Phylogenetic and functional structure

The phylogenetic and functional structure measures were taken to test the hypothesis that Caatinga vegetation near roads will be more phylogenetically and functionally clustered than Caatinga vegetation far from roads. For the phylogeny of the woody component, we used all the tree, shrub and liana species of the phytosociological survey identified at least to the family level (87 species). The phylogeny of herbaceous and sub-shrub species (non-woody component) included all non-woody species identified at least to the family level for a total of 86 species. Except for Selaginella convoluta (see phylogenetic signal section), all the morphospecies sampled in this survey were inserted into mega-tree R20160415.new (Gastauer & Meira-Neto, 2017) via phylomatic function, which was dated (Bell et al. , 2010) using the algorithm bladj in combination with the file ‘ages’ in Phylocom (Gastauer & Meira-Neto, 2017; Webb et al. , 2011). That mega-tree was chosen because it is an updated mega-tree using the updated APG IV classification (The Angiosperm Phylogeny Group, 2016).
With incidence data, we calculated phylogenetic structure for plots near roads and for plots further from roads, considering woody species or non-woody species, using the following phylogenetic measures with Phylocom 4.2 software (Webb et al. , 2011): mean pairwise distance (MPD), mean nearest taxon distance (MNTD), net relatedness index (NRI) and nearest taxon index (NTI). Phylogenetic analysis was done using the unconstrained model of Phylocom (Gastauer & Meira-Neto, 2015; Webbet al. , 2011) that maintains the species richness of each plot, but all species of the metacommunity (pool of species) have the same chance of being included (Webb et al. , 2002) within each of the 10,000 randomizations. We defined the metacommunity as all the species sampled in the collection area (8,000m2), identified at species, genus or family level. We used the ’comstruct’ function to calculate the phylogenetic metrics. We calculated these measures at different spatial scales: 10-m x 10-m and 20-m x 50-m.
In order to evaluate the functional structure of communities, we computed Gower distances among species based on the trait matrix (see above) using the ‘gowdis’ command from ‘FD’ package in R to perform the rarefaction and extrapolation of functional diversity (Villéger et al. 2008). From this we built a functional dendrogram in Newick format using ‘hclust2phylog’ from the ‘ade4’ package in R (Dray et al. , 2018). We then computed traitNRI and traitNTI values for each plot using “ses.mpd” and “ses.mntd” commands and the unconstrained null model that assumes the same probability for all species to enter randomized communities. This works similar to the computation of phylogenetic community structure, but instead of phylogenetic distances among species of a plot, functional distances among species with the focal trait are calculated. The functional tree was built with all woody and non-woody species (n=179).
We tested statistical significance through a bilateral t-test for one sample, using the generated NRI and NTI metrics (Gastauer & Meira-Neto, 2015).