OLIVIER J. HARDY and BRUNO SENTERRE*

Transcription

1 Journal of Ecology 2007 Characterizing the phylogenetic structure of communities Blackwell ublishing, Ltd. by an additive partitioning of phylogenetic diversity OLVER J. HARDY and BRUNO SENERRE* Behavioural and Evolutionary Ecology C 160/12, and *Laboratoire de Botanique systématique et de hytosociologie C 169, Université Libre de Bruxelles, B-1050 Brussels, Belgium Summary 1 Analysing the phylogenetic structure of natural communities may illuminate the processes governing the assembly and coexistence of species in ecological communities. 2 Unifying previous works, we present a statistical framework to quantify the phylogenetic structure of communities in terms of average divergence time between pairs of individuals or species, sampled from different sites. his framework allows an additive partitioning of the phylogenetic signal into alpha (within-site) and beta (among-site) components, and is closely linked to Simpson diversity. t unifies the treatment of intraspecific (genetic) and interspecific diversity, leading to the definition of differentiation coefficients among community samples (e.g. S, S ) analogous to classical population genetics coefficients expressing differentiation among populations (e.g. F S, N S ). 3 wo coefficients which express community differentiation among sites from species identity ( S ) or species phylogeny ( S ) require abundance data (number of individuals per species per site), and estimators that are unbiased with respect to sample size are given. Another coefficient (Π S ) expresses the gain of the mean phylogenetic distance between species found in different sites compared with species found within sites, and requires only incidence data (presence/absence of each species in each site). 4 We present tests based on phylogenetic tree randomizations to detect community phylogenetic clustering ( S > S or Π S > 0) or phylogenetic overdispersion ( S < S or Π S < 0). n addition, we propose a novel approach to detect phylogenetic clustering or overdispersion in different clades or at different evolutionary time depths using partial randomizations. 5 S, S or Π S can also be used as distances between community samples and regressed on ecological or geographical distances, allowing us to investigate the factors responsible for the phylogenetic signal and the critical scales at which it appears. 6 We illustrate the approach on forest tree communities in Equatorial Guinea, where a phylogenetic clustering signal was probably due to phylogenetically conserved adaptations to the elevation gradient and was mostly contributed to by ancient clade subdivisions. 7 he approach presented should find applications for comparing quantitatively phylogenetic patterns of different communities, of similar communities in different regions or continents, or of populations (within species) vs. communities (among species). Key-words: additive partitioning of diversity, community ecology, Equatorial Guinea, neutral community, phylogenetic structure of communities, phylogeny, rain forest, randomization test, species assemblage, tropical trees Journal of Ecology (2007) doi: /j x Ecological Society Correspondence: Olivier Hardy (tel. +32 (0) ; fax +32 (0) , ohardy@ulb.ac.be). ntroduction Community assembly of the individuals of different species depends on both historical (biogeographical)

2 494 O. J. Hardy & B. Senterre and ecological phenomena (Webb et al. 2002). o cooccur, species must have both overlapping geographical distributions and overlapping habitat affinities (although a species could occur in a suboptimal habitat through dispersal from other nearby habitats). n addition, when species ecological niches overlap excessively, competitive exclusion could limit coexistence. Ecological niches depend on the similarities between species traits. deally, an assessment of species geographical ranges and a detailed characterization of the species traits relevant for habitat preferences and biotic interactions would be necessary to understand and predict community assemblages, but this is an insurmountable task for species-rich communities. However, species phylogeny is highly relevant to understanding community assembly because it provides insight into the divergence time among species, and hence is a proxy for interspecific biogeographical similarity as well as for ecological similarity if niche conservatism occurs during evolution. nvestigating the phylogenetic structure of communities can thus provide useful insight to understand the historical and ecological factors shaping species assemblages (Webb et al. 2002; Cavender-Bares et al. 2004). he correlation between any two species ranges is expected to decrease with their divergence time because of the accumulation of independent dispersal and local extinction events over time. Likewise, the similarity in habitat preferences of species pairs should decrease with increasing phylogenetic distance because of the accumulation of independent trait changes over time, a pattern known as phylogenetic trait conservatism (Lord et al. 1995; Webb et al. 2002). herefore, a pattern of phylogenetic clustering (i.e. species co-occurring within communities are more related on average than species from different communities) is expected whenever the communities compared are geographically distant (i.e. biogeographical origin; Fig. 1a), or whenever the communities occur in contrasted habitat (ecological origin; Fig. 1c). However, the correlation between divergence time and geographical or ecological differentiation does not necessarily hold, in particular for closely related species, and a pattern of phylogenetic overdispersion (i.e. species co-occurring within communities are less related on average than species from different communities) may result. Such a pattern could result from allopatric speciation of widely distributed ancestral species caused by a biogeographical barrier (Fig. 1b). t could also occur among communities situated in distinct habitats because sister species have specialized into these habitats, as a result of sympatric speciation driven by ecological differentiation, or as a result of secondary habitat differentiation driven by competitive exclusion (Fig. 1d). Finally, phylogenetic overdispersion can occur among communities situated in similar habitats as a direct consequence of competitive exclusion between sister species with widely overlapping niches (Fig. 1e). As illustrated by Cavender- Bares et al. (2004), community phylogenetic patterns Fig. 1 ypes and origins of community phylogenetic patterns. Biogeographical origin: (a) phylogenetic clustering due to local speciation of allopatric clades; (b) phylogenetic overdispersion due to allopatric speciation of two ancestral sympatric species caused by the same biogeographical barrier. Ecological origin: (c) phylogenetic clustering due to habitat filtering of phylogenetically conserved traits; (d) phylogenetic overdispersion due to habitat filtering of phylogenetically convergent traits (when sister species arise from ecological speciation); (e) phylogenetic overdispersion due to competitive exclusion of related species showing phylogenetically conserved traits. he communities compared occur in contrasted habitats in cases (c) and (d), and in similar habitats in case (e). driven by ecological factors ultimately depend on the interplay between the evolution of species traits, which can be phylogenetically conservative or convergent, the environmental filtering of species traits, which favours phenotypic clustering, and the competitive interaction between species, which favours phenotypic overdispersion. A random pattern (i.e. absence of phylogenetic clustering and overdispersion) may mean that the impact of these three processes is not significant, as would be the case for a neutral community (Hubbell 2001). n fact, overdispersion may occur in some lineages, or just among closely related species, whereas clustering affects other lineages or more distantly related clades, so that the overall pattern may be difficult to distinguish from a random one. t must be emphasized that the scales of observation (geographical range, range of habitats covered, taxonomic delimitation of the communities) are very important to consider when interpreting community phylogenetic patterns. For example, studying oak species spread over a range of forest habitats in north central Florida, Cavender-Bares et al. (2004) detected phylogenetic overdispersion, but

3 495 hylogenetic structure of communities the pattern reverted to phylogenetic clustering when all plant species were considered in the same habitats, and this clustering became amplified when a larger pool of habitats (including wetlands and coastal communities from Florida) was included (Cavender-Bares et al. 2006). he same type of dependency towards taxonomic and species pool scaling was reported by Swenson et al. (2006) when analysing the phylogenetic structure of neotropical forest tree communities. hus, while community phylogenetic overdispersion may occur at a shallow phylogenetic depth because of competitive exclusion among species from a radiating clade, phylogenetic clustering could be present at deeper phylogenetic depth because of niche conservatism. Hence, methods to characterize the phylogenetic structure of a community at different time depths are very valuable (e.g. Webb 2000). Using species inventory data and the topology of a phylogenetic tree, Webb (2000) developed a method to assess whether species co-occurring locally are more related than species from a regional species pool. Similar studies have been published recently (e.g. Webb et al. 2002; Cavender-Bares et al. 2004, 2006; Horner-Devine & Bohannan 2006; Kembel & Hubbell 2006; Lovette & Hochachka 2006; Silvertown et al. 2006; Swenson et al. 2006), and given the recent availability of dated super-trees based on molecular phylogenetic data (e.g. Davies et al. 2004, for angiosperm families) and of new tools and software performing phylogenetic community structure analyses (e.g. Webb & Donoghue 2004; Webb et al. 2004), many new studies are likely to be forthcoming. As for any new research area, the development of appropriate statistical tools is fundamental. We present a statistical framework that quantifies and partitions additively into alpha and beta components (i) the phylogenetic diversity of communities expressed by the average divergence time between pairs of individuals, and (ii) the phylogenetic distinctness of species assemblages expressed by the average divergence time between pairs of species. his framework is based on several previous treatments of biodiversity organization (e.g. Rao 1986; Lande 1996; Ganeshaiah et al. 1997; Clarke & Warwick 1998; Shimatani 2001; Veech et al. 2002; Webb et al. 2002; Couteron & élissier 2004; avoine et al. 2004, 2005; avoine & Dolédec 2005; Chave et al., in press). he framework generalizes some classical diversity coefficients (such as Simpson s diversity) in a phylogenetic perspective, and defines differentiation coefficients analogous to classical population genetics indices (e.g. F S, N S ) that quantify the strength and direction of the phylogenetic signal. hese differentiation coefficients can also be assessed for pairs of community samples so that the phylogenetic signal can be interpreted according to ecological or geographical distances between communities. esting the phylogenetic signal can be achieved by randomizing species at the tips of the phylogenetic tree. n addition, we propose new partial randomization tests to detect a phylogenetic signal within different clades or within clades younger than some time threshold. Such randomizations can potentially discern phylogenetic clustering and overdispersion if both occur at different levels or depths of a phylogenetic tree. o illustrate this approach, we analyse floristic inventories performed in rain forests of Equatorial Guinea: (i) we test for phylogenetic clustering/overdispersion within sites, (ii) we assess in which clades a phylogenetic signal occurs, (iii) we compare the testing power of statistics based on species abundance vs. species presence/absence, (iv) we identify which ecological factors best predict the phylogenetic signal using pairwise differentiation coefficients, and (v) finally we assess the robustness of the approach with respect to the precision of the phylogenetic tree by comparing our results with those obtained using a rank-based species classification. heory artitioning species diversity within and among sites is fundamentally analogous to partitioning allele diversity within and among populations, and the basic processes determining the dynamics of populations and communities are also very similar in nature. Consequently, the way genetic diversity is partitioned given a genealogy of alleles (ons & etit 1996) can be used to describe the phylogenetic structure of communities (avoine & Dolédec 2005; Chave et al., in press). n the following, we present descriptive statistics of the phylogenetic structure of communities requiring species abundance data, or species incidence (presence/ absence) data, and we show how essentially unbiased estimates can be obtained. We then present randomization procedures to test whether phylogenetic clustering or overdispersion occur. Our definition of community is any assemblage of species spatially localized and possibly restricted to some clade, functional group and/or phenotypic features (e.g. all angiosperm trees with a diameter above 10 cm in a 1-ha plot). ARONNG HYLOGENEC DVERSY FROM SECES ABUNDANCES Different measures quantify species diversity of a community, some of the best known being the species richness, the Shannon Wiener index, the Simpson diversity index and Fisher s alpha (Magurran 2004). hese statistics differ in the way species frequency is accounted for (e.g. Couteron & élissier 2004), but also in their sensitivity to sample size (Gimaret-Carpentier et al. 1998). For example, Simpson diversity gives much weight to common species, nearly ignoring rare species, and is unbiased with respect to sample size. n contrast, species richness weights common and rare species equally, but is highly dependent on sample size so that unbiased estimates are difficult to obtain in species-rich communities (Gotelli & Colwell 2001).

4 496 O. J. Hardy & B. Senterre able 1 Main coefficients presented in this paper and correspondences with other publications Coefficient nterpretation Correspondences D D S S Π S robability that two individuals belong to different species (Simpson diversity index). Mean phylogenetic distance (e.g. divergence time) between individuals (an index of phylogenetic diversity). roportion of the overall species diversity (D ) expressed among sites. Analogue to F S and G S (ref. 1 ) in population genetics. roportion of the overall phylogenetic diversity (D ) expressed among sites. Analogous to N S (ref. 1 ) in population genetics. Mean phylogenetic distance (e.g. divergence time) between distinct species (an index of phylogenetic species distinctness). roportion of the overall phylogenetic species distinctness ( ) expressed among sites. Î (ref. 2); D (ref. 3) D (ref. 2); (ref. 3, 4); A (ref. 5); H (ref. 6) F S (ref. 2) + (ref. 4); 1 / 2 MD (ref. 7) he subscript S or (e.g. DS, D) is added to denote that the level of sampling is a subcommunity (or site) or the total set of subcommunities (sites). References: (1) ons & etit (1996); (2) Chave et al. (in press); (3) Shimatani (2001); (4) Clarke & Warwick (1998); (5) Ganeshaiah et al. (1997); (6) avoine et al. (2004, 2005); (7) Webb et al. (2002). hese statistics do not consider the phylogenetic relationships between species but the Simpson diversity index, D, can be extended to incorporate phylogenetic information. D is D = 1 i 2 f i where f i is the frequency of species i in a community. As 2 f i is the probability that a random pair of individuals 2 from the community belong to species i, f i is the probability that the pair belongs to the same species. Hence, D is the probability that two individuals from the community belong to a different species. t can also be expressed as D = δij fi f i j j (1) (2) if δ ij is an indicator variable equal to 0 when i = j (same species) and equal to 1 when i j (different species). Equation 2 can be extended to incorporate species phylogeny by letting δ ij be a continuous variable expressing the phylogenetic distance between i and j rather than being a binary (0, 1) variable. For example, if δ ij is the divergence time between species i and j (i.e. the age of the most recent common ancestor of i and j), D is the mean divergence time between two randomly sampled individuals in the community, and can be interpreted as a measure of phylogenetic diversity. n the following we will distinguish D, the original Simpson diversity index which accounts only for species identity (i.e. not their phylogeny), and D, a diversity measure accounting for species phylogeny and equivalent to Rao s quadratic entropy (Rao 1982) for the special case where δ ij is a phylogenetic distance (see able 1 for equivalence with other publications). Note that δ ij could represent other types of distances between species (e.g. taxonomic, morphological, functional). However, a diversity coefficient should ideally always increase when a species is added, a property of D ensured when δ ij is ultrametric (avoine et al. 2005), which is the case when δ ij is the divergence time or, more generally, when δ ij is obtained from the branch lengths of a rooted tree in which all the end nodes are equidistant from the root (ultrametric tree). When divergence time between species are known, D can be partitioned additively according to classes of divergence time, c (Shimatani 2001; Ricotta 2005). For instance, D = D c where D c is the probability that two individuals belong to distinct species whose divergence time is included in class c (classes must be non-overlapping and cover the full range of divergence times between species). n the limit of very narrow classes, D = δ c cdc where δ c is the average divergence time corresponding to class c. D and D can be used to define differentiation coefficients between local communities (sites) as follows. Diversity can be perceived at different levels and it is customary to decompose the total amount (gamma diversity) into a local component (alpha diversity) and an intersite or interhabitat component (beta diversity; Whittaker 1972). D can be partitioned additively, as in a nested ANOVA (e.g. Lande 1996; Couteron & élissier 2004; avoine & Dolédec 2005). Let f i and f ik be the frequency of species i overall in the region and within site k, respectively. he total (gamma) diversity is defined as D = δij fi. fj. i j he diversity within site k is D k = δijfik f i j jk (3) (4) he average within-site (alpha) diversity, D S, is the expectation of D k over all sites. he beta diversity is the difference D D S. his among-site component can be

5 497 hylogenetic structure of communities rewritten as a fraction of the total diversity, expressing differentiation among sites using information from species identity or species phylogeny: S S S D D = D S D D = D (5) (6) where the subscripts S and refer to the fact that diversity within site is compared with total diversity. S and S thus represent fractions of the overall species or phylogenetic diversity expressed among sites. hey are equivalent to, respectively, F S (or G S, the proportion of genetic diversity expressed among populations considering allele identity) and N S (the proportion of genetic diversity expressed among populations considering the phylogeny of alleles), which are defined at the within-species level (ons & etit 1996). Note that the same formalism of community diversity decomposition was proposed by Chave et al. (in press), who used the symbol F S to denote S in reference to population genetics literature (able 1). However, to avoid confusion, here we introduce new symbols. Moreover, we demonstrate below how to compare, estimate and test S and S to make inferences on the phylogenetic structure of communities. Among-site differentiation occurs when S > 0 or S > 0. An interesting property of these differentiation coefficients is that S = S when there is no community phylogenetic structuring, whereas S > S ( S < S ) indicates phylogenetic clustering (overdispersion), i.e. species found within a same site are more (less) related on average than species taken from different sites. he reason is that S expresses only the among-site differences in species frequencies whereas S also expresses the gain of phylogenetic divergence among species. he strength and direction of the phylogenetic signal can thus be quantified by the difference S S. o obtain unbiased estimators (ons & etit 1996), we assume that N randomly chosen sites have been sampled in a large community, and that n k individuals have been sampled in site k (n k > 3). Let f ik be the observed frequency of species i in site k (an estimate of the parameter f ik which is the actual value). he phyletic distance between species, δ ij, is assumed to be known without error. Unbiased estimates of diversity coefficients can be obtained from the principles of an ANOVA, considering that the factor site is a random effect (ons & etit 1996): Estimators of differentiation coefficients are obtained as: Î S S S D D = D S. D D = D (10) (11) S and S assess the proportion of the total diversity explained by species turnover among sites. hese coefficients can also be used to quantify pairwise differentiation between sites by applying the above formulae in the particular case where N = 2. Such between-site differentiation coefficients can then be regressed on explanatory variables that express the distance (geographical, ecological) between sites to infer which factors might be responsible for the phylogenetic signal. ARONNG HYLOGENEC DSNCNESS FROM SECES NCDENCE he diversity measures presented above require abundance data (counts of individuals) and rare species are underemphasized. As an alternative, we follow Clarke & Warwick (1998) to define a measure of phylogenetic distinctness based on species incidence (presence/absence data) that should remain robust with respect to sample size. For a given community = δ ij pp i j pp i j i j i i j i (12) where p i = 1 if species i is present ( f i > 0), otherwise p i = 0 ( f i = 0). he denominator is twice the number of pairwise comparisons between existing species. Note that, contrary to D, the double sums exclude comparisons of a species with itself. t is easiest to understand by analogy with D : while D is the mean phyletic distance between distinct individuals, is the mean phyletic distance between distinct species. t can thus be interpreted as a measure of phylogenetic distinctness between species (see able 1 for equivalence with other publications). Contrary to D, is not a measure of community phylogenetic diversity because it does not necessarily increase with the addition of new species. But like D, can be evaluated at different levels: S the average within site, and over all sites. Hence, a coefficient analogous to S can be defined as: Dk = nk/ ( nk 1) δij fik f D S = 1/N D k N k i 1 D = δ ij f ik f jl. ( 1) NN k l k i j j jk (7) (8) (9) S. S = (13) Π S expresses the gain of phyletic divergence between species occurring in different sites compared with species occurring in the same site. t is not sensitive to the gain in species richness among sites vs. within a

6 498 O. J. Hardy & B. Senterre site because expresses phylogenetic distinctness independently of species richness. ts expectation is Π S = 0 when there is no community phylogenetic structuring and Π S > 0 (Π S < 0) under phylogenetic clustering (overdispersion). o derive estimators that should be unbiased when phylogenetic distances and differences in species abundances are uncorrelated, let p ik = 1 if f ik > 0, otherwise p ik = 0: k = ij ik jk i j i 9 δ p p p p 9S = 1/ N 9k k N i j i ik jk (14) (15) 1 9 = δij pik pjl pik pjl NN ( 1) k l k i j i j (16) 0 S S. 9 9 = 9 (17) ESNG HE HYLOGENEC SRUCURE OF COMMUNES o detect phylogenetic clustering or overdispersion within sites, S or Π S can be computed after randomizing the species among the tips of the phylogenetic tree used to define the δ ij phyletic distances (Fig. 2a). Because such tree randomization breaks down the actual phyletic relationships among species (while keeping the tree architecture intact), the resulting pseudo S or Π S values (hereafter denoted p S or pπ S ) are representative of a community without phylogenetic structuring, conforming to the null hypothesis to be tested. Hence, the actual S (Π S ) can be compared with the distribution of p S (pπ S ) obtained for many independent randomizations, providing an estimate of the value for the null hypothesis that there is no phylogenetic signal. his procedure tests if S = S or if Π S = 0 because p S and pπ S have statistical expectations equal to S and 0, respectively (see Hardy et al for an analogy in population genetics). he same principle could be applied to test the phylogenetic diversity/distinctness measures themselves ( DS, D, S, ) rather than their ratios ( S, Π S ) but care must be taken in the interpretation of the results. ndeed, D < pd (or < p ) would mean that the difference in abundance (or frequency) between species is correlated with their phyletic distance, and thus that the most abundant species belong to particular clades of the phylogenetic tree (clustering/overdispersion is relative to the particular tree considered). t does not demonstrate that the species found within sites are more or less related than species found among sites. he latter situation can only be tested using the ratios ( S, Π S ). Fig. 2 Modes of phylogenetic tree randomizations for testing phylogenetic patterns at different evolutionary levels. he structure of the tree is left unchanged but species are randomly permuted within the grey areas so that the apparent divergence times between species are modified. (a) Complete tree randomization. (b) artial tree randomization for the clade shown by the arrow. (c) artial tree randomization for clades younger than the age threshold shown by the stippled line. ARAL RANDOMZAONS OF HE HYLOGENEC REE When a non-random community phylogenetic structure is observed, one may wonder which parts of the phylogenetic tree contribute to the pattern. artial tree randomizations, where species positions are randomized only within a defined clade, allow one to test phylogenetic clustering or overdispersion independently within each clade (Fig. 2b). A community phylogenetic structure may also show overdispersion within recent clades and clustering within old clades. o assess phylogenetic patterns according to evolutionary depth, partial tree randomizations where species positions are randomized only within clades younger than an age threshold (Fig. 2c)

7 499 hylogenetic structure of communities can provide interesting clues, because the resulting p S (or pπ S ) retains part of the phylogenetic information and its expectation lies between S and S (or between 0 and Π S for pπ S ). he difference p S S (or pπ S ) expresses then the extent of community phylogenetic structuring contributed by the separation of clades that diverged before the age threshold. f phylogenetic overdispersion occurs in some clades while phylogenetic clustering occurs in other clades, these patterns may cancel out. artial randomizations should thus permit the identification of such situations using S or Π S. Application to a tropical forest tree community he approach described above has been applied to detailed floristic inventories conducted in a forest from Atlantic central Africa by Senterre (2005). he Monte Alén National ark in Equatorial Guinea, an area that has not suffered substantial human disturbance, is situated on the transition between the Sacoglottis littoral evergreen rain forests and the Biafrean evergreen rain forests sensu stricto (Letouzey 1968; White 1983), and ranges from 300 m to 1300 m in altitude (Senterre 2001; Senterre & Lejoly 2001; Senterre et al. 2004). A submontane belt occurs above m. Rainfall is quite variable depending on exposure and elevation, and ranges from 1500 mm year 1 to 3800 mm year 1 with two short dry seasons (Fa 1991). emperature is about 25 C and is seasonally constant. Soils in this region are derived from granite and gneiss and are mostly ferralitic and acid. SAMLNG he flora of 28 sample plots was inventoried within a km 2 area within the Monte Alén National ark. hese plots were placed in order to represent the main mature forest types of the region, and are in homogeneous stands of about 1 ha. hey are distributed along five mountain slopes situated at different distances from the sea coast. Species inventories were made at several forest layers but, for simplicity in the present study, only the upper layer comprising canopy and emergent trees (i.e. trees generally over m in height exposed to direct solar radiation) are considered. n each plot, 100 individuals were randomly sampled along a transect. Morphospecies were recognized in the field and their exact identification was based on herbarium material (2874 specimens). Nomenclature follows Lebrun & Stork (1991, 1992, 1995, 1997, 2003). Among the 2800 trees inventoried, 40 (1.4%) were unidentified and 111 (4%) were identified to morphospecies known to genus. ECOLOGCAL DAA he following quantitative variables were measured at each plot: altitude, stand dynamic (intensity of perturbation assessed from the frequency of windfall gaps; three ordinal classes), hygrometry (i.e. air humidity; four ordinal classes), soil hydromorphy (i.e. the degree of water saturation in soil; five ordinal classes), soil depth (five ordinal classes), presence of rocks in the soil (binary) and presence of an impenetrable gravel layer in the soil (binary). We used Bryophyte cover as an indicator of hygrometry (Frahm & Gradstein 1991; Wolff 1993; Kessler 2001). he absolute difference of values between plots for each variable was used as interplot ecological distance. HYLOGENEC REE hylogenetic distances between species from different families are estimated from the dated Angiosperm families super-tree of Davies et al. (2004). his supertree is based on rbcl gene sequencing and is calibrated using fossil data related to the split between Fagales and Cucurbitales 84 million years ago (Wikström et al. 2001). he tree for the families found is shown in supplementary Fig. S1. Below the family level, the different genera were treated as polytomies with a divergence age arbitrarily set at two-thirds the age of the family, where the family age is the estimated age of the node between sister families of the whole super-tree (379 families), and species from the same genus were treated in the same way considering that they diverged at onethird the age of their family. o investigate the robustness of the measures of phylogenetic diversities with respect to the precision of the phylogenetic tree, we also considered a very simplified tree following a hierarchical AG classification (AG 2003). hylogenetic distances, δ, between individuals were then equal to 0 (same species), 1 (same genus), 2 (same family), 3 (same order), 4 (same higher clade, distinguishing asterids, rosids, magnoliids, monocots and orders not belonging to these clades) or 5 otherwise. DAA ANALYSES Coefficients representing local ( DS, DS, S) and total ( D, D, ) (phylogenetic) diversity/distinctness as well as their ratios ( S, S, Π S ) were computed. Simpson s diversities ( DS, D ) were also partitioned according to divergence time using 20 million year (Myr) wide age classes (i.e. classes of divergence times: 0 20 Myr, Myr,, Myr). n this way, the distributions of divergence time between individuals sampled within plots and among plots were compared. o test for phylogenetic structuring, tips (species) from the phylogenetic tree were permuted 999 times (complete randomization), considering a tree containing only the observed species. o assess how this test is affected by the species pool of the phylogenetic tree when species from another ecological guild are included, we also applied the randomization procedure on a tree containing two times more species by adding Angiosperm species found in the herbaceous layer

8 500 O. J. Hardy & B. Senterre able 2 artition of (phylogenetic) diversity/distinctness measures within and among 28 tree plots from an Atlantic central African forest (2760 individuals belonging to 273 species). For coefficients based on species phylogeny, mean values and 95% central distribution envelopes after 999 randomizations of the phylogenetic tree are given (italic values): a using a tree containing only the observed species, and b using a tree containing additional herbaceous species Coefficients based on: Local diversity α otal diversity γ = α + β Differentiation β/γ Species identity and abundance D S = D = S = Species phylogeny and abundance D S = D = S = a 96.6 (91.8, 103.5) a (95.9, 108.0) a (0.037, 0.049) b (105.2, 116.3) b (109.8, 121.6) b (0.037, 0.049) Species phylogeny and incidence S = = Π S = a (100.1, 106.2) a (101.2, 106.3) a ( , ) b (114.6, 121.5) b (114.6, 121.4) b ( , ) Note: D and are mean divergence times expressed in million years. Fig. 3 Decomposition of within- and among-plot Simpson s diversity indices according to the divergence time (i.e. frequency distributions of divergence time between individuals from different species for pairs of individuals sampled within plots or among plots). (which was very rich in Monocots), so that sampled species are also permuted with species not represented in the data set analysed. artial tree randomizations were performed by setting an age threshold above which the tree is kept intact (i.e. no species permutations among clades older than the threshold). We considered different threshold dates ranging from 150 Myr (equivalent to complete randomizations in our case) to 40 Myr. n addition, pairwise S, S and Π S between plots were computed and regressed on pairwise spatial and ecological distances between plots for each variable (multiple regression). he significance of explanatory variables was checked by partial Mantel tests. Results DVERSY Over all 28 plots inventoried, 273 species and morphospecies belonging to 168 genera from 36 families were identified. he most species-rich families (following AG classification) are the Fabaceae (which includes Caesalpiniaceae and Mimosaceae following older classifications), Euphorbiaceae, Annonaceae, Burseraceae, Clusiaceae and Rubiaceae, represented each by at least 10 species (supplementary Fig. S1). he maximal divergence time between species is 144 Myr following the super-tree of Davies et al. (2004). Within a plot, the probability that two individuals belong to different species is D S = , the mean divergence time between individuals is D S = Myr, and the mean divergence time between species is S = Myr (able 1). According to these coefficients, most diversity occurs within a plot, the amongplot contribution (β diversity) being always less than or equal to 6% ( S = 0.042, S = 0.059, Π S = ; able 2). When these measures were computed using taxonomic ranks to produce surrogates of phyletic distances, estimates of phylogenetic diversity/distinctness were naturally different because divergence times were not included (e.g. D S = 403., S = 442. ), but estimates of phylogenetic differentiation among plots were only slightly affected ( S = 0.055, Π S = ). he distributions of divergence time between individuals, which is a partition of D, show that more than half the pairs of individuals have diverged between 100 and 120 Myr ago, belonging to different major clades such as rosids and asterids (Fig. 3). Slightly more pairs of individuals are from species having diverged less than 100 Myr ago within plots

9 501 hylogenetic structure of communities than among plots, the reverse pattern occurring for species having diverged more than 100 Myr ago (Fig. 3). Hence, a trend of phylogenetic clustering is observed. ESNG COMMUNY HYLOGENEC SRUCURNG After complete randomization of the phylogenetic trees (for a tree containing only the observed species or also additional ones), mean p S = S and mean pπ S = 0, as expected on theoretical grounds (able 2). he observed S is above the 95% central p S distribution interval, indicating that species within plots tend to be more related than species among plots (phylogenetic clustering; able 2). Π S is marginally significantly larger than zero, the observed Π S being within the 95%, but outside the 90%, pπ S distribution interval. nterestingly, mean divergence times between individuals or species (i.e. DS, D, S, ) were within their 95% distribution intervals obtained after randomizing a tree containing only the observed species, but they were outside such intervals when randomizing a tree containing additional species from the herbaceous layer (able 2). his result occurs because tree species do not form a random sample of a phylogenetic tree containing both tree and herb species. hus, testing absolute phylogenetic diversity measures by randomizing a phylogenetic tree strongly depends on the phylogenetic tree used and is not adequate to test whether species co-occurring within plots are more (or less) related on average than species from different plots. On the contrary, testing for phylogenetic clustering/overdispersion within plots using ratios such as S and Π S seems robust with respect to the phylogenetic tree used. Under partial randomizations, when families are kept intact (age threshold at 40 Myr), p S does not differ from S (Fig. 4). he same trend is observed using Π S (results not shown). Hence, within a family, no tendency for phylogenetic clustering or overdispersion is observed. Significant phylogenetic patterns emerge using older age thresholds for partial randomizations: a substantial drop of the mean p S value (about half of the phylogenetic signal) is observed when the age threshold is larger than 143 Myr, i.e. when species from core eudicots and magnoliids (Lauraceae, Myristicaceae and Annonaceae) are allowed to be permuted (Fig. 4). Similar trends are observed using Π S (not shown). Hence, phylogenetic clustering is mostly explained by deep phyletic divisions. artial randomizations within each clade detected significant phylogenetic clustering within eudicots, rosids, eurosids1, eurosids2, malpighiales, malvales, rosales, sapindales and Annonaceae. hus, only one family (Annonaceae) shows some trend of nonrandom phylogenetic pattern, and phylogenetic overdispersion was never observed. t must be noted, however, that the resolution of our phylogenetic Fig. 4 Results of partial phylogenetic tree randomizations on S. Error bars represent the 95% central p S values obtained for 999 randomizations, where species permutations are done only among clades younger than the age threshold (in millions of years). Values can be compared with S and S (without randomizations). hypothesis below the family level may be too low to detect existing phylogenetic signals. HYLOGENEC SGNAL AND ECOLOGCAL DFFERENAON airwise differentiation between plots using S, S, Π S or the difference ( S S ) were not correlated with the spatial distance between plots, but did yield significant positive correlations with the ecological distance between plots for several variables, which together explained from 17% (for Π S ) to 29% (for S ) of the variance of differentiation coefficients (able 3). he differences in altitude explained most of the variance (able 3). When pairwise S and S between plots are averaged for a set of elevation difference intervals (0 100 m, > m, > m, > m, > m; Fig. 5), only pairs of plots situated at very similar elevation (first interval) show no phylogenetic signal, and the magnitude of phylogenetic clustering, expressed by the difference S S, increases steadily with the elevation difference between plots (Fig. 5). he difference S S represents the part of the differentiation between plots that is explained only by the increase in phyletic divergence between individuals. nterestingly, it is not affected by hygrometry or the presence of rocks in the soil, two variables that significantly explain S values (able 3). Discussion his paper provides a consistent theoretical framework to characterize the extent of community phylogenetic structuring through the partitioning of measures of phylogenetic diversity or distinctness into α (withinsite) and β (between-site) components. t also proposes new testing procedures to detect a phylogenetic signal in different clades or at different evolutionary time depths. his work is closely linked to previous efforts

10 502 O. J. Hardy & B. Senterre able 3 Variables explaining the (phylogenetic) structure of tree communities in an Atlantic central African forest. Values are partial regression slopes of the linear regression of pairwise S, S or Π S on spatial and ecological distances between plots. hey are tested by partial Mantel tests (999 permutations). he last line shows the amount of variance explained when only the variables showing a significant effect ( < 0.05) are included in the regression model (R 2 ) S S S S Π S Spatial distance Altitude 0.269*** 0.381** 0.387** 0.409*** Hygrometry 0.305*** 0.163* Soil depth 0.099* 0.149* 0.156* Soil hydromorphy 0.204** 0.263* 0.255* Rocks in soil 0.127* Gravel layer in soil Stand dynamic R *** 0.289*** 0.278** 0.172*** * < 0.05, ** < 0.01, *** < for comparing phylogenetic patterns, for example, of similar communities in different regions or continents, of different guilds of a same region, or at the levels of populations (within species) vs. communities (among species). Using differentiation coefficients ( S, S, Π S ) in a pairwise fashion, the origin of a phylogenetic signal can be investigated by regressing these coefficients on ecological or geographical distances between community samples. A software program performing these analyses is being developed by O. J. Hardy and interested readers are invited to contact him. Fig. 5 hylogenetic signal for interplot differentiation. Average pairwise S and S are represented according to the elevation difference between plots. he stippled lines represent the 95% envelope of the p S values obtained for 999 randomizations, showing that phylogenetic clustering ( S > S ) occurs when the elevation difference exceeds c. 150 m. aimed at defining general frameworks to treat diversity patterns in a way that includes similarities between species (Rao 1982, 1986; Ganeshaiah et al. 1997; Clarke & Warwick 1998; Webb 2000; Shimatani 2001; Webb et al. 2002; avoine et al. 2004; avoine & Dolédec 2005; Chave et al., in press), partition diversity additively within and among sites or habitats (e.g. Lande 1996; Veech et al. 2002; Couteron & élissier 2004; avoine & Dolédec 2005; Ricotta 2005; Chave et al., in press), or allow species or plot ordination (élissier et al. 2003; avoine et al. 2004). he proposed framework has the advantage of defining parameters with clear interpretations and, moreover, unifies the analysis of interspecific biodiversity patterns with the classical analysis of intraspecific (genetic) diversity pattern undertaken by population geneticists ( F-statistics, Wright 1965). n fact, phylogenetic diversity could be analysed without using any species concept (i.e. without categorizing individuals into a set of species) provided that phylogenetic distance between individuals can be assessed, for example using molecular data. he synthetic parameters describing diversity and/or phylogenetic distinctness (D, D,, S, S, Π S ) should be useful COMARSON WH OHER DESCRORS OF HYLOGENEC DVERSY Among the measures of phylogenetic diversity, Faith s D measure (Faith 1992, 1994) has gained popularity in conservation biology. D is the total phylogenetic branch length spanned by the species composing a given community, and can be interpreted as the amount of evolutionary history. he concept of D can be used to describe phylogenetic diversity and also phylogenetic similarity between communities from the proportion of shared evolutionary history (Faith et al. 2004). How do D and compare with Faith s D? First, D does not account for species abundance and is thus more related to than to D. Second, is an average divergence time between pairs of species and is not influenced by species richness, explaining why it is a measure of phylogenetic distinctness, whereas D increases with species richness and better fits the notion of phylogenetic diversity (Clarke & Warwick 1998). he disadvantage of D is that it requires an exhaustive sampling of all the species composing a community (if branches to missing species are not counted D would be underestimated), whereas and D do not suffer such estimation problems and are thus easy to estimate using community samples. Webb et al. (2002) defined the net relatedness index (NR) and nearest taxon index (N) to characterize the phylogenetic structure of communities. NR is a standardized measure of defined for each plot as

11 503 hylogenetic structure of communities NR plot ( E[ rnd ] where and plot) / σ[ rnd] E[ rnd ] σ[ rnd ] are the expectation and standard deviation of plot when the species in the plot are randomly resampled from a defined species pool, or randomly redistributed among plots under some constraints. hus, NR plot quantifies the extent of phylogenetic clustering in a plot relative to a reference tree (species pool), and/or a particular randomization null model (Kembel & Hubbell 2006). By comparison, Π S quantifies the extent of phylogenetic clustering within a plot relative to species found among plots. Both NR plot and Π S provide a way to compare community phylogenetic patterns across studies and permit significance testing in a very similar way. However, NR plot relies on the choice of a particular randomization null model, which affects its value (Kembel & Hubbell 2006; O. J. Hardy, unpublished data), and/or on a subjectively defined reference species pool, adding levels of complexity, whereas Π S and S are not affected by such factors. Swenson et al. (2006) suggested the scale sensitivity of NR plot could be exploited with respect to the species pool to identify critical scales at which local or regional influences gain primacy for the structuring of communities. he same objective can be reached using pairwise differentiation coefficients ( S, S or Π S ) by assessing how geographical distance or habitat differentiation between community samples affect the phylogenetic signal (e.g. Fig. 5). he advantage of the latter approach is that prior knowledge of species habitat affinities and of the existing species pools at different scales is not required. N is defined in a same way as NR but replacing plot by the mean phylogenetic distance to the nearest taxon of each species, so that it quantifies the extent of terminal clustering, focusing on recent evolutionary events. Although possible, we have not defined measures analogous to and D focusing on terminal phylogenetic clustering (but the data can always be reduced to a given clade). However, partial randomization tests permit an assessment of phylogenetic clustering/overdispersion up to any evolutionary depth, generalizing on a continuous time scale the distinction between NR and N. ROBUSNESS WH RESEC O HE RECSON OF HE HYLOGENEC REE When based on a dated phylogenetic tree, D and depend on the time calibration of the super-tree, which can be problematic because it is based on incomplete fossil evidence and assumptions regarding nucleotide evolution (the molecular clock ). For example, time calibrations of phylogenetic trees using single calibration points have very large confidence intervals and published trees can provide fairly different clade ages according to the set of species used and the gene regions analysed (e.g. the age of Malpighiales is 114 Myr according to Davis et al whereas we considered 79 Myr following Davies et al. 2004). Using a taxonomic proxy for age is also problematic. For example, Diniza excelsa in the Fabaceae is almost as old as the family with 54-Myr fossils, yet the genus nga is less than 6 Myr old (C. Dick, personal communication). he more detailed information presented in fossilcalibrated phylogenetic studies of individual families or genera will provide better resolution for future analyses. Fortunately, whereas the accuracy of absolute D or coefficients might be questionable given present knowledge, ratios of these coefficients, such as S or Π S, are much more robust with respect to the interpretation of fossil evidence, and our results have shown that even a taxonomic rank-based classification provides estimates very similar to those based on a dated phylogenetic tree. his does not mean that rank-based classification are as informative as a more resolved phylogenetic tree because the ability to detect a phylogenetic signal within a clade always depends on the phylogenetic resolution of this clade. his may explain why essentially no phylogenetic signal was detected below the family level in our study. ESNG HYLOGENEC AERNS All our tests of community phylogenetic structuring were based on a randomization of species at the tips of a phylogenetic tree. he underlying logic is that we create artificial data sets matching the null hypothesis to be tested. Besides removing any pattern of phylogenetic clustering (overdispersion) regarding species co-occurrence within plots, such randomization also removes any pattern of phylogenetic clustering (overdispersion) regarding species abundances/frequencies. Hence, two types of phylogenetic patterns can be tested: (i) one relative to species spatial distribution ( Are species within sites more or less related than species from different sites? ) for which differentiation coefficients ( S or Π S ) are the relevant measures to test against their distribution after randomization; and (ii) one relative to species abundance/frequency ( Are there clades of mostly abundant species and clades of mostly rare species? ) for which total diversity coefficients ( D or ) are the relevant measures to test. his study focuses on the species spatial distribution and, interestingly, testing this pattern is not greatly affected by the species content of the tree used to provide randomization (able 2). Simulations show that the randomization procedure of the phylogenetic tree gives an exact test (i.e. exact type error rate) for this pattern when overall species abundances are phylogenetically random (O. J. Hardy, unpublished data). For our data set, tests on D and are not significant using the phylogenetic tree containing only the observed species (able 2), and hence the tests on S or Π S should be exact. s species abundance important to consider when testing community phylogenetic patterns? For the data set analysed here, higher testing power was obtained using S than Π S (able 2). However, other data sets have sometimes given the opposite result (O. J. Hardy, unpublished data). t is likely that species abundance