Caroline M Tucker 1 , Marc W Cadotte 2 , 3 , Silvia B Carvalho 4 , T Jonathan Davies 5 , 6 , Simon Ferrier 7 , Susanne A Fritz 8 , 9 , Rich Grenyer 10 , Matthew R Helmus 11 , 12 , Lanna S Jin 13 , Arne O Mooers 14 , Sandrine Pavoine 15 , 16 , Oliver Purschke 17 , 18 , 19 , David W Redding 20 , Dan F Rosauer 21 , Marten Winter 17 , Florent Mazel 22
May 2017
Biological reviews of the Cambridge Philosophical Society
biodiversity hotspots, biogeography, community assembly, conservation, diversity metrics, evolutionary history, phylogenetic diversity, prioritization, range size
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.