Community assembly of the individuals of different species depends on both historical (biogeographical) and ecological phenomena (Webb et al. 2002). To co-occur, 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). In addition, when species ecological niches overlap excessively, competitive exclusion could limit coexistence. Ecological niches depend on the similarities between species traits. Ideally, 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. Investigating 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).
The 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). Therefore, 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). It 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 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). In 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. It 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 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). The 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. Thus, 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. This 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 & Pélissier 2004; Pavoine et al. 2004, 2005; Pavoine & Dolédec 2005; Chave et al., in press). The 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. FST, NST) that quantify the strength and direction of the phylogenetic signal. These 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. Testing the phylogenetic signal can be achieved by randomizing species at the tips of the phylogenetic tree. In 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.
To 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.