Modelling invasion success as a function of phylogenetic and functional distance.
To test whether phylogenetic distance predicted invasion success, we asked how mean phylogenetic distance (MPD) and nearest taxon phylogenetic distance (NTD) affected our two measures of invasion success by means of generalized linear mixed models (BPMM, hereafter). We built the models in the R packages MCMCglmm (Hadfield 2010) and BRMS (Bürkner 2017), which implement Bayesian generalized linear mixed models by means of a Markov chain Monte Carlo process. To test whether phylogenetic distance influenced the likelihood that the exotic occurred in a community, the occurrence of exotic species was modelled by means of BPMMs with binomial distribution and logit link, including either MPD or NTD (or related abundance-weighted metrics and metrics based on functional distances) as fixed predictor and study site, species (as some species occurred in more than one community) and phylogeny as random factors. The inclusion of study site as a random effect was important because it allowed modelling the likelihood that the exotic was present in a community to be evaluated within each study site, ensuring thus that the absence of the invaders did not result from the fact that the species had never been introduced in the region. To test whether phylogenetic distance influenced the relative abundance of the invaders within a community, we restricted the analyses to communities with two or more exotic species and used BPMMs with Gaussian structure of errors. The fixed and random effects were the same used to model occurrence with the only difference that study site was replaced by community as random factor. In this way comparisons were made within communities instead of across communities. For each BPMM model we ran two independent runs with 200,000 generations, from which a 10% was discarded as “burn in”. Convergence and good mixing of each of the MCMC chains was assessed visually, plotting the traces of each of the model parameters. We re-ran all models including the interaction between phylogenetic distance and habitat, and the potential confounds of body size and native species richness. Based on a model selection approach, habitat was simplified to two categories according to the degree of human-related alterations: habitats with high levels of alterations (highly and moderately urbanized habitats) and habitats with low levels alterations (little urbanized, rural and natural habitats).
Sampling procedure to study the effect of species richness on phylogenetic distances. We randomly selected pairs of species without replacement for different sample sizes (from 10 to 150 pairs) and extracted the nearest phylogenetic distance from the matrix of phylogenetic distances for the entire avian phylogeny. We repeated the sampling 1,000 times, and represented the results as means and standards errors of the mean. The sampling was conducted for all birds, as well for the subset of species that were introduced in regions outside their native range and those from the subset that became established at least once (data from GAVIA (Dyer et al. 2017)). The approach was also used to show that whether introduced and established species are a non-random subset of all avian species.
Ecological validation of phylogenetic distance. We evaluated the assumption that phylogenetic distance is a good surrogate of functional distance in two ways. First, we used the package “geomorph” (Adams & Otárola-Castillo 2013)to estimate the overall phylogenetic signal by means of a multivariate version of Blomberg’s K (Adams 2014). Major axes of niche variation were described by means of a Principal Coordinates Analysis (PCoA), based on Gower’s generalized distance. Second, we used Mantel correlations to compare phylogenetic distances with functional distances (Mouquet et al. 2012). This analysis was conducted both on the entire dataset and splitting it in the main clades where exotics were represented. Finally, we used a multi-mantel test to test whether, besides describing the resource niche, phylogenetic relatedness also contained information on the degree of similarity in response traits (i.e. relative brain size, resource niche breadth and brood value).