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An example of six communities associated to a hypothetical phylogenetic tree. All species are scored as present or absent in each example and all branch lengths are set to one. Phylogenetic beta diversity (PBD) values were computed for several pairs of communities according to the PhyloSor and UniFrac indices and their respective turnover and phylogenetic diversity components (see Table 1 and main text for more details). doi:10.1371/journal.pone.0042760.g002
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The evolutionary dissimilarity between communities (phylogenetic beta diversity PBD) has been increasingly explored by ecologists and biogeographers to assess the relative roles of ecological and evolutionary processes in structuring natural communities. Among PBD measures, the PhyloSor and UniFrac indices have been widely used to assess the level...
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... speciation/extinction rate along the tree [36,37]. Consequently, the Yule model induces a higher degree of phylogenetic similarity between species than PDA model does. The two simulated phylogenetic trees (see supplementary figures S2) were created using the R package ‘‘apTreeshape’’ [38]. When considering the PhyloSor index and the PDA phylogenetic tree (see Fig S2), results showed that both the turnover and PD components of PBD displayed a positive triangular relationship with PhyloSor (Fig. 3a and 3c), with an upper bound (first bisectrix) corresponding to the situation where PhyloSor Turn = PhyloSor (Fig. 3a) and PhyloSor PD = PhyloSor (Fig. 3c). PhyloSor Turn = PhyloSor when the two communities compared had the same phylogenetic diversity, whereas PhyloSor PD = PhyloSor when the two communities were completely nested in regards to their taxonomic composition (see also Fig. 2 and Table 1). When comparing PhyloSor and PhyloSor together, we found a negative triangular relationship with an upper bound (first bisectrix) corresponding to the cases where PhyloSor = PhyloSor- Turn + PhyloSor PD = 1. Similar results were obtained when considering UniFrac (Fig. 3b, 3d and 3f). Overall, these relationships based on simulated communities allow verifying the additive property of the proposed decomposition of PBD. Baselga [19] found similar triangular relationships when considering taxonomic beta diversity. We hypothesized that phylogenetic tree topology (i.e. balance vs. unbalanced trees) may influence the observed patterns of PBD. Results showed high levels of correlation (Pearson’s correlation coefficient: r p . 0.95) between PBD values (PhyloSor and UniFrac) obtained using the Yule and PDA phylogenetic trees (Fig. S2). This was also verified when analysing the turnover and PD components of PBD (Fig. S2), while the levels of correlation were found to be lower (Pearson’s correlation coefficient: r < 0.8). This suggests that the shape of phylogenetic trees may have a weak influence on PBD and its turnover and PD components. However, a deeper work covering a wider panel of tree topologies [39] is needed to fully investigate the influence of phylogenetic tree shapes on PBD measurements. Previous empirical studies emphasized that CBD and PBD may be highly correlated [3,8]. Our simulation-based approach confirmed that both the PhyloSor and UniFrac indices were highly correlated with the Sorensen (Pearson’s correlation coefficient: r p = 0.933, Fig. 4a) and Jaccard dissimilarity (Pearson’s correlation coefficient: r p = 0.942, Fig. 4b) indices, respectively. This was also verified when analysing the turnover and PD components of PBD that showed high levels of correlation with the turnover and nestedness components of CBD (Pearson’s correlation coefficient r p ranging from 0.80 to 0.84, see Fig. 4c,d,e,f). It is worth noting that the phylogenetic diversity component of PBD is not trivially related to the nestedness component of CBD (see Fig. 4a,b). For example, when two communities are non-nested (i.e. b sne or b jne = 0), values of PhyloSor PD and UniFrac PD can be higher than 0. This highlights that PhyloSor PD and UniFrac PD measure the amount of PBD caused by PD differences for both nested and non-nested communities. Overall, these results emphasize that appropriate null models are required to analyze patterns of PBD and underlying processes. For instance, using PhyloSor (or Unifrac) and its turnover and PD components, one can test whether two communities are phylogenetically more or less dissimilar than what is expected given their taxa dissimilarity (CBD). This can be achieved by comparing the phylogenetic dissimilarity of the observed communities to a null expectation obtained by randomizing species across the tips of regional phylogenies while holding species richness and CBD constant [3,8]. We illustrated the relevance of partitioning PBD into ‘true’ phylogenetic turnover and PD components by exploring patterns of PBD among local communities of coral reef fishes belonging to the family Labridae. The Labridae is a species rich fish family, circa 600 species [40], that is characteristic of coral reef fish faunas around the world [41]. We compiled labrid fish species occurrences for 6 sites distributed along a longitudinal gradient (from the Indian Ocean to the Eastern Pacific passing by the Indo- Australian Archipelago, hereafter IAA, see Table S1). At each site, species occurrences were based on 12 6 20-min. timed swims (four locations x three habitats; the reef slope, crest and flat), to provide an overview of the local labrid fauna (census details are provided in [42]). This gradient spanned almost the entire Indian and Pacific Oceans, and encompassed the major physical factors that are thought to affect the global distribution of reef fishes [42]. To explore PBD, we used a labrid reef fish phylogeny (108 coral reef fish species recorded from the 6 locations) that was constructed using a genetic algorithm approach based on a maximum likelihood criterion and dated using Bayesian Inference [43]. The PhyloSor index showed low levels of PBD between sites (i.e. values of PhyloSor ranging from 0.16 to 0.44, Table 2), except for the pairwise comparisons involving Panama where high levels of PBD were found (e.g. values of PhyloSor ranging from 0.74 to 0.82, see Table 2). Using UniFrac index provided similar results (Table 2). Arguably, one might conclude that high turnover of lineages occurs between Panama (East Pacific) and the other sites located in the Indian Ocean (Mauritius) and the IAA (Great Barrier Reef, Moorea, Togian and Vanuatu). However, distinguishing between the turnover and PD components of PBD showed that the level of phylogenetic turnover was roughly low for each pairwise comparison (e.g. values of PhyloSor Turn ranging from 0.07 to 0.35, see Table 2). In fact, the ...
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... difference [26]. In contrast, b jne reflects the increasing dissimilarity between nested assemblages due to the increasing differences in species richness. Baselga [26] showed that the results obtained by the closely related Jaccard and Sørensen’s dissimilarity indices were roughly equivalent. Considering two communities j and k for which biodiversity can be quantified in terms of phylogenetic trees ( T j and T k are the subset of a rooted regional tree T ), we can express a as the sum of lengths for branches that are shared between communities j and k , b as the sum of lengths for branches that are present in community j but not found in assemblage k , c as the sum of lengths for branches that are present in community k but not found in community j . We express b, c and a using the phylogenetic diversity index [30,31] that can be calculated as the total branch length of a phylogenetic tree T that contains all species present in a community. Each branch t in the tree T has a length of w . Overall, both the PhyloSor and UniFrac indices range from 0 (the two communities are composed of similar species and hence share the same branches in the rooted phylogenetic tree) to 1 (the two communities are composed of distinct species that share no branch in the rooted phylogenetic tree). The two indices differ only because PhyloSor double weights the branch lengths shared by the two communities (i.e. the denominator of PhyloSor corresponds to the sum of phylogenetic diversity characterizing each community). Following the formula (2), we obtained the turnover components of the PhyloSor and UniFrac indices, i.e. PhyloSor Turn and UniFrac , respectively: The phylogenetic diversity (hereafter PD) component of PhyloSor is simply the difference between PhyloSor and PhyloSor Turn , i.e. PhyloSor PD = PhyloSor – PhyloSor Turn . It can be expressed using the formula (3) and by replacing a , b and c by the formula (10), (9) and (8), respectively. Similarly, the PD component of UniFrac is the difference between UniFrac and UniFrac Turn , i.e. UniFrac PD = UniFrac – UniFrac Turn . It can be expressed using the formula (6) and by replacing a , b and c by the formula (10), (9) and (8), respectively. All the analyses presented in this study were performed using the R statistical and programming environment [32]. The R code required to apply the additive partitioning framework is provided as Supporting Information (File S1 and File S2), together with the community dataset and the phylogenetic tree used to exemplify our approach (File S3 and File S4, respectively). As a simple illustration of the proposed decomposition of PBD, the figure 1 presents three different examples. The first two examples (Fig. 1a and 1b, respectively) show two communities (A and B) that have no species in common ( b sor = b sim = 1 and b sne = 0). However, communities A and B (example 1, Fig. 1a.) display distantly related species, hence indicating locally phylogenetically clustered communities that have high PBD (PhyloSor = 1, i.e. the two communities compared do not share evolutionary history). In contrast, communities A and B illustrating the example 2 (Fig. 1b) display closely related species, hence indicating locally phylogenetically overdispersed communities that have little PBD (PhyloSor = 0.4, i.e. the two communities share a large amount of evolutionary history). For both examples, PhyloSor = PhySor Turn as PhySor PD = 0. For the first example, the PD component of PBD is zero because the two communities do not share any branch length and also display a similar level of PD (PD A = PD B = 7, see Fig. 1a). For the second example, the PD component of PBD is zero only because the two communities compared display the same level of PD (PD A = PD B = 10, see Fig. 1b). Indeed, if we reconsider the example 2 with two communities having slightly unequal levels of PD (Fig. 1c, PD A = 10 and PD B = 9), the overall level of PBD (PhyloSor = 0.421) is found to be different from the turnover component of PBD (PhySor Turn = 0.388). The difference between the two indices (expressed as PhySor PD ) quantifies the amount of PBD caused by a difference in PD between the two communities. Let now consider 6 different communities sharing only two species (species 2 and 3 in Fig. 2). The phylogenetic diversity unique to community A remains constant while the phylogenetic diversity unique to the other communities increases from B to F. Comparisons between the community A and the other communities (B to F, see Table 1) show that the increasing phylogenetic beta diversity (PhyloSor and UniFrac) is entirely caused by an increasing contribution of the PD component (PhySor PD and UniFrac PD ), while the turnover component remains constant across comparisons (PhySor Turn = 0.166 and UniFrac Turn = 0.286). It is worth noting that PhyloSor PD = PhyloSor (or UniFrac PD = UniFrac) when the two communities compared are completely nested in regards to their taxonomic composition (see Fig. 2 and Table 1). For instance, the community B has no unique species and hence the branch length unique to community B is zero. Comparisons between the community B and the other communities (C to F, see Table 1) show that the increasing phylogenetic beta diversity (PhyloSor and UniFrac) is entirely caused by an increasing contribution of the PD component while the turnover component remains equal to 0. Overall, the above examples (Fig. 1 and 2, Table 1) emphasize that PhyloSor Turn and UniFrac Turn are two ‘narrow-sense’ measures of PBD (i.e. ‘true’ measures of phylogenetic turnover) that are independent of total branch length difference between the two compared communities (see Fig. 2 and Table 2). Specifically, PhyloSor Turn and UniFrac Turn measure the relative magnitude of gain and loss of unique lineages between communities that is not attributable to their difference in PD (i.e. phylogenetic turnover expected if the two communities display similar levels of PD). In contrast, PhyloSor PD and UniFrac PD measure the amount of PBD caused by PD differences between phylogenetically nested communities (i.e. communities sharing at least one branch within a rooted phylogeny). We simulated pairwise comparisons of communities by taking random values of a , b and c matching components (see formula 1 to 6) from uniform distributions between 1 and 100, where a is the number of species common to both communities, b is the number of species that occur in the first community but not in the second and c is the number of species that occur in the second community but not in the first. The regional species pool is thus composed of 100 species. 10 000 local communities were generated. For each pairwise comparison, we quantified the corresponding PBD (i.e. using PhyloSor and UniFrac), and we applied the proposed decomposition of PBD. To do so, we simulated the phylogenetic relatedness among species by creating two types of regional phylogenetic trees [33], the former being generated from the PDA (proportional-to-distinguishable arrangements) model and the latter generated from the Yule model (see Fig. S1). Specifically, we aimed at testing the influence of phylogenetic tree structure (i.e. balanced vs. unbalanced trees) on the turnover and PD components of PBD. A phylogenetic tree generated under PDA (proportional-to-distinguishable arrangements) model tends to be more unbalanced than observed phylogenies because all trees with the same number of tips (i.e. species) are equally likely and the majority of potential arrangements are uneven [34,35]. Reversely, a Yule model tends to produce more balanced phylogenetic trees than empirical ones because it assumes ...
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... number of species common to both sites, b is the number of species that occur in the first site but not in the second and c is the number of species that occur in the second site but not in the first [19]. Recently, Baselga [26] proposed a similar decomposition based on the Jaccard’s dissimilarity index. The following pairwise dissimilarity indices (formulas 5 and 6) represent, the turnover and nestedness components of the Jaccard’s dissimilarity index ( b = b + b ), respectively. Specifically, b jtu measures the proportion of species that would be replaced between communities if both communities had the same number of species, and hence accounts for species replacement without the influence of richness difference [26]. In contrast, b jne reflects the increasing dissimilarity between nested assemblages due to the increasing differences in species richness. Baselga [26] showed that the results obtained by the closely related Jaccard and Sørensen’s dissimilarity indices were roughly equivalent. Considering two communities j and k for which biodiversity can be quantified in terms of phylogenetic trees ( T j and T k are the subset of a rooted regional tree T ), we can express a as the sum of lengths for branches that are shared between communities j and k , b as the sum of lengths for branches that are present in community j but not found in assemblage k , c as the sum of lengths for branches that are present in community k but not found in community j . We express b, c and a using the phylogenetic diversity index [30,31] that can be calculated as the total branch length of a phylogenetic tree T that contains all species present in a community. Each branch t in the tree T has a length of w . Overall, both the PhyloSor and UniFrac indices range from 0 (the two communities are composed of similar species and hence share the same branches in the rooted phylogenetic tree) to 1 (the two communities are composed of distinct species that share no branch in the rooted phylogenetic tree). The two indices differ only because PhyloSor double weights the branch lengths shared by the two communities (i.e. the denominator of PhyloSor corresponds to the sum of phylogenetic diversity characterizing each community). Following the formula (2), we obtained the turnover components of the PhyloSor and UniFrac indices, i.e. PhyloSor Turn and UniFrac , respectively: The phylogenetic diversity (hereafter PD) component of PhyloSor is simply the difference between PhyloSor and PhyloSor Turn , i.e. PhyloSor PD = PhyloSor – PhyloSor Turn . It can be expressed using the formula (3) and by replacing a , b and c by the formula (10), (9) and (8), respectively. Similarly, the PD component of UniFrac is the difference between UniFrac and UniFrac Turn , i.e. UniFrac PD = UniFrac – UniFrac Turn . It can be expressed using the formula (6) and by replacing a , b and c by the formula (10), (9) and (8), respectively. All the analyses presented in this study were performed using the R statistical and programming environment [32]. The R code required to apply the additive partitioning framework is provided as Supporting Information (File S1 and File S2), together with the community dataset and the phylogenetic tree used to exemplify our approach (File S3 and File S4, respectively). As a simple illustration of the proposed decomposition of PBD, the figure 1 presents three different examples. The first two examples (Fig. 1a and 1b, respectively) show two communities (A and B) that have no species in common ( b sor = b sim = 1 and b sne = 0). However, communities A and B (example 1, Fig. 1a.) display distantly related species, hence indicating locally phylogenetically clustered communities that have high PBD (PhyloSor = 1, i.e. the two communities compared do not share evolutionary history). In contrast, communities A and B illustrating the example 2 (Fig. 1b) display closely related species, hence indicating locally phylogenetically overdispersed communities that have little PBD (PhyloSor = 0.4, i.e. the two communities share a large amount of evolutionary history). For both examples, PhyloSor = PhySor Turn as PhySor PD = 0. For the first example, the PD component of PBD is zero because the two communities do not share any branch length and also display a similar level of PD (PD A = PD B = 7, see Fig. 1a). For the second example, the PD component of PBD is zero only because the two communities compared display the same level of PD (PD A = PD B = 10, see Fig. 1b). Indeed, if we reconsider the example 2 with two communities having slightly unequal levels of PD (Fig. 1c, PD A = 10 and PD B = 9), the overall level of PBD (PhyloSor = 0.421) is found to be different from the turnover component of PBD (PhySor Turn = 0.388). The difference between the two indices (expressed as PhySor PD ) quantifies the amount of PBD caused by a difference in PD between the two communities. Let now consider 6 different communities sharing only two species (species 2 and 3 in Fig. 2). The phylogenetic diversity unique to community A remains constant while the phylogenetic diversity unique to the other communities increases from B to F. Comparisons between the community A and the other communities (B to F, see Table 1) show that the increasing phylogenetic beta diversity (PhyloSor and UniFrac) is entirely caused by an increasing contribution of the PD component (PhySor PD and UniFrac PD ), while the turnover component remains constant across comparisons (PhySor Turn = 0.166 and UniFrac Turn = 0.286). It is worth noting that PhyloSor PD = PhyloSor (or UniFrac PD = UniFrac) when the two communities compared are completely nested in regards to their taxonomic composition (see Fig. 2 and Table 1). For instance, the community B has no unique species and hence the branch length unique to community B is zero. Comparisons between the community B and the other communities (C to F, see Table 1) show that the increasing phylogenetic beta diversity (PhyloSor and UniFrac) is entirely caused by an increasing contribution of the PD component while the turnover component remains equal to 0. Overall, the above examples (Fig. 1 and 2, Table 1) emphasize that PhyloSor Turn and UniFrac Turn are two ‘narrow-sense’ measures of PBD (i.e. ‘true’ measures of phylogenetic turnover) that are independent of total branch length difference between the two compared communities (see Fig. 2 and Table 2). Specifically, PhyloSor Turn and UniFrac Turn measure the relative magnitude of gain and loss of unique lineages between communities that is not attributable to their difference in PD (i.e. phylogenetic turnover expected if the two communities display similar levels of PD). In contrast, PhyloSor PD and UniFrac PD measure the amount of PBD caused by PD differences between phylogenetically nested communities (i.e. communities sharing at least one branch within a rooted phylogeny). We simulated pairwise comparisons of communities by taking random values of a , b and c matching components (see formula 1 to 6) from uniform distributions between 1 and 100, where a is the number of species common to both communities, b is the number of species that occur in the first community but not in the second and c is the number of species that occur in the second community but not in the first. The regional species pool is thus composed of 100 species. 10 000 local communities were generated. For each pairwise comparison, we quantified the corresponding PBD (i.e. using PhyloSor and UniFrac), and we applied the proposed decomposition of PBD. To do so, we simulated the phylogenetic relatedness among species by creating two types of regional phylogenetic trees [33], the former being generated from the PDA (proportional-to-distinguishable arrangements) model and the latter generated from the Yule model (see Fig. S1). Specifically, we aimed at testing the influence of phylogenetic tree structure (i.e. balanced vs. unbalanced trees) on the turnover and PD components of PBD. A phylogenetic tree generated under PDA (proportional-to-distinguishable arrangements) model tends to be more unbalanced than observed phylogenies because all trees with the same number of tips (i.e. species) are equally likely and the majority of potential arrangements are uneven [34,35]. Reversely, a Yule model tends to produce more balanced phylogenetic trees than empirical ones because it assumes ...
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... branches that are present in community k but not found in community j . We express b, c and a using the phylogenetic diversity index [30,31] that can be calculated as the total branch length of a phylogenetic tree T that contains all species present in a community. Each branch t in the tree T has a length of w . Overall, both the PhyloSor and UniFrac indices range from 0 (the two communities are composed of similar species and hence share the same branches in the rooted phylogenetic tree) to 1 (the two communities are composed of distinct species that share no branch in the rooted phylogenetic tree). The two indices differ only because PhyloSor double weights the branch lengths shared by the two communities (i.e. the denominator of PhyloSor corresponds to the sum of phylogenetic diversity characterizing each community). Following the formula (2), we obtained the turnover components of the PhyloSor and UniFrac indices, i.e. PhyloSor Turn and UniFrac , respectively: The phylogenetic diversity (hereafter PD) component of PhyloSor is simply the difference between PhyloSor and PhyloSor Turn , i.e. PhyloSor PD = PhyloSor – PhyloSor Turn . It can be expressed using the formula (3) and by replacing a , b and c by the formula (10), (9) and (8), respectively. Similarly, the PD component of UniFrac is the difference between UniFrac and UniFrac Turn , i.e. UniFrac PD = UniFrac – UniFrac Turn . It can be expressed using the formula (6) and by replacing a , b and c by the formula (10), (9) and (8), respectively. All the analyses presented in this study were performed using the R statistical and programming environment [32]. The R code required to apply the additive partitioning framework is provided as Supporting Information (File S1 and File S2), together with the community dataset and the phylogenetic tree used to exemplify our approach (File S3 and File S4, respectively). As a simple illustration of the proposed decomposition of PBD, the figure 1 presents three different examples. The first two examples (Fig. 1a and 1b, respectively) show two communities (A and B) that have no species in common ( b sor = b sim = 1 and b sne = 0). However, communities A and B (example 1, Fig. 1a.) display distantly related species, hence indicating locally phylogenetically clustered communities that have high PBD (PhyloSor = 1, i.e. the two communities compared do not share evolutionary history). In contrast, communities A and B illustrating the example 2 (Fig. 1b) display closely related species, hence indicating locally phylogenetically overdispersed communities that have little PBD (PhyloSor = 0.4, i.e. the two communities share a large amount of evolutionary history). For both examples, PhyloSor = PhySor Turn as PhySor PD = 0. For the first example, the PD component of PBD is zero because the two communities do not share any branch length and also display a similar level of PD (PD A = PD B = 7, see Fig. 1a). For the second example, the PD component of PBD is zero only because the two communities compared display the same level of PD (PD A = PD B = 10, see Fig. 1b). Indeed, if we reconsider the example 2 with two communities having slightly unequal levels of PD (Fig. 1c, PD A = 10 and PD B = 9), the overall level of PBD (PhyloSor = 0.421) is found to be different from the turnover component of PBD (PhySor Turn = 0.388). The difference between the two indices (expressed as PhySor PD ) quantifies the amount of PBD caused by a difference in PD between the two communities. Let now consider 6 different communities sharing only two species (species 2 and 3 in Fig. 2). The phylogenetic diversity unique to community A remains constant while the phylogenetic diversity unique to the other communities increases from B to F. Comparisons between the community A and the other communities (B to F, see Table 1) show that the increasing phylogenetic beta diversity (PhyloSor and UniFrac) is entirely caused by an increasing contribution of the PD component (PhySor PD and UniFrac PD ), while the turnover component remains constant across comparisons (PhySor Turn = 0.166 and UniFrac Turn = 0.286). It is worth noting that PhyloSor PD = PhyloSor (or UniFrac PD = UniFrac) when the two communities compared are completely nested in regards to their taxonomic composition (see Fig. 2 and Table 1). For instance, the community B has no unique species and hence the branch length unique to community B is zero. Comparisons between the community B and the other communities (C to F, see Table 1) show that the increasing phylogenetic beta diversity (PhyloSor and UniFrac) is entirely caused by an increasing contribution of the PD component while the turnover component remains equal to 0. Overall, the above examples (Fig. 1 and 2, Table 1) emphasize that PhyloSor Turn and UniFrac Turn are two ‘narrow-sense’ measures of PBD (i.e. ‘true’ measures of phylogenetic turnover) that are independent of total branch length difference between the two compared communities (see Fig. 2 and Table 2). Specifically, PhyloSor Turn and UniFrac Turn measure the relative magnitude of gain and loss of unique lineages between communities that is not attributable to their difference in PD (i.e. phylogenetic turnover expected if the two communities display similar levels of PD). In contrast, PhyloSor PD and UniFrac PD measure the amount of PBD caused by PD differences between phylogenetically nested communities (i.e. communities sharing at least one branch within a rooted phylogeny). We simulated pairwise comparisons of communities by taking random values of a , b and c matching components (see formula 1 to 6) from uniform distributions between 1 and 100, where a is the number of species common to both communities, b is the number of species that occur in the first community but not in the second and c is the number of species that occur in the second community but not in the first. The regional species pool is thus composed of 100 species. 10 000 local communities were generated. For each pairwise comparison, we quantified the corresponding PBD (i.e. using PhyloSor and UniFrac), and we applied the proposed decomposition of PBD. To do so, we simulated the phylogenetic relatedness among species by creating two types of regional phylogenetic trees [33], the former being generated from the PDA (proportional-to-distinguishable arrangements) model and the latter generated from the Yule model (see Fig. S1). Specifically, we aimed at testing the influence of phylogenetic tree structure (i.e. balanced vs. unbalanced trees) on the turnover and PD components of PBD. A phylogenetic tree generated under PDA (proportional-to-distinguishable arrangements) model tends to be more unbalanced than observed phylogenies because all trees with the same number of tips (i.e. species) are equally likely and the majority of potential arrangements are uneven [34,35]. Reversely, a Yule model tends to produce more balanced phylogenetic trees than empirical ones because it assumes ...
Citations
... Phylogeny is increasingly used to assess the relative roles of dispersal assembly, filtering, and limiting similarity in shaping community composition (Hauffe et al., 2016;Leprieur et al., 2012), while the functional traits of species cannot be measured completely (although we collected as many functional traits as possible). Phylogeny can be used as a suitable supplement (Gomez et al., 2010;Pellissier et al., 2018). ...
For temperate desert ecosystems, understanding the maintenance mechanisms of biodiversity has become a central concern of policymakers, practitioners, and academicians, especially in the context of human disturbances. However, most of the past desert research is based on a single survey and single functional traits or phylogeny, and may lead to incomplete estimates of community assembly. To assess climate factors, soil factors, and human interference in the processes of dispersal assembly, filtering, and limiting similarity in community assembly, we selected 50 sites along the Beijing‐Xinjiang Expressway and conducted plant surveys at sites 0.5, 1.0, and 1.5 km away from the expressway twice, one in spring and one in autumn, and collected 31 functional traits and phylogenetic information of 74 species. We found that incomplete detection can lead to bias in biodiversity estimation due to seasonal surveys. Road disturbance at different distances had no significant effect on species composition and community assembly processes. The trade‐off between dispersal limitation and environmental filtering is the process of maintaining desert plant diversity. Precipitation directly determined the dispersal limitation and environmental filtering. Soil pH mediated the effect of temperature on biotic interaction in the dimension of phylogeny, while soil pH and organic carbon acted as intermediate factors affecting biotic interaction in functional traits. Our findings suggest that climate change, notably precipitation, has a more pronounced impact on desert plant communities than human disturbances. A multifaceted approach considering functional traits and phylogeny across seasons is essential for understanding species coexistence in desert ecosystems.
... To account for phylogenetic relatedness among species in β-diversity, it is necessary to measure phylogenetic distance among areas, that is, phylogenetic β-diversity (Graham & Fine, 2008). Both taxonomic and phylogenetic β-diversity can be partitioned into turnover component, which represents species or lineage replacement between areas, and nestedness component, which represents the degree to which the species or lineages in the area with fewer species or lineages is a subset of the species or lineages in the area with more species or lineages (Baselga, 2010;Leprieur et al., 2012). Previous studies have shown that geographic distance, current climatic conditions, Quaternary climate change and topographic heterogeneity are major factors driving β-diversity of plants Xu et al., 2023). ...
... where a is the number of species shared by the two localities, b is the number of species unique to one locality and c is the number of species unique to the other locality (Baselga, 2010). For phylogenetic β-diversity, shared and unique species are replaced with shared and unique branch lengths, respectively (Leprieur et al., 2012). We denoted β-diversity and its two components as β sor.tax , β sim.tax and β nes.tax for taxonomic β-diversity, and as β sor.phy , β sim.phy and β nes.phy for phylogenetic β-diversity. ...
... The significance of the standardized regression coefficients and the coefficients of multiple determination (R 2 ) was assessed using a permutation test based on distance matrices (N = 999). For distance variables that explained more than 10% of the variation in a metric of β-diversity, we applied a variation partitioning approach (Legendre & Legendre, 2012) based on MRM to separate the total explained variance (R 2 ) explained by each pair of explanatory variable groups (e.g. variables of current climate vs. ...
Aim
Beta‐diversity quantifies the change in taxonomic and phylogenetic composition between areas, and is a scalar between local ( α ) and regional ( γ ) diversity. Geographic distance, which reflects dispersal limitation, and climatic distance, which reflects environmental filtering, are major drivers of β‐diversity. Here, we analyse a comprehensive data set of angiosperms in regional floras across Africa to assess the relationships of β‐diversity, and its components, to three major types of environmental variables (current climate, Quaternary climate change and topographic heterogeneity) thought to drive β‐diversity.
Location
Africa.
Taxon
Angiosperms.
Methods
Africa was divided into 27 regions. Species lists of angiosperms for each region were collated. The relationships of both taxonomic and phylogenetic β‐diversity, and their respective turnover and nestedness components, with geographic and environmental distances were assessed.
Results
This study showed that (1) regions of the lowest β‐diversity are located in moist tropical climates, (2) the turnover and nestedness components of β‐diversity are negatively correlated with each other, (3) taxonomic β‐diversity is higher than phylogenetic β‐diversity across Africa, (4) variation in β‐diversity of angiosperms is more strongly associated with current climate than with Quaternary climate change and topographic heterogeneity and (5) the variation in taxonomic β‐diversity and its turnover component that is independently explained by geographic distance is much larger than that is independently explained by climatic distance for angiosperms in Africa.
Main Conclusions
The finding that geographic distance explained more variation than climatic distance suggests that dispersal limitation plays a greater role than environmental filtering in shaping angiosperm β‐diversity in Africa. Of climatic factors, current climate plays a more important role than Quaternary climate change in shaping angiosperm β‐diversity in Africa.
... The β SIM value reflects the turnover components of total compositional heterogeneity within a species pool of several sites (Baselga, 2013). We calculated the β SIM using a color-based dendrogram (Petchey & Gaston, 2002) based on information on shared branch lengths in all sites considered together and the branch lengths unique to each site (Leprieur et al., 2012). Plumage color and lightness were used to measure pairwise color distances between species based on Gower's distance. ...
... Color-based relationships between species were incorporated into quantifying pairwise beta dissimilarities (Graham & Fine, 2008). These indices calculate the pairwise dissimilarity between two communities by replacing shared and unique species with shared and unique branch lengths, respectively (Leprieur et al., 2012), as follows: ...
Urbanization has altered natural landscapes and serves as an environmental filter that selects species with specific traits. Coloration is an important trait associated with biotic interactions and thermoregulation, enabling species’ survival and reproductive success. However, few studies have focused on how species coloration changes in response to urbanization. Here, we used 547 passerine bird species from 42 cities and their corresponding nonurban communities in China to test whether urban species are darker and if they have duller plumage colors than their non-urban counterparts. Furthermore, we examined whether and how urbanization influences avian plumage color homogenization and the extent to which urbanization has altered the strength of the color–latitude geographic pattern in passerine birds across China. We found a 3.2% loss in the coloration space of birds after urbanization, although there were no significant differences in the individual dimensions of colorfulness and lightness between urban and non-urban birds. Avian communities in cities exhibited more plumage color homogenization than those in non-urban communities. There were significant latitudinal gradients in plumage colorfulness and lightness in non-urban communities, but these correlations were weaker in urban communities. Non-urban communities that were more colorful and lighter tended to be duller and darker in urban environments, and vice versa. Our results provide national-scale evidence that urbanization has led to reduced color diversity, increased color-based community similarity, and altered geographic patterns of avian plumage color gradients in China. These findings provide new insights into how rapid human-induced environmental changes have affected animal coloration during the Anthropocene.
... Phylobetadiversity quantifies the evolutionary distance among assemblages using shared branch lengths, just as taxonomic beta diversity uses shared species 20 . The turnover component of phylobetadiversity partitions out beta diversity due to discrepancies in alpha diversity and only describes beta diversity due to 'replacement' or the presence of unique, unshared branch lengths after accounting for differences in phylogenetic alpha diversity 21 . This allows phylobetadiversity turnover to quantify shared evolutionary history at the assemblage level without being biased by species richness. ...
... Taxonomic beta diversity can be partitioned into two components: 'nestedness', the difference in diversity due to 'species loss' or discrepancies in species richness, and 'turnover', the difference in diversity due to 'species replacement' or the presence of unique, unshared species after accounting for differences in species richness 70 . Likewise, phylobetadiversity can be partitioned into nestedness and turnover components 21 . In the phylo.beta.pair ...
Biogeographic history can lead to variation in biodiversity across regions, but it remains unclear how the degree of biogeographic isolation among communities may lead to differences in biodiversity. Biogeographic analyses generally treat regions as discrete units, but species assemblages differ in how much biogeographic history they share, just as species differ in how much evolutionary history they share. Here, we use a continuous measure of biogeographic distance, phylobetadiversity, to analyze the influence of biogeographic isolation on the taxonomic and functional diversity of global mammal and bird assemblages. On average, biodiversity is better predicted by environment than by isolation, especially for birds. However, mammals in deeply isolated regions are strongly influenced by isolation; mammal assemblages in Australia and Madagascar, for example, are much less diverse than predicted by environment alone and contain unique combinations of functional traits compared to other regions. Neotropical bat assemblages are far more functionally diverse than Paleotropical assemblages, reflecting the different trajectories of bat communities that have developed in isolation over tens of millions of years. Our results elucidate how long-lasting biogeographic barriers can lead to divergent diversity patterns, against the backdrop of environmental determinism that predominantly structures diversity across most of the world.
... The increased interest in beta diversity comes from the recognition of its important roles in revealing community assembly mechanisms (Zellweger et al., 2017;Soininen et al., 2018;Du et al., 2021), and helping guide conservation practices (reviewed in Socolar et al., 2016). Major progresses have been made in the field of beta diversity studies by decomposing beta diversity into its turnover and nestedness components (Baselga, 2010), and/or by incorporating species' functional traits and evolutionary histories (Swenson et al., 2011;Leprieur et al., 2012). Specifically, the former enables deeper insights into mechanisms driving the variation in biodiversity Si et al., 2015;Soininen et al., 2018), while the latter offers a complete perspective on biodiversity and better captures community assembly (Perez Rocha et al., 2018;Branco et al., 2020;González-Trujillo et al., 2020;Jiang et al., 2021). ...
... Another promising approach which measures traits or lineage composition varying among sites (i.e., functional or phylogenetic beta diversity) could provide additional insights into community assembly (Swenson et al., 2011;Leprieur et al., 2012;Siefert et al., 2013). Specifically, if variation in trait or lineage composition is similar as random expectations given the observed species beta diversity, a product of neutral processes could be drawn. ...
... For taxonomic composition, the multiple-site β-diversity uses information on the total number of species in all sites and the number of species unique to each site (Baselga 2010). For phylogenetic composition, multiple-site β-diversity was calculated using a phylogenetic tree, where shared and unique branch lengths were used instead of shared and unique species (Leprieur et al. 2012). Functional multiple-site β-diversity was calculated using a similar method, replacing the phylogenetic tree with a functional trait-based dendrogram (Petchey & Gaston 2002). ...
... We further incorporated phylogenetic tree and functional trait-based relationships among species into the quantification of phylogenetic (Pβ sim ) and functional (Fβ sim ) pairwise β-diversity (Graham & Fine 2008), respectively. These indices calculate pairwise phylogenetic or functional dissimilarity between two communities by replacing shared and unique species with shared and unique branch lengths, respectively (Leprieur et al. 2012): ...
Urbanization-driven biotic homogenization has been recorded in various ecosystems on local and global scales; however, it is largely unexplored in developing countries. Empirical studies on different taxa and bioregions show conflicting results (i.e. biotic homogenization vs. biotic differentiation); the extent to which the community composition changes in response to anthropogenic disturbances and the factors governing this process, therefore, require elucidation. Here, we used a compiled database of 760 bird species in China to quantify the multiple-site β-diversity and fitted distance decay in pairwise β-diversities between natural and urban assemblages to assess whether urbanization had driven biotic homogenization. We used generalized dissimilarity models (GDM) to elucidate the roles of spatial and environmental factors in avian community dissimilarities before and after urbanization. The multiple-site β-diversities among urban assemblages were markedly lower than those among natural assemblages, and the distance decays in pairwise similarities in natural assemblages were more rapid. These results were consistent among taxonomic, phylogenetic, and functional aspects, supporting a general biotic homogenization driven by urbanization. The GDM results indicated that geographical distance and temperature were the dominant predictors of avian community dissimilarity. However, the contribution of geographical distance and climatic factors decreased in explaining compositional dissimilarities in urban assemblages. Geographical and environmental distances accounted for much lower variations in compositional dissimilarities in urban than in natural assemblages, implying a potential risk of uncertainty in model predictions under further climate change and anthropogenic disturbances. Our study concludes that taxonomic, phylogenetic, and functional dimensions elucidate urbanization-driven biotic homogenization in China.
... Phylogenetic dissimilarity (i.e., dissimilarity in lineage composition) was computed using a Jaccard-like index. Its turnover component (Leprieur et al., 2012) represents the degree of replacement of a lineage part between two assemblages. Functional dissimilarity was computed using a Jaccard-like index applied to convex hulls shaping species in the functional space. ...
The establishment of protected areas to face global diversity declines has mainly prioritized taxonomic diversity, leaving aside phylogenetic and functional diversities, which determine ecosystem functioning and resilience. Furthermore, the assessment of protected areas' effectiveness is mainly done using short‐duration surveys (<2 h), which may undermine the detection of rare species.
Through a long‐duration video approach, reef fish taxonomic, phylogenetic and functional facets of diversity were assessed for 3 days within a fully protected area and a nearby poorly protected area in Mayotte Island (Western Indian Ocean).
We found that temporally rare species contributed to more than 60% of the taxonomic facet and 85% of the functional facet of biodiversity found on each site. Those rare species, which harbour the most distinct trait values, also make reef fish diversity particularly vulnerable to their loss. Taxonomic, phylogenetic and functional richness were similar between the fully protected area and the poorly protected area, while the species, lineage and trait compositions were markedly different.
These results pinpoint the importance of considering taxonomic, functional and phylogenetic dissimilarities while assessing protected areas' effectiveness, instead of using only richness. In addition, benefits of the fully protected area were detected only using more than 15 h of video survey, which emphasizes the importance of long‐duration remote approaches to capture the within‐ and between‐day temporal variations.
... We also determined phylogenetic beta diversity between Africa and South America at a particular geological time (also at an interval of 1 Ma) and generated a phylogenetic-beta-diversity-through-time plot. We did so both for the total phylogenetic beta diversity and for the turnover component of phylogenetic beta diversity (i.e., phylogenetic turnover) (76). The total phylogenetic beta diversity was measured as the Sørensen dissimilarity index, using the formula (b + c)/(2a + b + c), and the turnover component of phylogenetic beta diversity was measured as the Simpson dissimilarity index, using the formula min(b,c)/[a + min(b,c)], where a is the shared branch length by the two continents, b is the branch length unique to one continent, and c is the branch length unique to the other continent (76,77). ...
... We did so both for the total phylogenetic beta diversity and for the turnover component of phylogenetic beta diversity (i.e., phylogenetic turnover) (76). The total phylogenetic beta diversity was measured as the Sørensen dissimilarity index, using the formula (b + c)/(2a + b + c), and the turnover component of phylogenetic beta diversity was measured as the Simpson dissimilarity index, using the formula min(b,c)/[a + min(b,c)], where a is the shared branch length by the two continents, b is the branch length unique to one continent, and c is the branch length unique to the other continent (76,77). ...
Although originating from a common Gondwanan flora, the diversity and composition of the floras of Africa and South America have greatly diverged since continental breakup of Africa from South America now having much higher plant species richness. However, the phylogenetic diversity of the floras and what this tells us about their evolution remained unexplored. We show that for a given species richness and considering land surface area, topography, and present-day climate, angiosperm phylogenetic diversity in South America is higher than in Africa. This relationship holds regardless of whether all climatically matched areas or only matched areas in tropical climates are considered. Phylogenetic diversity is high relative to species richness in refugial areas in Africa and in northwestern South America, once the gateway for immigration from the north. While species richness is strongly influenced by massive plant radiations in South America, we detect a pervasive influence of historical processes on the phylogenetic diversity of both the South American and African floras.
... To assess the degree of phylogenetic overlap between the guilds we explored phylogenetic beta diversity (PBD) using the PhyloSor index (Bryant et al. 2008) as a distance metric following Molina-Venegas et al. (2020) using a pruned phylogenetic tree including only the useful plant species since the idea is testing whether branches subtending useful species overlap. The PBD is defined as 1-PhyloSor index and can be decomposed into two additive components, namely 'true' phylogenetic turnover (hereafter 'turnover') and nestedness, which represent different aspects of beta diversity (Leprieur et al. 2012, Molina-Venegas et al. 2020. We evaluated whether the observed turnover component of PBD was higher (ses.PBD > 1.96) than expected for the given species composition in the guild-species matrix by computing SES scores for the phylogenetic turnover (ses.phylo. ...
Biodiversity in the Neotropics includes an extraordinary diversity of plant variation produced by evolution that is useful for human well-being. Traditional knowledge of the Tenek, a Huastec Mayan culture, represents an important biocultural heritage for this realm. Here, we used the information about their useful plants to explore evolutionary biocultural patterns occurring in Neotropics. Our goal was to analyse the phylogenetic distribution of usage guilds, their degree of evolutionary clustering, significant associations, and phylogenetic overlap between guilds to test the hypothesis that Tenek selection of plants is not random but phylogenetically clustered. We found significant phylogenetic clustering in all usage guilds except ceremonial and medicine. Tenek people use a variety of relatively deep plant lineages providing specific services that biocultural processes have promoted in the ecosystems they inhabit. The lineages Asterales, Caryophyllales, Fabales, Lamiales, Malpighiales, and Malvales in eudicots and Poales and Asparagales in monocots concentrated most of the Huastec Mayan useful plants. Multi-functional hot nodes, including Asterales, Fabales, Lamiales, Malvales, Poaceae Sapindales, and Solanales, with phylogenetic overlap between usage guilds, should be major priority targets in conservation planning.
... Moreover, Baselga's additive partitioning framework also allows the decomposition of phylogenetic and functional beta-diversity indices into turnover and nestedness components when they are estimated by using branch lengths and convex hull volume (Leprieur et al., 2012;Villéger et al., 2013). This allows for a systematic comparison of multifaceted beta-diversity and provides a more thorough view of community assembly and diversity maintenance determinants across ecological gradients and evolutionary histories. ...
... We chose abundance-based Sørensen dissimilarities (i.e., percentage difference dissimilarities) as index of total taxonomic beta-diversity (β sor ) and divided them into species turnover (β sim ) and nestedness (β sne ) components. In analogy with taxonomic dimension, total functional beta-diversity (β funsor ) based on calculations of convex hull volume in multidimensional functional space can be partitioned into functional turnover (β funsim ) and functional nestedness (β funsne ) components (Villéger et al., 2008(Villéger et al., , 2013, and total phylogenetic beta-diversity (β physor ) calculated using branch lengths of phylogenetic tree also allowing for turnover-nestedness decomposition (denoted by β physim and β physne ; Leprieur et al., 2012). Before calculating beta-diversity and turnover-nestedness decomposition at two scales, we removed individuals with DBH greater than the 90% quantile of all DBH values (i.e., individuals in the canopy) to avoid using forest structure to explain their compositional variation. ...
Beta‐diversity, or the spatio‐temporal variation in community composition, can be partitioned into turnover and nestedness components in a multidimensional framework. Forest structure, including comprehensive characteristics of vertical and horizontal complexity, strongly affects species composition and its spatial variation. However, the effects of forest structure on beta‐diversity patterns in multidimensional and multiple‐scale contexts are poorly understood. Here, we assessed beta‐diversity at local (a 20‐ha forest dynamics plot) and regional (a plot network composed of 19 1‐ha plots) scales in a Chinese subtropical evergreen broad‐leaved forest. We then evaluated the relative importance of forest structure, topography, and spatial structure on beta‐diversity and its turnover and nestedness components in taxonomic, functional, and phylogenetic dimensions at local and regional scales. We derived forest structural parameters from both unmanned aerial vehicle light detection and ranging (UAV LiDAR) data and plot inventory data. Turnover component dominated total beta‐diversity for all dimensions at the two scales. With the exception of some components (taxonomic and functional turnover at the local scale; functional nestedness at the regional scale), environmental factors (i.e., topography and forest structure) contributed more than pure spatial variation. Explanations of forest structure for beta‐diversity and its component patterns at the local scale were higher than those at the regional scale. The joint effects of spatial structure and forest structure influenced component patterns in all dimensions (except for functional turnover) to some extent at the local scale, while pure forest structure influenced taxonomic and phylogenetic nestedness patterns to some extent at the regional scale. Our results highlight the importance and scale dependence of forest structure in shaping multidimensional beta‐diversity and its component patterns. Clearly, further studies need to link forest structure directly to ecological processes (e.g., asymmetric light competition and disturbance dynamics) and explore its roles in biodiversity maintenance.
