line indicates the threshold of 1000 call. FB acknowledges support from the grant GACR random possibilities. The x-axis represents the age value used as a P505/12/1296. The ﬁeld research was conducted on the parameter for the IL-R and the vertical dotted line indicates the long-term research site Zone Atelier Alpes, a member of evolutionary scale used to generate Fig. 3. the ILTER-Europe network. ZAA publication n° 24. The computations presented in this paper were performed using the CIMENT infrastructure (https://ciment.ujf-grenoble.fr), supported by the Rhone-Alpes^ region (GRANT Species distribution models as a new tool for diversity CPER07_13 CIRA: www.ci-ra.org). pattern analysis

Our study highlights the potential of species distribution References models to refine the species pool concept by defining a pool of species adapted to local environmental condi- Albert, C.H., Thuiller, W., Yoccoz, N.G., Douzet, R., Aubert, S. & tions based on suitability estimates (introduced in Bou- Lavorel, S. 2010. A multi-trait approach reveals the structure langeat et al. 2012a). One potential drawback is that and the relative importance of intra- vs. interspecific variability in plant traits. 24: 1192–1201. suitability-based randomization (SB-R) might lead to Functional Ecology Blomberg, S.P., Garland, T. Jr & Ives, A.R. 2003. Testing for phy- false positives when using flawed suitability informa- logenetic signal in comparative data: behavioral traits are tion. This situation occurred in our simulation study, more labile. Evolution 57: 717–745. where the species suitability indices were based on a Boulangeat, I., Gravel, D. & Thuiller, W. 2012a. Accounting for trait unrelated to species’ niches in the random com- dispersal and biotic interactions to disentangle the drivers of munity assembly scenario. As a result, the test wrongly species distributions and their abundances. Ecology Letters 15: identified divergence and thus competition as a major 584–593. assembly process. This result calls for caution in real-life Boulangeat, I., Lavergne, S., Van Es, J., Garraud, L. & Thuiller, situations where the habitat zsuitability is assessed from W. 2012b. Niche breadth, rarity and ecological characteristics the observed distribution of species, and may reflect within a regional flora spanning large environmental gradi- other processes than environmental filtering. Such an ents. Journal of Biogeography 39: 204–214. approach should then be preceded by a cautious selec- Carboni, M., Acosta, A. & Ricotta, C. 2013. Differences in function of relevant abiotic variables driving species distributional diversity among plant communities on Mediterranean tions, niche differentiation and thus environmental coastal dunes are driven by their phylogenetic history. Jour- filtering. nal of Vegetation Science 24: 932–941.

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Journal of Vegetation Science 864 Doi: 10.1111/jvs.12031 © 2013 International Association for Vegetation Science Journal of Vegetation Science 24 (2013) 865–876 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Shifts in functional traits and functional diversity between vegetation and seed bank Robin J. Pakeman & Antonia Eastwood

Keywords Abstract Disturbance; Functional divergence; Functional evenness; Functional richness; Productivity; Question: Does the seed bank have a different functional trait signature to the Seed bank; Trait convergence vegetation and is this difference affected by productivity and disturbance? Do functional diversity differences exist between the vegetation and seed bank and Nomenclature are the differences modulated by productivity and disturbance. Stace & Thompson (2010) Location: An area of diverse land use on the west coast of Scotland. Received 16 March 2012 Accepted 23 August 2012 Methods: Parallel vegetation and seed bank surveys were carried out across 30 Co-ordinating Editor: Jason Fridley sites contrasting in land use, and hence productivity and disturbance regime. Data were analysed to assess overall differences between the seed bank and vegetation in community-weighted mean for selected functional traits and to test if Pakeman, R.J. (corresponding author, [email protected]) & the difference was affected by productivity or disturbance. Three functional – Eastwood, A. (antonia.eastwood@hutton. diversity indices were calculated for each seed bank and vegetation sample ac.uk): The James Hutton Institute, functional richness, functional divergence and functional evenness – and each Craigiebuckler, Aberdeen, AB15 8QH, UK index was assessed for overall differences between the seed bank, the vegetation and differences modulated by disturbance and productivity. Results: There were clear differences in many community-weighted mean traits between the seed bank and the vegetation. Plants in the vegetation were characterized by increased stature, more conservative leaf traits, wind pollination and by being perennial, whereas plants in the seed bank had higher seed longevity, higher seed masses and were more frequently insect- or self-pollinated. There was no difference in functional richness between the seed bank and the vegetation, and while both the seed bank and the vegetation functional richness were significantly lower than expected, the vegetation showed a bigger deviation from expectations compared to the seed bank. The functional diversity indices revealed different relationships between seed bank and vegetation against productivity and disturbance. Functional divergence (and its standardized effect size) indicated stronger habitat filtering for the vegetation at high levels of productivity or disturbance, while the effect size of functional richness and functional evenness suggested stronger filtering on the seed bank at high levels of productivity and disturbance. Conclusions: The significant difference in traits between the seed bank and vegetation mean that following disturbance this is likely to have a considerable impact (at least in the short term) on ecosystem function. Functional diversity patterns were less clear, with conflicting evidence on habitat filtering, depending on the metric chosen.

(Chippindale & Milton 1934; Champness & Morris 1948), Introduction and that the degree of difference in species between the It has long been recognized that the species represented in vegetation and seed bank varies between habitats (Thomp- the seed bank may not be present in the same relative son 1992; Pakeman & Marshall 1997). Given that there is abundance in the vegetation, or even present at all this difference between the seed bank and vegetation then

Journal of Vegetation Science Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science 865 Functional diversity in seed bank and vegetation R.J. Pakeman & A. Eastwood it is possible that the functional characteristics of the vege- abundances are distributed in trait space in relation to the tation after disturbance may be different from the original centre of gravity, and functional evenness describes the vegetation if the seed bank has played a large part in the evenness of abundance distribution in functional trait regeneration process. The contribution of the seed bank to space (Ville´ger et al. 2008). Given that the composition of regeneration varies upon the nature, timing and size of the vegetation is dependent on the current impacts of com- disturbance (Bullock et al. 1995; Pakeman & Small 2005), petitive interactions and environmental drivers, whereas as well as upon the potential contributions from other the species composition of the seed bank is dependent on sources (Pakeman et al. 1998). However, the seed bank the previous composition and seed longevity, then it might usually plays some role in regeneration, and hence the be expected that there are differences in these metrics functional characteristics of the vegetation may shift, between the seed bank and the vegetation. These differ- although depending on the rate of succession this shift ences and how they might change along gradients of key may be short term (Pakeman & Small 2005) or long-term environmental drivers may, in turn, provide information (Marks 1974). The shift in functional composition of the on processes related to regeneration following disturbance vegetation after disturbance may have knock-on effects on in different plant assemblages and help in understanding ecosystem function and ecosystem services, so quantifica- the role of the seed bank in secondary succession. tion of the impact is important. The natural metric to compare trait values between the Consideration of the species present in the seed bank, seed bank and the vegetation is the community-weighted particularly in grasslands, suggests that some shift in trait mean (Garnier et al. 2007). The functional diversity mea- values or attributes would be expected (Klimkowska et al. sures, functional divergence and functional evenness, also 2010). In particular, it is expected that seed banks would require information on abundance. The abundance mea- be characterized by species with high seed longevity, more sure for the vegetation is usually taken as the relative bio- contribution from species with an annual life history and masses present, or its proxy – relative cover. However, for traits related to rapid growth [e.g. high specific leaf area the seed bank the usual abundance scoring is density. That (SLA), low leaf dry matter content (LDMC); Pakeman & would mean that small-seeded species would be highly Marshall 1997; Pakeman & Small 2005]. In effect, the seed weighted in any analysis. However, there is no evidence bank should be characterized by species with a more that small-seeded species have any long-term general ruderal strategy than the vegetation, according to Grime’s advantage over large ones, as individual assemblages show (1979) CSR strategy theory, and so species in the seed bank a large range of seed masses (Moles et al. 2004). Conse- should be characterized by features associated with low quently, the weighting in this paper was taken as the mass investment in structures and a rapid life cycle. However, of seeds present for each species per unit area, as for the the degree of shift between the seed bank and the vegetation. This is in line with the conclusions of Bruun & vegetation may differ considerably depending on local Poschlod (2006) who found that the dominance of small conditions; in particular, as productivity and disturbance seed in herbivore faeces could be accounted for by the nec- both play a significant role in determining the species and essary seed mass:seed size trade-off. However, it does mean the functional signature of the vegetation (Pakeman that if there are differences in the likelihood of germination 2011a). Highly productive and disturbed environments between big and small seeds, then this would impact the might select for the same set of trait values for both the resulting mean value and bias it towards the larger-seeded seed bank and the vegetation, leading to a convergence in species (see Thompson et al. 1993). trait values. To understand the difference in species traits and poten- As well as shifts in the values of traits expressed in the tial community assembly patterns between the seed bank seed bank compared to the vegetation, there may also be and the vegetation, this paper tests the hypotheses that (a) shifts in the frequency distribution of these traits. This type there is a difference in the functional trait signature of information can be used to assess how processes related (expressed as the community-weighted mean, CWM) to community assembly may differ between the present between the vegetation and the seed bank; and (b) that vegetation and the vegetation that develops after the degree of difference in CWM is lower at sites with disturbance. Useful metrics to do this are the measures of higher productivity or disturbance levels as environmental functional diversity presented in Ville´ger et al. (2008): filtering restrict the possible strategies displayed by the functional richness, functional divergence and functional plants. It also tests the alternative hypotheses that func- evenness. Each of these largely orthogonal metrics tional richness is (c1) lower in the seed bank as a result of (Mouchet et al. 2010) captures information relating to the restricting species to those with a seed bank, or (c2) higher process of community assembly. Functional richness as the seed bank often contains species not present in the accounts for the amount of functional space occupied by vegetation. It also tests the hypothesis (d) that functional the assemblage, functional divergence relates to how divergence is higher in the seed bank as habitat filtering

Journal of Vegetation Science 866 Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science R.J. Pakeman & A. Eastwood Functional diversity in seed bank and vegetation may be stronger within the vegetation, and (e) that func- Site productivity and disturbance tional evenness is higher in the seed bank, again as a result of habitat filtering on the composition of the vegetation. Annual above-ground net primary productivity (ANPP) Finally, the relationships between the functional diversity was estimated as the difference between biomass present indices and measures of ecosystem productivity and distur- in four 50 cm 9 50 cm quadrats at the start of the grow- bance are assessed to test the hypothesis (f) that differences ing season and four different quadrats at the time of peak in functional diversity between vegetation and seed bank biomass (Garnier et al. 2007). On sites with grazing ani- are lower in more disturbed and more productive areas as mals, growth was protected by 80 cm 9 80 cm 9 60 cm a consequence of within-site trait convergence. It might be wire cages. Summer samples used to estimate ANPP were expected that environmental filtering in more disturbed analysed for carbon and nitrogen (Flash EA112 Elemental and more productive areas will result in reduced functional Analyser; ThermoFinnegan, Milan, Italy) and the C:N diversity for both the seed bank and the vegetation. ratio calculated. Live, dead and total standing biomass from the samples used to estimate ANPP were also Methods included as measures of ecosystem functioning. Soil nitrogen release was quantified using resin bags (Johnson et al. Site selection 2005) buried on 23 Apr 2007 and retrieved on 9 Jul 2007. Thirty sites were chosen within a small area on the west Eight 8 cm 9 10 cm nylon bags each containing 10 g of coast of Scotland that was characterized by crofting – asys- Dowex Marathon MR-3 mixed ion-exchange resin were tem of rented agricultural landholdings that are too small buried at 10-cm depth at locations spread across each site. for full-time farming. The area was selected to have a large Ammonium and nitrate ions were extracted from the range of land uses and intensities within a small area retrieved bags by shaking in 400 ml of 2 M KCl for 1 hr. (5 km 9 7 km, centred on 57.31N, 5.66W) sharing the The resulting solutions were then analysed for nitrate same geology and climate. Site managements included using ion chromatography (Dionex, Sunnyvale, CA, USA) arable, fallow areas cropped for hay, winter-grazed rough and colourimetrically for ammonium (Konelab Discrete grassland and tall herb communities, unimproved silage Analyser, Espoo, FI). fields, unimproved pasture, moorland and woodland Disturbance was quantified using the metrics developed (Appendix S1; Pakeman et al. 2011). Each site was effec- from those of Ku¨ hner & Kleyer (2008) for frequency/ tively a land-use unit with apparently homogenous vege- intensity of disturbance, biomass loss at disturbance, tation cover and management, with site sizes ranging from below-ground disturbance, start of disturbance in weeks 0.5 to 100.0 ha. and vegetation height (Table 1). Vegetation surveys were carried out in July 2007. Rela- To reduce the number of variables and because of collin- tive abundance by visual estimates of percentage cover of earity between variables, the disturbance and productivity all higher plant species, bryophytes and litter was estimated drivers were separately subject to ordination, specifically in between four and seven, randomly placed 1 m 9 1m computation of a Gower distance matrix followed by prin- quadrats per site. The site 9 species matrix was formed cipal coordinates analysis in ade4 (Dray & Dufour 2007), from the mean cover of species within a site converted to a with the first two axes then taken as the environmental proportion of the total species cover at each site. variables in subsequent analysis. For ordination of the dis- The soil seed bank was sampled in April 2007. Thirty turbance drivers, 76.1% was explained by axis 1 (hereafter cores of 5-cm diameter and 5-cm depth were removed per referred to as Disturbance 1) and 20.1% by axis 2 (Distur- site from random locations. This density of sampling gives bance 2). For both axes, greater and more complete distur- an approximate minimum detectable limit of 50 seeds mÀ2 bance was associated with positive values. The ordination (Thompson et al. 1997). Soil samples from each site were of the productivity values showed axis 1 explaining 59.0% mixed and spread across several seed trays to a depth of ca. of the variation (Productivity 1) and axis 2 explaining 1 cm. Samples were kept in an unheated greenhouse for 26.8% (Productivity 2). More biomass was associated with 1 yr and subjected to repeated disturbance. Seedlings were higher axes scores on Productivity 1, whereas Productivity removed when identified. Unidentified seedlings (mostly 2 was more associated with higher nutrient status and net Carex spp.) were grown on until identification was possible. primary productivity. Density information (seeds mÀ2) was converted to mass of seeds of each species per unit area (m2) by multiplying by Plant trait data the seed mass using data from LEDA (Kleyer et al. 2008). This mass per unit area was converted to a proportion of Trait data were assembled from two main sources the total mass of seed per unit area to use as data for the (Table 2) – BiolFlor (Klotz et al. 2002) and LEDA (Kleyer site 9 species matrix. et al. 2008) – with data on seed longevity from Thompson

Journal of Vegetation Science Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science 867 Functional diversity in seed bank and vegetation R.J. Pakeman & A. Eastwood

Table 1. Disturbance parameters and categories.

Disturbance measure Variable type Categories/range Details

Frequency Ordinal 0 Undisturbed 1 Extensive grazing 2 Moderate winter grazing 3 Mowing + moderate winter grazing 4 Year-round intensive grazing 5Arable Biomass loss Continuous 0–100 Percentage of biomass removed each year Below-ground disturbance Ordinal 0 No below-ground disturbance in previous 5 yr 0.5 Ploughing 1–5yrpreviously 1 Ploughing in year of sampling Start of disturbance Continuous 1–53 First disturbance event of year measured from start of growing season. Week 53 equates to undisturbed Vegetation height Continuous Linear Measured height of community (m) Derived from Ku¨ hner & Kleyer (2008).

et al. (1997). Traits were selected as those known to respond (i.e. response traits) to either management or pro- Table 2. Traits used in the analysis, with source and coding information. ductivity within the system studied (Pakeman et al. Traits and attributes Coding Attributes 2011). Certain nominal traits were omitted from the anal- Bud height (life form)* 0 Geophyte, Therophyte yses as they were effectively mirror images of each other, 0.333 Hemicryptophyte e.g. winter-green vs summer-green leaf phenology. Life 0.667 Chamaephyte span, canopy structure and life form were coded to reduce 1 Phanerophyte † the number of attribute columns in the analysis. No cod- Log-canopy height (m) Continuous ingwaspossibleforpollenvectororvegetativespread Canopy Structure* 0 Rosette 0.5 Hemirosette method, so these traits remained as three and two col- 1 Erosulate umns in the analysis, respectively. In total, 19 columns of Flowering – start (month)* 1–12 trait information were used in the full analysis. CWM trait Flowering – end (month)* 1–12 values were calculated by multiplying the matrix of À1 † Leaf dry matter content (mgÁg ) Continuous trait 9 species with the species 9 site abundance matrix. Log-leaf size (mm2)† Continuous Leafing period – summer green* 0 Evergreen 1 Summer green Statistical analysis Life span* 0 Annual 0.5 Biennial The shift in functional trait signature (hypothesis a) was 1 Perennial tested on the community-weighted mean or proportion Pollen vector – self* 0 Not self-pollinated (CWM) of each trait (Garnier et al. 2007) for the seed bank 1 Self-pollinated and the vegetation at each site. Traits were tested individu- – Pollen vector insect* 0 Not insect-pollinated ally with a paired t-test, while redundancy analysis (RDA; 1Insectpollinated Version 4.5.; Microcomputer Power, Ithaca, NY, US) was Pollen vector – wind* 0 Not wind-pollinated 1 Wind-pollinated used to test the trait data together. For the RDA, data were Seed longevity‡ Continuous centred and standardized, the single environmental vari- Log-seed mass (mg)* Continuous able was a dummy variable standing for vegetation vs seed Specific leaf area (mm2ÁmgÀ1)† Continuous bank, and significance was tested against 999 permutations À1 † Terminal velocity (mÁs ) Continuous with data permuted within site. Analysis was carried out ,1 Variance in seed dimensions* Continuous running ‘rda’ in vegan in R (R Foundation for Statistical Vegetative spread – rhizome* 0 Not rhizomatous Computing, Vienna, AT), as was an analysis of dissimilarity 1 Rhizomatous Vegetative spread – stolon* 0 Not stoloniferous within the seed bank and the vegetation (procedure 1 Stoloniferous ‘vegdist’, with the Bray–Curtis method). To test the effect † of environmental drivers on the relative response of the Sources of data: *BiolFlor (Klotz et al. 2002), LEDA (Kleyer et al. 2008) and ‡ Thompson et al. (1997). Traits in italics are those used in the effect traits CWM of traits between the seed bank and the vegetation analysis. (hypothesis b), the data were tested using a linear mixed 1Calculated as the variance of seed length, height and width. model with fixed factors of source (seed bank or

Journal of Vegetation Science 868 Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science R.J. Pakeman & A. Eastwood Functional diversity in seed bank and vegetation vegetation), each ordination axis and their interaction Gotelli & McCabe 2002). This is the (observed functional with source. The random model was specified as site. A sig- diversity À expected functional diversity) divided by the nificant interaction term indicated a difference in behav- SD of expected values from the 999 simulated assem- iour between the seed bank and the vegetation. The blages. Overall differences between the ES and zero were analysis was done using the lme4 library in R, and propor- tested using a sign test, with a value below zero meaning tional data were transformed using a logit transformation functional diversity was lower than expected given ran- (Warton & Hui 2011). As these analyses and those below dom assembly and thus indicated environmental filtering. involved multiple testing, false discovery rates were con- The differences in functional richness and its effect size trolled using the Benjamini & Hochberg (1995) procedure between the seed bank and the vegetation were tested as outlined in Verhoeven et al. (2005). with a paired t-test (hypotheses c1 and c2), as were the dif- Three measures of functional diversity were computed ferences for FDiv and ES FDiv (hypothesis d) and FEve with the trait data: the FRic, FEve and FDiv measures of and ES FEve (hypothesis e). Finally, differences in behav- Ville´ger et al. (2008). FRic, or functional richness, corre- iour of the vegetation and seed bank as environmental sponds to the functional space occupied by the commu- drivers changed across sites (hypothesis f) were tested for nity and is estimated using the FRic index of Cornwell using a linear mixed model, in the same way as for the et al. (2006) based on the convex hull volume; FEve, or CWM analysis, including logit-transformations for data functional evenness, represents the regularity of distribu- constrained between 0 and 1. Again, a significant interaction in abundance in this volume; and FDiv, or functional tion indicated different behaviour between the seed bank divergence, represents the divergence in the distribution and the vegetation. of species traits within the trait volume occupied. They were calculated using the methods and R scripts of Ville´ger Results et al. (2008). As the trait data contained qualitative and Differences in functional traits between seed bank and semi-quantitative traits, an initial matrix of Gower dis- vegetation tances was first computed, which was then subjected to a principal coordinates analysis (Ville´ger et al. 2008; Lali- The RDAs showed clear differences between the CWM berte´ & Legendre 2010) in ade4 (Dray & Dufour 2007). traits in the seed bank and the vegetation (hypothesis a), The first four axes were selected from the principal coordi- as vegetation vs seed bank explained 13.8% of the varia- nates analysis and represented 65.9% of the variance. It tion for the trait data (P < 0.001). The paired t-tests con- should be noted that this approach cannot capture all the firmed this analysis, with the seed bank having higher variation in the initial trait data and significant inter-trait proportions of insect- and self-pollinated species, as well as correlations can be lost. The computed values of func- higher CWM values for seed longevity, seed mass and ter- tional diversity were compared with values from 999 sim- minal velocity (Table 3). Conversely, the vegetation had ulated assemblages in order to show how patterns of higher CWM for canopy height, leaf dry matter content community assembly may differ between the vegetation and leaf size, and was dominated by wind-pollinated and the seed bank, and to assess how these patterns might perennials. There was no significant difference in CWM for be affected by the environment. These simulated assem- bud height, canopy structure, start and end of flowering, blages were constructed using all the species recorded leafing period, specific leaf area, variance in seed dimen- across the sites from both the seed bank and the vegeta- sions or vegetative spread. tion, and species were randomly drawn from this list without replacement and allocated an abundance from the list Response of functional traits to the environment of abundances of the species at that site by matching the order of draw with the rank order of abundance. As the Of the 76 linear mixed models fitted (Table 4), 23 (30.3%) sites are within the same small geographic area, and dis- showed significant effects of the environmental driver and persal limitation is not likely to constrain species occur- 17 (22.3%) showed significant interaction effects. CWM of rences, then this method of null model generation is bud height, LDMC, life span and pollen vector – wind – appropriate (Mason et al. 2013). This method of simulated were all negatively correlated with Disturbance 1, while assemblage construction maintained the species richness flowering – end –, leafing period – summer –,pollenvector and pattern of abundance within each site: a matrix swap and selfing, SLA and terminal velocity were positively cor- randomization (Manly 1995). A previous paper demon- related with Disturbance 1. No trait was correlated with strated that weighting the null model by the frequency of Disturbance 2. occurrence of each species (Kraft et al. 2008) had little Low leaf dry matter content, leaf size, life span, pollen effect (Pakeman 2011a). The deviation from expected vector – wind – and vegetative spread – rhizome – were was measured using the standardized effect size (ES; positively correlated with Productivity 1, while flowering

Journal of Vegetation Science Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science 869 Functional diversity in seed bank and vegetation R.J. Pakeman & A. Eastwood

Table 3. Means of the community-weighted mean (or proportion) traits For the significant interactions, there was a relatively and the result of a paired t-test between the seed bank and the vegetation even split between those where there was a convergence in = (n 30), with correction for multiple testing by the Benjamini & Hochberg means at high productivity or disturbance levels (Table 4), (1995) method. and those where the convergence was at low levels (eight Community weighted mean vs six, respectively, or 10.5% and 7.9%, hypothesis b). Bud height and vegetative spread – rhizome – converged at high Seed bank Vegetation P levels of Disturbance 1, while canopy height, leaf size and Bud height 0.286 0.365 n.s. life span converged at high Disturbance 2. Similarly, leaf Log-canopy height (m) À0.738 À0.367 <0.001 size converged at high levels of Productivity 1 and canopy Canopy structure 0.684 0.663 n.s. – – Flowering – start (month) 5.650 5.755 n.s. height and vegetative spread rhizome at high levels of Flowering – end (month) 8.476 7.980 n.s. Productivity 2. Conversely, leaf dry matter content and Leaf dry matter content (mg gÀ1) 211.3 255.4 0.001 pollen vector – insect and wind – converged at low Log-leaf size (mm2) 2.293 2.714 0.011 Disturbance 2, bud height and terminal velocity at low Pro- Leafing period – summer green 0.404 0.436 n.s. ductivity 1 and life span at low Productivity 2. Vegetative < Life span 0.733 0.935 0.001 spread – stolon – had three significant interactions, with the Pollen vector – insect 0.368 0.204 <0.001 response of the seed bank and the vegetation showing a Pollen vector – selfing 0.323 0.224 0.006 Pollen vector – wind 0.309 0.572 <0.001 crossover at mid-range on the axes for all three. Seed longevity 0.598 0.513 <0.001 Log-seed mass (mg) À0.043 À0.571 <0.001 À Differences in functional diversity between seed bank Specific leaf area (mm2Ámg 1) 21.71 19.88 n.s. Terminal velocity (mÁsÀ1) 2.746 1.988 <0.001 and vegetation Variance in seed dimensions 1.040 1.240 n.s. Species richness was similar between the seed bank and Vegetative spread – rhizome 0.262 0.342 n.s. the vegetation, and analysis of the response trait data Vegetative spread – stolon 0.464 0.447 n.s. showed a similar functional richness in the vegetation as Traits in italics are those used in the effect traits analysis. the seed bank (Table 5; hypotheses c1 and c2). Ten sites showed a FRic score for the vegetation in the lowest 2.5% of the simulations (significantly less than expected given – start and end – and pollen vector – selfing – were random community assembly), compared to two for the negatively correlated with this axis. Leaf size (as for seed bank (all two in common). This is reflected in the Productivity 1), pollen vector – selfing –, seed mass, SLA lower ES FRic score, which is significantly lower than that and terminal velocity were positively correlated with Pro- for the seed bank and significantly lower than zero (indi- ductivity 2, while bud height and LDMC were negatively cating functional convergence or the effects of environ- correlated with it. mental filtering). Of the 17 significant interactions, seven had seed bank Neither functional divergence nor ES FDiv was different and vegetation differing by the degree of response to the between the seed bank and the vegetation (hypothesis d, environmental factor, while ten differed in the direction of Table 5). Three sites from the data set had FDiv scores in response. Where the degree of response differed, the the lowest 2.5% of simulations for both the seed bank and response of CWM for the seed bank was steeper in fewer the vegetation (all different), while one site in the vegeta- cases (two out of seven). These were the response of life tion data had an FDiv score in the highest 2.5%. Despite span to Disturbance 2 and Productivity 2. A steeper this, the mean ES FDiv score across all sites and all traits response in CWM of the vegetation was found for the was significantly less than zero for the seed bank and the response of bud height to Disturbance 1 and Productivity vegetation. 1, terminal velocity to Productivity 1 and vegetative spread Functional evenness was not significantly different – rhizome – to Disturbance 1 and Productivity 2. Increasing between the seed bank and the vegetation for the response CWM in the vegetation contrasted with a decreasing traits (hypothesis e; Table 5), and they had one and two CWM in the seed bank for the response of leaf size and pol- sites in the lowest 2.5% of simulations, respectively (one len vector – insect – to Disturbance 2. In contrast, increased shared). ES of FEve was not significantly different between CWM of the seed bank coincided with a decreased CWM the seed bank and the vegetation. of the vegetation for the response of vegetative spread – stolon – to Disturbance 1, canopy height, LDMC and pollen Response of functional diversity to the environment vector – wind – to Disturbance 2, log-leaf size and vegetative spread – stolon – to Productivity 1 and canopy struc- Functional richness for the trait data was insensitive to ture and vegetative spread – stolon – to Productivity 2. the environment (Table 6). However, ES FRic of the

Journal of Vegetation Science 870 Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science R.J. Pakeman & A. Eastwood Functional diversity in seed bank and vegetation

Table 4. Relationship between the community-weighted mean traits for each site and the four environmental drivers from the linear mixed models.

Trait Factors Disturbance 1 Disturbance 2 Productivity 1 Productivity 2

Bud height V vs S n.s. n.s. n.s. 0.005 D 0.013 n.s. n.s. 0.037 À I 0.009e+ n.s. 0.014a n.s. Log-canopy height VvsS <0.001 <0.001 <0.001 <0.001 D n.s. n.s. n.s. n.s. I n.s. 0.030d+ n.s. n.s. Canopy structure V vs S n.s. n.s. n.s. D n.s. n.s. 0.019 I n.s. n.s. 0.004d+ Flowering – start V vs S n.s. D 0.005 I n.s. Flowering – end V vs S 0.011 0.009 0.010 0.009 D <0.001 n.s. <0.001 n.s. I n.s. n.s. n.s. n.s. Leaf dry matter content VvsS <0.001 <0.001 <0.001 <0.001 D <0.001 n.s. 0.017 0.003 I n.s. <0.004dÀ n.s. n.s. Log-leaf size V vs S 0.002 0.001 0.001 0.003 D n.s. n.s. 0.023 0.018 I n.s. 0.011c+ 0.016d+ n.s. Leafing period – summer V vs S n.s. D 0.006 I n.s. Life span V vs S <0.001 <0.001 <0.001 <0.001 D <0.001 n.s. <0.001 n.s. I n.s. 0.022f+ n.s. 0.016fÀ Pollen vector – insect V vs S <0.001 <0.001 <0.001 <0.001 D n.s. n.s. n.s. n.s. À I n.s. 0.011c n.s. n.s. Pollen vector – selfing V vs S 0.001 <0.001 0.001 0.001 D 0.001 n.s. 0.011 n.s. I n.s. n.s. n.s. n.s. Pollen vector – wind V vs S <0.001 <0.001 <0.001 0.001 D 0.006 n.s. 0.026 n.s. À I n.s. 0.009d n.s. n.s. Seed longevity V vs S 0.002 0.001 0.001 0.002 D n.s. n.s. n.s. n.s. I n.s. n.s. n.s. n.s. Log-seed mass VvsS <0.001 <0.001 <0.001 <0.001 D n.s. n.s. n.s. 0.013 I n.s. n.s. n.s. n.s. Specific leaf area V vs S 0.015 0.022 0.023 0.017 D 0.004 n.s. n.s. 0.017 I n.s. n.s. n.s. n.s. Terminal velocity VvsS <0.001 <0.001 <0.001 <0.001 D 0.006 n.s. n.s. 0.007 À In.s.n.s.0.025e n.s. Vegetative spread – rhizome V vs S n.s. n.s. D n.s. n.s. I 0.004e+ 0.001e+ Vegetative spread – stolon V vs S n.s. n.s. n.s. D n.s. n.s. n.s. I <0.001d0 <0.001d0 0.012d0 Traits in italics are those used in the effect traits analysis. The fixed factors in the analysis are indicated by D, driver; S, seed bank; V, vegetation; I, interaction. Where a significant interaction is present the relationships are indicated in the following way: (a) both +ve, slope of vegetation > seed bank; (b) both +ve, slope of seed bank > vegetation; (c) vegetation +ve, seed bank Àve; (d) vegetation Àve, seed bank +ve; (e) both Àve, slope of vegetation > seed bank; (f) both Àve, slope of seed bank > vegetation. Furthermore, convergence of trait values for seed bank and vegetation at high levels of each environmental driver are indicated by ‘+’, divergence by ‘À’ and a crossover in the range investigated by a ‘0’. Variance in seed dimensions had no significant relationships with any driver.

Journal of Vegetation Science Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science 871 Functional diversity in seed bank and vegetation R.J. Pakeman & A. Eastwood

Table 5. Mean values for species richness, functional diversity indices Functional evenness of the response traits was only (FRic Functional Richness, FDiv Functional Divergence and FEve Functional directly affected by disturbance; FEve of the vegetation Evenness) and their ES with the results of paired t-tests for both the increased with Disturbance 1 but that of the seed bank response and effect traits. declined (Fig. 1g). ES of FEve was not significantly affected Response traits by the environment. Overall, there was no indication that there was a con- SB Veg P vergence in functional diversity at high levels of distur- Number of species 24.73 25.40 n.s. bance or productivity. Of the seven significant FRic 13.43 11.99 n.s. interactions, all showed significant differences in behav- FDiv 0.805 0.807 n.s. FEve 0.429 0.448 n.s. iour between the seed bank and the vegetation (Fig. 1), † † ES FRic À0.811 À1.389 0.019 indicating that hypothesis f was not supported, although ES FDiv À0.519 À0.545 n.s. there was some indication that ES FRic had similar values ES FEve À0.103 À0.267 n.s. at high productivity and disturbance in the vegetation and †Significantly different from zero in a sign test. the seed bank.

Discussion Table 6. The relationship between the functional diversity indices and the four environmental drivers from the linear mixed models. Differences in functional traits between seed bank and vegetation Disturbance 1 Disturbance 2 Productivity 1 Productivity 2

ES FRic Multivariate analysis of the community-weighted means Veg vs SB 0.011 0.018 0.020 0.012 of traits showed there was a significant overall shift Driver n.s. n.s. n.s. n.s. between the vegetation and the seed bank in terms of the Interaction 0.016a n.s. n.s. 0.019c traits displayed (supporting hypothesis a). As might be FDiv expected, the seed bank was characterized by species with Veg vs SB n.s. n.s. n.s. longer seed longevity, shorter life span, lower heights and Driver <0.001 0.006 0.003 smaller leaves with less investment (Table 3; Thompson Interaction <0.001d n.s. 0.001d ES FDiv 1992). However, it was also characterized by having a Veg vs SB n.s. n.s. n.s. lower representation of wind-pollinated species, species Driver <0.001 0.002 0.005 with higher terminal velocity (less likely to be wind dis- Interaction <0.001d n.s. 0.002d persed) and species with a higher seed mass. This latter FEve result is against expectations (Thompson et al. 1993; Veg vs SB n.s. Hodkinson et al. 1998), but it should be remembered that Driver n.s. the weighting in the data set used was corrected so that it Interaction 0.005c represents seed mass within the soil. These data suggest Where a significant interaction is present the relationships are indicated in that, when expressed on a mass basis, there is no predomi- the following way: (a) both +ve, slope of vegetation > seed bank; (b) both +ve, slope of seed bank > vegetation; (c) vegetation +ve, seed bank Àve; nance of small seeds in the seed bank as there is on a (d) vegetation Àve, seed bank +ve; (e) both Àve, slope of vegeta- numeric basis (cf. Moles et al. 2004; Bruun & Poschlod tion > seed bank; (f) both Àve, slope of seed bank > vegetation. FRic and 2006). In fact, the pattern is reversed across the habitats FS Eve had no significant relationships with any driver. studied here. Overall, the greater dominance of ruderal species (sensu Grime 1979) in the seed bank was expected, as these traits vegetation increased as Disturbance 1 increased, whereas are necessary to quickly compete for available resources that of the seed bank was relatively insensitive (Fig. 1a). A and space against other species. However, a number of different pattern was seen for the response to Productivity traits that might be considered as unrelated to persistence 2 (Fig. 1b); ES FRic of the vegetation increased with pro- in the seed bank or presence only in the vegetation also ductivity by that of the seed bank fell. showed differences. This may be a case of phylogenetically Relationships between the functional divergence of the controlled trait linkages – e.g. the lower representation of response traits and the environment differed between the wind-pollinated species in the seed bank is substantially a seed bank and the vegetation. Significant interactions result of the lower proportion of grasses in the seed bank indicated that it increased in the seed bank and declined (mean across sites of 9.2%) compared to the vegetation in the vegetation as Disturbance 1 and Productivity 2 (41.2%). Shifts in key effect traits, such as canopy height, increased (Fig. 1c,d). ES FDiv showed the same pattern leaf dry matter content and leaf size (Pakeman 2011b), all (Fig. 1e,f). suggest that there would be a shift in ecosystem function

Journal of Vegetation Science 872 Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science R.J. Pakeman & A. Eastwood Functional diversity in seed bank and vegetation

2 (a)2 (b)

1 1

0 0

–1 –1 ES FRic ES FRic –2 –2

–3 –3

–4 –4 –0.4 –0.2 0.0 0.2 0.4 0.6 0.8 –0.4 –0.2 0.0 0.2 0.4 Disturbance 1 Productivity 2

1.0 (c)1.0 (d)

0.9 0.9

0.8 0.8 FDiv FDiv 0.7 0.7

0.6 0.6

0.5 0.5 –0.4 –0.2 0.0 0.2 0.4 0.6 0.8 –0.4 –0.2 0.0 0.2 0.4 Disturbance 1 Productivity 2

2 (e)2 (f)

1 1

0 0

–1 –1 ES FDiv ES FDiv –2 –2

–3 –3

–4 –4 –0.4 –0.2 0.0 0.2 0.4 0.6 0.8 –0.4 –0.2 0.0 0.2 0.4 Disturbance 1 Productivity 2

0.7 (g)

0.6

0.5

0.4

FEve 0.3

0.2

0.1

0.0 –0.4 –0.2 0.0 0.2 0.4 0.6 0.8 Disturbance 1

Fig. 1. Significant relationships (Table 6) between functional diversity indices and environmental drivers. For the response traits Disturbance 1 and (a)ES FRic, (c) FDiv, (e) ES FDiv and (g) FEve, and Productivity 2 and (b) ES FRic, (d)FDiv,(f) ES FDiv. Seed bank data are shown as (●), with the fitted linear regression as (─), and vegetation data as (Δ), with the fitted regression as (∙∙∙∙∙).

Journal of Vegetation Science Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science 873 Functional diversity in seed bank and vegetation R.J. Pakeman & A. Eastwood following disturbance across this system towards a situa- Differences in functional diversity between seed bank tion where nutrient turnover and productivity would be and vegetation higher and where carbon would be less likely to be locked into the system (De Deyn et al. 2008). All the raw functional diversity metrics did not differ between the seed bank and the vegetation (Table 5), suggesting similar levels of functional richness, divergence Response of functional traits to the environment and evenness between the vegetation and the seed bank. Also as expected, many traits showed a response to the envi- However, ES FRic was negative, significantly less than zero ronment (46.2%). Productivity favoured faster-growing and significantly lower for the vegetation. As ES FRic is a species with bigger leaves and seeds and leaves held higher good measure to test questions relating to habitat filtering in the canopy (Pakeman 2011a,b). Disturbance was corre- (Mouchet et al. 2010), it indicates that habitat filtering is lated with shorter life spans and shorter-lived leaves and a acting on both the seed bank and the vegetation, but that greater reliance on selfing and insect pollination. it is stronger across all traits in the vegetation than the seed However, there was also significantly different behav- bank. This offers some support to hypothesis c2, that the iour between the seed bank and the vegetation in a fifth vegetation has lower richness compared to the seed bank. (22.3%) of the trait:environment relationships tested There was no other evidence to suggest that habitat filter- (hypothesis b). Of these, two types of relationship pre- ing was stronger in the vegetation than the seed bank as dominated: the seed bank responds in the same way as FDiv, ES FDiv, FEve and ES FEve were not significantly the vegetation but is less sensitive (6.6% of all cases), and different and were not different from zero. Thus there was the seed bank and the vegetation respond in opposite no support for hypothesis d or e. ways (13.2%). Overall, it appears that the response of the Compared across all the sites, it appears that there is trait signatures in the vegetation and the seed bank are some evidence to suggest that habitat filtering is stronger in largely parallel despite differences in mean levels; hypoth- the vegetation than in the seed bank. This indicates that esis b is only partially supported. However, the exceptions any impact of disturbance on ecosystem function by shift- are too common to be dismissed. These exceptions are ing plant traits is more likely to act through the mean trait related to vegetative spread – rhizome and stolon (five values rather than through variance in traits present. Also, cases), growth strategy (bud height two cases, canopy it may be that different processes with opposite effects (forc- height and structure one), leaf investment (LDMC one ing convergence or divergence) on functional diversity and case, leaf size two cases) and life span (two cases.) There is habitat filtering act at the same time and that the balance no strong evidence that disturbance (ten significant inter- between them may be different in different situations. actions) is more important in these opposing patterns than productivity (seven significant interactions), but the num- Response of functional diversity to the environment ber of these interactions does suggest that there are different pressures on the vegetation and seed bank. For The FRic was relatively insensitive to the environment, instance, as disturbance increases, canopy height but the degree of habitat filtering or trait convergence was decreases in the vegetation, but it slowly increases in the sensitive and differed between the vegetation and the seed seed bank, indicating that the species present in the seed bank (significant interaction terms for ES FRic). The trait bank are more similar in height across the disturbance convergence was more apparent for the vegetation at low gradient than the species in the vegetation for this trait levels of disturbance (Disturbance 1) and low levels of pro- (although not overall in terms of FRic). This is a common ductivity (Productivity 2), while it was more apparent for pattern in the responses, suggesting that the seed bank is the seed bank at high levels of disturbance and productiv- less responsive to the environment than the vegetation. ity. This pattern for the vegetation opposes that seen for a This implies that the b-diversity of traits of the seed bank much reduced trait data set from the same sites (Pakeman is lower than that of the vegetation, even though it is clear 2011a), suggesting that these indices are sensitive to the that the a-diversity of the vegetation and the seed bank traits chosen). The pattern for FDiv and ESFDiv differed are similar within this system (Table 5). The difference in somewhat, with evidence for greater convergence/higher b-diversity was confirmed by comparing the mean Bray– habitat filtering for the vegetation at high levels of Curtis dissimilarity within the seed bank and within the productivity (Productivity 2) and disturbance (Disturbance vegetation: only 0.738 for the seed bank compared to 1) – i.e. a tendency for the abundant species to be more 0.826 for the vegetation. This then indicates that after dissimilar in traits than expected. However, while the vegeta- turbance there would be a convergence between the sites, tion was more functionally even at high levels of distur- as well as a shift towards more fast-growing, shorter-lived bance (Pakeman 2011a), the seed bank was less so. species. Overall, there is no real evidence to support hypothesis f,

Journal of Vegetation Science 874 Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science R.J. Pakeman & A. Eastwood Functional diversity in seed bank and vegetation as all the significant interactions indicated differences in Bruun, H.H. & Poschlod, P. 2006. Why are small seeds dispersed behaviour rather than any convergence in behaviour. The through animal guts: large numbers or seed size per se? Oikos difference in patterns between ES FRic/FEve and FDiv/ 113: 402–411. ESFDiv suggests that the metrics are capturing different Bullock, J.M., Clear Hill, B., Silvertown, J. & Sutton, M. 1995. aspects of habitat filtering. ES FRic measures the volume of Gap colonization as a source of grassland community trait space occupied by all species, and this indicates that change: effects of gap size and grazing on the rate and mode – the presence of species away from the centre of the volume of colonization by different species. Oikos 72: 273 282. is more likely in vegetation at high levels of disturbance Champness, S.S. & Morris, K. 1948. The population of buried and productivity. Similarly, species in the vegetation are viable seeds in relation to contrasting pasture and soil types. Journal of Ecology 36: 149–173. more evenly spread across trait space at high levels of dis- Chippindale, H.G. & Milton, W.E.J. 1934. On the viable seeds turbance. However, the data for FDiv and ES FDiv suggest present in the soil beneath pastures. Journal of Ecology 22: that the more abundant species are closer together in trait 508–531. space as disturbance and productivity rise. Cornwell, W.K., Schwilk, D.W. & Ackerly, D.D. 2006. A trait- based test for habitat filtering: convex hull volume. Ecology Conclusions 87: 1465–1471. De Deyn, G.B., Cornelissen, J.H.C. & Bardgett, R.D. 2008. Plant There were clear differences between the mean trait functional traits and soil carbon sequestration in contrasting values and frequency of attributes between the seed bank biomes. Ecology Letters 11: 516–531. and the vegetation. As expected, the seed bank was Dray, S. & Dufour, A.B. 2007. The ade4 package: implementing characterized by lower-growing, less conservative, the duality diagram for ecologists. Journal of Statistical Soft- shorter-lived plants than the vegetation, and this would ware 22: 1–20. clearly have an impact on ecosystem function after distur- Garnier, E., Lavorel, S., Ansquer, P., Castro, H., Cruz, P., Dolezˇal, bance. There was, however, little difference in functional J., Eriksson, O., Fortunel, C., Freitas, H., Golodets, C., Grigu- diversity between the seed bank and vegetation, and the lis, K., Jouany, C., Kazakou, E., Kigel, J., Kleyer, M., Leh- different functional diversity metrics differed in their rela- sten, V., Lepsˇ, J., Meier, T., Pakeman, R., Papadimitriou, M., tionships for disturbance and productivity, with FDiv/ES Papanastasis, V.P., Quested, H., Que´tier, F., Robson, M., FDiv indicating evidence for greater convergence in the Roumet, C., Rusch, G., Skarpe, C., Sternberg, M., Theau, J.- vegetation, especially at high levels of disturbance and P., The´bault, A., Vile, D. & Zarovali, M.P. 2007. A standard- productivity, while data on ES FRic and FEve indicated ized methodology to assess the effects of land use change on the reverse. This implies that if regeneration comes plant traits, communities and ecosystem functioning in – mainly from the seed bank, then following disturbance in grasslands. Annals of Botany 99: 967 985. sites of high productivity or disturbance the vegetation is Gotelli, N.J. & McCabe, D.J. 2002. Species co-occurrence: a meta-analysis of J.M. Diamond’s assembly rules. Ecology 83: liable to develop from a narrower suite of species (in 2091–2096. functional terms), but that they are less likely to be Grime, J.P. 1979. Plant strategies and vegetation processes. Wiley, functionally similar. This could then have knock-on Chichester, UK. effects on ecosystem function if this depends on func- Hodkinson, D.J., Askew, A.P., Thompson, K., Hodgson, J.G., tional diversity. Bakker, J.P. & Bekker, R.M. 1998. Ecological correlates of seed size in the British Flora. Functional Ecology 12: 762–766. Acknowledgements Johnson, D.W., Verberg, P.S.J. & Arnone, J.A. 2005. Soil extraction, ion exchange resin, and ion exchange membrane mea- The authors thank Iain Turnbull of the National Trust for sures of soil mineral nitrogen during incubation of a tallgrass Scotland for all help in arranging access, and the many prarie soil. Soil Science Society of America Journal 69: 260–265. crofters and grazing clerks of Balmacara, Drumbuie, Duiri- Kleyer, M., Bekker, R.M., Knevel, I.C., Bakker, J.P., Thompson, nish and Plockton for help with our work. Rob Brooker, K., Sonnenchein, M., Poschlod, P., van Groenendael, J.M., Jenni Stockan, Roger Cummins and Jelle van Rijmenant Klimesˇ, L., Klimesˇova´, J., Klotz, S., Rusch, G.M., Hermy, M., helped with the fieldwork. Adriaens, D., Boedeltje, G., Bossuyt, B., Dannemann, A., En- dels, P., Go¨tzenberger, L., Hodgson, J.G., Jackel, A.-K., Ku¨ hn, I., Kunzmann, D., Ozinga, W.A., Ro¨mermann, C., References Stadler, M., Schlegelmilch, J., Steendam, H.J., Tackenberg, Benjamini, Y. & Hochberg, Y. 1995. 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Journal of Vegetation Science Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science 875 Functional diversity in seed bank and vegetation R.J. Pakeman & A. Eastwood

Klimkowska, A., Bekker, R.M., van Diggelen, R. & Kotowski, W. Pakeman, R.J. & Small, J.L. 2005. The role of the seedbank, seed 2010. Species trait shifts in vegetation and soil seed bank rain and the timing of disturbance in gap regeneration. Jour- during fen degradation. Plant Ecology 206: 59–82. nal of Vegetation Science 16: 121–130. Klotz, S., Ku¨ hn, I. & Durka, W. 2002. BIOLFLOR – Eine Datenbank Pakeman, R.J., Attwood, J.P. & Engelen, J. 1998. Sources of mit biologisch-o¨kologischen Merkmalen zur Flora von Deutschland. plants colonizing experimentally disturbed patches in an Bundesamt fu¨ r Naturschutz, Bonn, Bad Godesberg. acidic grassland. Journal of Ecology 86: 1032–1041. Kraft, N.J.B., Valencia, R. & Ackerly, D.D. 2008. Functional traits Pakeman, R.J., Lennon, J.J. & Brooker, R.W. 2011. Trait assem- and niche-based tree community assembly in an Amazonian bly in plant assemblages and its modulation by productivity Forest. Science 322: 580–582. and disturbance. Oecologia 167: 209–218. Ku¨ hner, A. & Kleyer, M. 2008. A parsimonious combination of Stace, C. & Thompson, H. 2010. New Flora of the British Isles.3rd functional traits predicting plant responses to disturbance ed. Cambridge University Press, Cambridge, UK. and soil fertility. Journal of Vegetation Science 19: 681–692. Thompson, K. 1992. The functional ecology of seed banks. In: Laliberte´, E. & Legendre, P. 2010. A distance-based frame- Fenner, M. (ed.) Seeds: the ecology of regeneration in plant com- work for measuring functional diversity from multiple traits. munities, pp. 231–258. CABI, Wallingford, UK. Ecology 91: 299–305. Thompson, K., Band, S.R. & Hodgson, J.G. 1993. Seed size and Manly, B.J.F. 1995. A note on the analysis of species co-occur- shape predictpersistence in soil.Functional Ecology 7: 236–241. rences. Ecology 76: 1109–1115. Thompson, K., Bakker, J.P. & Bekker, R.M. 1997. The soil seed Marks, P.L. 1974. The role of pin cherry (Prunus pensylvanica L.) banks of north west Europe: methodology, density and longevity. in the maintenance of stability in northern hardwood eco- Cambridge University Press, Cambridge, UK. systems. Ecological Monographs 44: 73–88. Verhoeven, K.J.F., Simonsen, K.L. & McIntyre, L.M. 2005. Mason, N.W.H., de Bello, F., Mouillot, D., Pavoine, S. & Dray, S. Implementing false discovery rate control: increasing your 2013. A guide for using functional diversity indices to reveal power. Oikos 108: 643–647. changes in assembly processes along ecological gradients. Ville´ger, S., Mason, N.W.H. & Mouillot, D. 2008. New multidi- Journal of Vegetation Science 24: 781–793. mensional functional diversity indices for a multifaceted Moles, A.T., Falster, D.S., Leishman, M.R. & Westoby, M. 2004. framework in functional ecology. Ecology 89: 2290–2301. Small-seeded species produce more seeds per square metre Warton, D.I. & Hui, F.K.C. 2011. The arcsine is asinine: the anal- of canopy per year, but not per individual per lifetime. Jour- ysis of proportions in ecology. Ecology 92: 3–10. nal of Ecology 92: 384–396. Mouchet, M.A., Ville´ger, S., Mason, N.W.H. & Mouillot, D. Supporting Information 2010. Functional diversity measures: an overview of their redundancy and their ability to discriminate community Additional supporting information may be found in the assembly rules. Functional Ecology 24: 867–876. online version of this article: Pakeman, R.J. 2011a. Functional diversity indices reveal the impacts of land use intensiﬁcation on plant community Appendix S1. Details of site locations and brief – assembly. Journal of Ecology 99: 1143 1151. management details. Pakeman, R.J. 2011b. Multivariate identiﬁcation of plant functional response and effect traits in an agricultural landscape. Please note: Wiley-Blackwell are not responsible for Ecology 92: 1353–1365. the content or functionality of any supporting materials Pakeman, R.J. & Marshall, A.G. 1997. The seedbanks of the supplied by the authors. Any queries (other than missing Breckland heaths and heath grasslands, and their relation- material) should be directed to the corresponding author ship to the vegetation and the effects of management. Jour- for the article. nal of Biogeography 24: 375–390.

Journal of Vegetation Science 876 Doi: 10.1111/j.1654-1103.2012.01484.x © 2012 International Association for Vegetation Science Journal of Vegetation Science 24 (2013) 877–889 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Partitioning phylogenetic and functional diversity into alpha and beta components along an environmental gradient in a Mediterranean rangeland Maud Bernard-Verdier, Olivier Flores, Marie-Laure Navas & Eric Garnier

Keywords Abstract Beta dissimilarity; Calcareous rangelands; Community assembly; Environmental gradient; Questions: To what extent is the functional structure of plant communities Functional traits; Phylogenetic community captured by phylogenetic structure? Are some functional dimensions better rep- structure; Trait phylogenetic signal resented by phylogenetic relationships? In an empirical study, we propose to test the congruence between phylogenetic and functional structure at the alpha and Nomenclature the beta scale along an environmental gradient. Bernard (1996) Location: Causse du Larzac, southern France. Received 1 April 2012 Accepted 12 December 2012 Methods: We measured species abundances and eight key functional traits in Co-ordinating Editor: Francesco de Bello 12 plant communities distributed along a gradient of soil depth and resource availability in a Mediterranean rangeland. A phylogenetic super-tree of the species was assembled, and after quantifying the degree of phylogenetic signal pres- Bernard-Verdier, M. (corresponding author, ent in each trait, we quantified taxonomic (TD), phylogenetic (PD) and [email protected]): Universite´ functional (FD) diversity both within (alpha scale) and among (beta scale) com- Montpellier 2, Centre d’Ecologie Fonctionnelle munities, taking species abundances into account. We tested for trends in diver- et Evolutive (UMR 5175), 1919 route de Mende, sity along the environmental gradient, and looked for congruence among 34293 Montpellier, France different facets of diversity, both at the alpha and the beta scale. Garnier, E. ([email protected]): CNRS, Centre d’Ecologie Fonctionnelle et Evolutive Results: We found a significant phylogenetic signal for seven out of eight traits. (UMR 5175), 1919 route de Mende, 34293 However, when accounting for trends in taxonomic diversity (i.e. richness and Montpellier, France evenness), PD did not capture the strong functional structure observed within Flores, O. (olivier.fl[email protected]): UMR Peuplements Veg etaux et Bioagresseurs en and among the communities. At the alpha scale, we found an overall pattern of Milieu Tropical, UniversitedelaR eunion, phylogenetic convergence of abundant species, which did not reflect the 97715, St Denis Messageries, France observed functional divergence. At the beta scale, despite some congruence Navas, M.-L. ([email protected]): between betaPD and betaFD for three individual traits, phylogenetic dissimilari- Montpellier SupAgro, Centre d’Ecologie ties did not capture the overall environmental and functional sorting of species Fonctionnelle et Evolutive (UMR 5175), 1919 according to habitats. route de Mende, 34293 Montpellier, France Conclusions: We show that even when traits display a significant phylogenetic signal, PD does not capture the complex functional structure of communities in response to environmental gradients. Nevertheless, results suggest that phylogenetic relationships may partially capture differences in the beta niche of species and provide additional insights on assembly processes not captured by the set of measured functional traits. Only by accounting for patterns in taxonomic diversity were we able to disentangle the functional and evolutionary determinants of species assembly along the gradient.

netic structure, deﬁned as the pattern of phylogenetic Introduction relatedness among species in communities (Cavender- Recent years have seen a growing interest in the use of Bares et al. 2009), has been used as a tool to understand phylogenetic information in community ecology (Webb community assembly (Webb 2000; Mayﬁeld & Levine et al. 2002; Mouquet et al. 2012). Community phyloge- 2010), as well as to predict ecosystem functioning (Cadotte

Journal of Vegetation Science Doi: 10.1111/jvs.12048 © 2013 International Association for Vegetation Science 877 Partitioning phylogenetic and functional diversity M. Bernard-Verdier et al. et al. 2009; Flynn et al. 2011). These approaches originally studies have investigated PD within communities, where stem from the concept of ecological niche conservatism processes of species co-existence, such as limiting similar- (reviewed in Wiens et al. 2010), a hypothesis stating that ity, are expected to shape local alpha diversity (Webb closely related species should share more ecological and 2000; Cavender-Bares et al. 2009). However, recent stud- functional similarities than distantly related ones. Based ies suggest that phylogenetic diversity may better capture on this assumption, a number of studies have interpreted assembly processes at the beta scale than at the alpha scale phylogenetic diversity (PD) as a surrogate for functional (Emerson & Gillespie 2008; Graham & Fine 2008; De Vic- diversity (FD) in communities (Webb 2000; Cadotte et al. tor et al. 2010). According to one line of reasoning, traits 2009; Violle et al. 2011). However, some authors have related to habitat preferences (i.e. beta niche traits) may be questioned this assumption (Cavender-Bares et al. 2004; more phylogenetically conserved within lineages than Silvertown et al. 2006; Losos 2008), and a consensus now determinants of local co-existence within communities seems to emerge from the literature stating that PD cannot (i.e. alpha niche traits), due to processes of sympatric niche in fact be considered as a simple proxy for FD (Mouquet differentiation among close relatives (Silvertown et al. et al. 2012). Studies comparing FD and PD tend to show 2001; Prinzing et al. 2008). Thus, phylogenetic differences an overall lack of consistency between phylogenetic and among communities, i.e. phylogenetic beta diversity, may functional patterns (Losos 2008; Swenson & Enquist 2009; reveal niche-based processes such as environmental but see Baraloto et al. 2012). Even in cases where clear sorting of species across habitats (Graham & Fine 2008; patterns of phylogenetic clustering or over-dispersion are Cavender-Bares et al. 2009). Although this hypothesis has detected, interpretations of assembly mechanisms are not found unequal support in the literature (Emerson & straightforward because different assembly processes may Gillespie 2008), quantifying phylogenetic beta diversity, create the same phylogenetic patterns (Losos 2008; May- and comparing it to the turnover in species and functional field & Levine 2010). traits along gradients, is a promising way to understand If PD cannot be interpreted as a simple proxy for FD, how ecological and evolutionary processes shape biodiver- then what do the non-random patterns of phylogenetic sity across landscapes and environmental gradients structure, consistently reported in very different systems (Graham & Fine 2008; Vamosi et al. 2009; Pavoine & (reviewed in Pavoine & Bonsall 2011), capture exactly? Bonsall 2011). One hypothesis is that PD may be related only to a few In this study, we investigate the functional information phylogenetically conserved traits. In this case, PD should captured by phylogenetic relationships both within and capture only assembly processes acting upon these traits among communities in a Mediterranean rangeland of (e.g. Cavender-Bares et al. 2004). However, if different southern France. To do this, we quantified and compared phylogenetically conserved traits display opposite patterns taxonomic, phylogenetic and functional diversity along a of convergence or divergence within a community, as seen gradient of soil depth and resource availability, along in recent trait-based studies (e.g. Cornwell & Ackerly which plant communities have been found to exhibit 2009; Spasojevic & Suding 2012; Bernard-Verdier et al. strong taxonomic and functional structures in previous 2012), then PD cannot be expected to capture such a mul- studies (Bernard-Verdier et al. 2012; Perez-Ramos et al. tiplicity of information. Another hypothesis is that PD may 2012). Using data for eight key functional plant traits, phy- be related to a complex combination of multiple ecological logenetic information from the latest published phyloge- traits capturing whole plant strategies, rather than individ- nies, and local species abundances, we aimed to answer ual traits (Flynn et al. 2011). In this case, one would the following questions: (i) can we detect phylogenetic expect PD to be more closely correlated to a multivariate structure, both within and among plant communities, in diversity score based on relevant traits, than to individual response to the environmental gradient; (ii) do phyloge- traits. A third hypothesis is that PD may in some cases cap- netic and functional diversities show congruent distribu- ture some ‘hidden’ aspects of plant functioning that are tions at the alpha and/or the beta scale; and (iii) is the not captured by any of the functional traits commonly existence of phylogenetic signal in traits a good indicator of measured in community studies (e.g. pathogen sensitivity; the correlation between PD and FD both within and Webb et al. 2006), and thus may show no congruence among communities? To answer these questions, we first with the measured FD. quantified the phylogenetic signal in each trait, and then Moreover, relationships between PD and FD are used a unified framework based on Rao’s quadratic expected to shift with increasing spatial scale (Emerson & entropy (Rao 1982) to partition phylogenetic and func- Gillespie 2008). PD may capture different processes of tional diversity within and among communities. We then assembly as one moves from diversity within communities tested the congruence between these different facets of (alpha scale) to diversity among communities (beta scale; diversity along the gradient, while accounting for patterns Graham & Fine 2008; Cavender-Bares et al. 2009). Most in species richness and evenness in communities.

Methods Trait data Study site and environmental gradient Eight plant traits were chosen to characterize different dimensions of the functional niche with respect to species The study was carried out on dry calcareous rangelands of morphology, phenology and chemical composition. Spe- southern France, at the INRA La Fage experimental station cies trait values were measured in a previous study (Ber- (43°55′N, 3°05′E, 790 m a.s.l.), located on a limestone pla- nard-Verdier et al. 2012) using standardized protocols teau (Larzac Causse), 100 km northwest of Montpellier, (Cornelissen et al. 2003). Traits were measured on 42–61 France. The plateau has a sub-humid mediterranean cli- species depending on traits, but always representing more mate, with cool wet winters and warm dry summers. For than 90% of the biomass in each plot (Pakeman & Quested the past 35 yr, the 260 ha of rangelands that constitute the 2009). Leaf traits – leaf dry matter content (LDMC; in mg experimental station have been grazed by a sheep herd dry mass per gram leaf fresh mass), specific leaf area (SLA; raised outdoors all-year-round for meat production. m2kg 1), leaf blade thickness (LT; lm), leaf nitrogen con- For our purpose, 12 plots of grassland (6 9 9 m) were centration (LNC; % leaf dry mass) and leaf carbon isotope selected to span the widest possible range of soil depth and ratio (d13C) – were chosen for their relationship to resource texture across the 160 ha of dolomitic rendzinas in the use and growth strategies (Grime 1977; Westoby et al. study site. Plots were dispersed over the study site without 2002; Wright et al. 2004), while seed mass (SM; mg) and any particular spatial or topological organization (Appen- the onset of flowering (OFL; day of year) were chosen to dix S1). Geographical distances between plots ranged from represent regenerative strategies and temporal reproduc- ca. 10 m to ca. 1.5 km, such that we consider dispersal lim- tive niches (Grubb 1977; Thompson 2000). Reproductive itations to be unlikely among plots. Environmental vari- height (Hrep; cm) was chosen as a trait pertaining to both ables (soil depth, texture, pH, total organic carbon, plant competitive and reproductive strategies (Garnier & available phosphorus, C/N ratio, nitrogen nutrition index, Navas 2012). Hrep and SM were log-transformed prior to spring soil water content) were measured and analysed analyses to meet normality. We chose to normalize the using a principal components analysis (PCA; see details traits showing exponential distributions (i.e. size-related Bernard-Verdier et al. 2012). The first axis (PC1) of the traits: SM and Hrep) to make them more comparable with PCA explained over 67% of the variability and was used in other traits and to be able to standardize their distribution all subsequent analyses as a single variable representing before including them with other traits in a multivariate the environmental gradient. Along PC1, plots varied from diversity index. shallow (ca. 10-cm deep) dry soils with low nitrogen availability, to deeper (ca. 1-m deep) and moister soils with higher nitrogen availability. Standing biomass increased Taxonomic and functional structure of communities from ca. 50 gm 2 on the shallow soils (with 10% bare ground) to ca. 250 gm 2 on the deeper soils (no bare In a previous study investigating community assembly in ground). There was no correlation between the environ- these same communities, Bernard-Verdier et al. (2012) mental gradient (PC1) and geographical distances (see found a clear pattern of species turnover and functional Appendix S1, Fig. S1.2). community structure in response to the soil gradient. No trend in species richness was observed, but a steep decrease Vegetation data in the evenness of species abundances was detected towards the deeper and more productive soils (trend in Species abundances Gini–Simpson index; r = 0.76, P < 0.001). A strong turn- In June 2009, the abundances of vascular plant species over in species taxonomic composition was also detected were estimated using the pin-point method (Levy & Mad- along the gradient. Perennial grasses dominated all 12 den 1933) along five 3-m transects in each of the 12 plots communities, but the identity of the dominant grass (see Bernard-Verdier et al. 2012). We recorded species shifted as one moved from deep to shallow soils: Bromus abundance as the total number of times the pin hit a live erectus, the main dominant on deeper soils, gave way to organ belonging to a given species, across the 150 pin- Stipa pennata at intermediate soil depths, which was points distributed in each plot. The pin-point method of replaced by Festuca christianii-bernardii on the shallower botanical survey estimates the percentage cover of species, soils. Dicotyledonous species also became more abundant which also relates to relative biomass in grasslands (Jonas- on the shallower soils (Fig. 1), with dwarf shrubs such as son 1988). A total of 73 angiosperm species were recorded Helianthemum canum and Thymus dolomiticus becoming across the entire gradient, with plot (=local community) major co-dominant species. This taxonomic response to species richness ranging from 15 to 33 species (average the soil gradient was associated with a strong functional 19.7). response of communities. At both ends of the gradient,

100 Phylogenetic signal in traits

80 Phylogenetic signal, deﬁned as the tendency for related species to resemble each other more than they resemble 60 species drawn at random from the phylogenetic tree (Blomberg & Garland 2002), was tested for each trait using a range of metrics. Blomberg’s (Blomberg & Garland 40 K 2002) was used as an index to rank trait phylogenetic signals: the higher the K value, the stronger the signal, with 20 K = 1 corresponding to trait evolution according to a

Relative abundance (% cover) Brownian motion model. Because the K metric may be 0 Shallow Deep misleading for low samples, significance of the phyloge- Soil gradient (PC1) netic signal was also tested using the variance of PICs (phylogenetic independent contrasts; Felsenstein 1985). As a Other lineages Fabaceae Graminoids means of comparison, we also calculated two other met- Fig. 1. Relative abundance of lineages in communities along the soil rics: Moran’s I (Moran 1950) and Pagel’s Lambda (Pagel gradient. Graminoids represent species from the Poaceae and Cyperaceae 1999). PIC variances and K values (function phylosignal families, other lineages correspond to all other monocotyledon and non- in R package picante; R Foundation for Statistical Com- Fabaceae dycotyledon families. puting, Vienna, AT) were compared to null distributions obtained by shuffling (999 times) species labels at the processes of habitat filtering restricted the range of differ- tip of the phylogeny: observed values in the upper fifth ent traits, such as Hrep, SLA, LNC, LDMC, LT and SM. Leaf quantile of the null distribution indicated significant traits values (LDMC, LNC, d13C) tended to be divergent phylogenetic signal (Blomberg & Garland 2002; Kraft & within communities at shallow to intermediate soil depth, Ackerly 2010). Observed values of Moran’s I were tested but tended to converge on plots with deeper soils. By con- using a two–sided test as described in Gittleman & Kot trast, reproductive traits (SM and OFT) tended to be more (1990; function Moran.I in R package ape). Pagel’s divergent towards plots with deeper soils. Other leaf traits lambda was tested against a Brownian motion hypothe- (SLA and LT) tended to diverge only at intermediate soil sis using a maximum likelihood ratio test (function depths. phylosig in R package phytools; Revell et al. 2008). Due In the above-mentioned previous study, FD within local to an incomplete trait data set, the number of spe- communities, defined as functional divergence, was cies included in the phylogeny varied depending on assessed by calculating community-weighted variance traits, ranging from 45 for leaf thickness to 61 for the (CWV) and comparing it to a null model (identical to that onset of flowering. To check the bias induced by these used in the present study; see below). In the present study, unequal samples, we also tested trait phylogenetic signal we chose to use Rao’s quadratic entropy as a measure of on the subsample of 40 species common to all eight trait dispersion in order to have a common measure for traits. functional and phylogenetic diversity. Both indices are quite similar in calculation (de Bello et al. 2009), and Partitioning diversity using Rao’s entropy yielded comparable trends in FD within communities along the gradient (see Appendix S2). Rao’s quadratic entropy (Rao 1982) can be used to partition diversity within and among communities, into alpha, Phylogenetic data beta and gamma components (Pavoine et al. 2004; Hardy & Senterre 2007). Being distance-based, it provides a very Phylogenetic super-tree flexible framework that can be adapted to quantify and We assembled a phylogenetic super-tree for the 73 species compare different facets of diversity, such as taxonomic, recorded along the gradient, based on recently published phylogenetic or functional diversity among species (de molecular phylogenies and dated according to published Bello et al. 2010). It should be noted that we use the term fossil records and molecular ages (see Appendix S3). The ‘taxonomic diversity’ to refer to the diversity in species super-tree was well resolved (only 7/64 nodes were left as identity or composition, according to its recent use and def- unresolved polytomies) and spanned major angiosperm inition by de Bello et al. (2010) and other authors (e.g. De families (24 families), with their oldest common ancestor Victor et al. 2010; Richardson et al. 2012), and not accord- dating back to the divergence between eudicots and mono- ing to previous definitions referring to taxonomic hierar- cots, estimated as 322 million years ago. chy diversity.

Alpha diversity Distance measures Within each community k, the among-species diversity Several distance measures can be used to calculate Rao’s (alpha) was calculated using Rao’s coefﬁcient of diversity diversity index, depending on the facet of diversity under (Rao 1982; Pavoine et al. 2004): consideration. Taxonomic diversity was calculated using a

distance measure defined as dij = 1 when i ¼6 j,anddij = 0 Xn Xn when i = j, which is equivalent to calculating the Gini– aRaoðkÞ¼ dijpikpjk ð1Þ i¼1 j¼1 Simpson diversity index (Pavoine et al. 2004). Phyloge- netic distances were calculated for each pair of species as with pik the relative abundance of species i in commu- the age of their most recent common ancestor (in million nity k,and(dij) the distance between species i and j, years; Webb 2000). Euclidean distances were used to cal- which can be taxonomic, functional or phylogenetic (see culate functional distances between species. Multivariate below). This abundance-weighted formulation of alpha functional distances (i.e. Euclidean distances in the multi- diversity represents the expected distance between two trait space created by all eight traits), as well as single-trait individuals chosen randomly from the community. In functional distances were calculated. Euclidean properties other words, because it is weighted by abundances, it were ensured using R function quasieuclid (package ade4), represents the convergence or divergence of abundant when needed. To calculate multivariate Euclidean dis- species within communities. It is a generalization of the tances, species with at least data missing for one trait Gini–Simpson index of evenness that includes differences were removed from the analysis, which brought the among species. number of species to 40 in multivariate analyses. To check if the missing data introduced significant bias, we re-ran all single-trait and phylogenetic diversity analyses Beta dissimilarity on this subsample of 40 species and found similar Beta dissimilarity classically represents the turnover in results to those obtained with the larger sets of species species composition across communities and along envi- (results not shown). This was expected, given the fact ronmental gradients (Simpson 1949), but this notion has that Rao indices are abundance-weighted, and that the recently been extended to describe phylogenetic and func- missing species were all rare species in these communi- tional beta dissimilarities (Pavoine et al. 2004; Hardy & ties. Senterre 2007; Graham & Fine 2008). Rao’s dissimilarity As recommended in de Bello et al. (2010), in order to coefficient (Rao 1982) was used to compute taxonomic, make taxonomic, functional and phylogenetic distances functional and phylogenetic pair-wise beta dissimilarities comparable, we scaled all distances between 0 and 1 by between communities. This dissimilarity index represents dividing each type of distance by its maximum value in the expected distance (e.g. phylogenetic, functional) our data set prior to all analyses. between two individuals chosen randomly from two distinct communities. It is based on the additive partitioning of the overall gamma diversity into within (alpha) and Statistical analysis among (beta) community diversity, and can be calculated Using a single framework such as Rao’s quadratic for each pair of communities, k and l, with the following entropy has the advantage of making different facets of equation (Pavoine et al. 2004): diversity more comparable, but may introduce mathe- cð þ Þ að ; Þ matical artifacts when comparing these facets (De b ð ; Þ¼ k l k l ð Þ Raopairwise k l c 2 ðkþlÞ Victor et al. 2010; Pavoine & Bonsall 2011; see also Pavoine et al. 2013). Through construction of the Rao with ck+l the gamma diversity of the pair of communities diversity index, FD and PD are both influenced by spe- (calculated with equation (1) using the mean of species cies richness and evenness (i.e. TD), at the alpha and relative abundances across the two communities) and the beta scale. As a consequence, a strong signal in TD a(k, l) corresponding to the mean alpha diversity of the along the gradient (e.g. due to differences in evenness two communities. Following recommendations from de among communities) may overwhelm any possible Bello (2010), to accurately quantify beta diversity inde- more subtle functional or phylogenetic patterns in com- pendently of alpha diversity, we applied Jost’s correction munities, and create an apparent strong correlation (Jost 2007) to c and a values prior to calculations between indices. Thus, in order to compare the pat- (see de Bello et al. 2010). Calculations were performed terns of FD and PD along the gradient, we needed to using the R function ‘rao’ provided in de Bello et al. control for the effect of TD, both at the alpha and the (2010). beta scale.

Alpha diversity along the gradient on beta dissimilarities along the gradient (Appendix S1, Fig. S1.3). We used a Mantel test to quantify the corre- Variation in the different facets of alpha diversity was lation of bTD, bFD and bPD with environmental dissimi- investigated along the environmental gradient. First, we larities (999 permutations). Environmental dissimilarity tested the correlation of taxonomic (aFD), functional was calculated as the absolute pair-wise difference in (aFD; univariate and multivariate) and phylogenetic (aPD) plot scores along the soil gradient (PC1). Furthermore, alpha diversities among themselves and against plot scores to control for dissimilarities in bTD, we tested functional on PC1. Preliminary analyses showed no influence of geo- and phylogenetic turnover along the gradient with par- graphic distances on alpha diversity along the gradient tial Mantel regressions using the permutation method (Appendix S1). recommended by Legendre & Fortin (2010) for small To control for trends in aTD along the gradient, we stan- sample sizes (method 1: permutations within the dardized aPD and aFD using a null model that shuffled response matrix; Legendre & Fortin 2010). Similarly, we abundances 999 times among species within each commu- used a partial Mantel test to measure the congruence nity (Bernard-Verdier et al. 2012). This null model main- between bFD and bPD while controlling for the effect of tained the observed community evenness and species bTD. richness constant (i.e. a constant aTD), while breaking all possible ties between traits (or phylogeny) and local species Results abundances. This standardized index was calculated as an effect size (ES) based on the proportion of null values infe- Phylogenetic signal rior to the observed value (scaled from 1to1;seemeth- We found significant phylogenetic signal (PS) in all traits ods in Bernard-Verdier et al. 2012), with negative except specific leaf area (SLA; Table 1), although all had a (alternatively positive) ES indicating a trend towards func- PS lower than expected under a Brownian model (K < 1; tional or phylogenetic convergence (alternatively diver- Blomberg & Garland 2002). According to Blomberg’s K, gence). We tested for overall positive (or alternatively the trait with the highest phylogenetic signal was the leaf negative) effect sizes across all 12 communities using non- d13C isotope ratio (d13C), followed by leaf nitrogen concen- parametric two-sided Wilcoxon rank tests. We also tested tration (LNC), seed mass (SM) and leaf dry matter content for trends in alpha diversity along the gradient by testing (LDMC). These four traits were also loosely correlated the correlation between effect sizes and the soil gradient among themselves (Table S1). Reproductive height (Hrep), (PC1). onset of flowering (OFL) and leaf thickness (LT) had a lower but still significant phylogenetic signal in our regional species pool. It should be noted that the phylogenetic Taxonomic, functional and phylogenetic turnover along the signal measured in this study, and the ranking among gradient traits, is contingent on the pool of species under study, and We used pair-wise beta dissimilarities (bRao) to investi- may not be extrapolated to a broader range of angiosperm gate the turnover in taxonomic, phylogenetic and func- species and families. Down-sampling the trait data set to tional diversity along the soil gradient. Preliminary include only the 40 species common to all traits slightly analyses showed no influence of geographic distances modified trait phylogenetic signals, with the PS of leaf

Table 1. Phylogenetic signal in eight functional traits according to four different metrics: Blomberg’s K index, phylogenetic independent contrasts (PIC) variance across the phylogenetic tree, Moran’s I and Pagel’s lamda. Traits are ranked by increasing values of K, i.e. by increasing phylogenetic signal. Bold font indicates signiﬁcant phylogenetic signals (P < 0.05; see details in Methods). The number of species indicates the size of the species pool used to calculate the phylogenetic signal for each trait.

Trait nb. species Blomberg’s K PIC variance Moran’s I Pagel’s lambda

KPDPIC PI P k P

SLA 52 0.242 0.147 0.726 0.164 0.019 0.857 0.001 0.998 LT 48 0.302 0.016 106.66 0.016 0.083 0.002 0.228 0.033 Hrep 50 0.315 0.024 0.002 0.013 0.121 <0.001 0.38 <0.001 OFL 61 0.339 0.001 16.82 0.002 0.051 0.008 0.725 <0.001 LDMC 52 0.408 0.003 120.14 0.002 0.103 <0.001 0.579 <0.001 SM 48 0.412 0.002 0.006 0.001 0.096 <0.001 0.553 0.010 LNC 45 0.431 0.002 0.383 0.002 0.110 <0.001 0.705 <0.001 d13C520.4330.001 0.019 0.002 0.087 <0.001 0.528 <0.001

Journal of Vegetation Science 882 Doi: 10.1111/jvs.12048 © 2013 International Association for Vegetation Science M. Bernard-Verdier et al. Partitioning phylogenetic and functional diversity thickness and the onset of flowering becoming non-signifi- than in the deeper soils (mostly Poaceae; Fig. 1, Appen- cant, but it did not change the ranking of traits according dix S2). to the K index (results not shown). By contrast, null model testing of multivariate functional alpha diversity (aFD) revealed no pattern of overall Alpha diversity convergence: we observed a decreasing trend in effect sizes, with functional divergence in communities on inter- Variation in alpha diversity along the gradient mediate to shallow soil depths, and convergence on the As previously reported in Bernard-Verdier et al. (2012), deepest soils only (Fig. 2e). When considering the diversity we found a strong trend in taxonomic alpha diversity of each trait individually, d13C, LDMC, SM and LNC were (aTD) along the gradient (Fig. 2a), representing a decrease the only traits with a significant linear trend in aFD in evenness towards the deeper soils. We found similar along the gradient, which decreased towards deeper soils decreasing trends in phylogenetic (aPD) and multivariate (Table 2). However, as reported in a previous study functional (aFD) alpha diversity along the gradient (Bernard-Verdier et al. 2012), null model testing (Fig. 2b,c). revealed more contrasted patterns of trait diversity along However, null model testing to control for the effect of the gradient, with leaf traits diverging on shallow to evenness revealed different phylogenetic and functional intermediate soils, and converging on deep soils, trends along the gradient (Fig. 2d,e). We detected an over- whereas reproductive traits had opposite trends (Appen- all significant signal of phylogenetic convergence in all dix S3). communities along the gradient (significant one-sided Wil- coxon signed-ranks test; Fig. 2d). Nevertheless, results fur- Congruence between aPD and aFD ther suggest that this phylogenetic convergence may be less strong towards the shallower plots (less negative effect Overall, we found no congruence between phylogenetic size; Fig. 2d), where abundant species belonged to a wider and functional alpha diversities (Table 2). This was mostly array of families (Cistaceae, Rosaceae, Lamiaceae, Poaceae) due to the incongruence between aPD and aFD at interme-

(a) αTD (b) αPD (c) αFD 0.9 0.7 r2 = 0.76*** r2 = 0.68*** 0.35 r2 = 0.63** 0.8 0.6 0.30

0.7 0.25 0.5 0.20 0.6 0.4 0.15 0.5 0.10 Shallow Deep Shallow Deep Shallow Deep Soil gradient (PC1) Soil gradient (PC1) Soil gradient (PC1)

(d) αPD Effect size (e) αFD Effect size 1.0 1.0

r = –0.25 ns r = –0.6* 0.5 W : obs < null** 0.5 W : ns

0.0 0.0

–0.5 –0.5

–1.0 –1.0 Shallow Deep Shallow Deep Soil gradient (PC1) Soil gradient (PC1)

Fig. 2. Alpha diversities within communities along the gradient. (a–c)Variationintaxonomic(aTD), phylogenetic (aPD) and multivariate functional (aFD) alpha diversities along the soil gradient, tested using a linear regression. (d, e) Variation of standardized values of aPD and aFD along the gradient. Positive effect sizes indicate phylogenetic/functional divergence, while negative effect sizes indicate phylogenetic/functional convergence. Variation in effect size along the gradient is tested with a linear correlation (r), and overall signal of convergence or divergence is tested with one-sided Wilcoxon tests (W). (ns: P > 0.05, *P < 0.05, **P < 0.01, ***P < 0.001).

Table 2. Variation in functional diversity within (aFD) and among (bFD) Congruence of functional and phylogenetic dissimilarities communities along the environmental gradient, and correlations with phylogenetic diversity (PD). Pearson correlations were used to test for the Partial Mantel tests revealed no correlation between bPD variations in aFD along the gradient and its correlation with aPD. Partial and multivariate bFD (Table 2). However, when consid- Mantel tests were used to test the correlation of bFD with environ- ering traits individually, three single trait bFD did show a mental dissimilarities (D gradient) and with bPD, while controlling for bTD. significant correlation with bPD: LNC, SM and Hrep (*P < 0.05, **P < 0.01, ***P < 0.001). (Table 2). These three traits had a significant phyloge- Trait Alpha diversity Beta dissimilarity netic signal, but not necessarily the strongest (see aFD bFD Table 1). In fact, it is interesting to note that some traits with stronger PS and a significant turnover along the gra- ~Gradient ~a PD ~D gradient ~b PD dient, such as d13C, showed no correlation at all with Multivariate 0.79** 0.43 ns 0.65*** 0.17 ns bPD. These contrasting responses among traits may LNC 0.61** 0.43 ns 0.25 ns 0.04 ns explain the absence of correlation observed between bPD d13C 0.89*** 0.78** 0.33* 0.03 ns b b LDMC 0.81** 0.57 ns 0.2 ns 0.55 ns and the multivariate FD, or between PD and the envi- SM 0.66* 0.29 ns 0.34* 0.58*** ronmental gradient. Hrep 0.55 ns 0.41 ns 0.60*** 0.55*** OFL 0.27 ns 0.21 ns 0.29* 0.08 ns Discussion LT 0.48 ns 0.15 ns 0.53** 0.39** SLA 0.49 ns 0.17 ns 0.44** 0.11 ns In this study, we investigated how phylogenetic diversity relates to functional and taxonomic diversity in plant communities, both at the alpha and the beta scale. We found diate soil depths, in which phylogenetic clustering co- communities were strongly structured in terms of taxo- existed with functional divergence (Fig. 2d,e; see also nomic, functional and phylogenetic diversity along a gradi- Bernard-Verdier et al. 2012). This finding concerned uni- ent of soil depth and resource availability. Among the variate as well as multivariate measures of FD, except for eight functional traits under study, seven displayed signifi- d13 one single trait, C, whose alpha diversity was positively cant phylogenetic signal in our species pool. Despite these a correlated to PD (Table 2). Nevertheless, once compared results, we show that the different facets of diversity are d13 to the null model, even C did not show a pattern of con- not equivalent and may capture different processes of com- vergence congruent with phylogenetic convergence at munity assembly along the gradient. By taking care to con- intermediate soil depths (Appendix S3). trol for the effect of species richness and evenness on diversity indices, we were able to discriminate the subtle Beta dissimilarities among communities patterns of mismatch and congruence among functional Correlation between beta dissimilarities and environmental and phylogenetic diversities, both at the alpha and the beta differences scales. Taxonomic pair-wise beta dissimilarities (bTD) were signif- Phylogenetic convergence and functional divergence icantly correlated to environmental dissimilarities within communities (Fig. 3a), confirming the influence of environmental factors in driving a turnover in species composition and abun- At the alpha scale, we detected an overall phylogenetic dances (see Bernard-Verdier et al. 2012). Phylogenetic convergence within communities, a pattern frequently dissimilarities (bPD) showed a similar trend (Fig. 3b), but reported in the literature (reviewed in Vamosi et al. 2009). multivariate functional dissimilarities (bFD) presented an Given the significant phylogenetic signal detected in seven even stronger correlation with environmental dissimilari- out of eight key functional traits under study, one could ties (Fig. 3c). This tighter link between functional dissimi- have expected an overall functional convergence within larities and environmental factors was confirmed in the communities (Webb et al. 2002; Cavender-Bares et al. partial Mantel tests controlling for the effect of bTD, which 2009). However, functional diversity within communities detected no significant correlation for bPD, but a signifi- did not reflect this phylogenetic convergence. On the cant correlation for the multivariate bFD (Fig. 3d,e). When contrary, complex patterns of functional divergence and considering single-trait beta dissimilarities, we found con- convergence were observed all along the gradient, both for trasting patterns depending on traits (Table 2): six traits – the multivariate functional index and for individual traits Hrep, SLA, LT, d13C, SM and OFL – had dissimilarities sig- (Fig. 3e, Appendix S2). Our results thus support the idea nificantly correlated with the environmental variations, that phylogenetic diversity cannot be used as a simple while LNC and LDMC showed no turnover associated with proxy for functional diversity (Emerson & Gillespie 2008; the gradient. Mouquet et al. 2012).

β = β = β = (a) TD rM 0.69*** (b) PD rM 0.68*** (c) FD rM 0.81*** 50 20 20

40 15 15 30 10 10 20 5 5 10

0 0 0 0246 0246 0246 Environmental dissimilarity Environmental dissimilarity Environmental dissimilarity

= = (d) PD residuals rM 0.17 ns (e) FD residuals rM 0.65***

4 4

2 2

0 0

–2 –2

–4 –4

0246 0246 Environmental dissimilarity Environmental dissimilarity

Fig. 3. Pair-wise beta dissimilarities among communities along the environmental gradient. (a–c) Taxonomic (bTD), phylogenetic (bPD) and multivariate functional (bFD) pair-wise beta dissimilarities are tested against pair-wise environmental dissimilarities using a Mantel test on Pearson coefﬁcients (rM). Environmental dissimilarities are the differences in plot scores along the axis of the soil gradient. (d, e) Graphical representation of partial regressions of bPD and bFD against environmental dissimilarities, while controlling for bTD; the residuals of the regressions bFD ~bTD (FD residuals) and bPD ~bTD (PD residuals) are plotted along environmental dissimilarities. Statistics for the partial Mantel test (rM) are indicated in the upper right corner of panels (ns: P > 0.05, ***P < 0.001).

This mismatch between phylogenetic and functional as the result of the overwhelming dominance of grami- diversity within communities was mostly apparent at noids (i.e. representing over 70% of cover on average; intermediate soil depths: communities were phylogeneti- Fig. 1), particularly at intermediate soil depths. It is well cally convergent but, at the same time, harboured the known that the general success of graminoids in grazed highest functional diversity of the gradient (Fig. 2e, ecosystems is related to traits conferring better tolerance to Appendix S3; see also Bernard-Verdier et al. 2012). These grazing and trampling (e.g. capacity for tillering, heavy results suggest that closely related species may successfully sympodial rhizome system, basal leaf meristems; Gibson co-exist at intermediate levels of stress and productivity by 2009), which were not measured in this study. Such traits diverging functionally, mainly in terms of reproductive are not easily quantifiable with continuous measures height and resource acquisition strategy (captured by leaf (Weiher et al. 1999), and would need to be added as dis- traits such as LDMC and SLA; Westoby et al. 2002). This is crete or binary variables in a diversity index using more consistent with recent findings suggesting that intermedi- flexible distance indices (such as the Gower distance; Pavo- ate abiotic environments play an important role in main- ine et al. 2009). In such an analysis, we would expect a taining and generating the striking trait diversity present convergence of these traits on intermediate to deep soils, within certain lineages (Hermant et al. 2012). consistent with the dominance of graminoids, and thus If none of the eight functional traits taken individually, congruent with the phylogenetic convergence. or the multivariate index, reflect the strong pattern of phylogenetic convergence at the alpha scale, then it is possible Phylogeny only partially captures the beta niche in these that phylogenetic relationships may capture some other communities unmeasured functional feature that converges within these rangeland communities. Indeed, phylogenetic con- Contrary to hypotheses from the literature (Silvertown vergence within these communities can easily be identified et al. 2001; Graham & Fine 2008), we did not detect strong

Journal of Vegetation Science Doi: 10.1111/jvs.12048 © 2013 International Association for Vegetation Science 885 Partitioning phylogenetic and functional diversity M. Bernard-Verdier et al. patterns of phylogenetic differentiation among communi- expectations from the literature, stating that traits related to ties in response to the gradient (i.e. at the beta scale). Phy- habitat preferences (i.e. beta niche traits) may be better cap- logenetic dissimilarities among communities were not tured by PD than traits involved in processes of local co-exis- related to environmental dissimilarities (Fig. 3d), despite tence (Silvertown et al. 2001; Emerson & Gillespie 2008). the existence of a strong turnover in species abundance and composition (Fig. 3a; see also Bernard-Verdier et al. Beyond phylogenetic signal in traits 2012). In contrast, we detected a strong functional turnover, Significant phylogenetic signal in traits was necessary but which supports the existence of environmental sorting of not sufficient to predict a correlation between PD and FD species according to habitat preferences (i.e. beta niche at any scale (Losos 2008; Swenson & Enquist 2009). For traits) along the gradient. Functional turnover along the instance, a trait such as LDMC had one of the highest phy- gradient was driven by dissimilarities in six traits, related to logenetic signals, but was never correlated to PD, either at both the reproductive (Hrep, SM, OFL) and the vegetative the alpha or at the beta scale (Table 2). As pointed out by (SLA, LT, d13C) phases (Table 2). These results are consis- previous authors (e.g. Losos 2008), this is not really sur- tent with a previous study (Bernard-Verdier et al. 2012) in prising, because testing the congruence between FD and which these traits were found to be filtered, i.e. restricted PD can be understood as investigating a phylogenetic sig- in range, within these communities. These traits can thus nal at the community scale, and not at the species scale (as be interpreted as traits relevant to the beta niche of species in PS). At the community scale, relationships between PD along this gradient in soil resources (Emerson & Gillespie and FD integrate a more complex signal, which takes into 2008). account both the phylogenetic signal of traits across spe- Because we chose to use abundance-weighted diversity cies, but also the effect of species sorting among communi- indices, beta dissimilarity measures were largely influ- ties, as well as the local distribution of abundances. The enced by the most abundant species, in particular the dom- very discrepancies observed between the signal at the com- inant grasses Bromus erectus, Stipa pennata and Festuca munity scale and the PS among species may provide addi- christianii-bernardii, which gradually replace each other tional information concerning community assembly (e.g. along the soil gradient (see Appendix S2). The fact that Cavender-Bares et al. 2004). these dominants belong to the Poaceae family certainly Moreover, although seven out of eight traits had a sig- contributes a large part to the low and non-significant phy- nificant phylogenetic signal, our multivariate index of logenetic turnover observed along the gradient. But diversity was never well correlated to phylogenetic diver- despite belonging to the same family, these species did sity. This does not support the hypothesis that phyloge- show clear functional differences, especially in terms of netic diversity may be more related to a multivariate index leaf and phenological traits, thus participating in the signif- of functional diversity (e.g. Flynn 2011). However, these icant functional turnover detected along the gradient. This conclusions may depend on the list of functional traits illustrates how a phylogenetic approach may be inade- selected. The multivariate index used in this study may not quate to capture the subtle functional turnover occurring be the best multidimensional descriptor of species niche in among dominant grasses, and driven by soil resource het- these communities, and different multivariate indices erogeneity in this rangeland. would probably show different patterns depending on the Nevertheless, although phylogenetic and functional dis- selected traits. Although the eight functional traits mea- similarities were not congruent when considering a multi- sured in this study were chosen to cover a wide array of variate functional index, we did find significant functional dimensions, from vegetative growth to regener- correlations when considering, individually, three traits ative strategies, and showed a strong response to the gradi- related to the beta niche in these communities: Hrep, SM ent in our communities, we did not include potentially and LT (Table 2). In this case, phylogenetic dissimilarities crucial traits pertaining to underground foraging and stor- did capture part of the beta niche of species, but not all of age strategies, such as root traits (Kembel & Cahill 2011). It its dimensions. Indeed, the turnover of other important would be interesting to investigate the diversity of under- beta niche traits along this gradient, such as SLA or d13C, ground traits in future analyses, which may provide a bet- were not captured by phylogenetic dissimilarities, which ter understanding of the processes creating functional, and likely explains the absence of phylogenetic beta structure phylogenetic, convergence within communities. detected along the environmental gradient. Overall, we found that the relationship between FD and Conclusions PD shifted from the alpha to the beta scale, with a slightly better congruence at the beta scale than at the alpha scale Overall, our results show that investigating the phylogenetic when considering single-trait diversity. This tends to support structure of communities can be used as a complement,

Journal of Vegetation Science 886 Doi: 10.1111/jvs.12048 © 2013 International Association for Vegetation Science M. Bernard-Verdier et al. Partitioning phylogenetic and functional diversity but not a substitute, for trait-based approaches of plant understand patterns of plant community productivity. PLoS community assembly. We show that, even when traits ONE 4: e5695. have a significant phylogenetic signal, phylogenetic diver- Cavender-Bares, J., Ackerly, D.D., Baum, D.A. & Bazzaz, F.A. sity is not a good indicator of functional structure within 2004. Phylogenetic overdispersion in Floridian oak commu- communities. Moreover, moving from the alpha to the nities. The American Naturalist 163: 823–843. beta scale revealed that beta niche traits in our communi- Cavender-Bares, J., Kozak, K.H., Fine, P.V.A. & Kembel, S.W. ties were only partially captured by phylogenetic dissimi- 2009. The merging of community ecology and phylogenetic – larities, thus limiting the power of PD to detect ecological biology. Ecology Letters 12: 693 715. sorting of species along an environmental gradient. Cornelissen, J.H.C., Lavorel, S., Garnier, E., Dıaz, S., Buchmann, N., Gurvich, D.E., Reich, P.B., Ter Steege, H., Morgan, H.D., However, phylogenetic convergence within these commu- Van der Heijden, M.G.A., Pausas, J.G. & Poorter, H. 2003. A nities did capture a pattern of community assembly (i.e. handbook of protocols for standardised and easy measure- the dominance of graminoids) that had not been detected ment of plant functional traits worldwide. Australian Journal with our functional approach. Confronting different facets of Botany 51: 335–380. of diversity provides a promising approach bringing Cornwell, W.K. & Ackerly, D.D. 2009. Community assembly together community ecology and evolutionary ecology, a and shifts in plant trait distributions across an environmental crucial issue in fields of research such as conservation gradient in coastal California. Ecological Monographs 79: 109– biology (Mouquet et al. 2012). Environmental gradients 126. provide an ideal context to disentangle the functional and de Bello, F., Thuiller, W., Leps, J., Choler, P., Clement, J.-C., evolutionary determinants of the alpha and the beta niche Macek, P., Sebastia, M.-T. & Lavorel, S. 2009. Partitioning of of species. functional diversity reveals the scale and extent of trait convergence and divergence. Journal of Vegetation Science 20: 475 Acknowledgements –486. de Bello, F., Lavergne, S., Meynard, C.N., Leps, J. & Thuiller, W. This work was funded in part by the ANR programme 2010. The partitioning of diversity: showing Theseus a way O2LA (09-STRA-09) and by the DIVHERBE project (ACI out of the labyrinth. Journal of Vegetation Science 21: 992– ECCO, ECOGER programme). M.B.V. was supported by a 1000. doctoral fellowship from the French Ministry of Education De Victor, V., Mouillot, D., Meynard, C., Jiguet, F., Thuiller, W. and Research. We thank J. Richarte for his invaluable help & Mouquet, N. 2010. Spatial mismatch and congruence in the field and B. Testi for help correcting species names between taxonomic, phylogenetic and functional diversity: in the phylogeny. We also thank C. Violle, M. Vellend, the need for integrative conservation strategies in a changing J. Ruffault, F. de Bello and three anonymous referees for world. Ecology Letters 13: 1030–1040. helpful discussions and comments on earlier versions of Emerson, B.C. & Gillespie, R.G. 2008. Phylogenetic analysis of the manuscript. community assembly and structure over space and time. Trends in Ecology & Evolution 23: 619–630. Felsenstein, J. 1985. Phylogenies and the comparative method. 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Journal of Vegetation Science Doi: 10.1111/jvs.12048 © 2013 International Association for Vegetation Science 889 Journal of Vegetation Science 24 (2013) 890–897 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Impact of plant invasions on functional diversity in the vegetation of Central Europe Martin Hejda & Francesco de Bello

Keywords Abstract Functional diversity indices; Functional similarity; Invaded communities; Invasibility; Questions: How is the loss of plant species richness, associated with invasions, Invasive alien species; Native species related to changes in functional diversity? What is the relationship between the traits of invasive species and those of invaded communities? Nomenclature Kubat et al. (2002) Location: Different Central European vegetation types within the Czech Republic. Received 3 April 2012 Accepted 29 October 2012 Methods: Functional diversity was calculated for 260-paired releves, half non- invaded and half invaded by one of 13 widespread invasive species in Central Europe. Four traits (height, SLA, seed mass and clonal index) were considered Hejda, M. (corresponding author, martin. as a way to understand the functional space occupied by native and alien species [email protected]): Department of Invasion Ecology, Institute of Botany, Academy of in the data set (410 species altogether). Sciences of the Czech Republic, CZ-252 43, Results: Some of the functional diversity (FD) indices used (mean trait dissimi- Pruhonice, Czech Republic larity, mean nearest neighbour dissimilarity and SD of the mean nearest neigh- de Bello, F. ([email protected]): Department of Botany, Faculty of Biology, University of bour dissimilarity) revealed higher trait diversity for the invaded vegetation and South Bohemia, CZ-370 05, Budweiss, Czech negative relationship with species richness, while functional richness and even- Republic and ness gave higher values for the uninvaded vegetation and positive relationship Institute of Botany, Academy of Sciences of with species richness. Adding hypothetically the invader into the FD calculations the Czech Republic, CZ-379 82, Trebon, Czech for the uninvaded vegetation was found to increase most of the FD indices used, Republic while excluding it from the FD calculations of the invaded vegetation decreased functional richness and also mean trait dissimilarity. Conclusions: Results suggest that invading aliens tend to be functionally different from native species and are therefore likely to occupy an empty niche in the invaded vegetation. Similarly, the resident species in the non-invaded communities are not likely to occupy the whole potential niche space, which could remain available for the invasive species with different traits. This study suggests that the probability of a successful invasion is related to functional dissimilarities between the alien invader and native species of the resident communities.

communities with higher species diversity should be more Introduction stable and less easily invaded than species-poor communi- Recently, much attention has been paid to the impacts of ties (see Levine & D’Antonio 2010 for a review), however, invasive plants on the diversity and composition of com- evidence to support this assumption remains rather ambig- munities (Vila et al. 2010; Thuiller et al. 2011). It is widely uous, varying from supporting this “diversity – stability – accepted that the invasions of plant species are often asso- low invasibility” hypothesis (Dimitrakopoulos et al. 2005; ciated with a certain loss of species diversity (Hejda & Pysek McDougall 2005) to contradicting it (Foster et al. 2002). 2006; Valtonen et al. 2006; Hejda et al. 2009), which is These patterns are generally explained by the fact that especially apparent on smaller spatial scales. On a larger higher species richness should imply a more complete spatial scale, the invasions of non-native species tend to occupation of niches available within a site, making com- enrich ﬂoras, however, invaded ﬂoras tend to be similar munity invasibility lower. It has been suggested, however, over large areas (Winter et al. 2009). It is expected that that the relation between the diversity and invasibility

Journal of Vegetation Science 890 Doi: 10.1111/jvs.12026 © 2013 International Association for Vegetation Science M. Hejda & F. de Bello Plant invasions and functional diversity could concern the diversity of functional traits (i.e. func- ostruthium, Lupinus polyphyllus, Mimulus guttatus, Fallopia x tional diversity) existing in a community rather than sim- bohemica, F. japonica, F. sachalinensis, Rudbeckia laciniata, ply the numbers of species, and this principle has even Rumex alpinus, Solidago gigantea. These species are now been experimentally documented (Xu et al. 2004; Pok- invading and dominating a range of different vegetation orny et al. 2005). Xu et al. (2004) showed that the pres- types. The vegetation types invaded, including typical alli- ence of native species phylogenetically or ecologically ances in Central European vegetation, are described in the closely related to the invading alien enhances the commu- Appendix. The characteristics of the invaded habitats ran- nity resistance to invasion. On the other hand, the original ged from the alluvial plains of lowland rivers with large hypotheses of Darwin (known as Darwin’s naturalization stands of Aster novi-belgii agg., Helianthus tuberosus or Impa- conundrum; Thuiller et al. 2011) also support the view tiens glandulifera in the warmest areas of the southeastern that a successful invasive species should be similar, to a cer- part of the Czech Republic to sub-alpine meadows invaded tain extent, to the native species of the invaded commu- by Rumex alpinus in the Krkonose Mountains located in the nity, because species will tend to show similar adaptations north and northeast of the Czech Republic. to the prevailing environmental conditions. These obser- We aimed to sample the invaded vegetation types with vations together suggest that species invasiveness and the largest cover (dominance) of the invasive species men- community invasibility should be considered together to tioned above, i.e. where the alien impact could be consid- understand the patterns of species invasions and that the ered to be highest. This was possible for all species except community invasibility is affected by the traits of the resi- Mimulus guttatus, which is a widespread alien but it is never dent native species as well as invading aliens (Fynn et al. really dominant in the native vegetation (i.e. its cover on a 2009; Sperfeld et al. 2010). given scale did not exceed 40%). For each plot of invaded This approach likely requires comparing the functional vegetation, an adjacent plot of uninvaded, or minimally traits of invasive species against those of resident species, invaded, vegetation was sampled. A total of 260 plots were rather than considering species diversity alone. This sampled altogether – 13 aliens with ten pairs of invaded vs. approach should therefore explain that the impacts of uninvaded vegetation. Plots were squares of 4 9 4m plant invasions are context-dependent, as some species are where species cover was visually estimated. As such, all able to cope with the newly arrived invader while others the species of vascular plant within plots were recorded are not. In fact, it has also been documented that different and their relative abundances estimated on a percentage native species respond differently to the presence of the scale. The adjacent uninvaded vegetation for comparison dominant invader (e.g. Holmes & Cowling 1997; Cum- was chosen in close vicinity to the invaded vegetation with ming & Kelly 2007; Mason & French 2008; Mason et al. the aim of having site conditions as close to the invaded 2009). In this sense, when approaching this problem from vegetation as possible – see Hejda et al. (2009) for a the perspective of invading aliens, a question arises as to detailed discussion of the comparative, “space-for-time” whether invasive species adopt a life strategy similar to the approach and its limitations. native species or a different one. Tecco et al. (2009) documented that the strategy depends on the life form of alien Functional diversity species – naturalized herb species exhibited ecological similarities to native species, while woody species tended to Different indices of FD (Mouchet et al. 2010; Pavoine & grow faster and larger and could therefore be presumed to Bonsall 2011) can be used to estimate trait differences be superior competitors to native woody species. While between species, the only limitation for our study should evidence is accumulating, the question of whether inva- be that FD must be as much as possible independent of the sive species have similar or dissimilar traits to the native number of species. A suitable index, that which is our main species remains largely unexplored. To answer this ques- focus, for this approach is the mean dissimilarity between tion, this paper deals with the impact of invasive aliens on species (‘MPD’; Pavoine & Bonsall 2011). This index is the functional diversity (FD) of invaded communities. mathematically similar to other mainstream indices of FD that can include species′ relative abundances, such as the Methods widely used Rao’s quadratic entropy (Pavoine & Bonsall 2011). The MPD index calculates the mean trait dissimilar- Data sampling ity among all possible pairs of species and is one of the This study is based on comparative data that consist of 260 existing indices of functional divergence sensu Mason plots of vegetation invaded by 13 widespread invasive neo- et al. (2005). Most importantly, the MPD index indicates phytes alien to central Europe (see Pysek et al. 2004; for the expectation of trait dissimilarity between two ran- deﬁnitions): Aster novi-belgii agg., Helianthus tuberosus, Her- domly chosen species within a set of species. In this way, acleum mantegazzianum, Impatiens glandulifera, Imperatoria the FD expected by chance (i.e. if species are taken

Journal of Vegetation Science Doi: 10.1111/jvs.12026 © 2013 International Association for Vegetation Science 891 Plant invasions and functional diversity M. Hejda & F. de Bello randomly from a given pool of species) can be estimated St ep ankov a 2010). The nomenclature of species follows safely based on the observed FD for that set of species (de Kubat et al. (2002). Together with these indices of func- Bello et al. 2013). We computed again the index for all tional diversity, we computed trait means for each plot and plots. Then we computed it either after removing or adding each of the traits considered. the invasive species from the invaded and non-invaded plot, respectively. These calculations basically mimicked Data analysis the case of running null models comparing (1) the FD of invaded plots that would be expected without the effect of The differences in the values of various FD indices among invading species, and (2) the FD expected for not yet the invaded and pair-wise adjacent uninvaded vegetation invaded communities. This two-step approach also shows were tested using paired t-tests (Crawley 2007). Then we the relative contribution of invasive species to FD of assessed the relations between the species richness and invaded communities and the potential contribution in various indices of FD, both for the invaded an adjacent un- not-yet invaded ones. invaded vegetation separately, and also between the Together with the MPD index, we computed several changes of species richness and FD indices associated with other important FD indices that are increasingly being con- the invasions. The changes in species richness and in vari- sidered when studying patterns of species co-existence. ous FD indices associated with the invasions were First, we considered functional richness and functional expressed as uninvaded plot value/invaded plot value evenness, described in Villeger et al. (2008) and further ratios. Relationships between species richness and FD indi- discussed in Mason et al. (2013). The first index represents ces and also changes in species richness and changes in FD the functional space occupied in a community and, unlike indices were tested using Pearson′s correlation coefficients. other indices, it is often correlated to species diversity. The Due to the requirement of normally distributed error struc- second index highlights the patterns in even occupation of tures, all variables (FD indices and uninvaded/invaded trait space in a community. We further considered two ratios of these) were square root-transformed prior to the other indices that are recommended to study patterns in analysis, provided that the Shapiro–Wilk normality test species invasions: mean nearest neighbour dissimilarity revealed significant deviations from normality. All these (mnnd) and SD of the mean nearest neighbour dissimilar- tests were performed (1) with all plots together, i.e. includ- ity (sdnnd; Thuiller et al. 2011). The indices indicate the ing all the target invasive neophytes (n = 260, half mean and SD of the dissimilarity between all closely simi- invaded, half not), and (2) within each of the target neo- lar traits in a community from the trait viewpoint. We also phytes (n = 20, half invaded, half not). computed trait mean values for all traits (see below) in each plot. Results To compute species functional dissimilarity, we considered four main traits representing key axes of ecological When considering all the target neophytes together, all of strategies among vascular plant species: height, specific leaf the FD indices (mean trait dissimilarity, mean nearest area (SLA), seed mass and the extent of clonality (Westoby neighbour dissimilarity and SD of the mean nearest neigh- 1998; Klimesova & de Bello 2009). These traits were con- bour dissimilarity, functional richness and evenness) sidered as a way to understand the functional space occu- revealed significant differences among the invaded vs. un- pied by all native and alien species in the data set (410 invaded vegetation (Table 1). Functional richness and species altogether). For calculating trait dissimilarity based evenness revealed higher values for the uninvaded vegeta- on multiple traits, we used the commonly applied Gower tion, while the other indices revealed higher values for the distance (Leps et al. 2006; Pavoine et al. 2009), which can invaded vegetation. also account for a limited number of missing trait values in Mean nearest neighbour dissimilarity, along with its SD, the data set. Traits were log-transformed, with the excep- varied significantly (negatively) with species richness, tion for clonality (expressed as the clonal index), before while functional richness increased significantly with spe- calculations. The Gower index was computed including all cies richness (Table 1). Functional evenness increased sig- invasive species (see recommendations of de Bello et al. nificantly with species richness, but this relation existed 2012). Trait values were available for most of the species in for the invaded vegetation only and not for the uninvaded. the data set. SLA and seed mass of the recorded species was Regarding changes in FD indices associated with the inva- extracted from LEDA (Kleyer et al. 2008) and clonality sion (expressed as the ratios between the values for the un- from the CLO-PLA database (Klimes & Klimesova 1999). invaded/invaded vegetation), the mean nearest neighbour The data on species height were obtained from the Flora of dissimilarity responded to the changes in species richness the Czech Republic (Hejny&Slav ık 1988, 1990, 1992; significantly negatively, while the changes in functional Slavık 1995, 1997, 2000; Slavık & St ep ankov a 2004; richness and evenness related to it positively.

Table 1. Results of the pair-wise comparisons between the invaded vs. adjacent uninvaded vegetation, correlations relating the various FD indices to species richness and changes in FD indices to changes in species richness associated with the invasions.

Mpd mnnd sdnnd Fric FEve

Mean FD values for invaded and uninvaded vegetation Mean invaded 0.171 0.081 0.040 0.149 0.711 Mean uninvaded 0.162 0.060 0.036 0.168 0.732 Invaded/uninvaded difference (P-value) 0.020 <0.001 0.022 0.009 0.040 Relation of FD indices to species richness Relation of FD values to species richness of invaded vegetation (P-value) 0.215 <0.001 <0.001 <0.001 <0.001 Correlation coefficient À0.113 À0.749 À0.326 0.589 0.356 Relation of FD values to species richness of uninvaded vegetation (P-value) 0.316 <0.001 0.002 <0.001 0.936 Correlation coefficient 0.089 À0.547 À0.273 0.648 À0.007 Relation of changes in FD values to changes in species richness (P-value) 0.145 <0.001 0.714 <0.001 <0.001 Correlation coefficient 0.132 À0.580 À0.034 0.670 0.499 Expected values (adding and removing the alien) Invaded expected (mean) 0.161 0.077 0.042 0.115 0.708 Uninvaded expected (mean) 0.165 0.061 0.037 0.202 0.730 Invaded expected/invaded real value difference <0.001 0.369 0.433 <0.001 0.199 Uninvaded expected/uninvaded real value difference <0.001 0.004 0.199 <0.001 0.233 The table also shows the expected values, obtained after (1) excluding the aliens from the calculations for the invaded vegetation and (2) including itinto the uninvaded vegetation.

Including the alien in the FD calculations for the unin- of excluding or adding the invader. Excluding the invader vaded vegetation (“adding the invader”) caused a signifi- Impatiens glandulifera from the calculations for the invaded cant increase of mean trait dissimilarity, mean nearest vegetation (invaded – expected value) lowered mean SLA, neighbour dissimilarity and functional richness (Fig. 1). while excluding the alien Imperatoria ostruthium increased Excluding the invader from the calculations of FD indices mean height and seed mass. Seed mass also increased after of the invaded vegetation caused a significant decrease of excluding the invasive aliens Solidago gigantea and mean trait dissimilarity and functional richness, when Aster novi-belgii agg. Excluding the alien Mimulus guttatus compared to the real values for the uninvaded and invaded lowered mean SLA and clonality; clonality also decreased vegetation, respectively (Table 1, Fig. 1). The patterns after excluding the alien Reynoutria japonica. Including found for all of the target neophytes together were also the invaders Heracleum mantegazzianum and Mimulus gutta- rather consistent for single target aliens. In two cases (Aster tus in the calculations for the uninvaded vegetation novi-belgii agg. and Solidago gigantea), the changes in mean (uninvaded – expected values) increased the mean value trait dissimilarity associated with the invasion were posi- for SLA when compared to the real value for the uninvaded tively related to changes in species richness. In the case of vegetation. Aster novi-belgii agg., excluding the invader from the calculations for the invaded vegetation increased the mean trait Discussion dissimilarity, mean nearest neighbour dissimilarity and Impact of invasions on values of FD indices and the functional evenness, which is the opposite compared to relation between FD indices and species richness the majority of the data. The invaded plots had higher mean values for height, The values of FD provide a novel and interesting insight seed mass and clonality (Table 2, Fig. 1). When comparing into the community-level impacts of invasive alien the expected values with the real ones, excluding the inva- plants, with the patterns being partially contrasting to der decreased the mean value for height and seed mass, those revealed by either species richness or diversity. while it increased the mean value for SLA and clonality The results show that the way in which FD varies in (Table 2, Fig. 1a). Including the invader in the calculations relation to species richness and how it relates to its loss for the uninvaded vegetation (uninvaded expected) due to the invasions changes depending on the kind of increased the mean value for height and seed mass, while FD indices used. Logically, functional richness, express- it decreased the mean value for SLA (Fig 1b). At the level ing the ranges of trait values present within the commu- of individual target neophytes, significant exceptions to nity, is tightly connected to species richness – the more the patterns at the general level were found. These excep- species present within a vegetation sample, the wider tions did not actually concern the differences between the the ranges of the target functional traits (Villeger et al. invaded and uninvaded vegetation, but rather the impacts 2008). The same is true when two similar samples with

(a)

(b)

Fig. 1. Mean percentages of the FD indices and trait values of the invaded vegetation over expected values, obtained after excluding the invader (a)andof the uninvaded vegetation over expected values, obtained after including the invader (b). The ﬁgure shows percentages of real values are higher (positive value) or lower (negative value) compared to expected values. Mpd, mean trait dissimilarity; mnnd, mean nearest neighbour dissimilarity; sdnnd, SD of the mean nearest neighbour dissimilarity; FRic, functional richness; FEve, functional evenness; height, mean height; SLA, mean speciﬁc leaf area; seed mass, mean seed mass; clonality, mean value of clonality.

Table 2. Mean values of the traits used for calculation of FD indices and also corresponding expected values obtained after excluding the invader from the invaded vegetation and including the invader into the uninvaded vegetation.

All neophytes (mean trait values) Height SLA Seed mass Clonality

Mean invaded 0.358 0.444 0.122 0.553 Mean uninvaded 0.293 0.444 0.114 0.532 Invaded 9 uninvaded difference <0.001 0.69 0.024 0.044 Mean expected invaded (alien excluded from the invaded plots) 0.302 0.455 0.111 0.567 Mean expected uninvaded (alien included in the uninvaded plots) 0.302 0.443 0.115 0.531 Invaded (expected) 9 invaded (real value) difference <0.001 <0.001 <0.001 0.012 Uninvaded (expected) 9 uninvaded (real value) difference <0.001 0.001 0.002 0.629 The table also shows results of the paired tests on the differences between the invaded and adjacent uninvaded vegetation and also between the real values and expected values obtained after including/excluding the invader. different species richness are compared, as is the case on species richness for mathematical reasons rather then with the pair-wise uninvaded and invaded plots – lower biological ones. numbers of species mean narrower ranges of functional In contrast, other FD indices reveal negative (mean trait values. For these reasons, FD indices based on the nearest neighbour dissimilarity, SD of the mean nearest range of functional traits, such as functional richness, neighbour dissimilarity) or no (mean trait dissimilarity) reveal higher values for the adjacent uninvaded vegeta- relation to both values and changes in species richness, tion. Functional evenness, which is also related to trait and even give higher values for the invaded vegetation range (de Bello et al. 2013), might also vary depending (mean trait dissimilarity, mean nearest neighbour dissimi-

Journal of Vegetation Science 894 Doi: 10.1111/jvs.12026 © 2013 International Association for Vegetation Science M. Hejda & F. de Bello Plant invasions and functional diversity larity along with its SD). These indices are not directly Hejda et al. 2009). On the other hand, both of these spe- dependent on the species richness or diversity, but rather cies apparently have notably higher SLA values than other on the mean functional dissimilarity among the present species in the type of vegetation that they invade. species. The fact that these indices are not dependent on Obviously, alien status tends to be associated with species diversity has already been documented (de Bello functional dissimilarities compared to native species, et al. 2006; Pavoine & Bonsall 2011). The value of these FD which makes it a potentially important factor determin- indices can even decrease when a functionally similar ing the individual species′ success. Our results point to species is added to the community, or increase when such the same phenomenon as that proposed in Knapp & a species is excluded (Botta-Dukat 2005; Lanta & Leps Kuhn€ (2012), who show that native and alien species 2006). This is likely why even the target neophytes causing tend to be ecologically different. However, Knapp & the most massive reduction of native species richness and Kuhn€ (2012) document this phenomenon on a large diversity (Heracleum mantegazzianum, Fallopia sp. – see scale of floristic units, while this study shows it on a Hejda et al. 2009 for details) show significantly higher FD local scale of particular invaded communities. These indices for the invaded vegetation. results add to a recent discussion raised in Davis et al. (2011) as to whether it is actually worth distinguishing species according to their origin, since it is the traits of Functional dissimilarity between alien invaders and the species that determine individual species′ success or native species of the resident communities failure. However, growing evidence shows that the In general, excluding the alien from the invaded vegeta- alien status tends to be associated with traits different tion decreases the FD indices, while including it into the from those of native species, so dividing species into uninvaded vegetation increases most of the FD indices aliens and natives has meaning from this point of view. used. Therefore, the results show that invasive aliens tend Based on these results, the functional similarity between to be functionally different compared to native species of the potential invader and native species should be included the resident communities, and are likely to occupy a differ- when considering the probability of successful invasion. ent ecological niche in the invaded community. The only The probability of a successful invasion may not be given clear exception was Aster novi-belgii agg., since its exclusion by the traits of the invader or character of the invaded from the invaded plots causes a significant increase in community per se, but rather by the functional relation mean trait dissimilarity, mean nearest neighbour dissimi- between the potential invader and the majority of native larity and functional evenness. This shows that Aster species present within the invaded community. The func- novi-belgii is actually functionally similar to native species tional dissimilarity of invasive aliens compared to native of the invaded communities, dominated by species such species of the target communities may also be one of the as Alopecurus pratensis, Dactylis glomerata, Chaerophyllum key reasons explaining the success of invasive aliens – they bulbosum and Urtica dioica. are able to produce a large biomass using a different niche Comparisons of the mean values of functional traits space compared to native species. In the future, the con- used for the calculations of FD indices provide a further cept of FD could be used not only when studying the insight on how the invaded vegetation differs from the impact of invasion on the type and number of functional adjacent uninvaded plots and how including/excluding guilds within the invaded communities, but also for esti- the invader affects the vegetation, especially when com- mating the functional dissimilarity between the invasive paring the general trends with the exceptions to these at aliens and native species. This feature may turn out to be the level of single target neophytes. For example, exclud- important when estimating the probability of particular ing the alien invader from calculations for the invaded veg- invasions. etation usually slightly increases the mean value for clonality; however, the opposite is true when a highly clo- Acknowledgements: nal alien e.g. Reynoutria japonica is excluded. The opposite applies for SLA: in general, including the alien in the calcu- This work was funded by grants P505/11/1112 and P505/ lations of SLA for the uninvaded vegetation decreases the 12/1296 from the Grant Agency of the Czech Republic and mean value (suggesting that aliens tend to have a lower by long-term research development project no. RVO SLA than native species), but the mean value increases 67985939 (Academy of Sciences of the Czech Republic). after including the aliens Heracleum mantegazzianum and Many thanks to my colleagues Honza Pergl, Katerina Mimulus guttatus. The invasion by H. mantegazzianum was Stajerov a and Petr Pysek for consultations on the methodol- documented to have a massive negative impact on the ogy, and to Joe Mullen for careful revision of the language. diversity of native species, while M. guttatus was docu- A great thanks go to our JVS editor and to the referees for mented to have a negligible impact (Pysek & Pysek 1995; helping us improve the overall quality of the text.

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Journal of Vegetation Science Doi: 10.1111/jvs.12026 © 2013 International Association for Vegetation Science 897 Journal of Vegetation Science 24 (2013) 898–909 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Effects of land-use changes on plant functional and taxonomic diversity along a productivity gradient in wet meadows Sˇ .Janecˇek, F.de Bello, J. Hornı´k, M. Bartosˇ,T.Cˇ erny´ , J. Dolezˇal,M.Dvorsky´,K.Fajmon, P. Janecˇkova´ ,Sˇ .Jira´ska´, O. Mudra´k & J. Klimesˇova´

Keywords Abstract Abandonment; Biomass; Clonal index; Fertilization; Leaf dry matter content; Questions: To what extent do changes in management (abandonment and fer- Management; Plant height; Plant traits; tilization) affect plant functional and taxonomic diversity in wet meadow com- Seed mass; Speciﬁc leaf area munities? To what extent do the changes in functional and taxonomic diversity depend on site productivity? Nomenclature Kuba´t et al. (2002) Location: Zˇelezne´ hory Mts., Czech Republic.

Received 23 March 2012 Methods: Experimental plots were established on 21 wet meadows differing in Accepted 7 October 2012 productivity and species composition. In each meadow, in 2007, four 1 9 1m Co-ordinating Editor: Alicia Acosta plots were established, representing a full factorial design with abandonment and fertilization as the factors. In each plot, the number of species present was 9 Janecˇek, Sˇ . (corresponding author, recorded in 100 subplots (0.1 0.1 m) in the years 2007, 2009 and 2011. Dif- [email protected]), de Bello, F. ferent indicators of functional diversity (functional richness, functional even- ([email protected]), Bartosˇ,M. ness, and Rao′s quadratic entropy) were calculated using ﬁve functional traits ([email protected]), Cˇ erny´,T.(cerny@ibot. (SLA, LDMC, seed mass, plant height and clonality). Both abundance-weighted cas.cz), Dolezˇal, J. ([email protected]), and non-weighted diversity indices were calculated. Randomization tests (con- Dvorsky´,M.([email protected]), ducted with PERMANOVA) were used to assess the effect of site productivity Janecˇkova´,P.([email protected]), and management on both a-andb-diversity components. Mudra´k, O. ([email protected]) & Klimesˇova´,J.([email protected]): Results: Meadows along the productivity gradient differed in functional and Institute of Botany, Academy of Sciences of taxonomic diversity. Both abandonment and fertilization decreased taxonomic the Czech Republic, Dukelska´ 135, CZ-379 82, diversity. Whereas fertilization decreased functional richness and Rao′s qua- Trˇebonˇ , Czech Republic Janecˇek, Sˇ ., Hornı´k,J.([email protected]) , dratic entropy, abandonment decreased functional evenness. The changes in Bartosˇ,M., Janecˇkova´,P.& Jira´ska´,Sˇ . both taxonomic and functional diversity caused by abandonment and fertiliza- ([email protected]): Centaurea, Society tion occurred faster in more productive meadows. for Landscape Monitoring and Management, Stolany 53, CZ-53803, Herˇmanu˚ vMeˇ stec, Conclusions: The increased dominance of tall species with abandonment and Czech Republic fertilization, followed by the loss of species and the decrease in various indicators de Bello, F., Hornı´k, J., Bartosˇ,M.& of functional diversity, suggest that increased competition for light resulted in Dvorsky´,M.: Department of Botany, Faculty increased trait convergence among co-existing species. In addition, many proof Science, University of South Bohemia, cesses occurring after abandonment and fertilization depend on meadow pro- ˇ Branisˇovska´ 31, CZ-370 05, Ceske´ Budeˇ jovice, ductivity. Results suggest that abundance- and non-abundance-weighted Czech Republic diversity indices give complementary insights on community structure. These Fajmon, K. ([email protected]): Administration of the Bı´le´ Karpaty Protected results imply that changes are needed in current meadow management and Landscape Area, Bartolomeˇ jske´ na´m. 47, conservation. CZ-69801, Veselı´ nad Moravou, Czech Republic

2002, 2005). Traditionally, most research on biodiversity Introduction has focused on taxonomic diversity, measured as species Biodiversity is a multifaceted concept that can be measured richness, species evenness or by various indices that com- with a variety of indicators (Izsa´k & Papp 2000; Ricotta bine richness and evenness. Although many ecological

Journal of Vegetation Science 898 Doi: 10.1111/jvs.12012 © 2012 International Association for Vegetation Science Sˇ .Janecˇek et al. Effects of land-use change on functional and taxonomic diversity theories concerning the mechanisms underlying commu- (Milchunas & Lauenroth 1993; Venterink et al. 2002). nity structure focus on taxonomic diversity and its changes Theoretical predictions are, however, to a certain extent due to environmental and land-use modification, the contradictory. On the one hand, changes in taxonomic mechanisms by which communities respond to these diversity are expected to be more pronounced on less pro- changes are expected to depend on the functional traits of ductive, nutrient-limited sites than on productive ones the species composing these communities (Lavorel & because of the increasing competition that occurs after Garnier 2002). Environmental filtering, disturbance toler- nutrient addition to less productive sites (Tilman 1987). ance, competitive interactions or facilitation, for example, On the other hand, changes in taxonomic diversity are are processes that select for species with specific functional expected to be pronounced on more productive sites traits related to competitive ability, resource acquisition because plant growth and hence population dynamics are and storage, reproduction, dispersal, persistence, etc. (Violle faster with high productivity (Huston 1979). Like theoreti- et al. 2007; Spasojevic & Suding 2012). This sorting of spe- cal predictions, results of empirical studies have not been cies based on their traits often results in non-random distri- uniform (e.g. Milchunas & Lauenroth 1993; Gough et al. bution of traits in a community. Consequently, researchers 2000) perhaps because both theoretical and empirical assessing the effect of environmental changes on commu- studies have not considered functional diversity along with nity structure are increasingly considering both taxonomic taxonomic diversity. We expect that the assessment of diversity and functional diversity (Mayfield et al. 2010). functional diversity in combination with taxonomic diver- Like taxonomic diversity, functional diversity can be sity will provide important insights into how the commu- expressed using several indices (Ville´ger et al. 2008); nities respond to land-use changes. This approach should among these, functional richness, functional evenness and include the comparison of the different spatial components functional divergence indices should cover the main of diversity (a-, b-andc-diversities, i.e. within-commu- dimensions of functional diversity (Ville´ger et al. 2008). nity, between-community and total diversity; de Bello At a small spatial scale, the most taxonomically diverse et al. 2010). In particular, b-diversity within a site (i.e. the plant communities on Earth are European hay meadows turnover among experimental plots with different man- (Wilson et al. 2012), even though species richness in these agement established on one meadow) should verify the meadows has been reduced by modifications in agricul- expectations of a greater vs. lesser response to land-use tural practices during the last decades (Linusson et al. changes within more productive sites. b-Diversity among 1998; Jensen & Schrautzer 1999; Helm et al. 2006; Halda sites (i.e. turnover across meadows) will, in contrast, test et al. 2008). Many experimental studies have reported that for the effect of environmental filtering on species and the most important factors affecting meadow species diver- functional trait turnover along the productivity gradient. sity are eutrophication and abandonment (Kull & Zobel In this study, we therefore asked: to what extent do the 1991; Willems et al. 1993; Lepsˇ 1999; Klimesˇ et al. 2000; changes in taxonomic or functional diversity (both within Krahulec et al. 2001; Pavlu˚ et al. 2007; Ceulemans et al. and between plots) in response to abandonment and 2011). Although the negative effect of land-use changes on eutrophication depend on site productivity? This question, species richness is well documented, the changes in func- which has seldom been the topic of experimentation, has tional diversity may not simply follow the decrease in taxo- both theoretical and practical importance. From a practical nomic diversity because functional diversity and perspective, the results can help indicate whether limited taxonomic diversity can be both mathematically and conservation resources should be directed toward more biologically independent (Dıáz & Cabido 2001; Ville´ger productive or less productive meadows. In this study, we et al. 2008; Sasaki et al. 2009; Mayfield et al. 2010). It evaluated the effect of abandonment and fertilization on is becoming increasingly evident that characterizing the taxonomic and functional diversity on 21 semi-natural relationship between taxonomic and functional diversity wet meadows along a productivity gradient. can provide insight into how different disturbances or management strategies affect community assembly and ecosys- Methods tem functioning (Sasaki et al. 2009; Mayfield et al. 2010). Experimental setup Changes in both taxonomic and functional diversity due to land-use changes can be region-specific (Dıá´zetal. The study area is located in the Zˇelezne´ hory Mts. (eastern 2007; Mayfield et al. 2010) and can differ along environ- Bohemia, Czech Republic). The landscape is composed of mental gradients (Dıáz et al. 1998; de Bello et al. 2006, forest fragments, arable fields, villages, ponds and different 2012; Bernhardt-Ro¨mermann et al. 2011; Lavorel et al. semi-natural meadows. Meadows represent regional biodi- 2011; Pakeman 2004, 2011). Productivity has been versity hotspots and are often protected as nature reserves. pointed out as one of the most important gradients We selected 21 of these wet meadows, differing in species determining plant biodiversity response to management composition and productivity (Appendix S1). As a surro-

Journal of Vegetation Science Doi: 10.1111/jvs.12012 © 2012 International Association for Vegetation Science 899 Effects of land-use change on functional and taxonomic diversity Sˇ .Janecˇek et al. gate for meadow productivity, we used standing crop at (Johansson et al. 2011; Klimesˇova´ et al. 2011). Data on the peak of the growing season (July). The standing crop, SLA, LDMC and seed mass were taken from the LEDA as measured at the beginning of the experiment (see Traitbase (Kleyer et al. 2008), and the plant height was below), ranged from 255 to 680 g m2 of dry biomass. taken from Kuba´t et al. (2002). Clonal index was calcu- On each of the 21 meadows, four 1 9 1 m plots were lated as the sum of ordinal values of multiplication rate established early in the spring of 2007 (84 in total). In July and lateral spread reported in the CLO-PLA database (Kli- 2007, plant species occurrences in 100 subplots mesˇova´ & Klimesˇ 2006). For more details on clonal index (10 9 10 cm) in each of the 84 plots were recorded as calculation, see Johansson et al. (2011). baseline data. We then assigned treatments to each of the four plots per meadow: the plots were mown, mown– fertilized, abandoned or abandoned–fertilized. Mowing Alpha-diversity was done in July of every year. Fertilizer was applied at We used three indices of functional diversity (FD) and four 20 g of mineral NPK (10% N, 10% P O ,10%KO) per 2 2 2 parameters of taxonomic diversity (TD). FD was described m2 at the end of July 2007 and at 50 g m2 in the second by functional richness (FRich; Mason et al. 2005; Ville´ger half of April in subsequent years. These doses represent the et al. 2008), functional evenness (FEve; Mason et al. maximum amount recommended for extensive grasslands 2005; Ville´ger et al. 2008) and Rao′s quadratic entropy (30–50 g m2). Species composition in each of the expressing functional divergence (Rao index; Rao 1982). 10 9 10 cm subplots in each plot were recorded again FRich is deﬁned as the functional trait space that is occu- after 2 yrs (2009) and 4 yrs (2011). The standing crop bio- pied by the community, and for one trait it represents the mass was clipped from two 1 9 1 m plots on each meadow range of trait values in a community. FEve represents the in 2007 after the species were recorded (these two plots evenness in both the abundance and trait values of species were subsequently assigned to mown and mown–fertilized in a community (Ville´ger et al. 2008). The Rao index rep- treatments). The biomass was dried for 12 h at 85 °C, and resents the extent of functional dissimilarity between spe- weighed. The biomass per m2 was then calculated as the cies pairs (Rao 1982): mean value from these two plots.

XN XN Rao ¼ d p p ð1Þ Plant functional traits ij i j i¼1 j¼1 We selected five functional traits related to key plant functions. Three of them, specific leaf area (SLA), height and where dij is the dissimilarity between species i and j, and pi seed mass, were suggested in Westoby (1998) as traits that and pj is the relative abundance of species i and j,respec- express fundamental differences in ecological behaviour of tively. The relative abundance of the particular species was plant species. SLA was calculated as the leaf area of the calculated as the number of 10 x 10 cm subplots where fresh leaf divided by its dry mass (in m2 kg1). SLA is usu- the species was present over the sum of these presences for ally positively correlated with potential relative growth all species in the 1 x 1 m plot. For a more detailed descrip- rate and negatively correlated with investment into leaf tion of functional indices, see Appendix S2. Rao index and protection (Schierenbeck et al. 1994; Westoby 1998). functional evenness were computed both with and with- Plant height is closely related to competitive ability and out consideration of differences in species abundance (the represents the trade-off between benefits resulting from calculations without abundance effect assume that all spe- access to light and costs of stem construction and mainte- cies have equal abundances). This was done to assess how nance (Falster & Westoby 2003). Moreover, these costs species occurrence alone vs. species composition including can be higher when the regular mowing removes a large species abundance affected FD. All functional diversity proportion of above-ground biomass (Klimesˇova´ et al. indices were calculated using the five functional traits 2010). Seed mass is related to the ability to survive hazards mentioned above. Data on SLA, LDMC, plant height and that occur at the start of seedling growth (Westoby et al. seed mass were log transformed to decrease the effect of 1996). In addition to these three traits, we used leaf dry extreme values. Subsequently, multivariate species trait matter content (LDMC), which represents the ratio of leaf dissimilarities were combined and standardized using the dry mass to fresh mass (in g kg1), and the clonal index. Gower approach to compute FD (Gower 1971; Podani Although LDMC is often correlated with SLA, it does not 1999; Pavoine et al. 2009; de Bello et al. 2012). always have the same biological functions (Cornelissen To characterize TD, we used species evenness, species et al. 2003). The clonal index reflects the degree of clonal richness, the Simpson reciprocal diversity index and the reproduction and space occupancy and is important in total number of presences in a community (i.e. the sum of meadow communities where most species are perennial richness of all 100 10 x 10 cm subplots). To describe the

Journal of Vegetation Science 900 Doi: 10.1111/jvs.12012 © 2012 International Association for Vegetation Science Sˇ .Janecˇek et al. Effects of land-use change on functional and taxonomic diversity community evenness independently of species richness, lation) depended on site productivity of individual mead- we used the converted index of dominance (Williams ows. Changes were expressed as the diversity index or 1964): E=1/(D*S),whereS is the species richness and D is plant trait value in the third year divided by the value for 2 the index of dominance. D=∑(ni/N) , where ni is the the same parameter in first year of the experiment. Model number of species presences of the ith species in 100 sub- 3 was similar to model 2 but compared the first and fifth plots, and N is the total sum of number of all species pres- year, i.e. it determined whether changes after 4 yrs of ences in the community. The Simpson reciprocal diversity experimental manipulation depended on site productivity index was expressed as 1/D,whereD is the index of domi- of individual meadows. In models 2 and 3, fertilization and nance and corresponds to the correction proposed by Jost abandonment were used as fixed factors and productivity (2007) and de Bello et al. (2010) to decompose diversity was the continuous fixed effect variable. The same into a-, b-andc-diversities (see next section). approach was used for both a-diversity indices and for b- diversity (i.e. functional and taxonomic dissimilarity between plots). Beta-diversity To test whether dissimilarity between plots within par- The taxonomic and functional b-diversity (i.e. taxonomic ticular meadows (i.e. dissimilarity between mown, mown– and functional dissimilarity between individual pairs of fertilized, abandoned and abandoned–fertilized plots) plots) was computed according to de Bello et al. (2010) depended on meadow productivity, we calculated for each (see also Jost 2007; Ricotta & Szeidl 2009). The b-diversity meadow, year and dissimilarity index the mean distance to expresses a dissimilarity, or ‘turnover’, between each pair the common centroid (or ‘meadow heterogeneity’) using of plots based on either taxonomic or functional character- the PERMDISP utility in the program PERMANOVA+ for istics. We followed four steps. First, we calculated the Rao PRIMER. Dependence of these mean distances on produc- and reciprocal Simpson index for each plot in each year (a- tivity was then analysed with linear regression models in diversity) for summarizing FD and TD, respectively. Note which meadow heterogeneity (either taxonomic or func- that the Rao index is the generalization of the Simpson tional) was the dependent variable and standing biomass index of diversity (Lepsˇ et al. 2006), and if dissimilarity dij was the predictor. in eq. 1 is 1, then Rao = 1 – Simpson dominance. Therefore, taxonomic and functional diversity can be Results compared within the same mathematical framework. Sec- Taxonomic diversity ond, the a-diversity for taxonomic diversity was calculated for each plot as the Simpson reciprocal diversity index The meadows differed in all parameters of taxonomic (1/D) and similarly for functional diversity as 1/(1 –Rao). diversity (Table 1). Three taxonomic indices, i.e. species Third, the c-diversity was determined for each pair of plots: richness, the Simpson index and total number of species pi and pj in eq. 1 represent the mean abundance of species presences, decreased over time in response to both j and i in both plots. Fourth, b-diversity between each pair meadow abandonment and fertilization (Fig. 1a, b, c, of plots was subsequently calculated as b = ((c – mean a) Table 1; interactions were significant for Year*Treatment) *100)/c. R software (R Foundation for Statistical Comput- whereas species evenness did not show any significant ing, Vienna, AT) and the function ‘Rao’ (de Bello et al. trend (Table 1). 2010) were used for these calculations. The response of taxonomic diversity indices to changes in management, mainly fertilization, depended on meadow productivity particularly over the first 2 yrs (Table 1, Statistical analyses Model 2 and 3). These trends, however, disappeared when We used different models based on randomization tests changes after 4 yrs were considered in that the response and the program PERMANOVA+ for PRIMER (Anderson became more similar across meadows (Table 1, Model 3). et al. 2008) to assess management and site productivity. The changes after 2 yrs were most obvious for the Simpson We constructed three models for each diversity index diversity index. The Simpson index decreased rapidly (Table 1). Model 1 was used to test the effect of abandon- mainly in abandoned–fertilized plots on more productive ment and fertilization on changes in diversity indices over meadows (Fig. 2). These trends were also similar for the time. In this model, meadow identification was used as a other three taxonomic indices (Table 1, Appendix S3). random factor, abandonment and fertilization were used as fixed factors, and year was used as a continuous fixed Functional diversity predictor. Model 2 determined whether the changes between the first and third year of the experiment With regard to the effects of treatments and productivity (i.e. whether changes after 2 yrs of experimental manipu- on individual traits used for calculations of functional

Table 1. The effect of fertilization, abandonment and productivity on individual parameters of functional (FD) and taxonomic (TD) diversity as indicated by permutation P-values.

E Richness Sim. NSP FRich FEve FEve-Ab Rao Rao-Ab

Model 1 Meadow 0.000 0.000 0.000 0.000 0.000 0.000 n.s. 0.000 0.000 Year (Y) 0.030 0.000 0.000 0.000 0.000 0.001 n.s. 0.003 0.001 Abandonment (A) 0.076 0.000 0.004 0.000 0.022 0.001 n.s. n.s. n.s. Fertilization (F) n.s. 0.001 0.000 0.000 0.011 n.s. n.s. 0.033 0.006 Y*A n.s. 0.000(-) 0.001(-) 0.000(-) n.s. 0.012(-) n.s. n.s. n.s. Y*F n.s. 0.000(-) 0.000(-) 0.001(-) 0.017(-) n.s. n.s. n.s. 0.008(-) A*F n.s. 0.060 n.s. n.s. n.s. n.s. n.s. n.s. n.s. Y*A*F n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Model 2 (3rd/1st year) Biomass (B) 0.056(-) 0.056(-) 0.000(-) 0.080(-) n.s. n.s. n.s. n.s. 0.097(-) Abandonment (A) n.s. n.s. 0.001(-) 0.000(-) n.s. 0.079(-) n.s. n.s. n.s. Fertilization (F) n.s. n.s. 0.001(-) 0.000(-) n.s. 0.010(-) n.s. n.s. 0.001(-) B*A n.s. n.s. 0.008 n.s. n.s. 0.023 n.s. n.s. 0.061 B*F 0.091 0.091 0.010 0.074 n.s. n.s. 0.0590 0.079 n.s. AxF n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. B*A*F n.s. n.s. n.s. n.s. n.s. n.s. n.s. 0.079 n.s. Model 3 (5th/1st year) Biomass (B) 0.027(-) 0.006(-) 0.000(-) 0.008(-) n.s. 0.020(-) n.s. n.s. 0.023(-) Abandonment (A) n.s. 0.001(-) 0.001(-) 0.000(-) n.s. 0.011(-) n.s. n.s. n.s. Fertilization (F) n.s. 0.001(-) 0.000(-) 0.000(-) n.s. n.s. n.s. n.s. 0.004(-) B*A n.s. n.s. n.s. n.s. n.s. 0.057 n.s. n.s. n.s. B*F n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. AxF n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. B*A*F n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. Superscript signs () or (+) indicate a negative or positive effect of explanatory variables. E – Taxonomic evenness, Sim – Simpson reciprocal diversity index, NSP – Total number of species present, FRich – Functional richness, FEve –Functional evenness, Rao – Rao′s quadratic entropy, -Ab – Functional index considering the effect of abundance. Values with P < 0.05 and marginally significant P-values 0.1 > P < 0.05 are displayed in bold and in bold italics, respectively. Model 1: Effect of fertilization and abandonment on FD and TD changes in the course of the experiment. The explanatory variable year was considered as a continuous variable. Because interactions with year are of the greatest interest, they are the only ones accompanied with signs ( ) or (+). Model 2:Effectof fertilization, abandonment and biomass productivity on ratios between values of FD or TD parameters in the third vs. first year of the experiment (i.e. 2yrs after experimental manipulations began). Model 3: Effect of fertilization, abandonment and biomass productivity on ratios between values of FD or TD parameters in the fifth vs. first year of the experiment (i.e. 4 yrs after experimental manipulations began). For interaction effects of models 2 and 3, see Fig. 3 and Appendix S4. diversity indices, effects on plant height were the most low productivity (Fig. 2, Table 1). Moreover, fertilization consistent. The mean plant height, both weighted or not reduced functional richness (Fig. 1d), while the effects of weighted by species abundance, increased after abandon- fertilization along the productivity gradient on the Rao ment and fertilization (Appendix S3). The responses of index were only marginally significant. The marginal effect other traits to treatments differed depending on treatment on the Rao index was manifest mainly by an increase in (fertilization vs. abandonment) and whether community the Rao index in abandoned–fertilized plots after 2 yrs of means or community weighted means were considered experimental manipulation on meadows with high bio- (for details, see Appendix S3). mass production (Table 1, Appendix S4). The single and Except for functional evenness, when species abun- only marginally significant effect on changes in diversity dance was taken into account, the meadows differed in all along the productivity gradient after 4 yrs of the experi- examined parameters of functional diversity (Table 1). mental manipulation was the effect of abandonment on The effects of treatments were much more variable on functional evenness (Model 3 in Table 1). This effect was functional diversity than on taxonomic diversity. related mainly to a decrease in functional evenness in With regard to changes that were not affected by species abandoned–fertilized plots (Table 1, Appendix S4). abundance, abandonment decreased functional evenness When calculation of functional diversity indices (Fig. 1e, Table 1), and after 2 yrs of experimental included the effect of plant abundance, fertilization manipulations functional evenness decreased more after negatively affected the Rao index (Table 1, Fig. 1f). The abandonment on meadows with high productivity than effect of fertilization on functional evenness was

(a) (d)

(b) (e)

Fig. 1. Functional and taxonomic diversity in the last year of the experiment (2011). Taxonomic diversity expressed as (a) species richness, (b) Simpson diversity index, (c) NSP. Functional diversity expressed as (d) functional richness, (e) Functional evenness and (f) Rao index. Aba – abandoned. NSP – total number of species present. Mean ± SE. marginally signiﬁcant when calculations included plant treatments (as indicated by the distance to the meadow abundance; functional evenness decreased more in the centroid) on individual meadows was positively related to second year after experimental manipulation due to fertil- meadow productivity in both 2009 and 2011 (Fig. 3). The ization in meadows with high rather than with low pro- one exception was taxonomic diversity when the effect of ductivity (Table 1, Appendix S4). abundance was included in 2011 (Fig. 3, Table 3).

Taxonomic and functional dissimilarity among plots Discussion Treatments generally increased both taxonomic and func- Our study revealed that abandonment and eutrophication tional dissimilarity between plots within a meadow, espe- (due to fertilization) reduce both the taxonomic and func- cially when plant abundance was considered. When plant tional components of plant diversity in meadows. The abundance was not considered, fertilization and abandon- response to abandonment and eutrophication, however, ment (the latter was only marginally significant) affected often depended on meadow productivity. The more pro- only functional dissimilarity (Rao index; Table 2, Whole ductive meadows lost taxonomic as well as functional model). The two models, which were tested separately for diversity more quickly than less productive meadows. the third and fifth year of the experiment, showed basically These findings are important for understanding the ecolog- the same pattern (with the exception of the Rao index ical processes in semi-natural meadows and suggest that when abundance was not considered). Interestingly, the conservation efforts should be targeted not only on spe- extent of taxonomic and functional responses to cies-rich, low-productivity meadows but also on the more

degradation of meadows of both low and high productivity could level off over longer periods, while short-term effects are more intense in productive meadows.

Functional diversity The effects of abandonment and fertilization were more variable on functional diversity than on taxonomic diversity. This agrees with the absence of a simple linear relationship between functional and taxonomic diversity (see also Dıáz & Cabido 2001; Sasaki et al. 2009). In contrast to de Bello et al. (2006), who found a positive effect of a treatment (grazing) on species richness and a negative effect on functional diversity in the moistest locations, we found the same negative effect of treatments on both taxonomic and functional diversity. The different responses of species vs. plant traits were demonstrated by Pakeman (2004). He compared data from ten published experiments on grasslands and demonstrated that responses of traits to grazing are more often modulated by productivity than responses of plant species. Dıá´z et al. (2007) showed in their meta-analysis that effect of defoliation (grazing) on individual traits can differ according to different climate and herbivory history, and Mason et al. (2011) demon- Fig. 2. Dependence of changes in Simpson diversity and functional strated that mowing increase abundance-weighted niche evenness on meadow productivity after 2 yrs of experimental overlap (approximated as overlap of quantitative func- manipulation. The statistical significances of regression lines for individual treatments are presented: n.s. –not significant, *P < 0.05. tional traits) between species in a wet meadow in South Bohemia. What is the reason for context-specific effects of productive meadows that can more rapidly show changes changes in grassland management? One reason could be in plant diversity. different assemblage rules in individual plant communities (e.g. Keddy 1992). Due to environmental filters, the plants in a community represent only a certain range of func- Taxonomic diversity tional traits that are available in the species pool, and these We detected a general negative impact of abandonment ranges differ among communities (Dıáz & Cabido 2001). and fertilization on taxonomic diversity in that most diver- Consequently, decreases in species richness due to aban- sity indices decreased with both abandonment and fertil- donment of traditional management (e.g. grazing or mow- ization. These changes were most obvious in the ing) or increasing nutrient level can have different effects abandoned–fertilized treatment. Our results therefore cor- on species diversity in comparison with functional diver- respond with those of similar studies (e.g. Kull & Zobel sity. In our study, fertilization decreased the volume of 1991; Willems et al. 1993; Lepsˇ 1999; Honsova´ et al. 2007 functional space (i.e. functional richness), indicating that ;Pavlu˚ et al. 2007; Ceulemans et al. 2011) and highlight species with unique trait combinations were excluded the threats caused by recent land-use changes (Isselstein from the community and that resident species became et al. 2005 ; Henle et al. 2008). We found, however, that more similar (as indicated by the Rao index). These con- changes in taxonomic diversity differed along the produc- clusions are in agreement with the recent study of Gerhold tivity gradient, i.e. more productive meadows lost taxo- et al. (2013) , who report that fertilization of mesophytic nomic diversity more quickly than less productive grasslands in Estonia leads to exclusion of functionally dis- meadows 2 yrs after start of the experiment. Such a rela- similar species. The increased dominance of tall species tionship is consistent with the idea that eutrophication with abandonment and fertilization in this study (as sup- accelerates loss of species diversity (Grime 1973; Keddy ported by changes in the community trait mean; Appendix et al. 1997; Hautier et al. 2009; Li et al. 2011), but shows S3), followed by the loss of species and the decrease in vari- that these effects are productivity-dependent. Nevertheless, ous facets of functional diversity, suggest that increased this trend disappeared when we considered changes 5 yrs competition for light increased trait convergence between after land-use changes were introduced. This indicates that co-existing species. Our result that functional diversity

Fig. 3. Dissimilarity of plots on individual meadows as affected by meadow productivity. Points are related to individual meadows in individual years and represent the mean distance of four plots (i.e. mown, mown–fertilized, abandoned, and abandoned–fertilized) to their common centroid in functional or taxonomic space.

Table 2. Effect of abandonment and fertilization on functional and taxo- decreases with fertilization is also in accordance with Bow- nomic dissimilarity (b-diversity) as indicated by permutation P-values. In all man et al. (1995), who showed that some growth forms significant tests, abandonment and fertilization increased b-diversity. having similar traits (e.g. dominant graminoids) are able to Simpson Simpson-Ab Rao Rao-Ab increase nitrogen acquisition after fertilization and invest more in photosynthesis than other growth forms. In con- Whole model Meadow 0.000 0.000 0.000 0.000 trast to eutrophication, defoliation (mowing or grazing) is Year (Y) 0.015 0.000 0.000 0.000 expected to have negative effects on these growth forms as Abandonment (A) 0.048 0.000 n.s. 0.006 they can be more affected by loss of the above-ground bio- Fertilization (F) n.s. 0.000 n.s. 0.074 mass investment (Klimesˇova´ et al. 2010); assemblage-level Y*A n.s. 0.000 0.055 0.003 thinning resulting from increases in individual plant size * Y F n.s. 0.001 0.035 0.006 could enhance this effect (Oksanen 1996; Stevens & Carson A*F n.s. 0.004 n.s. n.s. 1999). These processes were especially obvious on more Y*A*F n.s. n.s. n.s. Model 2009 productive sites. The influence of meadow productivity on Meadow 0.000 0.000 0.000 0.000 treatment effects was similar for taxonomic as well as func- Fertilization (F) n.s. 0.000 n.s. 0.066 tional diversity and fell over time. Abandonment (A) n.s. 0.000 n.s. 0.014 Furthermore, we have shown that the observed changes AxF n.s. n.s. n.s. n.s. in a-andb-diversities are often dependent on whether or Model 2011 not we consider abundance of individual species. When Meadow 0.000 0.000 0.000 0.000 abundance is not considered, the diversity indices are Fertilization (F) n.s. 0.000 0.075 0.011 Abandonment (A) n.s. 0.000 n.s. 0.001 affected only by the changes in occurrence, whereas with AxF n.s. 0.069 n.s. n.s. abundance weighting, the results are also affected by changes in relative abundances of individual species. -Ab – the effect of plant abundance was included. The whole model tests dissimilarity across all years (including the first year – 2007 – when baseline Ecologists and conservationists should keep in the mind data were collected). Model 2009 and Model 2001 test dissimilarity in the that abundance-weighted and non-weighted matrices rep- third and fifth year of the experiment, respectively. resent complementary rather than alternative points of

Table 3. Dependence of dissimilarity between four plots on meadow productivity.

Year Simpson Simpson-Ab Rao Rao-Ab

r P r P r P r P

2007 0.2368 n.s. 0.3864 n.s. 0.2778 n.s. 0.1470 n.s. 2009 0.5424 0.0111 0.4517 0.0398 0.4528 0.0393 0.5487 0.0100 2011 0.6913 0.0005 0.1688 n.s. 0.4382 0.0469 0.4669 0.0329 Dissimilarity was measured as the mean distance of the four plots in the meadow to the centroid. Mean distances were calculated with PERMDISP. -Ab – the effect of plant abundance was included. views on processes in the plant communities (Pakeman meadows, where exclusion of functionally dissimilar spe- et al. 2008; Klimesˇova´ et al. 2011). cies resulted in convergence. Based on these findings, we suggest that conservation management plans should Implications for management involve not only species-rich, nutrient-poor meadows but also those that are relatively species-poor and nutrient-rich. The taxonomic and functional dissimilarities among plots within individual meadows increased with productivity. Acknowledgements This finding confirms the classical hypotheses of Milchunas & Lauenroth (1993) that dissimilarity between meadow We thank Elisˇka Pataćˇova´,Elisˇka Padysˇa´kova´, Jan Altman, communities under different management increases with Vojteˇch Lanta, Terezie Janecˇkova´, Lenka Lesˇtinova´, Karla site productivity. This hypothesis, however, has largely not Kunertova´, Barbora Zdvihalova´,Marke´ta Zdvihalova´, been verified empirically, and to the best of our knowledge, Michal Jira´sky´, Milan Jira´sky´, Alena Bartosˇova´, Zuzana the current study is the first to test the hypothesis in terms Chlumska´,Ade´la Cejnarova´, Toma´sˇ Krejcˇı´, ZdeneˇkIpser, of functional diversity (but see Carmona et al. 2012). More- Kamila Lencova´, Viktorie Ingerlova´, Lucie Drahnı´kova´, over, we have shown that this productivity-dependent Adam Klimesˇ,Milosˇ Dudycha, Jana Kantorova´,Martin response to management is particularly evident in the short Antzak, Jana Antzak, Jirˇı´ Jan, Eva Hojerova´,HanaHoje- term, but tends to weaken with time. In other words, the rova´ and Josef Hojer. This study would not have been pos- changes caused by eutrophication and/or abandonment in sible without cooperation and support from the Zˇelezne´ meadows occur faster on more productive meadows than hory Protected Landscape Area Administration, namely on less productive meadows, and are most evident shortly Josef Rusnˇ a´k and Vlastimil Perˇina. This research was sup- after the meadow fertilization or abandoned; this finding ported by the Grant Agency of the Czech Republic (GA has important implications for conservation. We suggest 526/07/0808, GA 526/09/0963, GA P505/12/1296), the that the appropriate frequency of monitoring to observe tra- long-term research development project no. RVO jectories in diversity changes could differ for different types 67985939 and the University of South Bohemia, Cˇ eske´ of meadow and priorities in management should be set with Budeˇjovice (GAJU 138/2010/P). respect to meadow productivity. Because budgets for meadow management are often limited, the conservation authorities usually allocate all References resources into regular management of a small number of Anderson, M.J., Gorley, R.N. & Clarke, K.R. 2008. PERMANO- the most diverse and less productive meadows. These VA+ for PRIMER: guide to software and statistical methods.PRI- meadows are then mown every year, whereas other less MER-E, Plymouth, UK. diverse and more productive meadows hosting fewer rare de Bello, F., Lepsˇ, J. & Sebastia´, M.T. 2006. Variation in species plants are mostly abandoned. Our results suggest, how- and functional plant diversity along climatic and grazing gra- ever, that extensive management of diverse meadows with dients. Ecography 29: 801–810. low productivity and intensive management of less diverse de Bello, F., Lavergne, S., Meynard, C.N., Lepsˇ, J. & Thuiller, W. meadows with high productivity could prevent their rapid 2010. The partitioning of diversity: showing Theseus a way degradation. out of the labyrinth. Journal of Vegetation Science 21: 992–1000. de Bello, F., Janecˇek, Sˇ., Lepsˇ, J., Dolezˇal, J., Mackova´, J., Lanta, Conclusions V. & Klimesˇova´, J. 2012. Different plant scaling in dry versus wet Central European meadows. Journal of Vegetation Science This study documented that abandonment and fertilization 23: 709–720. reduce both plant functional and taxonomic diversity of Bernhardt-Ro¨mermann, M., Ro¨mermann, C., Sperlich, S. & wet meadows along a productivity gradient. The changes Schmidt, W. 2011. Explaining grassland biomass – the were faster and more pronounced on more productive contribution of climate, species and functional diversity

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Journal of Vegetation Science Doi: 10.1111/jvs.12012 © 2012 International Association for Vegetation Science 909 Journal of Vegetation Science 24 (2013) 910–920 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Comparing functional diversity in traits and demography of Central European vegetation Tomas Herben, Zuzana Novakov a & Jitka Klimesova

Keywords Abstract Botanical garden; Habitat filtering; LHS traits; Seed reproduction; Standardized effect size; Question: A major obstacle to understanding non-random patterns in plant Trait under-dispersion; Vegetative traits (over-dispersion or under-dispersion) has been our limited knowledge of reproduction trait–demography relationships for large sets of species. Here, we suggest that some of the needed data on demographic processes can be gathered from growth Received 4 April 2012 records on plants in botanical gardens. We examine within-community patterns Accepted 11 January 2013 in demographic responses determined from such growth records, and ask Co-ordinating Editor: Francisco Pugnaire whether they are different from patterns in plant traits. Location: Czech Republic. Herben, T. (corresponding author, [email protected]): Institute of Botany, Methods: We assembled data on seed and vegetative reproduction for ca. 1000 Academy of Science of the Czech Republic, Central European species from the Botanical Garden of Charles University in CZ-252 43, Pruhonice, Czech Republic and Prague. We used these data as estimates of potential vegetative and seed repro- Department of Botany, Faculty of Science, Charles University, Benatska2,CZ-12801, duction of individual species under favourable conditions. We linked these data Praha 2, Czech Republic with co-occurrence data from the Czech National Phytosociological Database Novakov a, Z. ([email protected]): Botanical and with data on major species traits. We examined dispersion of both species Garden of the Charles University, Na Slupi 16, traits and garden reproduction using randomization tests on the data set as a CZ-128 01, Praha 2, Czech Republic whole and on the data stratified using EUNIS classification into seven or 32 habi- Klimesova, J. ([email protected]): tat types. Institute of Botany, Academy of Sciences of the Czech Republic, CZ-379 82, Trebon, Czech Results: The patterns found for species traits and for garden reproduction are Republic similar, with strong under-dispersion for the data set as a whole and diminishing under-dispersion in subsets of the data. Under-dispersion was much stronger for traits than for garden reproduction. No over-dispersion was detected in either trait or garden reproduction data. Conclusions: The major source of the pattern in the data is environmental filtering. Stronger filtering for traits indicates that the linkage between environment and traits is much tighter than that between environment and demography. In ecologically homogeneous communities, reproduction parameters are closer than trait values to a random distribution, indicating that co-existence of species are not limited by either similarities or differences in their demography. These findings show that trait dispersion need not be directly related to species demography and, more generally, that correct identification of trait–demography relationships is necessary for better understanding patterns of trait dispersion.

communities. First, mean values of many traits differ Introduction across community types (e.g. Dı´az et al. 2004; Messier Investigating functional diversity of plant communities et al. 2010). Further, trait structure within communities using species traits has become a major focus of research in may also show different types of non-random pattern. plant ecology in the past decade, revealing several types of Environmental ﬁltering makes trait values of individual pattern in the distribution of trait values in plant species within a community more similar to each other

Journal of Vegetation Science 910 Doi: 10.1111/jvs.12054 © 2013 International Association for Vegetation Science T. Herben et al. Functional diversity in demography than would be expected from random processes (‘trait In contrast, definitions of compound demographic parame- under-dispersion’; Ackerly & Cornwell 2007; de Bello ters such as reproduction are much less arbitrary. 2012). Trait under-dispersion may also result from selec- While the relationships between traits and occurrence tion for equalizing processes in competition (Chesson of plants in different environments are well established for 2000; Mayfield & Levine 2010). On the other hand, trait large sets of species (see e.g. Grime et al. 1997; Dıáz et al. values of individual species in a community can have val- 2004), data on demographic processes are largely restricted ues that are more dissimilar to each other than expected to relatively few species (Burns et al. 2010). This makes from a random distribution (‘trait over-dispersion’); this community-wide analysis of such parameters nearly has been ascribed to microenvironmental heterogeneity impossible. Here, we suggest that useful proxy variables for (Cornwell & Ackerly 2009), facilitation (Verdu et al. 2009) plant demographic processes for extensive sets of species or niche differentiation (Stubbs & Wilson 2004; for a can be gathered from growth records on plants in botanical review see Gotzenberger€ et al. 2012). gardens. Botanical gardens have been used as valuable Understanding these trait patterns is contingent on sources of data for a number of ecological subjects (Gratani understanding the links between these traits and plant per- et al. 2008; Dawson et al. 2009; Primack & Miller-Rushing formance within communities. Species values of each par- 2009; Ferenczy et al. 2010). They could also provide rea- ticular trait (‘soft’ traits of Lavorel & Garnier 2002) define sonable assessment of demographic processes under good the potential of a given plant species to perform particular or optimum conditions for large sets of species, given that ecological functions, but the actual performance of any species are kept in suitable environments and proper individual involves responsive behaviour to environmental records are kept on performance of each species. Such factors, both abiotic and biotic (Suding et al. 2003; McGill records can provide parameters (e.g. growth and reproduc- et al. 2006; see also Fig. 1). Relationships between organis- tion) that are more ecologically relevant than most soft mal traits and community composition thus depend on a traits, although they do not constitute strict ‘hard’ traits as chain of mediating processes that include the growth, sur- they typically document performance in only a given, vival and reproduction (Suding et al. 2003; Clark et al. rather favourable, environment. While there can be a 2004; Gross et al. 2009; these processes largely correspond number of issues regarding data from these records (e.g. to the ‘hard traits’ of Weiher et al. 1999). Due to the high semi-quantitative data only, small sample sizes, reduced number of plant traits affecting demographic responses, inter-specific competition, low genetic variation), these there is a fair amount of arbitrariness in the choice of traits disadvantages are easily outweighed by the large number to be examined (Bernhardt-Ro¨mermann et al. 2008). of species that can be compared.

Garden LHS traits reproduction

Environment Environment Trait x Productivity environment constraints interaction Filtering Demography and dispersal in the field

Community assembly

Fig. 1. Conceptual relationships between garden reproduction, LHS traits and field demography. Dotted line indicates environmental effects (filtering). Garden reproduction and LHS traits are proxies for different processes in the field, and the environment acts on each of them, although to some extent differently. Demographic processes (both in the garden and in the field) integrate effects of a number of traits into a single demographic response.

In this paper, we examine whether such data on repro- differences in these means critically constrain our capac- duction, and relative roles of vegetative and seed repro- ity to interpret dispersions (Ackerly & Cornwell 2007; de duction, show non-random patterns similar to the Bello et al. 2009). patterns shown by species trait values. We hypothesize that habitat filtering will be weaker for reproductive per- Methods formance than for soft traits, as reproduction values are Reproduction data from the botanical garden close to the ‘common currency’ of ecological processes, and thus differences in overall magnitude of reproduction The data were gathered from the native plant collection in and preferences for either generative or vegetative repro- the Central European flora of the Botanical Garden of the duction will show up only in extreme environments (see Faculty of Science, Charles University in Prague (http:// e.g. Grace 1993). In contrast, habitat filtering in the LHS www.bz-uk.cz). Each of the species is kept under condi- traits will be stronger due to the key role of environment tions assumed to be as close to their natural conditions as in their action (see also Fig. 1). We further hypothesize possible within the garden. The habitats in the garden that at the within-community level these reproduction range in moisture from open, dry, sandy habitats and lime- values would tend to show similarity across co-existing stone, rocky habitats, through mesic, open habitats and species (under-dispersion or random distribution) due to shaded forest stands, to moist (shaded and unshaded) the universal nature of the processes of reproduction. places. Plants are grown in open soil, with weeding done – We use data assembled for ca. 1000 Central European including removal of individuals of the planted species – in species grown in the Botanical Garden of Charles Univer- order to keep stands of each species separate. For all plant sity in Prague (see also Herben et al. 2012). As the gar- species that have been growing in the garden for at least den is rather environmentally heterogeneous, with each 10 yr (for the list see Herben et al. 2012), we assigned species maintained separately in conditions that can rea- scores from 1 to 5 for vegetative and seed reproduction for sonably be assumed to be close to its natural habitat, we that period, based on contemporaneous records. Based on use these data as estimates of potential vegetative and these growth records and informal knowledge of the spe- seed reproduction of individual species under favourable cies behaviour, seed and vegetative reproduction were conditions. We link these data with co-occurrence data scored separately by one person using the same ordinal from a stratified version of the Czech National Phytoso- scale (Z.N.; for further details see Herben et al. 2012). ciological database (Chytry&Rafajov a 2003) to obtain Weeding/thinning visits were done on a regular basis that garden-estimated reproduction values for species that was the same for all species. In some plants with vigorous co-occur in individual database records. In addition, we vegetative reproduction, assessment of seed reproduction link the same co-occurrence data with data on LHS traits was impossible due to seedlings being potentially mixed of Westoby (1998) from the LEDA traitbase (Kleyer et al. with the vegetative progeny. For these plants (43 species), 2008). seed reproduction is treated as a missing value. Altogether, We examine patterns in within-sample dispersion of 1013 species were scored. As the Czech flora contains ca. plant reproduction, searching separately for negative val- 2500 species (depending on taxonomic treatment), includ- ues of trait dispersion (reproduction convergence) and ing common aliens and woody species, this constitutes positive values (reproduction divergence). Detection of over 40% of the total flora. This included 951 non-woody convergence/divergence in plant reproduction allows us to species, of which 823 could be matched to co-occurrence determine whether species that co-exist in the field show data (see below) and thus were used in the current study. any non-random patterns in their reproduction (under Complete data (i.e. including seed reproduction) were conditions favourable for each species). We next examine available for 778 of these species; the rest are species for the same patterns for a few key soft traits (plant height, which only vegetative reproduction is known. For further seed mass, specific leaf area and life span), and compare information on the data set see Herben et al. (2012). these patterns with those shown by potential (i.e. garden) reproduction. Trait data To examine the role of habitat filtering and species pool size in generating these patterns, we divide the The following trait data were taken from the LEDA trait- co-occurrence data set based on major habitat types and base (Kleyer et al. 2008), with the number of species for perform the same analyses within each habitat type which the trait data were available provided in parenthe- using only the pool of species that occur in it. We also ses: specific leaf area (SLA; 1253 species), maximum height examine whether within-community mean reproduction (1794 species) and seed mass (1131 species). Records were values show non-random patterns across habitat types taken from the whole LEDA database (i.e. including (as an additional indication of habitat filtering), as records not from Central Europe); the number of records

Journal of Vegetation Science 912 Doi: 10.1111/jvs.12054 © 2013 International Association for Vegetation Science T. Herben et al. Functional diversity in demography varying considerably among species as they ranged from SD for each of these four measures for all species present in one to 24 (for SLA) and one to 50 (for seed mass). If several the plot for which the information was available (use of records were available for one species, the simple cover-weighted means and SD yielded qualitatively similar (unweighted) arithmetic mean value was used. Plant results, not shown here). Unless otherwise stated, results height data missing from LEDA and life span data (annual/ for only non-woody species (herbaceous species and dwarf perennial; 1974 species) were taken from Kubat et al. shrubs) are shown in the paper. Because the taxonomic (2002); mean plant height values were used. concept used for the co-occurrence data was different from that for the garden collections, only a subset of species scored in the garden could be matched. Plots in which Species co-occurrence data <50% of the species present were scored were discarded; Species co-occurrence data were taken from the Czech this yielded 13 828 plots with sufficient garden reproduc- National Phytosociological Database (Chytry&Rafajov a tion data available. In a similar fashion, we calculated 2003). A stratified subset of the database containing unweighted mean value and SD of log values of SLA, 20 468 plots sampled after 1970 was used (see Chytry height and seed mass. Because life span had only two states et al. 2005 for the stratification procedure). We refer to (annual, perennial), we used the proportion of annual these units as ‘plots’ or ‘communities’ (using these terms plants as a measure of central tendency and the Simpson here interchangeably). diversity index as a measure of dispersion Before stratification, the set was standardized with respect to plot sizes separately for each major vegeta- 1 D ¼ tion type; plots were 50–500 m2 for woodland habitats, 2 2 nA þ nP 2 2 þ þ 10–100 m for scrub, 4–100 m for grassland, wetland nA nP nA nP and aquatic habitats, and 1–50 m2 for low-growing vegetation in stressed or disturbed habitats. These size where nA is the total number of annual species and nP is differences make comparison of trait dispersion across the total number of perennial species. Plots in which habitats less reliable, but we believe that this allows a <50% of the species present were scored were discarded, qualitative correction for different mean sizes of plant yielding the following number of plots with sufficient trait individuals. The plot sizes used are too small to include data available: SLA (18 211 plots), seed mass (17 832 large-scale environmental gradients (e.g. in wetness or plots), height (18 378 plots) and life span (18 396 plots). productivity) even in large forest plots, while even the Values for each garden reproduction parameter and trait smallest ones are large enough to include sufficiently were averaged over the whole set of plots to yield a mean high numbers of plant individuals to avoid data distor- value of the plot-wise means tion due to constraints on number of individuals. The plots were assigned to seven major EUNIS habitat 1 X 1 X types (see Chytry et al. 2005): grasslands (6702 plots), for- ðM ¼ xijÞ n Si ests (3391 plots), scrub and heathlands (354 plots), water i j habitats (4173 plots), rocky habitats (286 plots), peatlands and a mean value of the plot-wise SD and mires (531 plots), and synanthropic habitats (5030 plots). These are further referred to as EUNIS-7 habitat sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 X 1 X types. Plots not assigned to any EUNIS habitat type by Chy- ðD ¼ ðx x Þ2Þ; n S 1 ij i try et al. (2005) were omitted from any analysis using i i j EUNIS classification. Further, all plots were assigned to 32 small EUNIS habitat types (see Chytry et al. 2005), further where n is the number of plots, Si is the number of species referred to as EUNIS-32 habitat types. in the plot i,andxij is the trait value of species j in plot i. (Slightly modified formulas were used for life span.) We examined these values by a randomization proce- Data analysis dure in which we randomized garden reproduction param- We represented reproduction in the garden using four eters and LHS traits by randomly assigning each plant parameters. In addition to vegetative and seed reproduc- species in the data set a value randomly drawn from the tion scores, we calculated total reproduction as the sum of pool of all species, while keeping the lists of co-occurring both values, and prevalence of vegetative reproduction as the species for each plot intact (Stubbs & Wilson 2004; Schamp difference between vegetative and reproductive scores. We et al. 2008). We chose this approach because it does not refer to all four values as garden reproduction parameters. require additional assumptions about how the community For each plot, we calculated unweighted mean values and structure is generated, and it examines only the

Journal of Vegetation Science Doi: 10.1111/jvs.12054 © 2013 International Association for Vegetation Science 913 Functional diversity in demography T. Herben et al. non-randomness of the trait–species relationship. Species Table 1. Tests of non-randomness of trait means (M) and dispersions (D) with missing values were not included in the randomiza- over the whole data set. Values in the table are standardized effect sizes. tion (i.e. if a plot had the information available for <100% Trait Mean Dispersion of its species, only known values were randomized). This Garden reproduction randomization process was done 1000 times. We used the Seed reproduction 0.644 2.208* randomization procedure to determine the significance of Vegetative reproduction 4.973** 2.338** trait mean and trait dispersion for individual garden Prevalence of vegetative reproduction 3.133** 1.905* reproduction parameters and traits, and to calculate stan- Total reproduction 5.24** 1.907* dardized effect sizes (SES), defined as LHS traits Life span 2.74** 6.449** Height 1.644 6.675** Xobs Xexp SES ¼ ; SLA 2.668** 4.563** s X Seed mass 3.787** 6.851** *P < 0.05, **P < 0.01. Significant values are in bold. SES calculation and where Xobs is the true value of the parameter, Xexp and sX significance tests are based on 1000 randomizations. are its mean and SD after randomization. The same analyses were done for each of the seven EUNIS habitat types separately. In this case, we calculated tion) showed significantly higher mean plot values than M and D values by averaging plot-wise values only over expected by random sampling of the species pool (Table 1). plots in each habitat type. Significance and SES values Dispersion at the plot level for all four garden reproduction were calculated by randomly assigning to each species in parameters was negative (Table 1). Values of all the traits each plot a value (for garden reproduction parameters and tested had significantly non-random means: lower than LHS traits) drawn only from the pool of species that occur expected in life span and seed mass, and higher than within the habitat type in which the given plot belongs; expected in SLA. Dispersion values in all traits tested were values of remaining species were treated as missing values. negative (Table 1). The species pool for a habitat type was defined as all species The separate analyses of the seven EUNIS-7 habitat with frequency >1% in that habitat type. This yielded the types (i.e. with smaller subsets used as the reference pool following numbers of plots with sufficient (>50% species) for randomization) showed only a few significant values of trait data: scrub and heathlands (278 for garden parame- means: total reproduction in grasslands and water habitats, ters, 232 for garden parameters of herbs only, 194–310 for prevalence of vegetative reproduction in water and synan- measurable trait data), forests (2928, 2556, 2290–3231), thropic habitats (positive), and seed reproduction (nega- grasslands (5962, 5930, 6093–6309), peatbogs and mires tive) in synanthropic habitats. Significantly non-random (438, 408, 489–522), rocky habitats (217, 214, 155–235), dispersions were more common, but not universal; all of synanthropic habitats (1626, 1511, 4359–4716) and water them were negative, indicating convergence (Table 2). habitats (970, 953, 2476–2552). Differences among habitat types were highly significant Finally, the same set of analyses was repeated separately (Table 1). Peatbogs and water habitats showed the highest for individual habitat types from the 32 EUNIS habitat type prevalences of vegetative reproduction, whereas rocky classification. Only habitat types with more than 380 plots habitats and grasslands showed the lowest prevalences of were analysed, comprising the following 15 habitat types: vegetative reproduction (Fig. 2). Total reproduction was C1, Standing waters; C3, Littoral zone; D2, Poor fens and highest in water habitats and lowest in scrublands and transition mires; E1, Dry grasslands; E2, Mesic grasslands; rocky habitats. For all four reproduction parameters, differ- E3, Wet grasslands; E5.2, Woodland fringes; E5.6, Anthro- ences among EUNIS-7 habitats were highly significant pogenic tall-forb stands; G1, Broad-leaved woodland; G3, using one-way ANOVA (Fig. 2). Coniferous woodland; G4, Mixed woodland; G5, Forest Trait means in the separate analyses of the seven EUN- clearings; H5.6, Trampled areas; I1, Arable land; J6, Waste IS-7 habitats were quite often non-random (Table 2). deposits. Heights were less than expected (on the basis on the species All calculations were done in R ver. 2.8.1 (R Foundation pool of the given habitat type) in all habitat types except for Statistical Computing, Vienna, AT). for grasslands and synanthropic habitats. SLA was higher than expected in forest and rocky habitats, and seed mass lower than expected in scrub, forests and rocky habitats. Results The proportion of annual species was higher than expected In the analysis across the whole data set, three out of four in synanthropic habitats, and lower in grasslands and peat- garden reproduction parameters (vegetative reproduction, bogs. Differences between mean trait values among indi- total reproduction and prevalence of vegetative reproduc- vidual EUNIS habitat types were highly significant (not

Table 2. Tests of non-randomness of trait means and dispersions for the seven EUNIS habitat types. Values in the table are standardized effect sizes.

Trait Scrub Forests Grasslands Peatbogs Rocky Synanthropic Water

Mean Seed reproduction 0.174 0.445 0.912 0.409 0.816 2.589** 0.128 Vegetative reproduction 0.609 1.643 1.584 0.183 0.084 2.09* 1.833* Prevalence of vegetative reproduction 0.696 1.426 0.458 0.463 0.709 2.887** 1.398 Total reproduction 0.398 1.208 1.938* 0.019 0.485 0.348 2.706** Dispersion Seed reproduction 1.813* 1.546 0.249 0.813 1.067 0.531 0.831 Vegetative reproduction 1.637 1.18 1.513 0.772 2.303** 1.07 2.598** Prevalence of vegetative reproduction 2.86** 1.645* 0.005 0.686 2.571** 0.447 2.313** Total reproduction 0.206 0.614 1.734* 0.514 0.467 1.313 0.339 Mean Life span 0.175 1.182 2.945** 1.382* 0.116 3.334** 1.325 Height 5.473** 4.609** 1.065 2.256** 3.152** 1.354 0.781 SLA 1.108 4.69** 0.783 0.159 1.925* 1.467 0.791 Seed mass 4.49** 2.562** 1.339 0.097 3.217** 1.657* 0.115 Dispersion Life span 0.088 0.806 3.18** 1.135 0.926 5.099** 1.589 Height 4.884** 5.851** 3.669** 2.554** 3.323** 3.414** 0.026 SLA 1.48* 1.613* 2.794** 0.002 0.756 2.399** 1.209 Seed mass 4.54** 4.379** 3.096** 1.491 2.971** 2.639** 1.256 *P < 0.05, **P < 0.01. Significant values are in bold. SES calculation and significance tests are based on 1000 randomizations. shown). Trait dispersions, if significant, were invariably Dahlgren & Ehrlen 2011), to our knowledge this is the negative (Table 2, Fig. 3). The only positive dispersion first demonstration of non-random within-community value found was dispersion of height in forests, calculated patterns of a demographic parameter. from the data on all species (including woody species). The prevalence of under-dispersion observed in analyses Patterns at the level of narrower habitat types (classifica- across the entire data set (i.e. not broken down by individ- tion into 32 EUNIS habitat types) showed non-random ual habitat types) is clearly due to environmental filtering of patterns in means and dispersions of garden reproduction the species pool (Colwell & Winkler 1984; Kraft et al. 2007; in few habitat types (two to five out of 15 examined; see de Bello 2012). Indeed, in all the analyses, the existence also Fig. 3). All significant dispersions were negative. Traits and intensity of under-dispersion in the data strongly showed more pronounced patterns. In approximately half depended on the species pool used for randomization. (six to seven) of the habitat types, trait means were Narrower species pools invariably reduced under-dispersion significant (with the exception of SLA, which was both in the LHS traits and in garden reproduction. The significant in only three habitat types); trait dispersions strong role of habitat filtering is further supported by fact where significant and negative in eight out of ten of the that means of both LHS traits and garden reproduction habitat types for all traits. There were no significant positive differ among individual EUNIS-7 habitat types. dispersions. However, the nature of the habitat filtering differs between garden reproduction and LHS traits. For the LHS Discussion traits, strong differences in mean trait values from individual habitat types result from different trait values maximiz- Habitat filtering and dispersion in garden reproduction ing fitness in different environmental conditions (e.g. high and LHS traits SLA in forests vs low in peatbogs, scrublands and grass- The results show strong non-random structure of within- lands). Due to non-negligible habitat heterogeneity even community dispersions and community-level means in within the rather narrowly defined habitat types, changes both garden reproduction data and LHS traits. On a gross in trait values thus simply reflect these gradients with dif- level, the patterns found for LHS traits and for garden ferent trait values, maximizing fitness in different environ- reproduction data are quite similar, with strong under- mental conditions. In contrast, reproduction is a property dispersion for the data set as a whole and diminishing that must be maximized in any environment within the degrees of under-dispersion in subsets of the data. With constraints imposed by habitat productivity and species’ one exception, no over-dispersion was detected in the environmental tolerances. Therefore, it is much less likely data. While differences in mean trait values across habitat to show strong differences within broadly defined individ- types are well known (see e.g. van Groenendael et al. ual habitat types, leading to only a few cases of under- 1996; Dıáz et al. 2004; Klimesova et al. 2011; Rusch dispersion, either at the EUNIS-7 or EUNIS-32 level. The et al. 2011; Gough et al. 2012), and demographies of filtering effect in the garden reproduction data is thus species are known to be environmentally dependent (e.g. confined mainly to the extremes of environmental

Prevalence of vegetative reproduction LHS traits 0.8

0.6

SLA 0.4 SeedM Height

0.2 Standardized effect size 0.0 –6 –4 –2 0 2

–0.2 s ky er Whole E7 E32 nd nds c a ests a o at or R W thl F a Peatbogs anthropic He Grassl yn S Garden reproduction

Total reproduction 5

4 PrevVeg TotRep

2 Standardized effect size

1 –6 –4 –2 0 2

Whole E7 E32 0

r Fig. 3. Standardized effect sizes for dispersion of garden reproduction s s y e nds nd og ck at b o la t R ropic W and LHS traits across the whole data set, and using EUNIS-7 and EUNIS-32 th Forests sla h a ea e as P habitat type classifications. Negative values of SES (below the dotted line) H Gr Synant indicate under-dispersion, positive values indicate over-dispersion. Dots indicate means, with vertical lines connecting minimum and maximum Fig. 2. Differences among the EUNIS-7 habitat types in prevalence of values for the given habitat type classification. Data for EUNIS-32 habitat vegetative reproduction and in total reproduction of non-woody plants. types are calculated from the 15 types with a sufficiently high number of Differences among habitat types are significant using one-way ANOVA samples (see Methods for details). SeedM – seed mass, PrevVeg – (Prevalence of vegetative reproduction: F = 274.4, df = 6, 13 097, prevalence of vegetative reproduction, TotRep – total reproduction. P < 0.001; Total reproduction: F = 242.4, df = 6, 13 097, P < 0.001). Bars indicate SE. Dashed line indicates grand mean. gradients, such as water availability or temperature; in such are steep enough to bring about differences in mean poten- habitats, generative reproduction can be hindered and tial reproduction (see also, e.g. Sosnovaetal.2010). therefore vegetative reproduction becomes dominant Further, this difference between distributions of LHS traits (Grace 1993). Indeed, garden reproduction shows higher and garden reproduction could have been augmented by under-dispersion in more extreme habitats (such as scrub- the ordinal nature of the garden reproduction data (only lands, peatbogs and water habitats), indicating that, in five values) in comparison with the LHS traits (quantitative these habitat types, differences (e.g. in water availability) data for three traits).

Functional differentiation within communities

Over-dispersion, i.e. higher differences among co-existing AB C D species than expected by random sampling from the species pool, was never observed in the data on non- woody species, either in the LHS traits or in the growth data. The only case of over-dispersion was for height in for-

est communities (EUNIS-7 habitat type forest, and several Field reproduction forest EUNIS-32 habitat types), using the data set of all plants (i.e. including woody species). Thus, the only over- dispersion in the LHS traits is due to vertical stratification of forests. However, neither LHS traits nor garden reproduction data would seem to be good a priori candi- Environment dates to show over-dispersion. All the traits considered are Fig. 4. Conceptual issues in assigning the garden reproduction values to related to competitive processes, in which co-existence vegetation records from the field. Lines show field reproduction would be more favoured by species similarity (equalizing environmental responses of four species. The arrow indicates the position mechanisms, see Mayfield & Levine 2010) than by differ- of a vegetation record from one field site; open circles show reproduction entiation in the trait values, and show strong habitat filter- values in an optimum environment (garden reproduction) of the species ing effects. found at that site; full circles show true reproduction values in the habitat It is conceivable that a community could be composed conditions at that site. The difference between the value of reproduction of species with different reproduction patterns (e.g. differ- in optimum conditions and in the site’s conditions depends on the shape of the response of reproduction to habitat conditions as well as the ent proportions of vegetative and seed reproduction), location of the species’ optimum along the environmental gradient. It which would freely co-exist, but there is no indication of increases with the proportion of suboptimal conditions in which the any instances of this. Although the species data indicate species occurs. The difference is small in species B, which has a flat the existence of a trade-off between vegetative and seed response, more pronounced in species A, and strongest in species D. reproduction in the garden (Herben et al. 2012; see also Species C is little affected because the site conditions are close to its e.g. Reekie & Bazzaz 1987; Chaloupecka´ & Lepsˇ 2004), the optimum. distribution of different reproduction modes in the field is nevertheless driven mainly through filtering in extreme than others, there is no indication that any particular habitats. ecological group of species has been performing poorly relatively to others. On the other hand, the second assumption is less supported, as realized niches often differ from Use of garden reproduction as a proxy for species fundamental niches and this relationship differs among demography in the field species (Wisheu & Keddy 1992). Differences between fun- The validity of our inferences rests upon the degree to damental and realized niches in the field are largely driven which garden reproduction serves as a reliable indicator of by competition with other species, which is not accounted field reproduction. Reproduction of a population in the for in garden reproduction, with this effect likely to differ field depends both on the potential reproduction of the both across species and the different communities that species (i.e. reproduction in optimal conditions) and on include the species. If a species generally occurs only or pri- the actual environmental conditions of the field site, marily in suboptimal habitat (typically due to competitive where the population may be occurring in suboptimal con- exclusion from part of its fundamental niche), using a gar- ditions. Thus, our approach relies on two assumptions: (1) den proxy for its reproduction in the field is much less reli- that in the garden all the study species have been main- able than in a species that typically occurs close to its tained in conditions reasonably close to their optima optimum conditions or has a flat response curve (Fig. 4). (enabling valid estimation of the heights of the bell-shaped Temporal variation in habitat quality can impose similar fundamental niche-response curves), and (2) that propor- limitations on the applicability of garden data as a proxy tions of optimum and suboptimum habitats in the field are for field performance. similar in all species (see also Fig. 4). Finally, it must be stressed that, for perennials, We believe that there is no major difficulty with the first reproduction is only one of the two key components of assumption, as the garden’s environmental heterogeneity, demography. Although mortality is typically less environ- coupled with the siting of species within it, generally mentally dependent (i.e. survival environmental responses enables the species to grow in rather favourable habitats. are typically wider than reproduction responses), this is While some species may be more easily ‘domesticated’ not necessarily true universally. Additionally, mortality

Journal of Vegetation Science Doi: 10.1111/jvs.12054 © 2013 International Association for Vegetation Science 917 Functional diversity in demography T. Herben et al. may be correlated with reproduction via density-depen- Chaloupecka, E. & Leps, J. 2004. Equivalence of competitor dent mechanisms or life-history patterns (e.g. in monocar- effects and tradeoff between vegetative multiplication and pic plants). generative reproduction: case study with Lychnis flos-cuculi and Myosotis nemorosa. Flora - Morphology, Distribution, Func- tional Ecology of Plants 199: 157–167. Conclusion Chesson, P. 2000. Mechanisms of maintenance of species diver- – Comparative analysis of dispersion in LHS traits and gar- sity. Annual Review of Ecology and Systematics 31: 343 366. den reproduction shows that, in spite of weak correlations Chytry, M. & Rafajova, M. 2003. Czech National Phytosociologi- between these two sets of species parameters, their disper- cal Database: basic statistics of the available vegetation-plot data. Preslia 75: 1–15. sions within communities are rather similar to each other. Chytry, M., Pysek, P., Tichy, L., Knollova, I. & Danihelka, J. This is at least partly due to the predominant effect of envi- 2005. Invasions by alien plants in the Czech Republic: a ronmental filtering, both on trait values and reproduction quantitative assessment across habitats. Preslia 77: 339–354. parameters. However, at the finer level, reproduction Clark, J.S., LaDeau, S. & Ibanez, I. 2004. Fecundity of trees and parameters are closer than trait values to random patterns, the colonization–competition hypothesis. Ecological Mono- indicating that co-existence of species is not limited by sim- graphs 74: 415–442. ilarities or differences in their reproduction parameters. Colwell, R.K. & Winkler, D.W. 1984. A null model for null mod- There is also no indication in the data that species with dif- els in biogeography. In: Strong, D.R. Jr, Simberloff, D., ferent reproduction modes (vegetative vs seed) would be Abele, L.G. & Thistle, A.B. (eds.) Ecological communities: con- more likely to co-exist than species randomly selected from ceptual issues and the evidence, pp. 344–359. Princeton Univer- the species pool. sity Press, Princeton, NJ, US. Cornwell, W.K. & Ackerly, D.D. 2009. Community assembly and shifts in plant trait distributions across an environmen- Acknowledgements tal gradient in coastal California. Ecological Monographs 79: The research reported here would not have been possible 109–126. without the vision and long-term generous support of the Dahlgren, J.P. & Ehrlen, J. 2011. Incorporating environmen- garden collections by the Faculty of Science, Charles Uni- tal change over succession in an integral projection versity. Data from the Czech National Phytosociological model of population dynamics of a forest herb. Oikos Database were kindly provided by Milan Chytryand 120: 1183–1190. Lubomır Tichy, data from the LEDA project by Michael Dawson, W., Burslem, D.F.R.P. & Hulme, P.E. 2009. Herbivory is Kleyer. We thank Jonathan Rosenthal, Graciela Rusch, related to taxonomic isolation, but not to invasiveness of – Francesco Pugnaire, Francesco de Bello and an anony- tropical alien plants. Diversity and Distributions 15: 141 147. mous reviewer for comments and/or edits that greatly de Bello, F. 2012. The quest for trait convergence and divergence in community assembly: are null-models the magic wand? helped us to improve the paper. 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Journal of Vegetation Science 920 Doi: 10.1111/jvs.12054 © 2013 International Association for Vegetation Science Journal of Vegetation Science 24 (2013) 921–931 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Intra-specific and inter-specific variation in specific leaf area reveal the importance of abiotic and biotic drivers of species diversity across elevation and latitude Catherine M. Hulshof, Cyrille Violle, Marko J. Spasojevic, Brian McGill, Ellen Damschen, Susan Harrison & Brian J. Enquist

Keywords Abstract Community assembly; Intra-specific; Limiting similarity; Niche packing Questions: Are patterns of intra- and inter-specific functional trait variation consistent with greater abiotic filtering on community assembly at high latitudes Nomenclature and elevations, and greater biotic filtering at low latitudes and elevations? N/A Locations: Area de Conservacion Guanacaste, Costa Rica; Santa Catalina Abbreviations Mountains, Arizona; Siskiyou Mountains, Oregon. SLA = specific leaf area; CWM = community- weighted mean; CWV = community-weighted Methods: We measured woody plant species abundance and a key functional variance trait associated with competition for resources and environmental tolerance (specific leaf area, SLA) along elevational gradients in low-latitude tropical Received 31 March 2012 (Costa Rica), mid-latitude desert (Arizona) and high latitude mediterranean Accepted 5 November 2012 (southern Oregon) biomes. We explored patterns of abiotic and biotic filtering Co-ordinating Editor: Francesco de Bello by comparing observed patterns of community-weighted means and variances along elevational and latitudinal gradients to those expected under random Hulshof, C.M. (corresponding author, assembly. In addition, we related trait variability to niches and explored how [email protected]) & Enquist, B.J. total trait space and breadth vary across broad spatial gradients by quantifying ([email protected]): Department of the ratio of intra- to inter-specific variation. Ecology and Evolutionary Biology, University of Arizona, 1041 East Lowell, Tucson, AZ, 85721, Results: Both the community-wide mean and variance of SLA decreased with USA increasing latitude, consistent with greater abiotic filtering at higher latitudes. Violle, C. ([email protected]): Centre Further, low-elevation communities had higher trait variation than expected by d’Ecologie Fonctionnelle et Evolutive, 1919 chance, consistent with greater biotic filtering at low elevations. Finally, in the route de Mende, 34293, Montpellier 5, France tropics and across latitude the ratio of intra- to inter-specific variation was nega- Spasojevic, M.J. ([email protected]) & Harrison, S. ([email protected]): tively correlated to species richness, which further suggests that biotic interac- Department of Environmental Science and tions influence plant assembly at low latitudes. Policy, University of California–Davis, One Conclusions: Intra- and inter-specific patterns of SLA variation appeared Shields Avenue, Davis, CA, 95616, USA McGill, B. ([email protected]): School of broadly consistent with the idea that the relative strength of biotic and abiotic Biology and Ecology, University of Maine, drivers on community assembly changes along elevational and latitudinal gradi- Deering Hall Room 202, Orono, ME, 04469, ents; evidence for biotic drivers appeared more prominent at low latitudes and USA elevations and evidence for abiotic drivers appeared more prominent at high lat- Damschen, E. ([email protected]): itudes and elevations. Department of Zoology, University of Wisconsin–Madison, 430 Lincoln Drive, Madison, WI, 53706, USA Enquist, B.J. ([email protected]): The Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA

tors should limit diversity at high latitudes and elevations Introduction while biotic factors should underlie diversity patterns at Understanding how local processes influence diversity low latitudes and elevations (Pianka 1966). In order to link patterns across environmental gradients has been central abiotic and biotic mechanisms to diversity patterns across to the study of plant community ecology (von Humboldt elevation and latitude, we can begin by assuming that trait 1849; Kraft et al. 2011). Studies across latitude (e.g. Willig variability reflects variation in resource use within a popu- et al. 2003; Schemske et al. 2009; Stegen et al. 2009) and lation, thereby reflecting niche space and breadth (Rough- elevation (e.g. Korner€ 2007; Bryant et al. 2008; McCain & garden 1972; Violle & Jiang 2009; Violle et al. 2012). Grytnes 2010) have highlighted both biotic and abiotic Recent insights from trait-based ecology, building on clas- mechanisms to explain diversity patterns across these gra- sical niche theory, have outlined the use of intra- and dients (Dobzhansky 1950; Pianka 1966; MacArthur 1972; inter-specific trait variation for understanding diversity Schemske et al. 2009). However, connecting local ecologi- patterns across local to broad spatial scales (Jung et al. cal processes to broad latitudinal and elevational gradients 2010; Violle et al. 2012). First, abiotic factors are known to has been challenging. For example, elevational gradients shape the diversity of traits within a community; specifi- differ from latitudinal gradients in several key ways includ- cally, a change in the mean (either higher or lower mean ing smaller species pools and land area and increased isola- values, depending on the trait and environmental gradient tion (Lomolino 2001). Further, while many of the considered) and lower variance of trait values across an potential processes invoked to explain latitudinal diversity environmental gradient can indicate whether abiotic filter- patterns co-vary along latitudinal gradients (i.e. area, his- ing is occurring (Weiher & Keddy 1995). In addition, over tory, climate), they generally do not co-vary across eleva- evolutionary time scales, stronger stabilizing selection in tion (Korner€ 2007). increasingly harsh environments should further filter or Recently, trait based approaches have been used to reduce the total amount of phenotypic variation within a understand how multiple mechanisms influence commu- community (Fischer 1960; Violle et al. 2012). Second, bio- nity assembly across environmental gradients (McGill tic interactions can also shape the diversity of traits within et al. 2006; Weiher et al. 2011), including elevational a community. For example, on the one hand, competitive (Kluge & Kessler 2011; Hoiss et al. 2012; Spasojevic & Sud- exclusion will eliminate extreme phenotypes so that intra- ing 2012) and latitudinal (Swenson & Enquist 2007; Swen- and inter-specific trait variance is reduced (Grime 1973). son & Weiser 2010; Kooyman et al. 2011; Swenson et al. On the other hand, if competition imposes limiting similar- 2012) gradients. These studies often focus on two assembly ity with resource partitioning, the diversity of traits will mechanisms thought to influence diversity patterns along depend on the ratio between species’ niche breadth (e.g. environmental gradients: environmental filtering, which intra-specific variation) and total niche space (e.g. inter- can increase species trait similarity through abiotic con- specific variation; MacArthur & Levins 1967; MacArthur & straints (Weiher & Keddy 1995), and competitive interac- Wilson 1967). Thus, for limiting similarity and resource tions (i.e. niche partitioning, limiting similarity) that partitioning to occur in a community: (1) the inter-specific prevent co-existing species from being too similar (MacAr- packing of traits along a niche or trait axis will tend to be thur & Levins 1967; Chesson 2000). However, these two uniformly distributed (e.g. Roughgarden 1972; Brown hypotheses have been difficult to reconcile in species-rich 1975) and (2) either the ratio of intra- to inter-specific vari- communities in environments that seem to be severe (i.e. ation should decrease with increasing species richness the Sonoran desert; Whittaker & Niering 1964; Huston (MacArthur & Wilson 1967; Violle et al. 2012) and/or (3) 1979). Thus, making generalizations about how abiotic fil- the total niche space (phenotypic diversity of the entire tering should influence communities across latitude or ele- community) must increase (Tilman et al. 1997). vation has been challenging (Swenson & Enquist 2007). Here, we focus on how patterns of one ecologically Furthermore, while evidence for greater competitive inter- important trait, specific leaf area (SLA), change along three actions at lower elevations is more strongly supported elevational gradients located in tropical (Costa Rica), desert (Callaway 1998; Wang et al. 2008; Spasojevic & Suding (Arizona) and mediterranean (southern Oregon) biomes. 2012), the idea of greater competitive interactions at low By quantifying the distribution of intra- and inter-specific latitudes is equivocal at best (Vazquez & Stevens 2004; trait variation in local communities across broad elevation- Ricklefs 2009; but see Schemske 2009; Schemske et al. al and latitudinal gradients we address two long-standing 2009). It has thus been difficult to link the mechanisms questions in plant community ecology. Specifically, we that underlie diversity patterns across both elevational and ask: (1) are assembly mechanisms similar across elevation- latitudinal gradients (Swenson & Enquist 2007). al and latitudinal gradients, and (2) are patterns of trait One prominent, and seemingly simplistic, hypothesis variation consistent with greater abiotic filtering on com- relating assembly across broad gradients is that abiotic fac- munity assembly at high latitudes and elevations, and

Journal of Vegetation Science 922 Doi: 10.1111/jvs.12041 © 2013 International Association for Vegetation Science C.M. Hulshof et al. Functional variation across environmental gradients greater biotic pressures on assembly at low latitudes and Oregon (part of the California Floristic Province) are char- elevations? acterized by mediterranean-type climate with warm, dry To answer these questions we use three trait-based summers (mean max. July temperature 27 °C) and cool, metrics. First, we determine whether shifts in commu- wet winters (mean min. January temperature 2 °C); mean nity trait mean and variance across latitude are similar annual precipitation ranging from 1400 to 5000 mm, with to those across elevation. Second, we compare the simi- <15% occurring during May through September (Daly larity of community trait values relative to random trait et al. 2002). similarity, which can further describe the nature of assembly patterns (MacArthur & Levins 1967; Pacala & Floristic surveys and plant trait collection Tilman 1994; Weiher & Keddy 1995; Grime 2006) where high functional similarity is thought to be a Plant abundance (number of woody individuals) was mea- signature of abiotic filtering and low functional similar- sured in 20 9 50 m (0.1 ha) plots arrayed across the three ity is thought to be a signature of limiting similarity elevational gradients in which all stem diameters more (Weiher & Keddy 1995). Third, we describe the rela- than 2.5 cm DBH were measured and species identified. tionship between intra- and inter-specific variation and Only woody trees and shrubs were included, as woody species richness. The ratio of intra- to inter-specific vari- plants consistently dominate the total abundance and bio- ation expresses the rate at which niche breadth (i.e. mass in each sampled community relative to non-woody intra-specific trait variation) changes relative to total species. A total of 25 plots were surveyed in Costa Rica, 22 niche space (i.e. inter-specific variation) and can thus be plots in Arizona and 12 plots were surveyed in Oregon. In used to determine how total niche space and niche Costa Rica, the plots spanned an elevational range of 9 breadth change with increasing species richness across –1111 m, 740–2502 m in Arizona and 438–1255 m in diversity gradients. Oregon. The location of plots were determined using a stratified sampling regime (Gauch 1982). Using vegetation Methods maps for each site (Oregon: Whittaker 1960; Arizona: Whittaker & Niering 1964; Costa Rica: Holdridge et al. Study sites 1971), at least one plot was included in each plant zone We conducted our study in three locations that span a 30° across elevation. Locations that had evidence of recent dis- latitudinal gradient and an average elevational range of turbance (e.g. fire, logging) were avoided. Where habitats 2000 m a.s.l. (Table 1). Area de Conservacion Guanacaste were more heterogeneous, additional plots per habitat type (ACG) in northwestern Costa Rica is characterized by a 6- were included (Gauch 1982). In topographically complex mo dry season in low-elevation (0–300 m a.s.l.) dry tropi- habitats, for example, plots were placed in various slope cal forests with a mean annual temperature of 25 °C. How- directions and inclinations in order to represent the contin- ever, the length of the dry season decreases, and mean uum of soil moisture availability within a particular habitat annual precipitation increases with elevation; mean type (Whittaker 1960; Whittaker & Niering 1964). This annual precipitation in a rain forest site at 700 m a.s.l. is was particularly true in the lowland dry tropical forests of ca. 3500 mm in comparison with 1500 mm in a lowland Costa Rica where plant communities differ in deciduous- dry tropical forest site at 300 m a.s.l. The Santa Catalina ness, ranging from about 20% evergreen hillsides to almost Mountains in southern Arizona are characterized by sum- completely evergreen lowlands (Janzen 1986; Powers mer and winter monsoons, which together can bring about et al. 2009). After surveying the obvious extreme habitats 330 mm of precipitation annually to lower elevations and (e.g. ridge, valley, slope), plots were continually added 750 mm at the highest elevations (Whittaker & Niering until the number of new species encountered with increas- 1964). Finally, the Siskiyou Mountains in southern ingareanearedzero.

Table 1. Site characteristics including latitude (Lat), longitude (Lon), elevational range (m a.s.l.), major vegetation zones and relevant previous studies along each elevational gradient.

Site Lat/Lon Elevation (m) Major vegetation zones Previous studies

Area de Conservacion Guanacaste, Costa Rica 10°51′ N 0–1500 Lowland dry tropical forest; transitional Lowlands: Janzen (1986), 85°37′ W moist forest, rain forest, cloud forest Powers et al. (2009) Santa Catalina Mountains, Arizona 32°26′ N 740–2790 Sonoran desert scrub; oak woodlands; Shreve (1915), Whittaker & 110°47′ W coniferous forest; sub-alpine forest Niering (1964) Siskiyou Mountains, Oregon 41°49′ N 0–2100 Coniferous forest; montane forest; Whittaker (1960), Damschen 123°40′ W alpine and sub-alpine forests et al. (2010) Harrison et al. 2011

For all woody species within each plot we measured spe- whether the observed trait patterns differ from random, a cific leaf area (SLA), a key functional trait associated with null modelling approach is necessary (Gotelli & Graves competition for resources and environmental tolerance 1996). A null model compares whether the observed varia- (Poorter et al. 2009). SLA (cm2g1) is defined as the light tion in CWM or CWV across elevation is greater than or capturing surface area per unit of dry biomass and corre- less than expected given the observed species richness. To lates with differences in life-history strategies (e.g. net pho- create a null community, trait data for each sampling tosynthetic capacity, leaf longevity, relative growth rate region (i.e. Costa Rica, Arizona or Oregon) were pooled and competitive ability; Reich et al. 1997). For example, into a regional trait community. For each plot, we calcu- SLA is known to reflect a trade-off in plant resource-use lated the null trait mean and variance value and the 95% strategy from rapid biomass production (high SLA) to effi- confidence interval (CI) based on 999 iterations by shuf- cient nutrient conservation (low SLA; Wright et al. 2004). fling the species-by-plot trait values and thus conserving Further, variation in SLA is tightly coupled with variation species richness and abundance within each plot (Gotelli & in resource gradients such as light and productivity (Grime Graves 1996). This approach avoids averaging the mean 1998). For the collection of plant traits, five mature, and variance of species’ SLA values across all plots, which healthy, sun leaves were collected from five different indi- ultimately reduces the observed trait variation both viduals for each species within a plot (Cornelissen et al. between and within species. Shuffling trait values, as 2003). Once leaves were collected, the fresh area of each opposed to species’ abundances, can also indicate which leaf was measured within a few hours of collection. Fresh assembly processes, including abiotic filtering and limiting leaf area (cm2) was measured using a Canon CanoScan similarity, structure communities. LiDE 110 portable electronic scanner (Canon Inc., Lake First, to understand how plant function varies with ele- Success, NY, USA), and leaf area was calculated using the vation and whether abiotic filtering drives assembly at ImageJ imaging software (Abramoff et al. 2004). All leaves high elevations and latitudes, we performed simple linear were then placed in a drying oven for a minimum of 72 h regressions of CWM and CWV of SLA against elevation for at 70 °C until a constant mass was reached, and the final each community. Second, to determine whether the dry mass was recorded. observed trait values were clustered or over-dispersed If there were not sufficient individuals within the plot to compared to random assemblages (and thus the influence meet the minimum criteria of five individuals, then leaves of abiotic or biotic drivers), we compared the observed were collected from nearby individuals located outside of, CWM and CWV to random communities. Finally, we but never more than 500 m away from, a plot. Rare species explored the central ideas of limiting similarity and niche with <5 sampled individuals were not included in the anal- packing by comparing how the ratio of intra- and inter- yses but did not total more than 8% of the total relative specific trait variation changed with increasing species abundance in any plot. All individuals in Arizona were richness. For each plot, we calculated the ratio of intra- sampled during March and April 2010; in Oregon during specific to inter-specific variation. Plot inter-specific varia- early May 2010; and in Costa Rica in mid-May through tion was calculated as the variance of all species’ mean trait June 2010. These times correspond with the early growing values (i.e. variance of species’ means). Plot intra-specific season in each location. variation was calculated as the mean of all species’ intra-specific variances for each trait (i.e. mean of species’ Statistical analyses variances). We also included a null model to resample trait variation with increasing sample size (i.e. increasing To explore patterns of abiotic filtering across elevation and species richness) without replacement using the sample latitude, we first compared how community-wide trait package in R (R Foundation for Statistical Computing, means and variances change with elevation and latitude. Vienna, AT). For example, for a sample size of 45 species, To do this, the abundance-weighted plot mean and vari- we randomly drew 45 values of trait mean and 45 values ance (community-weighted mean and variance, CWM of intra-specific trait variance. We then calculated the null and CWV, respectively; Violle et al. 2007) of SLA values inter-specific variation as the variance of 45 mean values were calculated for each plot k as: and the null intra-specific variation as the mean of 45 variance values and calculated the ratio between intra- ¼ R ð Þ CWMk aiktik 1 and inter-specific variance. This was done for each of 999 iterations for each increase in sample size. Finally, we 2 CWVk ¼ Raikðtik CWMkÞ ð2Þ compared the observed and randomized slopes and intercept coefficients from linear regressions between variance where aik is the relative abundance of species i in plot k, (intra-specific, inter-specific and intra:inter) and species and tik is the trait mean of species i in plot k. To explore richness using the smatr package in R.

Although variance is often correlated with mean elevation in Costa Rica (r2 = 0.089, P = 0.14), decreased values (Taylor 1961), this ratio is informative for three with increasing elevation in Arizona (r2 = 0.71, P < 0.001) reasons. First, intra-specific variance includes standard- and was not correlated to elevation in Oregon (r2 = 0.16, ized measurements from a set number of leaves and P = 0.20; Fig. S1). With increasing latitude, both CWM individuals (five) from each species found within each and CWV of SLA decreased (Fig. 1d,h) and the ratio of plot and is thus measured systematically across all plots. intra- to inter-specific variation decreased (Fig. 2d) with Second, calculating the average of intra-specific vari- increasing species richness. Decomposing this ratio into ances in each plot partially accounts for differences in individual components, both intra- and inter-specific vari- species richness between plots. Third, by including a ations increased with increasing species richness at differ- resampling model, we can compare the observed pat- ent rates (i.e. slopes; Fig. 2h). terns of trait variation to those expected under random At the lowest latitude (Costa Rica) CWM (Fig. 1a) and sampling of trait variation. We used this ratio to explore CWV (Fig. 1e) of SLA were not correlated to elevation. the biological patterns of variance with increasing spe- Compared to a random model of assembly, SLA was more cies richness (Violle et al. 2012). We regressed intra-spe- variable than expected at low elevations (Fig. 1e), and the cific variation, inter-specific variation and the ratio of ratio of intra- to inter-specific variation was negatively cor- intra- to inter-specific variation against species richness. related to species richness (r2 = 0.27, P = 0.0083; Fig. 2a) If abiotic and biotic drivers are truly asymmetric across even though the individual components of this ratio were environmental gradients, then a decrease in the ratio of not correlated to species richness (Fig. 2e). At the mid-lati- variation with increasing species richness should be evi- tude site (Arizona) CWM (r2 = 0.60, P < 0.0001; Fig. 1b) dent at low latitudes, suggesting greater limiting similar- and CWV (r2 = 0.26, P = 0.016; Fig. 1f) of SLA decreased ity in tropical latitudes. All statistical analyses were with increasing elevation. In comparison with a random conducted in R (v. 2.15.1). model of assembly, community mean SLA was also higher than expected in the low-elevation communities and lower than expected in high-elevation communities (open Results data points; Fig 1b). Similarly, CWV SLA was more vari- There were a total of 275 woody plant species in our plots able than expected at low elevations (Fig. 1f) compared to in Costa Rica, 65 in Arizona and 18 in Oregon. Species a random model of assembly. The ratio of intra- to inter- richness decreased with latitude and was not correlated to specific variation of SLA was not significantly correlated to

(a) (b) (c) (d)

(e) (f) (g) (h)

Fig. 1. Community-weighted mean (CWM) and variance (CWV) of specific leaf area (SLA; cm2g1) against elevation (m a.s.l.) for Costa Rica (CR; a, e), Arizona (AZ; b, f) and Oregon (OR; c, g) and across latitude (d, h). Solid black lines indicate a significant (P < 0.05) relationship; dashed black lines indicate a non-significant relationship. Each data point represents a single community (plot). Solid data points indicate communities that are not statistically distinguishable from random communities; open data points indicate communities that are distinguishable from random communities. For latitudinal comparisons, sites separated by letters are statistically distinguishable in a one-way ANOVA. For each site, the lower-elevation communities tend to have increased trait diversity or variance than expected by chance.

(a)(b) (c) (d)

(e)(f) (g) (h)

Fig. 2. The ratio of intra-specific to inter-specific variance (Intra:Inter Variance, upper panel), intra-specific variation (grey lines, lower panel) and inter- specific variation (black lines, lower panel) of specific leaf area (SLA; cm2g 1) as a function of species richness for elevational gradients in Costa Rica (a, e), Arizona (b, f) and Oregon (c, g) and across latitude (d, h). Each data point represents a single community (plot). Solid lines indicate a significant (P < 0.05) relationship; dashed lines indicate a non-significant relationship. species richness (Fig. 2b) even though the individual com- (MacArthur & Levins 1967; Chesson 2000). In addition, ponents of this ratio were both positively correlated to spe- we found that both intra- and inter-specific variation cies richness (Fig. 2f). Finally, at the highest latitude site increased with species diversity but at different rates, so (Oregon), CWM and CWV of SLA were not correlated to that the total ratio between intra- and inter-specific varia- elevation (Fig. 1c,g). Relative to a null model of assembly, tions decreased with increasing species richness. This sug- CWM and CWV of SLA in the Oregon plots were more gests that the total trait space indeed increases towards variable than expected at low elevations (open data points; tropical latitudes; however, because intra-specific variation Fig. 1c,g), but neither the ratio of intra- to inter-specific increases at a slower rate, species are more ‘tightly packed’ variation of SLA nor the individual components were sig- in tropical systems. Together, these community-level find- nificantly correlated to species richness (Fig. 2c,g). The ings are consistent with the often cited but rarely tested randomized values of intra-specific variation, inter-specific assumption that variation in diversity across broad scale variation and the trait variation ratio did not increase with gradients is constrained by available niche space (see Willig increasing species richness (not shown). For each regres- et al. 2003; Wiens 2011), but that biotic pressure at lower sion analysis between SLA variance and species richness, elevations and latitudes increases the total niche volume the observed communities significantly differed in slope and thus phenotypic diversity. These results are consistent from the randomized communities and generally did not with previous findings that both biotic and abiotic forces differ in intercept (not shown). have likely been important in the evolution of plant function and diversity on contrasting ends of elevational and latitudinal gradients (Shepherd 1998; Stevens et al. 2006; Discussion Swenson et al. 2012). We discuss these results in light of The origins of diversity gradients continue to remain a cen- both the strengths and current challenges of a trait-based tral area of focus and debate (Wiens 2011; Stegen et al. approach for understanding diversity patterns across broad 2012). Few studies, however, have examined whether environmental gradients. traits vary in similar ways across gradients of latitude and elevation. Here, we show that at higher latitudes, but not Patterns of trait variation across elevation and latitude necessarily high elevations, the variance in SLA decreased, consistent with increased abiotic filtering (Weiher & Keddy Across latitude, CWM and CWV of SLA decreased with 1995) at higher latitudes. Further, lower-elevation plots increasing latitude. These patterns are similar to global across sites tended to have more variance in SLA than trends (Reich et al. 1997; Wright et al. 2005). Specifically, expected by chance, consistent with limiting similarity SLA has been shown to shift across productivity gradients

(Reich et al. 1997; Grime 2006), and increases in mean trait-based ecology will be to determine whether functional annual precipitation and temperature are thought to be the strategies of non-woody species respond similarly across two best predictors of mean SLA across broad geographical environmental gradients compared to woody species. On gradients (Swenson et al. 2012). Although this study uses the one hand, non-woody species can evade harsh abiotic a single functional trait, the patterns shown here mirror conditions by being non-persist so that patterns of trait var- those found for other plant traits including wood density, iation across environmental gradients may not reflect pat- maximum height and seed mass (Swenson & Enquist terns of abiotic filtering as seen in woody species. On the 2007; Swenson & Weiser 2010; Swenson et al. 2012), con- other hand, competition between non-woody and woody sistent with the idea that whole-plant level function species may be critical for assembly (see House et al. 2003), responds in concert to gradients of elevation and latitude. particularly during the seedling stage. An emerging Across elevation in Costa Rica, CWM and CWV of SLA research question is thus how patterns of trait variation dif- were not correlated with elevation (Fig. 1a,e). This pattern fer between co-existing life forms and how trait differences suggests that abiotic filtering is a weak driver of trait values may confer competitive and/or fitness differences. across elevation in Costa Rica, the abiotic differences Finally, in Oregon, a gradient dominated by gymno- between lowland dry forests and higher elevation commu- sperms, the lack of clear correspondence between trait nities are reduced during the rainy season (Gotsch et al. mean and variation with elevation may be due to either 2010), abiotic filtering of SLA is not relevant at this site or the stabilizing maritime influence on climate across the Si- spatial scale (see Swenson et al. 2006), or the high habitat skiyou Mountains (Whittaker 1960) or that the use of SLA heterogeneity characteristic of dry tropical forests obscured as a proxy for functional strategies is not consistent our ability to detect patterns in the context of broader envi- between gymnosperms and angiosperms. We can thus ronmental gradients (Baraloto & Couteron 2010). For infer that, first, the processes that influence assembly example, the high heterogeneity found in Costa Rica may across elevation are not the same between the three be related to topographical differences or historical distur- mountain gradients sampled. This is intuitive since eleva- bance regimes (i.e. land use, timber extraction, fire; Powers tional gradients reflect the combined effect of regional et al. 2009), and each can affect the structure and compo- peculiarities and general altitude phenomena (Korner€ sition of forest communities. Although areas that had obvi- 2007), and the three mountain gradients sampled here dif- ous evidence of disturbance were avoided, the long-term fer in climate, seasonality, topography, age and isolation, legacies of past disturbance events (see Foster et al. 2003) among other factors that affect biodiversity along eleva- could potentially limit our ability to detect non-random tional gradients (see Korner€ 2007). Thus, it would be assembly patterns at larger spatial scales. That the commu- highly desirable to systematically sample many replicate nity means were similar to random assemblages in Costa elevational gradients across a wide spectrum of climatic Rica may further indicate the opposing effects of both abi- zones. Although patterns of variation in SLA across envi- otic and biotic filtering at local scales (Swenson et al. ronmental gradients provide reasonable support for assem- 2006) due to either long-term successional processes or the bly mechanisms, the use of SLA as a proxy for functional effects of local topographic differences. strategies may break down when making comparisons In Arizona, the decrease of mean trait values with eleva- between distinct clades, or even life forms within the same tion mirrors the known decrease in SLA with increasing clade. Thus, experimental or simulation approaches may latitude (Reich et al. 1997). This latitudinal trend is better link differences in SLA to plant function and, ulti- thought to be primarily due to the increasing dominance mately, fitness across environmental gradients. of gymnosperms at high latitudes (McCarthy et al. 2007). Despite climatic and topographic differences between Similarly, the increasing dominance of gymnosperms at elevational gradients, low-elevation communities across high elevations in Arizona likely underlies the observed all sites had higher trait variance than expected by chance. shift in trait mean. Gymnosperms are known to have rela- This finding points to the role of competitive interactions tively low values of SLA (Royer et al. 2010) as well as low that prevent co-existing species from being too function- plasticity compared to angiosperms (Bond 1989). Thus, ally similar. Yet recent debate challenges whether trait var- whether the range of SLA variation is a consistent proxy of iation can be used to infer the relative importance of functional strategies for gymnosperms and angiosperms abiotic vs biotic filters (HilleRisLambers et al. 2012), since requires further exploration. competitive exclusion, like abiotic filtering, can also lead to Similarly, although this study offers the first standard- low-trait variation within and between species. Although ized quantification of how trait variation differs across ele- clear relationships between environmental gradients and vation and latitude, sampling a single functional life form traits provide reasonable support that an abiotic filter is (i.e. woody species) likely underestimates the total important for assembly (HilleRisLambers et al. 2012), observed variation in SLA. Thus, a critical next step for experimental approaches that explicitly link abiotic and

Journal of Vegetation Science Doi: 10.1111/jvs.12041 © 2013 International Association for Vegetation Science 927 Functional variation across environmental gradients C.M. Hulshof et al. biotic factors to the distribution of trait values within com- intra- and inter-specific trait variation at broad scales are munities will further shed light on trait assembly patterns consistent with the idea that local abiotic and biotic interac- across environmental gradients. tions influence diversity patterns across environmental gradients. While our findings support the use of a trait- based approach for understanding broad scale diversity gra- Trait variation and species richness dients (see Roughgarden 1972; Violle & Jiang 2009; Violle In order to determine the relationship between trait varia- et al. 2012), several key challenges remain for linking local tion and species richness we quantified the ratio between ecological processes to broad environmental gradients. intra- and inter-specific variations. By doing so, we were First, our analyses include three elevational gradients able to test a specific prediction of limiting similarity, that differ in a number of ways, including regional climate. namely that the amount of intra-specific variation (niche Future studies are needed to compare elevational gradients breadth) compared with inter-specific variation (total with similar climatic regimes (such as in the moist tropics) niche space) does indeed vary with community species in order to disentangle the effects of potentially confound- richness. The limiting similarity hypothesis predicts that ing abiotic and biotic factors (Korner€ 2007; Malhi et al. (1) niche breadth, or intra-specific variation in resource 2010). Further, by only measuring SLA of woody species, use, should decrease with increased richness (MacArthur our conclusions are limited to a single axis of plant & Wilson 1967) and/or (2) that the total amount of niche variation in ecological strategies for one life form and we space should increase with species richness (Tilman et al. cannot infer how reproductive, regenerative (Grime 2006) 1997; Weiher et al. 1998). An open question for niche- or whole-plant strategies differ across broad spatial scales, based ecological theory is whether the amount of intra- life forms or even different taxa. In addition, testing for abi- specific variation compared with inter-specific variation otic and biotic filters using a trait-based approach should actually varies with species richness as predicted by niche be expanded to include experimental and demographic theory (Violle et al. 2012). analyses (HilleRisLambers et al. 2012) to more strongly Consistent with niche theory, we showed that the ratio link traits to plant fitness. Finally, in order for intra-specific of intra- to inter-specific variation does decrease with trait variation to be integrated into functional ecology, increasing species richness across latitude and elevation. there is still a need to determine the magnitude and Decomposing the individual components of this ratio, we patterns of intra-specific variation within and across showed that inter-specific variation of SLA increased with ecosystems. In summary, quantifying patterns of commu- species richness at a faster rate compared to intra-specific nity assembly and trait variation across diverse environ- variation. This finding, while consistent with limiting simi- mental gradients will advance our understanding of the larity in tropical forest communities (e.g. Pianka 1966), mechanisms that give rise to large-scale biogeographic may also suggest that other processes, such as develop- gradients. mental constraints, can limit intra-specific trait expression. Despite the use of functional traits to define species’ niches, Acknowledgements there is still a large gap in our understanding of how traits relate to fitness, as well as to the evolutionary mechanisms We thank the National Park Service in Arizona and Oregon that drive niche differentiation (Sterck et al. 2011). for permits and logistics. C.M.H. is indebted to the staff of Area de Conservacion Guanacaste, Costa Rica, especially R. Blanco, M.M. Chavarrıa, F. Chavarrıa, R. Espinosa, W. Conclusions Hallwachs, D.H. Janzen, A. Masis and D. Perez; B. Anac- We provide an investigation into the predominant patterns ker, S. Copeland and B. Going for technical and logistical of trait variation across elevational gradients using moun- support in Oregon; T. Birt, V. Buzzard, D. Hill, G. Hulshof, tains at three widely separated latitudes in the New World. J. Hulshof, C. McDonnell and S. Wisniewski-Smith for This study combines measures of both intra- and inter-spe- field assistance in Arizona and Costa Rica; and B. Blonder cific trait variation observed within each forest plot across for assistance with the randomization models. C.M.H. was multiple elevational gradients. Although intra-specific trait supported by a National Science Foundation Graduate variation is thought to promote species diversity and Diversity Fellowship and an Institute of the Environment improve detection of abiotic filtering and limiting similarity Dissertation Improvement Grant from the University of (Jung et al. 2010) and, as such, has appeared at the fore- Arizona. C.V. was supported by a Marie Curie Interna- front of trait-based ecology (see Bolnick et al. 2011; Violle tional Outgoing Fellowship within the 7th European Com- et al. 2012), there is a limited understanding of how both munity Framework Programme (DiversiTraits project, no. intra- and inter-specific variation change over latitudinal 221060). B.J.E was partially supported by an NSF Macro- and elevational gradients. We demonstrate that patterns of systems award.

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Journal of Vegetation Science Doi: 10.1111/jvs.12041 © 2013 International Association for Vegetation Science 931 Journal of Vegetation Science 24 (2013) 932–941 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Are differences in functional diversity among plant communities on Mediterranean coastal dunes driven by their phylogenetic history? Marta Carboni, Alicia T.R. Acosta & Carlo Ricotta

Keywords Abstract Community assembly; Functional composition; Phylogenetic diversity; Rao’s coefﬁcient; Question: Variability in species function is often studied by using phylogenetic Syncsa relatedness as a surrogate for functional similarity between species, rather than by measuring functional traits directly. The phylogenetic-based method is far Nomenclature less data-intensive than trait-based methods. However, to what extent commu- Conti et al. (2005) nity-level variability in species function is driven by phylogenetic history has Received 30 March 2012 rarely been explored in terms of composition and diversity. In this paper we test Accepted 1 May 2013 this empirically by asking: do differences in the functional composition and Co-ordinating Editor: Norman Mason diversity of species assemblages (i.e. plots) along an environmental gradient mirror differences in phylogenetic structure?

Carboni, M. (corresponding author Location: Coastal dune communities of the Tyrrhenian coast of Italy. [email protected]) & Acosta, A.T. ([email protected]): Department of Methods: We calculated fuzzy-weighted mean trait values and the Rao index Environmental Biology, University ‘Roma Tre’, for dominant species in 405 plots using 16 functional traits that were measured Viale Marconi 446, 00146, Roma, Italy in the ﬁeld or from other sources. Based on a phylogenetic distance matrix Ricotta, C. ([email protected]): among species obtained from an aged phylogenetic supertree, we applied matrix Department of Environmental Biology, correlation tested against appropriate null models to calculate how much of the University of Rome ‘La Sapienza’, Piazzale Aldo plot-to-plot variability in functional composition (measured through fuzzy Moro 5, 00185, Rome, Italy weighting) and diversity (measured with the Rao index) is predicted by the corresponding phylogenetic metrics. Results: At the species pool level there was evidence for a phylogenetic signal in trait variation. Furthermore, we found that differences in species functional diversity among plots were closely related to their phylogenetic variability, but this was not true for functional composition. Conclusions: The results show that even when there is evidence of phylogenetic trait conservatism at the species pool level, phylogeny may be unable to capture all aspects of functional community structure. This emphasizes the need for caution when interpreting measures of phylogenetic community structure as proxies of functional community structure.

and plant strategies are used to decipher the relative Introduction importance of different niche-based processes for commu- Understanding the mechanisms driving community nity assembly (Stubbs & Wilson 2004; Cornwell et al. assembly has been an important focus in plant ecology. 2006; Kraft et al. 2008; Cornwell & Ackerly 2009; Pillar Species co-existence within communities, and species sort- et al. 2009). Which traits most influence the processes ing along environmental gradients, are thought to be regu- governing community assembly is not always clear. There- lated by the degree to which species share similar fore, a second approach attempts to quantify the relative adaptations. Both functional and phylogenetic approaches importance of species function by using phylogenetic relat- have been used for testing these hypotheses. In the first edness as a surrogate for functional similarity (Webb 2000; approach, functional traits reflecting species adaptations Cavender-Bares et al. 2004a; Kraft et al. 2007; Vamosi

Journal of Vegetation Science 932 Doi: 10.1111/jvs.12095 © 2013 International Association for Vegetation Science M. Carboni et al. Phylogeny and functional composition/diversity et al. 2009). The underlying assumption is that traits of (ca. 250 km). The area has a mediterranean climate (Car- closely related species are more similar than traits of species ranza et al. 2008) and the holocenic dunes generally more distant on a phylogenetic tree because of trait conser- occupy a narrow strip along the seashore. These dunes are vatism, which can be tested by searching for a ‘phyloge- not very high (usually <8–10 m) and are relatively simple netic signal’ (Blomberg et al. 2001). Evolutionary trait in structure, with beaches varying in breadth from a few divergence within lineages can also occur (Losos et al. meters to ca. 40 m, low embryo dunes, generally only one 2003), but when traits exhibit a phylogenetic signal, then mobile dune ridge, dune slacks and stabilized dunes. The related species are also likely to share similar ecological main environmental gradient is due to varying wind inten- requirements. When this is the case, the phylogenetic dis- sity, sand burial, salt spray, drought and soil development persion of a species assemblage should reflect its functional in relation to distance from the sea (Carboni et al. 2011). dispersion (Swenson & Enquist 2009), and composition in The vegetation on the dune profile follows a compressed terms of phylogenetic lineages should reflect functional zonation along the sea–inland environmental gradient: composition. Hence, the two approaches can be used to from the pioneer communities of the upper beach to the express community structure in terms of both composition woody communities (Mediterranean macchia and ever- and diversity. green forests) of the inland fixed dunes (Acosta et al. The phylogenetic-based method is far less data-intensive 2003). These distinct coastal dune community types (or than the trait-based method and has therefore been habitats) are reasonably homogeneous in composition broadly applied to different communities and trophic levels along the entire Central Italian coast (Acosta et al. 2003). and across different spatial and phylogenetic scales (for a Most of the remaining contiguous dune systems in this review, see Vamosi et al. 2009). However, both methods region are distributed within five sites spanning a total of have seldom been applied together (but see Cavender- ca. 50 km along the coast of Lazio and separated by rocky Bares et al. 2004a; Ingram & Shurin 2009). Recently, promontories, silty river outlets and totally urbanized lit- Swenson & Enquist (2009) and Kraft & Ackerly (2010) torals (Fig. 1). For this study, we used a vegetation data- applied both approaches to a Neotropical dry forest and an base available for these dune sites (Carboni et al. 2011; Amazonian forest, respectively. They both found that, Santoro et al. 2012). The database contains georeferenced taken together, the two methods can be complementary plots of 2 9 2 m in size, which were randomly sampled and give more insight into the processes that determine during spring–summer (April–June) from 2004 to 2008 community assembly. Furthermore, new frameworks after restricting the sampling area to recent holocenic have been proposed for decoupling functional, phyloge- dunes identified on orthophotographs. In each plot, all netic and taxonomic diversity, with the aim of identifying vascular plant species were recorded and the cover of all the phylogenetic component of trait variation (Pillar & species was visually estimated using a 10% interval rank Duarte 2010; Diniz et al. 2011; Ives & Helmus 2011; Peres- scale. Based on the species assemblage recorded, each plot Neto et al. 2012). However, to what extent plot-to-plot was assigned to a plant community type of the coastal functional turnover along an environmental gradient is driven by phylogenetic history has rarely been explored in terms of both composition and diversity. The aim of our paper is to test whether variation in the functional composition and diversity is driven by phylogenetic history in plant communities on sand dunes along the Tyrrhenian coast of Italy. These communities provide an ideal system to test this question because they occur along a clearly defined stress gradient that drives considerable turnover in species composition and physiognomy over short distances. We hypothesize that differences in the functional composition and diversity among distinct species assemblages along the environmental gradient should mirror differences in phylogenetic structure.

Methods Study area, vegetation data and environmental gradient

The study was performed on recent holocenic dunes of the Fig. 1. Location of the study sites along the Tyrrhenian coast of Lazio Central Tyrrhenian coast of Italy, within the Lazio region (Central Italy).

Journal of Vegetation Science Doi: 10.1111/jvs.12095 © 2013 International Association for Vegetation Science 933 Phylogeny and functional composition/diversity M. Carboni et al. dune zonation described for these areas. See Carboni et al. the most common and abundant species within each habi- (2011) for more details on the sampling and database con- tat along the sea–inland gradient (Santoro et al. 2012) and struction. In a previous study we measured environmental of the complete vegetation database used in this study. We variables (pH, total organic matter, moisture and granul- estimated that the selected species collectively made up at ometry of the soil, as well as wind erosion, salt spray and least 80% of the maximum standing live biomass of each sand burial) in a subset of plots and showed that all vari- dune habitat (estimated through the cumulative cover of ables significantly correlated with distance to the sea (Car- all species available for the 4-m2 plots). This threshold has boni et al. 2011). In particular, values associated with been shown to ensure a satisfactory description of overall higher environmental stress and disturbance were community properties (Pakeman & Quested 2007). recorded in plots closer to the sea. Hence, here we used For each species we collected data on 16 functional traits the distance to the sea as a single variable representing a that are related to plant responses to the environment proxy for the main environmental gradient. For the pur- (Table 1). We measured seven continuous traits (Table 1) pose of this study, we selected 405 plots distributed in the related to the leaf–height–seed (LHS) plant strategy five study sites (Fig. 1), covering all community types of scheme (Westoby 1998). Each trait was obtained by mea- the zonation and at varying distances from the sea. This suring at least ten replicate samples on different individuals ensured that we could examine variation in species func- per species and averaging. Only individuals growing on tion in plots along the strong sea–inland environmental well-conserved coastal dunes at one of the study sites were gradient. In the following, we refer to the plots as ‘commu- sampled (Fig. 1). For each species, individuals were mea- nities’ or ‘species assemblages’ and to the community sured within the habitat of the coastal zonation where the types of the coastal dune zonation as ‘habitats’. species was most abundant/dominant. The different leaf traits tend to be inter-correlated and are mostly indicators of leaf life span, but do not always capture the same func- Traits tions. For example, higher specific leaf area (SLA) is associ- Traits were collected or measured on a subset of 46 domi- ated with shorter leaf life span, higher leaf nitrogen, higher nant species. This subset of species was chosen by selecting photosynthetic capacity, shorter nutrient residence times

Table 1. Description of the functional traits used in this study. Sources: Pignatti (1982), Tutin et al. (1964–1993). Seed shape is calculated according to Thompson et al. (1993).

Trait Description Data Type Attribute Source

Plant height Plant height at maturity Quantitative [cm] Measured Seed mass Weight of air dried dispersules Quantitative [mg] Measured SLA Speciﬁc leaf area (leaf area/dry weight) Quantitative [mm2mg1]Measured LDMC Leaf dry matter content (dry Quantitative [mgg 1]Measured weight/fresh weight) Leaf size Leaf area Quantitative [cm2]Measured Leaf thickness Leaf thickness Quantitative [mm] Measured Seed shape Variance of the three main dimensions Quantitative variance Measured Flowering phenology Flowering phenology Ordinal 1. April and before 2. May 3. Literature June 4. July and after Clonality Vegetative propagation Binary 0. Clonal; 1. Non-clonal Literature Leaf persistence Leaf phenology Binary 0. Deciduous 1. Evergreen Literature Life span Plant life span Binary 0. Annual 1. Biennal-Perennial Literature Pollination Pollination system Binary 0. By wind or non-specialized Literature 1. By insects or birds Life form Raunkiaer life form Nominal 1. Phanerophyte 2. Chamephyte Literature 3. Hemicryptohyte 4. Geophyte 5. Therophyte Growth form Growth form Nominal 1. Short basal 2. Long-semibasal Literature 3. Erect leafy 4. Cushions, tussocks and dwarf shrubs 5. Shrubs, trees and climbers Dispersal Dispersal mode Nominal 1. Anemochorous 2. Barochorous Literature 3. Zoochorous Leaf texture Leaf texture Nominal 1. Succulents 2. Malacophyllous Literature 3. Semi-sclerophyllous 4. Sclerophyllous

Journal of Vegetation Science 934 Doi: 10.1111/jvs.12095 © 2013 International Association for Vegetation Science M. Carboni et al. Phylogeny and functional composition/diversity and higher relative growth rates (Westoby et al. 2002). entire set of traits used (Pillar & Duarte 2010; Hardy & Leaves with high leaf dry matter content (LDMC) tend to Pavoine 2012). be relatively tough and more resistant to herbivory and decomposition (Cornelissen et al. 2003; Garnier et al. Analysis 2007). Plant height (height of flowering shoot) relates to both competitive ability and tolerance of disturbance Various indices have been proposed to measure relevant (Westoby et al. 2002). Finally, the two seed traits are a aspects of community trait variability. Among these, the proxy of dispersal ability and germination. ‘community-weighted mean trait value’ (CWM) and the Furthermore, we collected information for nine categor- Rao coefficient have been widely used in ecological ical traits that relate to species growth form, phenology, research for summarizing different facets of functional dispersal ability and pollination from regional and national composition and diversity (e.g. Lavorel et al. 2008). While floras (see Table 1 for a full description and sources). CWM represents the average functional traits within a species assemblage, the Rao coefficient can be seen as a measure of trait dispersion (diversity) within the assemblage. Phylogeny and phylogenetic signal at the species pool Together, these two complementary measures can be used level to effectively describe two different aspects of the func- For the 46 dominant species we constructed an aged phy- tional turnover along environmental gradients (Ricotta & logenetic tree using the Phylomatic and Phylocom soft- Moretti 2011). While shifts in mean trait values within ware (Webb et al. 2008). We assigned branch lengths by communities can be ascribed to environmental selection using a branch length adjustment algorithm (BLADJ), for certain functional traits, shifts in trait dispersion are based on the minimum age of nodes estimated from the related to patterns of trait convergence or divergence in fossil record (Wikstrom et al. 2001). We next calculated a response to assembly mechanisms. Recently, Pillar & matrix of pair-wise phylogenetic distances between all spe- Duarte (2010) proposed using phylogenetic-weighted species (summed branch lengths separating pairs of species). cies composition to compare communities in terms of their Although the phylogenetic tree constructed by the soft- phylogenetic resemblance along environmental gradients. ware Phylomatic contains many polytomies at the species Hence, to examine to what extent phylogenetic history and genus level, Phylomatic is virtually the sole freely drives variability in within-plot functional composition available operational tool that enables ecologists without and diversity in the coastal dune communities studied deep knowledge of evolutionary biology to reconstruct a (community level), we used two complementary meaningful community phylogeny. Accordingly, due to its approaches. All analyses were carried out in the R statisti- simplicity, we consider it an acceptable tool for the integra- cal environment (R Foundation for Statistical Computing, tion of phylogenetic information into studies of commu- Vienna, Austria) nity ecology (Ricotta et al. 2012a). We next quantified the degree of phylogenetic signal at Method1: phylogenetic composition vs. functional the species level for the traits used in this study. First, we composition calculated a multivariate matrix of pair-wise functional dissimilarities between species using the Gower distance To summarize the influence of phylogenetic history on the Gower (1966) for mixed variables proposed by Pavoine community functional composition, we adopted the gen- et al. (2009b). Then, we used standard Mantel statistics to eral analytical approach described in detail by Pillar & test whether this functional distance matrix was signifi- Duarte (2010). Briefly, this procedure allows estimation of cantly correlated with the matrix of pair-wise phylogenetic the degree of correlation between phylogenetic and func- distances between species. Although the Mantel test has tional composition (termed phylogenetic signal at the been criticized because of its poor statistical performance in metacommunity level in Pillar & Duarte 2010). First, a terms of low power and high type-I error rates (Harmon & matrix P containing species compositions for each plot after Glor 2010), this method allowed us to deal with the cate- fuzzy weighting by phylogenetic similarities is generated gorical traits used in our study in a relatively simple man- (expressing clade composition in each plot). The ner. Furthermore, the species functional performances are corresponding matrix T is generated with a similar expected to be driven by complex interactions among traits procedure and in essence contains the trait averages in that are not fully independent from each other (e.g. Milla each community or community-weighted mean values et al. 2009). In this framework, combining single trait (CWM). Phylogenetic similarities were calculated as pair- differences between species into a multivariate pair-wise wise phylogenetic distances between species in million dissimilarity matrix, the Mantel test is essentially the sole years, while for calculating multivariate functional similar- approach that allows testing for phylogenetic signal for the ity, we used the Gower functional distance (Gower 1966,

Pavoine et al. 2009b). We consider as evidence of phyloge- plot phylogenetic and functional dissimilarity matrices to netic signal at the metacommunity level a significant Man- quantify how much of the functional diversity within plots tel correlation between the distance matrices derived from is predicted by the corresponding phylogenetic diversity. P and T (DP and DT, respectively), which is tested against a Since functional and phylogenetic diversity in this frame- null model predicting that phylogenetic structure work are both influenced by species richness and evenness described in matrix P is independent from structure pres- (i.e. taxonomic diversity), a partial Mantel test was used to ent in matrix T (details in Pillar & Duarte 2010). A strong confirm the fit after partialling out the variation due to the correlation is expected when communities that are more corresponding pair-wise taxonomic distances J.Finally,as similar in terms of phylogenetic structure are also similar in Method 1, we checked for relationships between plot- regarding their average trait values. Furthermore, this to-plot phylogenetic/functional dissimilarity matrices and framework also allows us to estimate the relationship the main environmental gradient through Mantel correla- between variation in phylogenetic/functional composition tion with matrix DE. (DP and DT) and the main environmental gradient (matrix DE; here obtained based on the Gower distance) through Results matrix correlations tested against appropriate null models. Analyses were performed using the R-based package ‘sync- At the species level, we found a significant Mantel correla- sa’ (Debastiani & Pillar 2012). tion (R = 0.169, P = 0.001) between the pair-wise species functional distances and their phylogenetic relatedness, meaning that, overall, the traits used in this study showed Method2: phylogenetic diversity vs. functional diversity significant phylogenetic signal. To investigate the effects of the phylogenetic dispersion At the community level, for Method 1, the correlation within communities on the corresponding functional dis- between functional and phylogenetic matrices (P and T) persion, we first calculated the phylogenetic and func- was higher than at the species pool level (R = 0.219), tional diversities (Q) of single plot as (Rao 1982): although this correlation was not significant when tested XS against a null model randomizing phylogenetic relation- Q ¼ dijpipj ð1Þ ships (P = 0.412). Furthermore, there was a significant i;j relationship between functional composition and the environmental gradient (R = 0.349, P = 0.003), but no rela- where dij is any uni- or multivariate distance of choice tionship of phylogenetic composition with the between species i and j with dij = dji for all i ¼6 j,anddii = 0. = = For calculating the Rao phylogenetic diversity, we used environmental gradient (R 0.037, P 0.5). In contrast, pair-wise phylogenetic distances between species, while Method 2 (Fig. 2a) showed a very high and significant fit for calculating the functional diversity we used the Gower between pair-wise differences in functional diversity and functional distance (Pavoine et al. 2009b). The Rao index corresponding differences in phylogenetic diversity = = is defined as the expected dissimilarity between two indi- (R 0.81, P 0.001). Furthermore, this congruence viduals of a given species assemblage selected at random remained highly significant, when controlling for the effect with replacement; as such it represents an analogy of vari- of the plot-to-plot taxonomic turnover through a partial = = ance that summarizes the amount of trait dispersion in Mantel test (R 0.37, P 0.001). Plot-to-plot variation in multivariate space (Ricotta & Moretti 2011; Pavoine 2012). both functional and phylogenetic diversity was also signifi- – Based on the decomposition of Q into additive alpha, cantly related to the sea inland environmental gradient = beta and gamma components (de Bello et al. 2010; Ricotta (P 0.001 in both cases; Fig. 2b,c). These results suggest et al. 2012b), we next calculated the pair-wise phyloge- that at least the species functional diversity within plots is netic and functional distances (J) for each pair of plots as: largely reflected by the underlying phylogenetic diversity, while phylogenetic composition seems to be only partially qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi related to functional composition at the community level J ¼ Q 1ðÞQ þ Q ð2Þ ab ab 2 a b in our system. where Q and Q are the Rao diversities for plots a and b, a b Discussion respectively and Qab is the Rao diversity for the pooled pair of plots (for details see Champely & Chessel 2002; de Bello Community-weighted trait means (CWMs) and functional et al. 2010). For calculation of the pair-wise functional dis- diversity (FD) indices are commonly used in plant ecology tances J, we used the function ‘disc’ in the R-based package to describe different facets of functional turnover along ade4 (Dray & Dufour 2007) as implemented in de Bello gradients: one related to changes in the dominant ecologi- et al. (2010). We then applied a Mantel test to the plot-to- cal adaptations and the other to changes in the number of

(a) reveal shifting assembly processes along the gradient (i.e. through FD shifts). Coastal dune ecosystems have long been typical model systems for the study of community level changes along natural gradients (Oosting & Billings 1942; Hesp 1991; Stubbs & Wilson 2004; Forey et al. 2008; Feola et al. 2011). Previous studies in the same communities exam- 0 20406080100 ined here highlighted the link between the complex gradi- 0 20406080100 Pairwise differences in FD ent in water availability, wind intensity and soil (b) complexity with vegetation turnover (Carboni et al. 2011). In this study, matrix correlation with plot-to-plot environmental dissimilarities showed that, parallel to this strong turnover in species composition and abundance, there is also a signiﬁcant functional turnover along the sea–inland gradient in terms of average trait composition. Hence, dominant species with particular suites of traits 0 20406080100 Pairwise differences in FD Pairwise differences in PD (e.g. short annual species with succulent leaves on the 0 50 100 150 200 250 300 350 beach) are gradually replaced by other dominant species Environmental dissimilarity (m) (c) with different traits (e.g. tall perennial rhizomatous species on the main dune ridge). These results support the existence of environmental sorting of species according to habitat preferences mediated by distinct suites of traits. Pavoine et al. (2011) using a different approach (RLQ ordination) also found highly signiﬁcant functional and phylogenetic turnover along a salinity gradient in a coastal dune 0 20 40 60 80 100

Pairwise differences in PD system in northeast Algeria. Nevertheless, in our case we 0 50 100 150 200 250 300 350 found no evidence for a correlation between the variation Environmental dissimilarity (m) in community functional composition and phylogenetic Fig. 2. Pair-wise dissimilarities among communities along the composition. The lack of significant congruence between environmental gradient. (a) phylogenetic (PD) against functional (FD) pair- the functional and phylogenetic composition metrics at wise dissimilarities (Mantel-R = 0.81, P = 0.001); (b–c) PD and FD pair-wise the community level was reflected by the absence of a = dissimilarities against pair-wise environmental dissimilarities (P 0.001). clear phylogenetic turnover in terms of community com- Environmental dissimilarities are the differences in plot distances to the position along the environmental gradient. This supports sea (the main gradient). Simple linear regression lines are depicted on the graphs for a clearer visualization of the trends. the hypothesis that phylogeny only partially captured the environmental selection for certain functional traits along the gradient in this system, even in the presence of signifi- available niches leading to convergence or divergence cant phylogenetic signal at the species pool level. (Ricotta & Moretti 2011). Analogue phylogeny-based met- With respect to diversity, we detected high plot-to-plot rics are being increasingly used, assuming that variability variability in the dispersion of trait values within commu- in community functional composition and diversity along nities (FD), as well as in the dispersion of phylogenetic lin- the gradient is reflected by phylogenetic variability. In this eages (PD) along the main sea–inland gradient. A previous paper we provided a formal test of this assumption. We study focusing on the same communities had already found that variation in community functional diversity at shown a clear pattern of variation in FD among the habi- the plot level could be effectively summarized by applying tats of the coastal zonation through functional rarefaction analogue phylogenetic metrics when traits show phyloge- (Ricotta et al. 2012b). Here, we confirmed a directional netic signal at the species pool level, while little congru- pattern in the dispersion of trait values, with a decreasing ence exists for variation in community composition trend in FD along the sea–inland gradient (Fig. S1). More- metrics. We discuss our results in relation to the underly- over, we detected greater FD differentiation between com- ing environmental gradient and in light of recent studies munities parallel to greater environmental dissimilarity addressing similar questions. In conclusion, we caution between communities (i.e. significantly positive Mantel against the use of phylogeny as a predictor of environmen- correlation; Fig. 2b). In addition, we found that not only tal selection for dominant traits (i.e. through CWMs), functional but also phylogenetic dissimilarities among while providing some support for the use of PD patterns to communities were related to environmental dissimilarities

(although more moderately; Fig. 2c). Finally, functional genetic community tests captured much of the same filter- and phylogenetic dissimilarities were highly congruent ing patterns detected by trait-based methods. Taken along the gradient, meaning that plots that were very dif- together, their conclusions and our results provide some ferent from each other in the dispersion of trait values support for methods based on phylogenetic relatedness to were also phylogenetically very different. This was true investigate community assembly along environmental gra- even when accounting for variation in taxonomic diver- dients. The presence of many other contrasting results, sity. Hence, we can conclude that variability in PD proba- however, suggests that more studies analysing specifically bly captured the same assembly processes that lead to the turnover in functional and phylogenetic diversity are functional turnover. Overall, these results suggest that needed along a variety of environmental gradients. analysing community phylogenetic patterns, rather than While the Rao coefficient is broadly applied in commu- functional ones, by means of diversity measures to reveal nity phylogenetics, together with a multitude of other the number of supported niches along this ecological gradi- indices for summarizing phylogenetic diversity (Crozier ent would lead to similar conclusions. 1992; Faith 1992; Pavoine et al. 2009a; Chao et al. 2010; In our empirical study, we considered a comprehensive Bernard-Verdier et al. 2013), the combined evaluation of set of traits summarizing relevant aspects of the plant strat- the congruence between phylogenetic and functional egies and adaptations in the coastal environment (e.g. composition is rare. Here, we showed that congruence in overcoming the summer drought period by ‘escaping’ in shifts of functional and phylogenetic diversity by no the seed bank or preventing dehydration through special- means implies the same fit at the level of community com- ized leaf anatomy and structure). In good agreement with position, even in the presence of significant phylogenetic recent works (Kraft & Ackerly 2010; Pillar & Duarte 2010; signal overall at the species pool level. This is because shifts Uriarte et al. 2010), here at the species pool level we in composition and in trait dispersion capture signals of observed a moderate although significant overall phyloge- different ecological mechanisms, which may be more or netic signal. We found that in this coastal system the exis- less adequately reflected by phylogenetic variation. For tence of phylogenetic signal in traits was a good indicator instance, if traits related to habitat filtering were more of the correlation between functional and phylogenetic labile than traits related to competition/facilitation, phy- diversity. A possible explanation (confirmed by prelimin- logeny should reflect shifting divergence/convergence ary analyses) is that, at least in our study system, func- patterns better than environmental sorting along the gra- tional traits exhibit on average higher levels of dient. Competitive exclusion (through niche overlap) can phylogenetic signal for the most dominant species than for lead to divergence of relevant traits, with the consequence the more subordinate ones. Given that dominant species that competition-related traits will contribute best to the tend to be strongly sorted along the coastal zonation, this is diversity of communities, while traits associated to envi- likely to lead to a significant relationship between phylog- ronmental preferences will determine the dominant trait eny and functional diversity, since the Rao index is sensi- composition of communities. Hence, if habitat-filtering tive to species relative abundances in its formulation. traits are less well conserved, we would expect less con- However, it is not obvious how well phylogenetic history gruence for functional and phylogenetic composition met- can predict functional diversity variability in other systems. rics, in accordance with our results. However, in contrast For example, both Mason & Pavoine (2013) and Pavoine with this hypothesis, some authors have suggested that et al. (2013) suggest that PD is a poor surrogate of FD. Pav- traits related to habitat preferences should rather be more oine et al. (2013) point out that PD and FD indices are phylogenetically conserved within lineages than traits often found to correlate simply because variation in species determining convergence or divergence (i.e. functional richness and evenness influences both FD and PD values. diversity) within communities (Silvertown et al. 2001). However, in our case the relationship between pair-wise There may also be other reasons why congruence of func- dissimilarities in PD and FD remained significant even tional metrics with phylogeny could be more strongly when accounting for the effect of taxonomic diversity dependent on phylogenetic signal for composition than (TD). Nevertheless, in a recent study in a Mediterranean for diversity metrics. For instance, phylogenetic signal at rangeland, Bernard-Verdier et al. (2013) found that phylo- the species level could be generally poorly maintained at genetic diversity was generally a poor predictor of multi- the meta-community level in terms of differences in spe- variate functional diversity along the main environmental cies composition. Mason & Pavoine (2013), this issue), in a gradient even after partialling out the effect of TD. How- simulation study, consistently found poor congruence of ever, they did find significant correlations for some traits functional and phylogenetic composition at the meta- when considering plot-to-plot variability in diversity mea- community level (measured as in our study) along a hypo- sures, as in our study, which is partially in accordance with thetical stress gradient, even when traits were strongly our results. Kraft & Ackerly (2010) also showed that phylo- phylogenetically conserved in the species pool. As in our

Journal of Vegetation Science 938 Doi: 10.1111/jvs.12095 © 2013 International Association for Vegetation Science M. Carboni et al. Phylogeny and functional composition/diversity study, even when the functionally determined species tion assessment in sandy coastal environments: an composition of communities was significantly correlated application in central Italy. Environmental Monitoring and with their position along the stress gradient, there was no Assessment 140: 99–107. such correlation for phylogenetic composition. However, Cavender-Bares, J., Ackerly, D.D., Baum, D.A. & Bazzaz, F.A. the causes of this mismatch at the meta-community level 2004a. Phylogenetic overdispersion in Floridian oak com- – are not yet clear, and this remains an open question that munities. American Naturalist 163: 823 843. merits further investigation. Champely, S. & Chessel, D. 2002. Measuring biological diversity In conclusion, on the one hand our results suggest that using Euclidean metrics. 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Journal of Vegetation Science Doi: 10.1111/jvs.12095 © 2013 International Association for Vegetation Science 941 Journal of Vegetation Science 24 (2013) 942–948 SPECIAL FEATURE: FUNCTIONAL DIVERSITY A novel framework for linking functional diversity of plants with other trophic levels for the quantiﬁcation of ecosystem services Sandra Lavorel, Jonathan Storkey, Richard D. Bardgett, Francesco de Bello, Matty P. Berg, Xavier Le Roux, Marco Moretti, Christian Mulder, Robin J. Pakeman, Sandra Dıaz & Richard Harrington

Keywords Abstract Functional trait; Ecosystem functioning; Biotic interactions; Plant-soil interactions; Biotic A novel conceptual framework is presented that proposes to apply trait-based control; Environmental change; Pollination; approaches to predicting the impact of environmental change on ecosystem ser- Grassland; Soil nitrogen; Field margins vice delivery by multi-trophic systems. Development of the framework was based on an extension of the response–effect trait approach to capture functional Received 31 December 2011 relationships that drive trophic interactions. The framework was populated with Accepted 13 March 2013 worked examples to demonstrate its ﬂexibility and value for linking disparate Co-ordinating Editor: Norman Mason data sources, identifying knowledge gaps and generating hypotheses for quantitative models.

Lavorel, S. (corresponding author, de Bello, F. ([email protected]): Institute of Ecology, Via Belsoggiorno 22, CH-6500, [email protected]) & Dıaz, S. Botany, Academy of Sciences of the Czech Bellinzona Switzerland ([email protected]): Laboratoire d’Ecologie Republic, Dukelska135,CZ-37982Trebon, Mulder, C. ([email protected]): Centre Alpine, CNRS, UMR 5553, UniversiteJoseph Czech Republic for Sustainability, Environment and Health, Fourier, BP 53 38041, Grenoble Cedex 9, France Berg, M.P. ([email protected]): Department of RIVM, Box 1, Bilthoven 3720 BA, the Storkey, J. (jonathan.storkey@ Ecological Science, VU University Amsterdam, Netherlands rothamsted.ac.uk) & Harrington, R. De Boelelaan 1085, 1081 HV, Amsterdam, the Pakeman, R.J. ([email protected]): ([email protected]): Netherlands The James Hutton Institute, Craigiebuckler, AgroEcology Department, Rothamsted Le Roux, X. ([email protected]): Aberdeen AB15 8QH, UK Research, Harpenden Herts, AL5 2JQ, UK Laboratoire d’Ecologie Microbienne, Universite Dıaz, S. ([email protected]): IMBIV Bardgett, R.D. (richard.bardgett@ de Lyon, Universite Lyon 1, INRA (USC 1193), (CONICET-UNC) and FCEFyN, Universidad manchester.ac.uk): Faculty of Life Sciences, CNRS (UMR 5557), B^at Mendel, 43 bd du 11 Nacional de Cordoba, Casilla de Correo 495, Michael Smith Building, The University of novembre 1918, 69622, Villeurbanne France 5000, Cordoba Argentina Manchester, Oxford Road, Manchester, M13 Moretti, M. ([email protected]): Swiss 9PT, UK Federal Research Institute WSL, Community

these ‘effect traits’ that determine service delivery and the Introduction ‘response traits’ that determine how the functional Understanding the processes that underpin ecosystem ser- diversity of a community responds to an environmental vice delivery is crucial if the impact of change on current driver has been hypothesized as a way of enhancing and future ecosystem services is to be quantified (Kremen predictability of ecosystem functioning (Lavorel & Garnier 2005; Cardinale et al. 2012). Recent syntheses of empiri- 2002; Suding et al. 2008), known as ‘the response–effect cal studies have highlighted that functional diversity more model’. often determines ecosystem functioning than does species An increasing number of studies support this hypothesis richness per se (Dıaz et al. 2006). This has led to the devel- for plant communities. For example, functional traits that opment of trait-based approaches designed to identify bio- determine plant response to resource availability (e.g. spe- tic control over ecosystem service delivery (Dıaz et al. cific leaf area, leaf N content, height) also affect the effi- 2007; de Bello et al. 2010; Luck et al. 2012; Lavorel ciency of key functions such as biomass production 2013), and the mechanisms underpinning synergies and (Minden & Kleyer 2011; Pakeman 2011; Laliberte & Tyli- trade-offs among ecosystem services (Lavorel & Grigulis anakis 2012; Lienin & Kleyer 2012). However, many eco- 2012). Quantifying the overlaps or correlations between system services ultimately rely on interactions between

Journal of Vegetation Science 942 Doi: 10.1111/jvs.12083 © 2013 International Association for Vegetation Science S. Lavorel et al. Functional traits and ecosystem services plants and organisms belonging to other trophic levels to identify the trophic effect and response traits that can be (Zhang et al. 2007; Cardinale et al. 2012; Mulder et al. used to quantify functional linkages which cascade 2012), for example the maintenance of soil fertility (Bardg- through the primary producer community to the con- ett & Wardle 2003; Brussaard et al. 2007; Schmitz 2008) sumer ecosystem service providers. In the case of the polli- and pollination (Kremen et al. 2007). Combining a multi- nator example (Fig. 1), there is good agreement that, at trophic perspective and interaction networks with a trait- species level, floral ‘trophic effect traits’ including based approach has thus been proposed in principle as the morphology, colour, fragrance and reward to pollinators next breakthrough for advancing biodiversity–ecosystem (Fenster et al. 2004; Ibanez 2012), influence pollinator functioning research (Reiss et al. 2009). communities. At the community level, the amount and Here, we present a novel conceptual framework for nature of flowering resources, and their spread over time, addressing this research need in practice. The framework are important determinants of pollinator abundance and explicitly incorporates into the original ‘response–effect species diversity, and ultimately of pollination success model’, trait linkages of plants with higher trophic levels to (Kremen et al. 2007). For instance, higher floral diversity capture indirect effects of environmental change on ecosys- promotes a diversity of functional groups of pollinators tem services delivered by consumers. The framework rep- (Potts et al. 2003; Fenster et al. 2004). The linkage resents an important step in moving from qualitative to between floral traits and pollinator traits has been demon- quantitative predictions of these systems by formulating strated at species level, for instance linking proboscis hypotheses for statistical models, organizing existing data length with nectar holder depth, or with nectar holder on individual functional linkages within a system and iden- depth and width (Stang et al. 2007; Ibanez 2012). The tifying knowledge gaps. As such, the proposed framework third step in populating the framework is to identify the is not meant to be a tool for a comprehensive systems’ anal- ‘functional effect traits’ of the consumer community and ysis. Rather, it is intended to identify and test key trait- appropriate metrics (CWM, functional dissimilarity or, based mechanisms that underlie ecosystem service deliv- where processes are driven by idiosyncratic species effects, ery, with the ultimate objective of quantifying the direction trait attributes for individual species) that determine eco- and magnitude of the response of an ecosystem service to system service delivery (Dıaz et al. 2007). To our knowl- environmental change. Unlike food web or interaction net- edge, such an analysis has not yet been carried out at work approaches, this trait-based approach does not community level for pollination services; although there is require a detailed, mechanistic understanding of complex evidence that increased functional diversity of pollinator species-specific trophic interactions (Mulder et al. 2012). communities can increase pollination success (Bluethgen &Klein2011). The final step is to identify linkages between response The framework and effect traits within each trophic level to predict the The framework is broken into a series of four sequential likelihood of the driver of change impacting on service steps, although in practice they could be completed in any delivery. A study quantifying the effects of habitat man- order. Figure 1 presents a simple case with two trophic lev- agement on pollinators found that the assemblage of bee els, where an environmental driver affects trophic level 1 communities responded to the CWMs of flower colour and and the ecosystem function of interest is determined by forage index (Carvel et al. 2006). These, in turn, appear to trophic level 2. This would apply to fertilization effects on a be correlated with plant response traits via phylogenetic plant–herbivore system, with secondary production (e.g. effects such as the presence of specific families/growth herbivore biomass) as the ecosystem service, or to grass- forms (Pakeman & Stockan 2013). Although pollinator land management effects on a plant–pollinator system traits were not included in these previous studies, it is with wild flower or crop pollination as the ecosystem ser- likely that there will be functional differences between bee vice of interest. We use this second example to populate communities in terms of trophic response and pollination the framework. efficiency. For example, if management selects for short- First, the relevant trophic levels and groups of organisms tongued bees, pollination services for plants requiring are identified along with the traits that are expected to long-tongued species will decline. If this is found to be the respond directly to the environmental driver of interest. In case, predictions of the impact of management on pollina- the example developed in Fig. 1, intensification of grass- tor services based on the direct effects on pollinator abun- land leads to decreased plant height, lower leaf dry matter dance alone may differ from models that include the content and a decreased legume component (Garnier et al. indirect effects of plant traits on pollinator function. How- 2007). The possible direct effects of management changes ever, this level of understanding of the system will require on pollinators are not considered explicitly in Fig. 1, but more comprehensive data on the relevant plant effect and could be incorporated. The second, and most novel, step is bee response traits and their coupling.

Step 1: Identify traits that respond to environmental driver of Step 2: Identify the trophic effect and response traits of the interest lower and upper trophic levels respectively.

Environmental driver Environmental driver

Grassland management intensity Grassland management intensity

Trophic level 2

Driver response traits Driver response traits Trophic response traits Height Height LDMC LDMC Body size Legumes Legumes Proboscis length

Trophic level 1 Corolla length Flower colour

Trophic effect traits

Trophic level 1

Step 3: Define and identify appropriate metrics of functional Step 4: Analyse linkages among different response and effect effect traits that determine efficiency of service delivery. traits within each trophic level.

Environmental driver Environmental driver

Grassland management intensity Grassland management intensity

Trophic level 2 Trophic level 2

Driver response traits Trophic response traits Driver response traits Trophic response traits Height Height LDMC Body size LDMC Proboscis length Legumes Legumes ~ Body size Corolla length, Proboscis length Flower colour Body size Corolla length Proboscis length Foraging range Flower colour Foraging range Trophic effect traits Functional effect traits Trophic effect traits Functional effect traits Trophic level 1 Trophic level 1 Ecosystem service Ecosystem service Pollination efficiency Pollination efficiency

Fig. 1. Method for articulating functional responses and effects within and across two trophic levels to predict changes in ecosystem functioning, and methodological steps for its application. Step 1 identifies response traits for each of the trophic levels to the environmental driver of interest. In this case, only effects on the plants are considered. Within each trophic level i, the response of organisms to the environmental driver can be related to particular functional traits (driver response traits). Step 2 identifies the trophic effect traits of a lower trophic level which affect the next trophic level up, and the corresponding trophic response traits of the upper trophic level. Step 3 defines the identity and appropriate metrics of the functional effect traits contributing to the ecosystem function. Step 4 analyses linkages among the different response and effect traits within each trophic level. Such linkages can occur through direct overlap (response trait = effect trait) or through association (indicated by ~), e.g. where traits are linked through evolutionary trade- offs.

The framework is intended to be used as a conceptual tool to challenge these hypotheses (Shipley 2000). SEMs tool to identify relevant traits and integrate data from dis- have recently been applied to test response–effect linkages parate studies on individual linkages, and to generate for plants, making it possible to conﬁrm the pivotal role of hypotheses on the response of the whole system to given plant height and the leaf economics spectrum as linkages drivers, which can lead to quantitative models. Structural between abiotic and management factors, and a variety of equation modelling (SEM), or path analysis, is a powerful ecosystem processes involved in carbon and nitrogen

of biomass consumption by grasshoppers proportional to Grassland management intensity their body size.

Discussion Legumes Height LDMC Plant response traits The example developed in Fig. 1 has a single driver of change and two trophic levels. However, the modular structure of the framework means that it has the flexi- Flower Corolla Plant trophic colour length effect traits bility to incorporate more than two trophic levels or multiple services and drivers. Two examples from temperate agro-ecosystems, for which extensive knowledge Proboscis Body size Pollinator can be synthesized from the literature, have been devel- length response traits oped in Appendix S1 (Supporting Information) to demonstrate this flexibility. They illustrate the potential of the framework for articulating often fragmented knowl- Pollination efficiency edge from complex systems into a comprehensive analysis. The first example shows how, by introducing traits Fig. 2. Hypothesis for a structural equation model (SEM) depicting effects explicitly for the soil microbial component, the applica- of grassland management intensity on pollination. The SEM tests how tion of the framework provides a conceptual basis for management effects on plant traits feed forward to pollinator traits testing the mechanisms that underpin a well-known involved in pollination efficiency. Plant height and leaf dry matter feedback loop of the nitrogen cycle involving plants and concentration (LDMC) would not be retained in the final model given their soil micro-organisms. This example also highlights the lack of direct links with floral traits relevant to pollinators. Black arrows potential to incorporate the direct impact of the environ- indicate positive effects; grey arrows indicate negative effects. mental driver on multiple trophic levels. The second example demonstrates how the framework can support cycling (Minden & Kleyer 2011; Laliberte & Tylianakis the analysis of trait-based trade-offs and synergies among 2012; Lavorel & Grigulis 2012; Lienin & Kleyer 2012). multiple ecosystem services using the functional compo- Figure 2 illustrates a possible hypothesis for a SEM of sition of the plant community to integrate functions. the impact of grassland intensification on wild flower or Although both examples only include a single driver of crop pollination mediated by plant–pollinator functional change, in many cases several drivers, such as land use interactions derived from the framework illustrated in and climate change, are likely to interact with unpredict- Fig. 1. To date, this approach has only been used to test the able effects on biotic interactions and the functions that framework in its comprehensive form in an analysis of they drive (Tylianakis et al. 2008). In such cases the coupled plant- and grasshopper-trait effects on primary framework would be used to identify multiple groups of productivity (Moretti et al. 2013). A combination of uni- response traits and analyse independence or association variate and multivariate approaches was used first to select among them as well as their linkages with effects traits traits relevant to grassland management response (step 1), of interest. to plant–grasshopper interactions (step 2), and to primary In applying the framework to multiple case studies, the production (step 3), while linkages flowing through the authors encountered a number of constraints. First, two trophic levels were identified manually as both although linear interaction networks are relatively responding to management and/or the other trophic level straightforward to formulate using the framework, difficul- and traits affecting primary production (step 4). The two ties arise as more trophic levels, with intrinsic feedbacks, functional metrics thus retained, i.e. CWM leaf dry matter are added (e.g. the full decomposer food web) unless there concentration (LDMC) and CWM body mass, were then is a clear effect of ecosystem engineers (e.g. earthworms) used to build a structural equation model demonstrating that overrides all other trophic groups (Lavorel et al. the effects of management on primary production, both 2009). Future applications of the framework should directly through CWM LDMC, and indirectly through the explore its value and limits for more complex cases (Mul- effects of these plant metrics on CWM body mass. The fact der et al. 2012). Second, the framework is most suited to that the final SEM retained the path through grasshopper addressing processes operating at local scale. Addressing body mass and its response to plant LDMC, rather than services depending on non-linear spatial processes, only a direct path through plant traits, provides strong whether for ecosystem fluxes or for the dynamics of eco- evidence for the relevance of using our framework which system service-providing organisms (e.g. Woodcock et al. included a quantification of the trophic path, and thereby 2010), will require that the framework be used in conjunc-

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Figure S1. Framework implementation for analysing Supporting Information the effects of changes in the intensity of grassland manage- Additional supporting information may be found in the ment through grazing and its inﬂuence on soil N provision online version of this article: via N transformations. Figure S2. Using trait linkages to assess the impact of Appendix S1. Formalizing available knowledge into ﬁeld margin management on multiple ecosystem services. the framework.

Journal of Vegetation Science 948 Doi: 10.1111/jvs.12083 © 2013 International Association for Vegetation Science Journal of Vegetation Science 24 (2013) 949–962 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Linking traits between plants and invertebrate herbivores to track functional effects of land-use changes Marco Moretti, Francesco de Bello, Sebastien Ibanez, Simone Fontana, Gianni B. Pezzatti, Frank Dziock, Christian Rixen & Sandra Lavorel

Keywords Abstract CWM; Ecosystem functioning; Ecosystem services; Functional dissimilarity FD; Functional Questions: Ecosystem functions and underlying services are strongly inﬂu- traits; Herbivory; Multitrophic trait cascade; enced by multitrophic relationships, with functional traits playing a central role Plant–herbivore interaction in structuring them. Which traits and functional metrics mediate the impact of different types of land use on ecosystem function within and across trophic Nomenclature levels? http://www.faunaeur.org/ Methods: We studied the functional relationships between plants and grass- Received 6 April 2012 hoppers in sub-alpine grasslands under different management regimes in the Accepted 28 September 2012 – Co-ordinating Editor: Robin Pakeman Central French Alps. We applied the theoretical multitrophic response effect framework described by (Journal of Vegetation Science, 24,thisissue)to identify key traits linking plants and grasshoppers to biomass production. The Moretti, M. (corresponding author, linkages between selected plant and grasshopper traits were analysed using [email protected]), Ibanez, S. community-weighted mean traits (CWM) and functional diversity (FD; Rao’s ([email protected]), Fontana, S. quadratic diversity). ([email protected]) & Pezzatti, G.B. ([email protected]): Swiss Federal Research Results: Uni- and multivariate models provided evidence about the relative Institute WSL, Community Ecology, Via importance of trait linkages within and across trophic levels. We showed that Belsoggiorno 22, 6500 Bellinzona, Switzerland management affected both plant and grasshopper traits and that the interaction de Bello, F. ([email protected]): Institute of Botany, Academy of Sciences of the Czech between them was linked to biomass production. While a number of CWM traits Republic, Dukelska 135, 379 82 Trebon, Czech and FD were involved in the interaction, CWM of leaf dry matter content Republic (LDMC) and grasshopper dry body mass (GMass) chieﬂy mediated the impact of de Bello, F.: Department of Botany, Faculty of management change on biomass production. Sciences, University of South Bohemia, Na Zlate Stoce 1, 370 05 Cesk eBud ejovice, Czech Republic Conclusions: Our study suggests that both trait values of the most abundant Dziock, F. ([email protected]): Faculty species and functional trait variation within and across trophic levels in combi- of Agriculture and Landscape Management, nation may best explain the impact of land-use changes on ecosystem function. Dresden University of Applied Sciences, To improve our mechanistic understanding across trophic levels, a better knowl- Pillnitzer Platz 2, 01326 Dresden, Germany edge of response and effect traits remains a major goal, especially for animal Rixen, C. ([email protected]): WSL ecologists, while a strong collaboration among disciplines is needed to bridge the Institute for Snow and Avalanche Research SLF, Community Ecology, Fluelastrasse€ 11, existing gaps. 7260 Davos, Switzerland Lavorel, S. ([email protected]): Laboratoire d’Ecologie Alpine CNRS UMR 5553, Universite Joseph Fourier, BP 53, 38041 Grenoble Cedex 9, France

Recent syntheses and empirical studies have highlighted Introduction that functional traits (sensu Violle et al. 2007) better predict Predicting how global changes will affect ecosystem func- the effects of global changes on ecosystem services than tions and resultant services through changes in biological taxonomic parameters alone (Hooper et al. 2005; Dıaz communities is one of the most urgent tasks in ecology. et al. 2007a,b; de Bello et al. 2010b; Lavorel et al. 2011).

Many ecosystem functions ultimately rely on interac- quantified by the community-weighted mean of trait val- tions between primary producers and other trophic levels, ues (CWM), and (2) the degree to which trait values dif- such as pollinators, soil decomposers and herbivores (Lo- fer in a community, quantified by functional diversity reau et al. 2001; Kremen et al. 2007). For example, nitro- (FD) (see Ricotta & Moretti 2011 for a synthesis). CWM gen cycling involves complex interactions between plants effects translate the mass ratio hypothesis (Grime 1998), and soil biota (Hyvonen et al. 2002), and between plants which proposes that ecosystem processes are mainly and herbivores (Heidorn & Joern 1987; Bardgett & Wardle determined by the trait values of the dominant species 2003; Dubey et al. 2011). However the mechanisms that present in a community, hence, that a species’ influence drive the relationships between biodiversity and ecosystem on ecosystems should be proportional to its relative con- functioning are still poorly known despite growing evi- tribution to the total number or biomass of the commu- dence for the importance of trophic interactions (Thebault nity (Garnier et al. 2004; Quested et al. 2007). On the & Loreau 2006; Suding & Goldstein 2008). Up to now only other hand, FD is associated with the complementary a few studies have considered multitrophic systems resource use hypothesis (Tilman et al. 1996), and pro- (Downing & Leibold 2002; Fox 2004; Duffy et al. 2005; poses that when there is higher FD among the species Ives et al. 2005; Thebault & Loreau 2006), and to our present in a community, reflecting higher diversity of knowledge they considered species diversity rather than resource use strategies, there will be a more complete functional components of biodiversity. exploitation of resources than in less functionally diverse There is a growing hope that extending the trait concept communities (Petchey et al. 2004; Hooper et al. 2005; to multitrophic systems will improve our ability to identi- Spehn et al. 2005). It still remains basically unexplored as fying mechanisms that drive biotic control over ecosystem to whether multitrophic trait linkages are affected by service delivery (e.g. Schmitz 2008; de Bello et al. 2010a,b; these community parameters (Reiss et al. 2009). Ibanez 2012). Most studies of the trophic interactions have In this paper we use plants and grasshoppers as model been focused at the species level, addressing the question organisms to assess the links between their traits and to ‘who eats whom’ and ‘how much’. Few attempts have quantify the relative importance of environmental and tro- been proposed to quantify relationships across trophic phic factors to explain species and functional traits compo- levels using functional traits of single species, while no sition. We used the term ‘grasshoppers’ for simplicity, but attempt has been made to link functional traits at we considered both true grasshoppers (Caelifera) and community level across trophic guilds (Stang et al. 2006; bush-crickets (Ensifera). These groups can have a rela- de Bello et al. 2010a; Brooks et al. 2012; Ibanez 2012). In tively high impact in alpine grasslands, consuming up to this paper we apply the multitrophic response–effect 30% of the green biomass (Blumer & Diemer 1996). They traits framework proposed by Lavorel et al. (2013) to ana- display great inter-specific differences in mandibles and lyse functional relations that underlie responses to exoge- head morphology, leading to distinct food preferences and nous drivers and effects on ecosystem functioning. The diets (e.g. Bernays et al. 1991). Here, we aimed to identify framework is based on response–effect traits theory (Lavo- (1) which trait metrics respond to different management rel & Garnier 2002; Violle et al. 2007; Suding & Goldstein types; (2) which trait metrics relate to biomass production; 2008), and further considering traits responsible for trophic and (3) which trait metrics link the response to effect traits interactions. The framework therefore considers three within and across trophic levels. With this last question, types of traits: (1) morphological, physiological which to our knowledge has not yet been addressed at the and behavioural traits related to fitness and ability of community level under natural conditions, we propose to organisms to cope with different environmental conditions (1) advance the fundamental understanding of mecha- (i.e. ‘response traits’); (2) ‘trophic traits’ that are involved nisms underlying relationships between biodiversity and in the interaction across trophic levels (e.g. nutritional ecosystem functioning by considering FD at multiple tro- characteristics, palatability and toughness on one side, and phic levels, and (2) provide a first attempt to identify key size, shape and strength of the consumption organs on the traits and metrics that link effects of environmental other), and finally (3) traits involved in ecosystem effects, changes with the ecosystem processes and ecosystem ser- i.e. ‘effect traits’ (e.g. consumption rate of herbivores or vices delivered by biodiversity in a multitrophic context. decomposers). The problem is that often such traits are not Based on existing knowledge of plant and grasshopper yet known, so proxies (i.e. soft traits – sensu Hodgson et al. trait responses to changing grassland management regime, 1999), often from the literature, are used with a risk of los- their potential trophic linkage and effects on the biomass ing explanatory power in the model. production, we expect that fertilization favours plants Two widely used distinct metrics of community trait with higher specific leaf area, higher concentrations of leaf composition are hypothesized to affect ecosystem pro- nutrients such as nitrogen or phosphorus and lower dry cesses: (1) the dominant trait value in the community, matter content (e.g. Quetier et al. 2007). Such plants

Journal of Vegetation Science 950 Doi: 10.1111/jvs.12022 © 2013 International Association for Vegetation Science M. Moretti et al. Plant–grasshopper trait linkages would provide highly palatable food for grasshoppers verted to terraced grasslands used for hay or grazing. At (Perez-Harguindeguy et al. 2000) that are large, have little mid-slope (1800–2100 m), ancient, never-ploughed hay mobility and live in dense vegetation. Grazing is expected meadows are increasingly left for light summer grazing by to favour well-protected tussocks of small and rather sheep or cattle. The study area is part of a larger experi- unpalatable plants with lower nitrogen content (Dıaz mental area with several management trajectories and et al. 2007a,b; Loranger et al. 2013) and small tough experimental plots representing past and current manage- leaves (Perez-Harguindeguy et al. 2003; Peeters et al. ment regimes. For more details see Quetier et al. (2007) 2007). Such plant traits are likely to sustain grasshopper and Lavorel et al. (2011). species with a large head (Bernays & Hamai 1987) and mandibles (Isely 1944), while influencing oviposition site Experimental design and data sampling substrate due to the overall change in the canopy structure of the herbaceous layer, with both patches of bare soil We selected five management trajectories (sensu Quetier among grass clumps due to trampling effect and accumu- et al. 2007) representing the main management practices lated unpalatable plant litter (O’Neill et al. 2010). Based on that best reflected the past and current management of Specht et al. (2008) and Unsicker et al. (2010), we expect these sub-alpine grasslands (Table 1). In this system, any plant diversity and plant functional identity, i.e. the varia- quantitative variable such as disturbance regime or soil fer- tion rather than the dominance of plant traits, to positively tility only partially captures management effects by ignoring affect grasshoppers’ fitness and traits related to potential past land-use legacies (Quetier et al. 2007). In each trajec- fecundity (e.g. number of ovarioles). Concerning repercus- tory we selected three sampling plots (replicates), of 200 m2 sions of vegetation and grasshopper changes on primary each, at different locations and altitudes from 1650 to productivity, we expect low leaf nitrogen content and high 1900 m a.s.l. to avoid spatial autocorrelation. Hereafter, we leaf tissue density to reduce both fodder productivity and will use the term ‘management’ when we refer to the five quality of plant communities (Perez-Harguindeguy et al. trajectories, and ‘plots’ when we refer to sampling plots. 2000). Lower leaf palatability would then negatively impact herbivory by grasshoppers (Bownes et al. 2010), Plants slowing down ecosystem-level nutrient cycling, and leading to lower productivity in grazed compared to mown Floristic composition of each plot was assessed using three and fertilized grasslands (Lavorel & Garnier 2002; Quetier non-intersecting point-quadrat survey lines (Daget & et al. 2007). Finally, based on the most recent investiga- Poissonet 1969). These were 8-m long, with one point tions, we expect the combination of dominant traits every 20 cm for a total of 120 points per plot. The total (CWM) and variation in traits (FD) to drive ecosystem number of contacts for each species was counted at each processes (e.g. Mokany et al. 2008; Schumacher & Ro- point of the point-quadrat survey. Species accounting for scher 2009; Mouillot et al. 2011), although very little is 80% of total accumulated abundance were selected for known on the relative importance of the two functional trait measurements. Based on current knowledge (see metrics across trophic levels. We are aware that using Introduction) we selected traits (Table 2a) expected to observational studies can leave uncertainties as to the cau- respond to management and to affect both biomass pro- sal relationships between trait metrics and trophic levels and that our study presents mainly co-occurrence rather Table 1. Past and current management of the five selected management trajectories. Past management indicates the main practice in the last cen- than strict trophic relationships. Nevertheless, we believe tury until the most recent change, which determined the current use (for that our study represents a valuable insight into trait link- more details, see Quetier et al. 2007). ages within and across trophic levels. Trajectory Alt (m a.s.l.) Past management Current management Methods 11810–1920 Cropped and Fertilized and mown for fertilized hay meadow since Study area 1950/60 21810–1853 Cropped and Unfertilized mown for The study area is located in the upper valley of the Roman- ° ′ ″ fertilized hay meadow since che River, central French Alps (Villar d’Arene, 45 2 24 N, 1950/60 ° ′ ″ 6 20 24 E). The substrate is homogenous calc-shale and 31810–1902 Cropped and Strongly grazed (sheep) the climate is sub-alpine with a strong continental influ- fertilized since 1970 ence. The landscape is dominated by sub-alpine meadows 41990–2030 Mown Unfertilized mown for and pastures with distinct past and current management hay meadow since 1800 – regimes. At the lower altitudes (1650–2000 m), former 519602025 Mown Lightly grazed (cattle) since 1986–2000 arable fields have been abandoned and subsequently con-

Journal of Vegetation Science Doi: 10.1111/jvs.12022 © 2013 International Association for Vegetation Science 951 Plant–grasshopper trait linkages M. Moretti et al. duction (i.e. annual net primary production) and grass- lect the animals by hand). A 2-m long aluminium support hopper traits (mainly through nutritional properties, palat- allowed operators to lift, move and place the biocenome- ability, but also as source of habitat structures and habitat ter at the different sampling sites without disturbing the conditions). We selected six traits: plant vegetative height grasshoppers. Each time the biocenometer was placed (VegH), leaf tensile strength (leaf toughness, Tough), leaf firmly on the ground and the enclosed 1 m2 was exam- dry matter content (LDMC), specific leaf area (SLA), leaf ined for grasshoppers. All collected grasshoppers were kept nitrogen content (LNC) and leaf phosphorus content in alcohol in separate vials (one per plot) and transported (LPC) (Quetier et al. 2007; Lavorel et al. 2011). The to the lab. All adults and sub-adults were identified at spe- detailed trait measurements followed standard protocols cies level using standard keys (Coray & Thorens 2001; (Cornelissen et al. 2003; Quetier et al. 2007). Plant traits Bellman & Luquet 2009). At each vegetation plot, we were measured on ten randomly selected plant individuals sampled grasshoppers 15 times, i.e. five samples along per species and per trajectory in order to capture intra-spe- three parallel transects cific variation in response to management (Garnier et al. (5-m distance between samples). The sampling was 2007). Primary production was assessed at peak biomass repeated at three distinct times during the vegetation and date (from early to mid-July, depending on trajectories) grasshopper season, i.e. at the end of July, at the end of using calibrated visual estimates over 12 quadrats per plot August and at the end of September 2008. Samples of (see Lavorel et al. 2008; Redjadj et al. 2012). each plot were pooled. We selected grasshopper traits (Table 2b) expected to respond to management (mainly mowing, grazing and fer- Grasshoppers tilization). These are traits sensitive to habitat structure Grasshoppers were sampled using a biocenometer, a and climate conditions, such as body size, dry body mass quantitative box sampling method (Gardiner & Hill 2006) and oviposition site (on the ground or in the vegetation), that enables the estimation of grasshopper population traits enabling response to disturbance, such as mobility densities and community structures. The biocenometer (ability to escape or recolonize disturbed surfaces), and consists of a 1 9 1 9 1 m cube made of tissue and open traits expected to be linked to plant traits as habitat and at the bottom (to be placed on the ground) and top (to col- food sources (e.g. diet mainly based on grass or forb, mandible size and body size). Some of these traits are expected Table 2. Plant (a) and grasshopper (b) traits used in the analyses to respond to both management and plant traits, and to (*indicates that the trait was measured on at least ten individuals). have a link to primary production, mainly through con- a) Plants1 sumption (e.g. body size, dry body mass, mandible size; see Trait Description Unit Type Introduction). Trait data was sourced from literature infor- * VegH Vegetative height mm Continuous mation (i.e. Detzel 1998) and complemented by morpho- SLA* Specific leaf area cm2 Continuous logical measurements taken on ten individuals (five males LNC* Leaf nitrogen concentration mg Continuous LPC* Leaf phosphorus concentration mg Continuous and five females) of each adult species, randomly sampled LDMC* Leaf dry matter content mg Continuous among the five trajectories (see Appendix S1). À Tough* Leaf toughness NÁmm 1 Continuous (leaf tensile strength)1 b) Grasshoppers Trait metrics Code Description Unit Type For each plant and grasshopper trait we calculated two NOvar Number of ovarioles2 – Integer – functional metrics widely used in functional community OvGround Oviposition on the ground Fuzzy NLarvS Number of larval stages – Integer studies (e.g. Dıaz et al. 2007a,b; Vandewalle et al. 2010). DietHerb Species feeding on herbs – Fuzzy First, the community-weighted mean (CWM) trait value HMobil Highly mobile species – Nominal (Garnier et al. 2004), which expresses the mean trait BodyL*3 Body length mm Continuous value in the community weighted by the relative abun- 3 TibiaL* Tibia length mm Continuous dance of the species. CWM reflects the average trait value *3 MandW Mandible width standardized mm Continuous of the most dominant species in a community, which has on body size been interpreted as translating the mass-ratio hypothesis GMass* Dry body mass mg Continuous by (Grime 1998), i.e. the dominant traits in a community 1 Plant traits were measured by Quetier et al. (2007) following Cornelissen exert the greatest effect on ecosystem functions. Second, et al. (2003). Rao’s quadratic entropy of functional diversity (FD), 2Organs containing the eggs in insects and spiders. In insects they are estimators of potential. which equals the sum of the dissimilarity in trait space fecundity. among all possible pairs of species, weighted by the prod- 3See details in Appendix S1. uct of the species’ relative abundance (Botta-Dukat 2005;

(a) Environmental driver 1a 1b Trophic DR1TR1 TR2 DR2 2b

4aLinkage1 4b Linkage2 level 2 2a FE1 TE1 TE2 FE2 Trophic level 1 3a 3b Ecosystem function

(b) Management PLANTS Species M Traits (t) Steps 1a,b: Trait response to land use Proportionproportion (p)[p] CWM + FD [1a] ANOVA(Pt[x]~M) [1b] ANOVA(Gt[x]~M) Plots Pt

CWM = ∑ p*t Steps 2a,b: Link between plant and grasshopper FD = ∑∑ dij*pi*pj traits (full and partial models) GRASSHOPPERS [2a] stpw(Pt~Gt); RDA(Pt ~Gt ); pRDA(Pt ~Gt |M) Gt [x] sel [x] sel Traits (t) [2b] stpw(Gt~Pt); RDA(Gt[x]~Ptsel); pRDA(Pt[x]~Gtsel |M) Sqrt-transf. + Proportion (p) Steps 3a,b: Trait linkages to fodder biomass (full Biomass production Plots and partial molels)

[3a] stpw(B~Gt); RDA(B~Gtsel); pRDA(B~Gtsel |Ptsel,M) B [3b] stpw(B~Pt); RDA(B~Ptsel); pRDA(B~Ptsel |Gtsel,M)

Fig. 1. (a, b) Theoretical multitrophic response–effect trait framework of Lavorel et al. (2013) to link traits, i.e. DR, driver response traits; TE, trophic effect traits; TR, trophic response traits; FE, functional effect traits (Linkage: DR, TR = FE, TE) across trophic levels 1 and 2. Numbers 1a,b – 4a,b correspond to the different steps (step 1a,b – step 4a,b) in the scheme of the analyses below. (b) Diagram of the different data sets used and the analytical steps performed with respect to the theoretical multitrophic response–effect trait framework (see scheme above and Methods). CWM, community-weighted mean; FD, functional dissimilarity (Rao’s quadratic entropy); dij, dissimilarity between each pair of species i and j; Pt, plant traits; Gt, grasshopper traits; [x]: single trait; sel: selected traits from the step-wise procedure (stpw); stpw: forward step-wise selection of the traits in RDA; RDA/pRDA: Redundancy analysis/partial RDA; sqrt-transf.: square-root transformation applied on the grasshopper abundance data; ~: linear relationship between response and explanatory variables; |: covariables used in the partial models.

Leps et al. 2006; Ricotta & Szeidl 2006; de Bello et al. shown in Fig. 1a) and further described by Lavorel et al. 2010a). Details of the formulas can be found in Ricotta & (2013). We applied a multistep analysis using a variety of Moretti (2011). For plants and grasshoppers, mostly un- uni- and multivariate linear regressions. Models were correlated functional metrics for each trait (< 0.5 Pearson based on both full and partial analyses (using covariables) correlation) were selected. Traits providing distinct and on different sets of variables to find relationships between complementary ecological and functional information land use, plant and grasshopper traits and their relative were included in the analyses. In the text, the functional contributions to biomass production. Figure 1b shows an metric of the different traits appears beside the name of overview of the different data sets used and the analytical each trait (TraitCWM; TraitFD). steps performed with respect to the multitrophic response– effect traits framework (Fig. 1a). In a first step, we identified the plant (step 1a in Fig. 1) Data analyses and grasshopper (step 1b) response traits (DR1 and DR2, Data analyses were designed to identify the traits involved respectively, in Fig. 1a) that were significantly affected by in the multitrophic response–effect traits framework (as management (i.e. the five trajectories) used as explanatory

Journal of Vegetation Science Doi: 10.1111/jvs.12022 © 2013 International Association for Vegetation Science 953 Plant–grasshopper trait linkages M. Moretti et al. variables in ANOVA (see Fig. 1b). In step 2a, we identified Results single grasshopper traits (TR2) responding to plant traits (TE1) in Fig. 1a. To do this, we first ran a forward selection General overview with the R package packfor (R Foundation for Statistical In the next four sections we first present the results for Computing, Vienna, AT) using redundancy analyses each single step (1–4) used in the analyses to untangle the (RDA) with plant traits as explanatory variables and grass- different components of the trait framework across trophic hopper traits as response variables to identify, among all levels (Fig. 2). At each step we quantify the unique and plant traits, those trait metrics affecting the ensemble of shared variance explained by the different sets variables grasshopper traits (P < 0.05 after 9999 random permuta- (management and traits) involved (results synthesized in tions). We minimized problems of classical forward selec- Fig. 2). Finally, we assessed the links between the traits tion by applying the double-step procedure proposed by responding to management (environment response traits) Blanchet et al. (2008): (1) inflated Type I error was and the other traits across trophic levels (trophic response– avoided by forward selecting only models for which a glo- effect traits linkage) with the traits affecting ecosystem bal test with all explanatory variables was significant; (2) functions (ecosystem effect traits). Appendix S2 provides to avoid overestimation of the amount of variance Pearson correlation values and sign of the correlation explained, an additional stop criterion was introduced, in between single plant and grasshopper traits and biomass that the adjusted coefficient of multiple determination (R2 production. This is useful to understand the direction of adj) of the model could not exceed the R2 adj obtained the linkages between variables to interpret the multitroph- when using all explanatory variables. The variables that ic framework (Fig. 2) and path analysis (Fig. 4). fulfilled both stop criteria were identified as significant Overall, we observed 501 grasshopper individuals from environmental variables shaping the communities of the 18 species. Of these, four species were dominant (Staurode- focal taxa. rus scalaris, Omocestus haemorrhoidalis, Chorthippus apricarius Being aware of the limits of any selection procedure, we and Euthystira brachyptera) with relative abundances ran exploratory regressions on several variable combina- > 10% in each of the 15 plots and 49% of the total number tions, yielding the same qualitative results (not shown). of individuals (Appendix S3). For the vegetation, patterns We then tested the effect of significant selected plant traits have already been described in Quetier et al. (2007). on each single grasshopper trait metric, again using RDA, to identify which specific grasshopper parameters responded specifically to vegetation. The same procedure Trait responses to management [steps 1a,b] was applied in step 2b (Fig. 1) to identify possible links of Management significantly affected both plant and grass- grasshopper traits (TR1) to plant traits (TE2) in Fig. 1a. In hopper traits (Table 3 column M, i.e. management). The step 3 we finally assessed the link between plant/grasshop- CWM of all six plant traits (Table 3a) was significantly per traits and biomass production (i.e. FE1 and FE2, affected, with fertilization and mowing (Trajectory 1) posi- respectively, in Fig. 1a), using the standing dry biomass of tively affecting LNC ,LPC and SLA ;viceversa the vegetation as response variable. The plant and grass- CWM CWM CWM for LDMC in Trajectory 1 and, partially, in 5 (lightly hopper traits that were identified as responding to man- CWM agement and/or the other trophic level and traits affecting biomass production represented the linkage between Management response and effect traits (step 4). Finally, variation partitioning (Borcard & Legendre 1994; 0.865a 0.084b 0.442b Legendre et al. 2005) was used to quantify the relative SLACWM, VegHCWM, BodyL MandW GRASSHOPPERS CWM CWM CWM CWM importance of the variables (management and traits) LPCCWM, ToughCWM,FD FD FD

b TibiaLFD involved in the different steps. By using management as a GMass FD NOvar HMobil LNCCWM 0.293 covariable, we intended to remove the variance explained PLANTS CWM LDMCFD GMassCWM by the main environmental factors that may confound the SLAFD trophic links within and across trophic levels. Based on these LDMC LPCFD results, we tested the paths connecting management and 0.442c 0.398c key plant/grasshopper traits to biomass production, using 0.687c Biomass production path analysis, which is a suitable method to describe the direct dependencies among a set of variables (Shipley 2002). Fig. 2. Synthesis of the results of our study (see also Table 3) ﬁtted into The statistical analyses were performed in R version the theoretical framework of Fig. 1a, showing variance explained (R2adj) of 2.13.1 using the library ‘vegan’ 1.17–3, ‘sem’ 0.9–21 and unique and combined fractions of the different variables involved, based ‘packfor’. on Fig. 3 (i.e. a 3a; b 3b; c 3c). Full trait names are given in Table 2.

Table 3. (a,b) P-values of the uni- and multivariate analyses of full and partial models of the different explaining set of variables linking traits across trophic levels.

Response variables Univariate response of traits Forward selection of predictors

Response to: Effect on: a) Plant traits M M | Gt Gt Gt | M Gt B [1a] [1a|2b] [2b] [2b|1a] [2a] [3a]

CWM LDMCCWM <0.0001 –– – 0.0188 0.0068

VegHCWM <0.0001 –––– –

LNCCWM <0.0001 –– – 0.0561 –

LPCCWM <0.0001 –––– –

SLACWM 0.0041 –––– –

ToughCWM <0.0001 –––– –

FD LDMCFD 0.3918 –– – 0.0510 –

VegHFD 0.0022 –––– –

LNCFD 0.2372 –––– –

LPCFD 0.1421 –– – 0.0671 –

SLAFD 0.7958 –– – 0.0601 –

ToughFD 0.0003 –––– – b) Grasshopper traits M M | Pt Pt Pt | M Pt B [1b] [1b|2a] [2a] [2a|1b] [2b] [3b]

CWM NOvarCWM 0.0193 0.1210 0.0050 0.0630 ––

OvSoilCWM 0.1930 0.3740 0.1000 0.2780 ––

NLarvSCWM 0.1642 0.6960 0.4000 0.8540 ––

DietHerbCWM 0.5956 0.8330 0.1400 0.0560 ––

HMobilCWM 0.0497 0.1100 0.0650 0.2010 ––

BodyLCWM 0.7714 0.7980 0.0510 0.0570 ––

TibiaLCWM 0.5708 0.8100 0.1000 0.2040 ––

GMassCWM 0.4337 0.6570 0.0200 0.2510 – 0.0069

MandWCWM 0.0198 0.1150 0.4100 0.6680 ––

FD BodyLFD 0.4705 0.1260 0.0200 0.0570 ––

TibiaLFD 0.1813 0.4890 0.0288 0.1330 ––

GMassFD 0.4565 0.2840 0.0150 0.0390 ––

MandWFD 0.0288 0.4810 0.1800 0.7540 –– (Signiﬁcant values are in bold; ‘–’ not signiﬁcant or not selected). M, management types; Gt, grasshoppers traits; Pt, plant traits; B, biomass production. Numbers and letters indicate steps of the analyses shown in Figs 1b and 2 (the symbol ‘|’ means that the effect of the following variable was removed (partial model), e.g. M|Gt: effect of M after removal of Gt variance. Trait names are given in Table 2. grazed hay meadow). The latter, together with mowing partitioning using full and partial models; see Methods). without fertilization (Trajectory 4), positively affected Plant traits alone accounted for 29.3% of the variance of toughness (ToughCWM; data no shown). FD responded grasshopper traits and this increased to 83.5% when com- only for leaf toughness (ToughFD) and vegetation height bined with management. Thus a large variation in grass- (VegHFD), with a positive effect in all management types hopper traits seems to depend on the effects of vegetation, except strong grazing. directly or via changes in management. Grasshopper traits responded to management in more diverse ways. CWM of number of ovarioles (NOvar ) CWM Linking plant and grasshopper traits [steps 2a,b] and FD of mandible width (MandWFD) were higher in fertilized and mown hay meadows (Trajectory 1), while While plant traits affected grasshopper traits (step 2a in mobility (HMobilCWM) and mandible width (MandWCWM) Fig. 2, see also above), no grasshopper trait showed a clear were lower. Mobility was also low in lightly grazed mead- effect on plant traits (step 2b). Forward selection high- ows, while mandible width was high. lighted four plant traits (LDMCCWM,FD,LNCCWM,LPCFD,

Overall we observed that management alone explained SLAFD) as signiﬁcantly (or marginally) linked to grasshop- 86.5% of the variance in plant traits, while it accounted for per traits (Table 3a column Gt). These four plant traits par- only 8.4% of the grasshopper traits, but 52.6% when in ticularly effected several grasshopper traits, i.e. CWM of combination with plant traits (Fig. 3; results of variance number of ovarioles (NOvarCWM), mobility (HMobilCWM),

(a) Plant response traits (b) Grasshopper response traits (c) Biomass production M Gt M Pt M Pt [1a] [2b] [1b] [2a] [1b] [3a]

0.214 0.075 0.000 0.865 0.000 0.000 0.084 0.442 0.293 0.227 0.030 0.001

0.140

Gt [3b]

Fig. 3. (a–c) Variation partitioning with relative variance explained (R2adj) of the various unique (outside of the circle interceptions) and combined fractions (inside) of the different explanatory variables of management (M), grasshopper traits (Gt) and plant traits (Pt) involved in the trait framework speciﬁed by step numbers [1–4, a–b] as in Fig. 1a.

body length (BodyLCWM), dry body mass (GMassCWM)and tional divergence within plant communities, LPCFD and

FD of the two latter traits (GMassFD; BodyLFD) and tibia SLAFD, were only linked to grasshopper traits, while length (TibiaLFD) (see Table 3b column Pt and Appendix LDMCCWM was also linked to biomass production S2 for direction of the correlations). Four traits were still (Table 2a col. B and Fig. 2 rows 2b and 3a). Thus the direct signiﬁcantly affected by the four plant traits above after effect of plant traits on grasshoppers and biomass produc- removing the effect of management (see Table 3b column tion appeared to be mediated by LDMCCWM. For grasshop-

Pt|M). Overall, NOvarCWM and HMobilCWM were the only pers, CWM of dry body mass (GMassCWM)respondedto two grasshopper traits affected by both plant traits and plant traits and was linked to biomass production (Fig. 2 management (Fig. 2). rows 2b and 3b). No single grasshopper metric connected trait responses to management to biomass production. In other words, the connection between management and Trait effects on biomass production [steps 3a,b] biomass production via grasshoppers stemmed from plant

Biomass production was negatively affected by CWM of community properties, LDMCCWM,FD and LNCCWM affect- both leaf dry matter content (LDMCCWM) and grasshopper ing GMassCWM. Thus, LDMCCWM and GMassCWM appeared dry body mass (GMassCWM) (forward selection of plant as the key traits in linking management to biomass produc- and grasshopper traits used separately as explanatory vari- tion across trophic levels. ables and biomass production as response variable; Table 3 The general patterns that emerged from our data analy- columnB).Mostofthelinkages,however,couldbe sis were confirmed when we tested the hypothesized links explained by the combination of plant and grasshopper between management, LDMCCWM,GMassCWM and bio- traits when structured by management (61.2%, see mass production using path analysis and structural equa- Fig. 3c, i.e. 0.214 + 0.227 + 0.030 + 0.140 + 0.001). The tion modeling/SEM (Model Chi squared = 0.475, df = 1, contribution of management to the overall variance of bio- P = 0.491; note P > 0.05 indicates significance for SEM; mass production was 54.6% (i.e. 0.075 + 0.214 + see Fig. 4). While significant pathways were found from

0.227 + 0.030) mainly structuring plant effect traits management to LDMCCWM (standardized regression path 2 (44.1% shared variance, i.e. 0.214 + 0.227) vs. 25.7% (i.e. coefﬁcients R 0.75) and from LDMCCWM to biomass pro- 2 0.227 + 0.030) shared with grasshoppers. Unique contri- duction (R À0.58), the negative link between GMassCWM butions to biomass production by management only and biomass production (R2 À0.47) was mediated by a

(7.5%), vegetation traits only (0%) and grasshopper traits positive significant trophic link between LDMCCWM and 2 only (14%) were comparably low. GMassCWM (R 0.40). The direct pathway from management to biomass production (R2 0.35) was, instead, not significant. Response–effect trait linkages within trophic levels Two community mean plant traits, LDMC and CWM, FD Discussion LNCCWM, were affected by management and linked to grasshopper traits: (Table 3a column Gt[2a] and Table 3b Our study represents one of the first attempts to apply the col. Pt[2a]). Two additional trait metrics depicting func- trait framework proposed by Lavorel et al. (2013),

study, response traits of both trophic levels were consistent 1.0 with other studies and with our expectations (see Intro- Management duction for an overview). Only two plant traits responding to management were further involved in either biomass 0.75*** production (leaf dry matter content. LDMCCWM)orinthe

0.44 0.64 trophic linkage (leaf nitrogen content, LNCCWM and 0.4* LDMC ). LDMC GMass CWM,FD CWM CWM In particular, plant species with high leaf dry matter content (LDMC ) were mainly favoured in unfertilized

0.35 CWM –0.58** –0.47** meadows (Trajectory 4) and in lightly grazed pastures (Tra-

0.36 jectory 5), and played a key role in the trait cascade. This Biomass trait linked the response of plant community composition production to management and biomass production both directly as an effect trait and indirectly as a trophic effect trait on Fig. 4. Path diagram showing paths (arrows) connecting management, grasshoppers, and especially on the dominance of species key plant/grasshopper traits and biomass production. Standardized with high dry body mass (GMassCWM). Grasshopper dry regression path coefficients are given on the arrows, with the thickness body mass indeed acted as an effect trait on primary pro- proportional to the path coefficient value. Negative correlations are duction, with a negative link to plant biomass. The reasons denoted by dotted and positive by solid lines. P-values *< 0.05; **< 0.01; for the positive link between LDMC and GMass ***< 0.001. Model Chi squared = 0.475, df = 1, P-value = 0.491; Adj. CWM CWM goodness-of-fit index = 0.836. For details of the statistics, see could be trophic, making, e.g. hard leaves more palatable Appendix S4. and assimilated by grasshoppers, but also related to habitat conditions and vegetation structure. Underlying mechanisms explaining the links between leaf chemical and although we are aware that others are working on similar structural properties and grasshopper body mass and topics. Our analyses clearly demonstrate the usefulness of related traits (e.g. mandible width) remain to be tested the approach using plant and grasshopper communities with appropriate experiments to remove the influence of under natural conditions. Our analytical approach allowed biotic and abiotic habitat constraints, such as microhabitat, us to tease out the relative contributions of the several climate, plant architecture and predators (e.g. Joern & components involved in the multitrophic trait framework. Lawlor 1980; Marini et al. 2008; Gardiner & Hassal 2009). It also provided insights into possible key trait-based mech- Biomechanical properties of the mandibles and other anisms that underlie ecosystem services responses to envi- mouthparts (Patterson 1983) or physiological and mor- ronmental change. We are aware that such correlative phological adaptations of the gastrointestinal tract (Martin relationships cannot necessarily be interpreted as cause– et al. 1987) may play an important role. Furthermore, sev- effect, but rather simple links between variables. Using eral studies have confirmed the relevance of nutrient partial uni- and multivariate models with management as acquisition/conservation trade-off governing plant nutri- a covariable should, however, have removed most of the ent economy (Dıaz et al. 2004; Wright et al. 2004; Quetier variation due to confounding environmental factors, at et al. 2007; Lavorel & Grigulis 2012) to predict responses least those that are structured by management. Our results of plant communities to decreasing management intensity. highlight two major aspects, which we discuss below: first, Here, we demonstrated explicitly how such trade-offs the trait identity affecting biomass production, and second, could have important consequences on other trophic levels the relative role of community-weighted mean traits and on ecosystem functioning (see Lavorel & Grigulis (CWM) and functional divergence (FD). 2012). Indeed, grasshoppers are highly sensitive to the protein:carbohydrate ratio, a trait governed by the nutrient acquisition–conservation trade-off (Simpson & Raubenhei- Trait changes within and across trophic levels affecting mer 1993). biomass production Management also largely influenced dominant func- Our study is the first, as far as we are aware, to show the tional characteristics of grasshoppers related to feeding effect of an environmental driver (management) on an capacity and variation (MandWCWM,FD), dispersal (HMo- ecosystem function (biomass production) mediated by bilCWM) and reproduction (NOvarCWM), with mown and response-and-effect traits across two trophic levels, i.e. fertilized grasslands (Trajectory 1) favouring communities plants and grasshoppers. Taxonomic and functional dominated by species with low mobility (indicating rather responses of plants and grasshoppers to management in stable conditions), as well as small and diversified mandi- meadow ecosystems are relatively well known. In our bles (indicating availability of different trophic niches)

Journal of Vegetation Science Doi: 10.1111/jvs.12022 © 2013 International Association for Vegetation Science 957 Plant–grasshopper trait linkages M. Moretti et al. that have a high number of ovarioles (i.e. high potential reveal a confounding effect by several components govern- fecundity). Only mobility and number of ovarioles were ing trophic interactions in the field, e.g. ‘habitat’ (i.e. plant involved in the plant–grasshopper linkage, with high community structuring of the environment occupied by quality food (high LNC and LPC) potentially favouring grasshoppers), ‘food availability’ (i.e. the same plant com- fecundity of grasshoppers (e.g. Branson 2008). Neverthe- munity in the habitat also representing food sources) and less, none of these traits was further linked to primary ‘food preferences’ (i.e. the plant species selected by a grass- production, which does not exclude a possible overlap hopper based on nutritional and biomechanical proper- between grasshopper response and effect traits on other ties). Since plant composition in the sampling plots ecosystem processes not considered in our study. No link- represented both the habitat and food resources for the ages were found between grasshopper and plant traits grasshoppers, we cannot untangle these two components (step 2b). The strong impact of management on plant to address the mechanism(s) underlying the effect– traits found in our results could have masked possible response trait linkages between plants and grasshoppers. top-down control of plant traits by grasshoppers. At the Trait dissimilarity (FD) of plant communities might be same time, we believe that the missing link between important to provide a sufficient variety of food sources in grasshoppers and plants could also add new insights into a diversified habitat satisfying specific abiotic requirements the effect of herbivory on plant composition when func- (Unsicker et al. 2010), reflecting niche partitioning and tional traits rather than species are considered (see Bran- relative resource exploitation, while dominant plant traits son & Sword 2009). (CWM) might drive food selection by the different grass- Only 3% of the overall variation of biomass production hopper species, satisfying precise nutritional needs and was explained by the combination of management and biomechanical constraints. grasshopper effect traits, while 22.7% was mediated The complementary role of CWM and FD in ecosystem through the linkage between plant and grasshopper effect processes, such as primary production, in natural systems traits. This confirms the important role of the trait link is consistent with recent evidence (e.g. Mokany et al. across trophic levels mediating ecosystem function. 2008; Schumacher & Roscher 2009; Mouillot et al. 2011) suggesting that both trait values of the most abundant species and functional trait variation within and across trophic The relative roles of community-weighted mean traits levels in combination may best explain the impact of envi- and functional divergence ronmental changes on ecosystem function. Our study revealed two main aspects related to the different steps in the multitrophic response–effect trait frame- Conclusions and perspectives work. First, we found that community-weighted means (CWM) were the main driving functional metrics relevant Overall our results (1) provided strong evidence to the two key response–effect traits (LDMC and GMass) in highlighting the functional links between trophic levels the multitrophic trait framework, highlighting the impor- via community trait metrics described by Lavorel et al. tant role of the dominant trait values in both communities, (2013); (2) showed the contribution of each partial step rather than the variance in trait composition (FD). CWM in the trait framework; and (3) gave an insight into was also the major overlapping functional metric between key trait linkages across trophic levels and their link to management response traits and functional effect traits biomass production. Our study suggests that while affecting biomass production in both plants and grasshop- changes in community dominance hierarchies deserve pers. This finding confirmed the prediction of the biomass most attention when managing communities for the ratio hypothesis (Grime 1998). maintenance of ecosystem processes, communities with Second, trait variation within communities of plants diverse trait values should be maintained to optimize and grasshoppers (trait dissimilarity, FD) was also impor- trophic interactions. tant in the link across trophic levels, but it did not have a There is an urgent need to quantify traits for multiple direct or indirect effect on biomass primary production. trophic guilds interacting with plants. Particular effort Plant species diversity (Unsicker et al. 2010) and plant should be devoted to defining and measuring trophic and functional identity (Specht et al. 2008) have been demon- ecosystem function effect traits. Concerning terrestrial ar- strated to increase the performance and the fitness of gen- thropods, most of the traits available in the literature are eralist grasshoppers. Both resource dominance and response traits, which do not directly affect ecosystem variability thus appear to be important in plant communi- functions. We hope that this contribution will stimulate ties for the trophic linkage. Nevertheless, the combined ecologists and statisticians to collaborate to explain the presence of both functional metrics (CWM and FD) in the multitrophic interaction webs and the mechanisms medi- trophic linkage between plants and grasshoppers might ated by functional traits.

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Osada, N., Poorter, H., Poot, P., Prior, L., Pyankov, V.I., Rou- Appendix S2. Pearson correlation matrix between met, C., Thomas, S.C., Tjoelker, M.G., Veneklaas, E.J. & Vil- plant and grasshopper traits and between the latter and lar, R. 2004. The worldwide leaf economics spectrum. Nature biomass production. See full trait names in Table 2. Corre- 428: 821–827. lation values > 0.5 are in bold. Appendix S3. List of the grasshopper species sam- Supporting Information pled in the ﬁve land-use trajectories in the upper valley of the Romanche River (Villar d’Arene) in the central French Additional supporting information may be found in the Alps. online version of this article: Appendix S4. Detailed results of the path analyses in Fig. 4. Appendix S1. Measurements taken on grasshoppers (dotted line), i.e. (a) body length (BodyL), (b) tibia length (TibiaL) and (c) mandible width (MandW).

Journal of Vegetation Science 962 Doi: 10.1111/jvs.12022 © 2013 International Association for Vegetation Science Journal of Vegetation Science 24 (2013) 963–974 SPECIAL FEATURE: FUNCTIONAL DIVERSITY Functional redundancy and stability in plant communities Valerio D. Pillar, Carolina C. Blanco, Sandra C. Muller,€ Enio E. Sosinski, Fernando Joner & Leandro D. S. Duarte

Keywords Abstract Causal models; Community resilience; Disturbance; Functional diversity; Functional Questions: Functional redundancy in assemblages may insure ecosystem pro- traits; Grazing intensity; Path analysis; Species cesses after perturbation, potentially causing temporary or permanent local spe- diversity cies extinctions. Yet, functional redundancy has only been inferred by indirect evidence or measured by methods that may not be the most appropriate. Here, Abbreviations we apply an existing method to measure functional redundancy, which is the FR = Functional redundancy OTU = Operational taxonomic unit fraction of species diversity not expressed by functional diversity, to assess whether functional redundancy affects community resilience after disturbance. Received 16 April 2012 ° ′ ″ ° ′ ″ Accepted 17 December 2012 Location: Subtropical grassland, south Brazil (30 05 46 S, 51 40 37 W). Co-ordinating Editor: Francesco de Bello Method: Species traits and community composition were assessed in quadrats before grazing and after community recovery. Grazing intensity (G)wasmea- Pillar, V.D. (corresponding author, sured in each quadrat. We used traits linked to grazing intensity to define func- [email protected]), Blanco, C.C. tional redundancy (FR)asthedifferenceofGini–Simpson index of species ([email protected]), Muller,€ S.C. diversity (D) and Rao’s quadratic entropy (Q). Also, with the same traits, we ([email protected]), Sosinski, E.E. defined community functional stability (S) as the similarity between trait-based ([email protected]), Joner, F. community composition before grazing and 47 and 180 d after grazing ending. ([email protected]) & Duarte, L.D.S. Using path analysis we assessed different postulated causal models linking ([email protected]): Departamento de Ecologia, Universidade Federal do Rio Grande functional diversity (Q), functional redundancy (FR), grazing intensity (G)and do Sul, Porto Alegre, RS, 91540-000, Brazil community-weighted mean traits to community stability (S) under grazing. Sosinski, E.E.: Current address: Laboratorio Results: Path analysis revealed the most valid causal model FR ? S G,with de Planejamento Ambiental, Embrapa Clima ? Temperado, Pelotas, RS, 96010-971, Brazil a significant positive path coefficient for FR S and a marginally significant

Joner, F.: Current address: Agronomia, negative one for S G.SinceFR and G were independent in their covariation Universidade Federal da Fronteira Sul – UFFS, and in their effects on S, the model discriminated community resistance to graz- Chapeco, SC, 89812-000, Brazil ing (the effect of G on S) from community resilience after grazing caused by functional redundancy (indicated by the effect of FR on S). Conclusion: We show that expressing functional redundancy mathematically is a useful tool for testing causal models linking diversity to community stability. The results support the conclusion that functional redundancy enhanced community resilience, therefore corroborating the insurance hypothesis.

Elmqvist et al. 2003). This maintenance in the long term is Introduction a kind of stability, deﬁned as resilience,i.e.theabilityofa It has been proposed that functional redundancy in com- community to return its effects on ecosystem processes to a munities is an insurance for the maintenance of ecosystem previous state after changing due to a disturbance, which processes in case of perturbations causing local species is distinguished from resistance, i.e. the ability of a commu- extinctions, which would be compensated by the presence nity to avoid any change (Harrison 1979; Pimm 1991; of species that are functionally similar but differ in their Carpenter et al. 2001). For instance, the functional equiva- responses to changes in environmental factors or distur- lence and the compensation between dominant and minor bances (Walker 1992; Naeem 1998; Yachi & Loreau 1999; species confer resilience for maintaining ecosystem

Journal of Vegetation Science Doi: 10.1111/jvs.12047 © 2013 International Association for Vegetation Science 963 Functional redundancy and community stability V. D. Pillar et al. processes under changing environmental conditions diversity into functional diversity and redundancy, which (Walker et al. 1999). This compensation can occur among was originally proposed by de Bello et al. (2007) (see also resident species or by colonization of new species, consid- de Bello et al. 2009b, 2010). Functional redundancy is so ering the metacommunity scale. Further, the effect of defined as the fraction of species diversity not expressed by functional redundancy may be demonstrated by species functional diversity. Functional diversity is measured with loss compensation in removal experiments in which spe- Rao’s quadratic entropy (1982), which is computed using cies are classified into functional groups and different com- the species abundances and their trait dissimilarities. From binations of species belonging to these groups are removed this definition, there is no functional redundancy when from the communities (Walker 1992; Dıaz et al. 2003; the species are completely different for the traits, i.e. when Joner et al. 2011). However, a survey in the literature Rao’s quadratic entropy is equal to species diversity; and shows that functional redundancy has often been mea- functional redundancy is equal to species diversity when sured using methods that may not be the most appropriate all the species are identical for the traits, i.e. when Rao’s (as discussed below), or it is only inferred from indirect evi- quadratic entropy is zero. dence (see Reiss et al. 2009, for a review on ecosystem The choice of traits is crucial and the distinction effects of biodiversity). between effect and response traits is fundamental in the con- Most frequently, functional redundancy has been mea- cept of functional redundancy. Functional response traits sured as species diversity (species richness) within func- refer to the way communities respond to environmental tional groups (Walker 1992; Naeem 1998; Fonseca & drivers (e.g. resources, disturbance), while functional Ganade 2001; Laliberte et al. 2010), where the result will effect traits refer to the effects of the communities in eco- depend on the method used for classifying the species into system processes (Lavorel & Garnier 2002; Lavorel et al. functional groups. This problem may be alleviated if the 2013; Moretti et al. 2013). In the above-mentioned meth- species classification is based on traits that are functionally ods, functional groups or functional dissimilarities important for the ecosystem process for which redundancy between species should be based on traits related to an is being considered (Walker et al. 1999; Pillar & Sosinski ecosystem process (effect traits). Thus, functional redun- 2003; Petchey & Gaston 2006; Laliberte et al. 2010) and if dancy refers to the diversity of species performing similar the groups are sharp and thus more stable in resampling functions in the ecosystem. Functional redundancy is (Pillar 1999; Chillo et al. 2011). Nevertheless, even when actually a fraction of species diversity that is not function- guided by objective methods, the choice of the number of ally related to the ecosystem effect under consideration groups is often an arbitrary decision, which will affect the but may be functionally related (by means of response value of species diversity within functional groups. Fur- traits) to variation in environmental factors (called ther, the functional groups are usually taken as crisp enti- ‘response diversity, Elmqvist et al. 2003’). ties, ignoring their similarities in terms of traits and, hence, Here, we measure functional redundancy as defined by in terms of their role for the ecosystem process under de Bello et al. (2007) and examine its effects on resilience consideration. using data from a grazing experiment on subtropical grass- Functional redundancy has also been evaluated indi- land communities of south Brazil. Foraging, as measured rectly by examining patterns in the joint scatter plot of by cattle grazing intensity, is the relevant ecosystem process functional diversity and species diversity, by fitting linear guiding us in the search for functional traits and in the or non-linear models, in a set of communities, and there- evaluation of resilience. In our analysis, grazing intensity fore as a property of the metacommunity. A saturation in measured at the plot scale is taken as an ecosystem effect of the relationship between functional diversity on species the plant communities previously described by plant traits. diversity indicates functional redundancy (Petchey et al. We find traits maximally related to this process and use 2007), being low when there is a strong positive relation- them to compute functional diversity and redundancy ship between species diversity and functional diversity before grazing. The same traits define community recovery (Micheli & Halpern 2005). This method was applied for after grazing as an indicator of functional stability. Both interpreting losses of functional diversity under land-use resistance and resilience may explain observed functional intensification (Flynn et al. 2009) and in a gradient of stability, but by taking low grazing intensity as an indicator grazing intensity in Mongolian rangelands (Sasaki et al. of community resistance we are able to disentangle resis- 2009). Yet, this method would not apply if interest lies in tance and resilience. Based on this, we examine empirically the measuring of functional redundancy as a component the links between species diversity, functional diversity, of species diversity within the smallest units of community functional redundancy, grazing intensity, plant traits and description and not within the whole metacommunity. community functional stability, and test the validity of pos- A novel definition of functional redundancy is based on tulated causal models relating functional redundancy and the partitioning of the Gini–Simpson index of species resilience under disturbance using path analysis.

Methods intensity (G) was estimated as the percentage of biomass 9 The grazing experiment removed by cattle in each 0.2 0.2-m quadrat, calculated as We used trait-based community data available from a grazing experiment (Blanco et al. 2007) conducted in a h1 h2 paddock of natural grassland of ca. 3 ha in the Agricul- G ¼ 100 h tural Research Station of the Federal University of Rio 1 Grande do Sul, Brazil (30°05′46″S, 51°40′37″W, 45 m a.s.l.). The climate is subtropical with annual mean tem- where h1 and h2 were successive measures of biomass perature of 18.8 °C, and annual precipitation of 1445 mm height. We used data on grazing intensity only for the normally well distributed throughout the year (Bergama- February grazing, considering h1 6 d before the grazing schi et al. 2003). The grassland vegetation is typical south period and h2 on the 17th day (1 d after grazing end- Brazilian campos, with high richness of species, most of ing). Biomass height was obtained using a thin metal them perennial and with resprouting ability (Overbeck & plate (140 g weight) with the same dimensions as the Pfadenhauer 2007; Overbeck et al. 2007) and above- sampling quadrats sliding on a marked ruler (Stewart ground primary productivity of ca. 4700 kgha1yr1. et al. 2001). Grazing intensity measured in this way for The south Brazilian campos have been subject to grazing the first grazing period was used in the next steps of since the introduction of domestic cattle and horses in the the analysis. G also includes unknown plant tissue loss 18th century, but there is evidence of a long history of co- from senescence and cattle trampling in each plot. Also, evolution with large grazers until the end of the Pleisto- plant growth between h1 and h2 was not evaluated. € cene (Muller et al. 2012). Indeed, in a few cases h2 was higher than h1 and thus The experiment assessed the effects of plant traits on G was negative, likely due to low or no grazing that did grazing by cattle, which was directly measured by the not compensate plant growth. Thus, G is actually a grazing intensity of small plots (Blanco et al. 2007). Graz- measure of standing biomass disappearance minus ing was taken as the relevant ecosystem process for the above-ground plant growth. However, we considered G analysis. Although the experiment is the same as an acceptable approximation of grazing intensity, described in Blanco et al. (2007), here we used a different assuming that losses by senescence and trampling and data subset and analytical approach. For this analysis we variation in plant growth were randomly distributed used data from 70 0.2 9 0.2-m quadrats, which were across quadrats. arranged in groups of five contiguous quadrats located sys- Here we used plant community data collected 6 d tematically in the paddock, i.e. equally distributed on top, before the first grazing period, hereafter called T1,and slope and lowland zones inside the paddock, and 30 m 47 and 110 d after the end of the first and second apart from each other. Frequently flooded areas as well as grazing periods, respectively, hereafter called T2 and a margin of 10-m width from the fence were excluded T3.Atotalof180delapsedbetweentheendofthe from the sampling. first grazing period and T3.Thespecieswereidenti- The paddock was established in a large area in the fied, visually estimated for cover percentage, and research station, which had been managed under con- locally evaluated in the 0.2 9 0.2-m quadrats for tinuous grazing by cattle, sheep and horses since Euro- plant traits (Table 1). The recording of traits refers to pean settlement. The paddock was fenced and then each species population in the 0.2 9 0.2-m quadrat, managed with moderate intensity rotational grazing by and in the case of leaf traits one representative leaf bovine cattle and sheep for 3 yr, then was intensively (or leaflet) that was young and completely expanded grazed by bovine cattle in August 2002 and left un- was picked for measurements. A total of 77 grassland grazed until the start of the experiment in February species was recorded in the quadrats. The chosen 2003, when the area was grazed for 16 d by bovine cat- plant traits were related to plant structure and palat- tle (54 heifers with an average weight of 226 kg). There ability or leaf acceptability, which are considered eco- was a second grazing period of 13 d in April–May 2003 logically relevant for grazing (see Blanco et al. 2007 with the same stocking rate adjustment applied. For and references therein). Species were locally described both grazing periods, the stocking rate was adjusted in for traits in each quadrat. Thus, immediately before T1 order to have an average of 12% forage dry matter on a total of 373 local species populations (hereafter offer (a grazing level of ca. 12 kg of forage dry matter called operational taxonomic units – OTUs) were on offer per 100 kg of cattle live weight) using the ‘put recorded, and within-species trait variation was con- and take’ method (Mott & Lucas 1952). The quadrats sidered in the analysis, analogous to Carlucci et al. were exposed to free grazing by the cattle. Grazing (2012).

Table 1. Plant traits describing species in the grassland experiment in south Brazil evaluating the effect of plant traits on the intensity of cattle grazing.

Traits Labels Description

Plant height HE Height measured from the soil surface to where most of the leaves were subjectively judged to occur Leaf tensile strength LT Calculated as the ratio of leaf resistance to traction (R)overleafarea(A); where R was measured with a tensioning device and A was the product of leaf width (LW) and leaf thickness measured at a mid-point of the leaf subjected to traction. Expressed as Nmm2. For compound leaves, these measurements were performed on a single leaflet Woody biomass WB Visual estimation of the percentage of above-ground woody biomass in relation to total above-ground biomass Maximum height MH Maximum measured plant height (cm) Senescent leaves SL Visual estimation of the percentage of senescent leaves in the above-ground biomass Upper leaf density UL Visual estimation of the percentage of leaf biomass above HE Leaf width LW Measured in mm. For compound leaves, a single leaflet was taken Leaf thickness TH Measured in mm. For compound leaves, a single leaflet was taken Rhizomes V1 Presence of vegetative propagation by rhizomes (binary trait) Stolons V2 Presence of vegetative propagation by stolons (binary trait) Leaf surface: smooth L1 Presence of smooth leaf surface (binary trait) Leaf surface: prickles L2 Presence of prickles on leaf surface (binary trait)

W X Identifying functional effect traits for grazing (Pillar et al. 2009). Matrix retains more trait information at the community level than community- The first step in the analysis was to identify plant traits in weighted means would do. The significance of r(XG), the data that would likely have an effect on the foraging by thecongruencebetweentrait-basedcommunitystruc- grazing animals. In the second step we used these traits to ture and grazing intensity, was tested against a null define functional diversity (Q), functional redundancy (FR) model involving the permutation of the OTUs in the and community functional stability (S) of each 0.2 9 0.2-m trait similarity matrix between OTUs. Although the quadrat (community unit). In the third step we used path test criterion is the same as in the PROTEST procedure analysis to examine postulated causal links between these (Jackson 1995), the null model is different. Since variables, including traits and grazing intensity. community matrix W is kept unchanged in these per- For trait optimization we used community data col- mutations, any existing spatial autocorrelation, espe- 9 lected before the first grazing (T1). We searched for a cially among the 0.2 0.2 m contiguous quadrats, is trait subset revealing community patterns significantly incorporated in the null model. For further details, see relatedtograzingintensitymeasuredimmediately Pillar et al. (2009). thereafter. The analytical method used as input matrix We used principal components analysis (PCA) of the B of OTUs by traits, matrix W of OTU cover propor- trait matrix B of OTUs 9 12 plant traits listed in Table 1 tionsinthecommunities(coverestimatesstandard- (only for T1). This analysis allowed us to identify main ized to unit total in each 0.2 9 0.2-m quadrat), and axes of trait variation at the population level. Further, we X vector G of grazing intensity immediately after T1 subjected community matrix to principal coordinates (ecosystem effect) in the same quadrats. The method analysis (PCoA) using Euclidean distances between quad- in general terms involved the following procedure: (1) rats. This analysis aimed at visualizing and interpreting cover proportions of OTUs in each community were community patterns and their links to community- fuzzy-weighted (Pillar et al. 2009) by the OTUs’ pair- weighted means for the traits, species diversity, func- wise trait similarities, generating a matrix X of OTUs tional diversity, functional redundancy and community by communities; (2) congruence r(XG) between functional stability (see definitions below) by projecting matrices X and G was computed based on Procrustes onto the ordination diagram the correlations of these rotation (Peres-Neto & Jackson 2001; Legendre & variables with the ordination axes. Ordination methods Legendre 2012); and (3) optimal trait subsets were and construction of triplots are described in e.g. Legendre searched to maximize r(XG) using an algorithm anal- & Legendre (1998). ogous to Pillar & Sosinski (2003). An element of X matrix is the probability of finding a given OTU in Measuring functional redundancy a quadrat considering the similarities of this OTU to the OTUs that occur in the quadrat (Pillar et al. 2009). Functional redundancy (FR) was defined by de Bello et al. Additionally, community-weighted means (Dıaz et al. (2007) as the difference between species diversity and 2007) were computed by matrix multiplication T = B′ functional diversity:

FR ¼ D Q community. In this case, intra-speciﬁc trait variation is

considered (Carlucci et al. 2012; Violle et al. 2012) and dij In this, D is the Gini–Simpson index for species diversity would be the dissimilarity between species i and j based on XS their locally evaluated trait records in the community. ¼ 2 D 1 pi The same species in another community may be different i¼1 for the same traits. In our data analysis, functional redundancy was computed based on traits recorded before where pi is the proportion of the abundance of species i in a community with a total of s species. the first grazing period (T1). Q is Rao’s quadratic entropy (Rao 1982): Measuring community functional stability XS XS Q ¼ d p p ij i j We measured community functional stability between i¼1 j¼1 time i and j at a given quadrat as the similarity in the trait- – where dij is the dissimilarity, in the range of 0 1, based community composition between Ti and Tj. For this, between species i and j based on a set of specified func- with the same traits selected for Q and FR we defined a tional traits. For this, we use the square root of the one- matrix of OTUs by traits, by pooling in one matrix Bi-j the complement of Gower’s similarity index, in order to have data of Ti and Tj. Parallel to this, we defined a pooled a dissimilarity matrix with Euclidean metric properties matrix Wij of these OTUs with their cover proportions in

(see further explanation below). Gower’s index ranges 140 community records (70 quadrats at Ti and Tj). By defi- from 0 to 1 and can handle traits with different measur- nition, in matrix Wij each OTU was present only in one ing scales (nominal, binary, ordinal, interval and ratio quadrat and survey. With these data, a matrix Xij was scales; Podani 1999). Rao’s Q has been considered a more derived using the above-mentioned procedure of fuzzy- appropriate measure for functional diversity than other weighting. Community stability of quadrat k between Ti indices (Izsak & Papp 2000; Botta-Dukat 2005; Ricotta and Tj was defined as 2005). pffiffiffi It is easily verified that FR ranges from 0 to 1. When the Skij ¼ 2 dkij species are completely different for the traits (i.e. for all ¼6 = = pairs i j, dij 1), then Q D (Rao 1982) and thus where d is the chord distance between the vectors X = kij pffiffiffi ki FR 0. When all the species are identical for the traits, and X of quadrat k and 2 is the theoretical maximum of = = kj then Q 0andFR D, and when in addition the number chord distance. With this method, we measured commu- of species is very large and the species are equally abun- nity stability S12 between T1 and T2, which was based on dant, then D (and in this case FR) approaches 1. When the 895OTUsinpooledmatrixB12,andS13 between T1 and dissimilarity matrix between species holds Euclidean met- T3, which was based on 970 OTUs in pooled matrix B13. ric properties (Gower & Legendre 1986), functional diver- Community functional stability will reflect both resistance sity Q presents the concavity property (Champely & and resilience. Chessel 2002), which is the analogue of set monotonicity for functional diversity measures that are computed from Testing causal models the summed branch lengths of the dendrogram constructed from functional dissimilarities (Champely & Ches- We used path analysis for testing causal models to sel 2002; Ricotta 2005). Thus, by adding new species to the explain community functional stability and to disen- set, functional diversity Q as defined here may remain con- tangle resistance and resilience. For path analysis, we stant, but not be reduced. The lack of monotonicity has adopted the d-separation approach (Shipley 2000) in been pointed out as a problem for adopting Rao’s quadratic which for each proposed causal model linking Q, FR, entropy (Petchey & Gaston 2006), but monotonicity is G, S and community-weighted mean traits, a set of maintained by choosing an appropriate dissimilarity func- independent relationships between the variables tion between species (Podani & Schmera 2006). Concavity included in the model was defined. Each of these is required for the additive decomposition of functional independent relationships involved correlations and diversity into alpha, beta and gamma terms (Ricotta 2005). partial correlations that were tested by permutation Whether an analogous decomposition of FR is useful (Manly 2007). Each causal model generated a value remains an open question. for a composite probability statistic (Fisher’s C statistic; The FR as defined also applies when community compo- Shipley 2000) that was tested by using the v2 proba- sition and trait description refer to units such as species bility distribution. A valid causal model must present populations (OTUs) that are locally described in each a P-value larger than an acceptable probability thresh-

Journal of Vegetation Science Doi: 10.1111/jvs.12047 © 2013 International Association for Vegetation Science 967 Functional redundancy and community stability V. D. Pillar et al. old (we adopted P 0.1). In our case, we selected the small quadrats were permuted as sets of five among valid models the one with the highest P-value among the 14 larger quadrats, while the factor(s) and for C. Also, regression models were used to determine co-variables remained unchanged. for a given valid model a path coefficient and corresponding probability found by permutation (Manly Results 2007) for each causal link, and a non-determination coefficient for each response variable. Factors and Trait subsets revealing maximum congruence r(XG)were response variables were centred and standardized to identified (Appendix S1). Presence of rhizomes (V1) or sto- unit variance, therefore the path coefficients (b) were lons (V2) and leaf tensile strength (LT), when used jointly, comparable across models. Since each set of five maximized the congruence between trait-based commu-

0.2 9 0.2-m quadrats were not true replicates within nity structure and grazing intensity at T1,whileasubset the larger 0.2 9 1.0-m quadrats, the permutations for including these traits and percentage of senescent leaves the testing of correlations and regression coefﬁcients (SL) resulted in a slightly lower and still highly signiﬁcant were restricted in order to control for spatial autocor- congruence (r(XG) = 0.2563, P = 0.007). The trait subset relation, e.g. the values of response variable S12 for SL V1 V2 LT was used in the next steps of the analysis. These

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (k) (l)

Fig. 1. Relations (a–l) between species diversity, functional diversity, functional redundancy, grazing intensity and community functional stability in grassland communities of south Brazil. Quadrats were evaluated for species composition and traits, the area was grazed, and then after 47 (for S12)or

180 d (for S13) community stability was measured. Gini–Simpson index is used for species diversity (D), Rao’s quadratic entropy for functional diversity (Q), and functional redundancy is their difference (FR = D Q). Percentage of senescent leaves, presence of rhizomes or stolons and leaf tensile strength deﬁned Q and FR, which were traits most likely functional for the community effect on grazing intensity by cattle. In (g)and(k) functional redundancy is indicated by the size of the circles. P-values obtained by restricted permutation tests.

traits, except SL, are well represented by the main axes of redundancy and functional diversity were not indepen- the PCA (Appendix S2) with the matrix of traits by OTUs, dent and the correlations with community stability were which revealed three significant principal components at much weaker and would not allow any conclusion regard- the population level. ing causal links (Appendix S3). We found a positive correlation between species diver- The PCoA ordination in Fig. 2 is based on the fuzzy- sity and functional diversity (Fig. 1a), and between species weighted composition of the communities (matrix X) diversity and FR (Fig. 1b). Yet, the correlation between according to percentage of senescent leaves (SL), presence functional diversity and FR was very low and non-signifi- of rhizomes (V1) or stolons (V2) and leaf tensile strength cant (Fig. 1c). The correlation between FR and grazing (LT). The community-weighted mean traits are also pro- intensity was low (Fig. 1d; see same in third dimension in jected on the diagram based on their correlations with the Fig. 1g,k). Also, species diversity and functional diversity ordination axes. The community scores on the first axis had low correlation to community stability between T1 and represent a gradient from communities characterized by T2 (Fig. 1e,f), but grazing intensity showed weak negative caespitose, taller plants with tougher leaves on the left, to correlation (R = 0.260, P = 0.0934) to the same measure rhizomatous or stoloniferous shorter plants with tender of community stability (Fig. 1g). Further, the correlation leaves on the right. The second axis represents increasing between FR and community stability S12 was positive and percentage of leaf senescence (SL) from bottom up. The significant (R = 0.312, P = 0.0470; Fig. 1h). The same projection of functional redundancy (FR) and community trends were observed for community stability S13 (Fig. 1–i stability (S12 and S13) on the diagram based on their corre- –l) but the correlations were slightly weaker. We have also lations with the ordination axes indicates that communi- explored these correlations by defining functional diver- ties characterized by less senescent and not rhizomatous sity, functional redundancy and community stability with plants presented higher FR and regenerated more similar the whole set of 12 traits of Table 1; in this case, functional communities after recovering from grazing (were more stable). This axis of variation related to senescence (SL)was orthogonal to that related to leaf tensile strength (LT), but SL both negatively influenced grazing intensity. We have proposed causal models linking FR, Q, G,

S12 and plant traits (Fig. 3). Among these models, the path model FR ? S G (Fig. 3a) presented the highest P-value (P = 0.999), well above the threshold, Axis 2 (22.7%) demonstrating that the postulated model is a plausible representation of the causal relationships that gener- LT ? L1 Q ated the data. Further, the path coefficient for FR S in this causal model was positive and significant, HE TH S13MH WB V1 while that for S G was negative and marginally sig- L2 LW D V2 S12 UD nificant. Yet 83% of the community stability could FR not be explained by the variables used in the model. G Whether unaccounted factors would include functional diversity is tested in the other causal models depicted in Fig. 3b,c. These models, compared to the model in Fig. 3a, presented lower P-values that, despite being higher than the threshold, indicated less plausible representations of the causal relationships Axis 1 (61.4%) generating the data. Indeed, the model in Fig. 3b pos- Fig. 2. PCoA ordination triplot of grassland communities in south Brazil. tulating that functional diversity causes community The analysis was based on matrix X of 70 0.2 9 0.2-m quadrats described stability showed a non-significant path coefficient for by the composition of operational taxonomic units (OTUs) after fuzzy- that effect. Similarly, the model in Fig. 3c indicated a weighting by the OTU similarities in terms of percentage of senescent non-significant effect of functional diversity on graz- leaves (SL), presence of rhizomes (V1) or stolons (v2) and leaf tensile ing intensity. The model in Fig. 3d including commu- strength (LT). The following variables are projected on the diagram based nity-weighted means for percentage of senescent on their correlations with the ordination axes: community-weighted means leaves (SL) and leaf tensile strength (LT) was also valid, for the traits (labels indicated in Table 1), and species diversity (D), = functional diversity (Q), functional redundancy (FR), community functional despite the lower P-value (P 0.484) than the other stability between times T1 and T2 (S12) and times T1 and T3 (S13). See main models; but in this case the path coefficient for text for further explanation. FR ? S was not significant, while the effect of SL on S

β = –0.260 (a) Functional β = 0.312 Community Grazing Fisher's C = 0.001 redundancy P = 0.0426 stability S12 P = 0.0931 intensity d.f. = 2, P = 0.999

U = 0.83

(b) Functional β = 0 .29 redundancy P = 6 β 0.0 = –0.236 584 Community Grazing Fisher's C = 5.14 stability S12 intensity d.f. = 6, P = 0.526 2 P = 0.1365 .13 Functional –0 01 β = .37 diversity = 0 P U = 0.82

U = 0.97

redundancy P = 0.0426 stability S12 P = 0.0931 intensity d.f. = 6, P = 0.701

U = 0.83 Functional β = 0.183 diversity P = 0.2035

β (d) R = –0.386 Senescent = –0.251 P = 0.0082 leaves P = 0.087 U = 0.89 β = –0.402 P = 0.0052

β Functional β = 0.157 Community = –0.366 Grazing Fisher's C = 5.47

redundancy P = 0.3046 stability S12 P = 0.0130 intensity d.f. = 6, P = 0.484

β = –0.200 P = 0.1780 U = 0.71 R = –0.189 Leaf tensile P = 0.1281 strength

Fig. 3. Causal relationships between variables indicated by different path models (a–d) in a grassland ecosystem in South Brazil. Seventy grassland quadrats were evaluated for species composition and traits, the area was grazed, and then after 47 d community stability was measured. These models were not rejected based on the composite probability for statistic C, under the null hypothesis that the independent relationships postulated in the given model are valid. However, the model in (a)gavemaximumP-value and therefore was further interpreted. The postulated independent relationships were tested by correlations and partial correlations (Shipley 2000). U is the non-determination coefficient of the ith response variable: the portion of variation that is explained by factors not in the model. Except in the case of the undirected relationships in (d), path coefficients (b) are indicated, which are regression coefficients using factors and response variables that were previously centred and standardized to unit variance. P-values were obtained using restricted permutation tests. was negative and significant and on G was negative knowledge, the functional redundancy measure proposed and marginally significant. Other postulated causal by de Bello et al. (2007) fills this gap and has not been used models (not shown) produced lower P-values. Similar before to examine that link. Our results showed the utility results, but slightly weaker path coefficients, were of such a measure for testing whether functional redun- found when the same path models included S13 dancy enhanced resilience of the grassland communities instead of S12 (see Appendix S4). with respect to a specific ecosystem process (cattle foraging). Using effect traits related to this specific ecosystem process enabled us to effectively evaluate the results of Discussion redundancy concerning functional effects on ecosystem Conceptually, functional redundancy is a critical property resilience. for the resilience of ecological communities (Walker 1992; Our results indicated that the path model postulating Naeem 1998), which has been demonstrated experimen- that functional redundancy and grazing intensity directly tally (e.g. Joner et al. 2011). Yet, the lack of an appropriate cause community stability (FR ? S G) was more plau- measure prevented analysis of data for the link between sible than the other causal models we proposed with differ- functional redundancy and resilience at plot scale. To our ent causal links between functional redundancy,

functional diversity, grazing intensity, community stability directly by one functional trait (SL) and by grazing inten- and community-weighted mean traits. Further, this model sity. The higher stability of community patches with less showed significant positive path coefficients for FR ? S, leaf senescence, which are grazed, suggests that grazing is and negative (marginally significant) for S G.Our controlling plant dominance, allowing the co-occurrence results therefore supported the conclusion that functional of more redundant species, and preventing the community redundancy enhances community stability. However, it is changing to a less attractive state for grazing. The resilience necessary to distinguish resistance from resilience (Harrison observed in the short term is likely mostly related to the 1979; Pimm 1991; Carpenter et al. 2001). Our measure of resprouting of plants that were grazed, and less to the community stability was actually a measure of resistance recruitment of new individuals from seeds (Fidelis 2008). plus resilience. Since resistance is ‘the amount of external However, the plausibility of this model was lower than the pressure needed to bring about a given amount of distur- model in Fig. 3a, and therefore it will not be discussed fur- bance in the system’ (Carpenter et al. 2001), we can say ther. In path analysis, the inclusion of community- that in grazed ecosystems low grazing intensity of a given weighted mean traits may indicate relationships that patch (due to rejection by grazers) indicates high resistance become obscure when only synthetic alpha-diversity of that patch to grazing disturbance, i.e. community resis- parameters such as functional diversity and redundancy tance is an inverse function of grazing intensity. But under are included. However, an in-depth examination of the extremely high grazing pressure (which is not the case of links between the set of measured plant traits, functional the study site), the system may likely move toward degra- diversity, redundancy and community stability would dation and instability due to loss of resistance and resil- require a more elaborate analytical approach that is ience (Sasaki et al. 2009). It should be noted that grazing beyond the scope of this paper. intensity at a given patch is dependent upon the forage on Hence, if we accept the model FR ? S G as the most offer. That is, the lower the amount of forage available per valid one, the logical conclusion is that functional redun- grazing animal unit at the paddock scale, the less selective dancy enhanced community resilience under disturbance. will be the foraging, and cattle will graze patches that That is, grassland communities that had higher functional otherwise would be rejected (Senft et al. 1987; Coughe- redundancy before one grazing period recovered a trait- nour 1991). Our study grassland, at the scale of a paddock, based community composition after grazing that was had been subjected to moderate grazing during the experi- more similar to that observed before grazing. A similar ment. Our results indicate that there was a negative effect effect of functional redundancy on resilience was found of grazing intensity on community stability, so that com- when we considered community functional stability at a munity patches that were more intensively grazed were longer period of 180 d that included two grazing periods less stable afterwards. Yet, considering also that functional in between, yet the causal link was weaker, which may redundancy positively affected community stability at the be related to grazing behaviour effects slightly shifting plot scale, stability reflected both resilience (affected by some plant communities towards a different functional functional redundancy) and resistance (affected by grazing composition between T1 and T3 with respect to foraging intensity). Furthermore, since functional redundancy and by grazers (Senft et al. 1987; Coughenour 1991; Blanco grazing intensity were independent in their covariation et al. 2007). and in their effects on community stability, the path model Our results support the hypothesis that biodiversity discriminated community resistance to grazing (the effect insures ecosystem processes ‘because many species proof G on S) from community resilience after grazing caused vide greater guarantees that some will maintain function- by functional redundancy (indicated by the effect of FR on ing even if others fail’ (Yachi & Loreau 1999). However, S). Therefore, more redundant communities, indepen- we found that the component of species diversity respon- dently of grazing intensity or community resistance, were sible for this insurance is functional redundancy, and not more stable. functional diversity. Indeed, notwithstanding the high However, if we consider the causal model including correlation between species diversity and functional community-weighted mean traits for percentage of senes- diversity, and between species diversity and functional cent leaves (SL) and leaf tensile strength (LT), the signifi- redundancy, which corroborates de Bello et al. (2009a), cance of the path coefficient FR ? S vanishes. Based on functional redundancy and functional diversity were this model, communities with higher senescence are less independent. This also explains why the correlation stable, and are grazed less intensively (although the path between species diversity and community functional sta- coefficient is weaker). Again, there is a negative effect of bility was weaker compared to that between functional grazing intensity on community stability. Therefore, if we redundancy and stability; i.e. species diversity contains consider this model, community stability would not be functional diversity, which in turn did not show a signifi- affected by functional redundancy but would be affected cant link to community functional stability and therefore

Journal of Vegetation Science Doi: 10.1111/jvs.12047 © 2013 International Association for Vegetation Science 971 Functional redundancy and community stability V. D. Pillar et al. adds noise to the correlation between species diversity question ‘redundancy for what?’ should always be asked. and stability. In our analysis we have first searched for a subset of traits While functional redundancy is considered important more likely functional for the ecosystem effect being con- for the maintenance of ecosystem processes and the sidered (foraging by grazers) and then these traits were resilience [in the sense of ‘engineering resilience’ or used for defining functional diversity and functional ‘ecological resilience’ (Peterson et al. 1998)], response redundancy. 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International Association for Vegetation Science JOURNAL OF VEGETATION SCIENCE Governing Board for 2011–2015 Offi cial organ of the International Association for Vegetation Science (www.iavs.org) PRESIDENT VICE PRESIDENT & MEETINGS COMMITTEE CHAIR Martin Diekmann, University of Bremen, Germany; [email protected] Valério De Patta Pillar, Federal University of Rio Grande do Sul, The Journal of Vegetation Science publishes papers on all aspects of plant community ecology, with particular emphasis on papers that develop new SECRETARY Porto Alegre, Brazil concepts or methods, test theory, identify general patterns, or that are otherwise likely to interest a broad readership. Papers may focus on any aspect of Susan Wiser, Landcare Research, Lincoln, New Zealand; [email protected] VICE PRESIDENT & MEMBERSHIP COMMITTEE CHAIR vegetation science including theory, methodology, spatial patterns (including plant geography and landscape ecology), temporal changes (including pal- VICE PRESIDENT & PUBLICATIONS OFFICER Michael Palmer, Oklahoma State University, Stillwater, OK, USA aeoecology and demography), processes (including ecophysiology), and description of ecological communities (by phytosociological or other methods), Robert K. Peet, University of North Carolina, Chapel Hill, NC, USA VICE PRESIDENT & GLOBAL FUND COMMITTEE CHAIR provided the focus is on increasing our understanding of plant communities. Papers that apply ecological concepts, theories and methods to the management VICE PRESIDENT & VEGETATION CLASSIFICATION Alicia Acosta, University of Rome Three, Italy and/or description of vegetation should be directed to our associated journal, Applied Vegetation Science. The Journal accepts original papers (including COMMITTEE CHAIR ADMINISTRATOR (ex offi cio) shorter ones), synthesis papers, forum contributions and reports. It commissions reviews of selected software. Javier Loidi, University of the Basque Country, Bilbao, Spain Nina Smits, Alterra, Wageningen, The Netherlands; [email protected] The Journal of Vegetation Science was founded by Eddy van der Maarel, Robert K. Peet and Robert Neuhäusl in 1990. 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Special Feature: Functional Diversity

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