Inferring the Evolutionary Model of Community-Structuring Traits with Convolutional Kitchen Sinks.

When communities are assembled through processes such as filtering or limiting similarity acting on phylogenetically conserved traits, the evolutionary signature of those traits may be reflected in patterns of community membership. We show how the model of trait evolution underlying community-structuring traits can be inferred from community membership data using both a variation of a traditional eco-phylogenetic metric-the mean pairwise distance (MPD) between taxa-and a recent machine learning tool, Convolutional Kitchen Sinks (CKS). Both methods perform well across a range of phylogenetically informative evolutionary models, but CKS outperforms MPD as tree size increases. We demonstrate CKS by inferring the evolutionary history of freeze tolerance in angiosperms. Our analysis is consistent with a late burst model, suggesting freeze tolerance evolved recently. We suggest that multiple data types that are ordered on phylogenies, such as trait values, species interactions, or community presence/absence, are good candidates for CKS modeling because the generative models produce structured differences between neighboring points that CKS is well-suited for. We introduce the R package kitchen to perform CKS for generic application of the technique.

Phylogenetic and functional clustering illustrate the roles of adaptive radiation and dispersal filtering in jointly shaping late-Quaternary mammal assemblages on oceanic islands.

Islands frequently harbour unique assemblages of species, yet their ecological roles and differences are largely ignored in island biogeography studies. Here, we examine eco-evolutionary processes structuring mammal assemblages on oceanic islands worldwide, including all extant and extinct late-Quaternary mammal species. We find island mammal assemblages tend to be phylogenetically clustered (share more recent evolutionary histories), with clustering increasing with island area and isolation. We also observe that mammal assemblages often tend to be functionally clustered (share similar traits), but the strength of clustering is weak and generally independent from island area or isolation. These findings indicate the important roles of in situ speciation and dispersal filtering in shaping island mammal assemblages under pre-anthropogenic conditions, notably through adaptive radiation of a few clades (e.g. bats, with generally high dispersal abilities). Our study demonstrates that considering the functional and phylogenetic axes of diversity can better reveal the eco-evolutionary processes of island community assembly.

A guide to phylogenetic metrics for conservation, community ecology and macroecology.

The use of phylogenies in ecology is increasingly common and has broadened our understanding of biological diversity. Ecological sub-disciplines, particularly conservation, community ecology and macroecology, all recognize the value of evolutionary relationships but the resulting development of phylogenetic approaches has led to a proliferation of phylogenetic diversity metrics. The use of many metrics across the sub-disciplines hampers potential meta-analyses, syntheses, and generalizations of existing results. Further, there is no guide for selecting the appropriate metric for a given question, and different metrics are frequently used to address similar questions. To improve the choice, application, and interpretation of phylo-diversity metrics, we organize existing metrics by expanding on a unifying framework for phylogenetic information. Generally, questions about phylogenetic relationships within or between assemblages tend to ask three types of question: how much; how different; or how regular? We show that these questions reflect three dimensions of a phylogenetic tree: richness, divergence, and regularity. We classify 70 existing phylo-diversity metrics based on their mathematical form within these three dimensions and identify 'anchor' representatives: for α-diversity metrics these are PD (Faith's phylogenetic diversity), MPD (mean pairwise distance), and VPD (variation of pairwise distances). By analysing mathematical formulae and using simulations, we use this framework to identify metrics that mix dimensions, and we provide a guide to choosing and using the most appropriate metrics. We show that metric choice requires connecting the research question with the correct dimension of the framework and that there are logical approaches to selecting and interpreting metrics. The guide outlined herein will help researchers navigate the current jungle of indices.