A guide to phylogenetic metrics for conservation, community ecology and macroecology
Genre
Journal ArticleDate
2017-05-01Author
Tucker, CMCadotte, MW
Carvalho, SB
Jonathan Davies, T
Ferrier, S
Fritz, SA
Grenyer, R
Helmus, MR
Jin, LS
Mooers, AO
Pavoine, S
Purschke, O
Redding, DW
Rosauer, DF
Winter, M
Mazel, F
Subject
biodiversity hotspotsbiogeography
community assembly
conservation
diversity metrics
evolutionary history
phylogenetic diversity
prioritization
range size
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http://hdl.handle.net/20.500.12613/4204
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10.1111/brv.12252Abstract
© 2016 The Authors. 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.Citation to related work
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http://dx.doi.org/10.34944/dspace/4186