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

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          ABSTRACT

          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.

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          Most cited references 82

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          Testing for phylogenetic signal in comparative data: behavioral traits are more labile.

          The primary rationale for the use of phylogenetically based statistical methods is that phylogenetic signal, the tendency for related species to resemble each other, is ubiquitous. Whether this assertion is true for a given trait in a given lineage is an empirical question, but general tools for detecting and quantifying phylogenetic signal are inadequately developed. We present new methods for continuous-valued characters that can be implemented with either phylogenetically independent contrasts or generalized least-squares models. First, a simple randomization procedure allows one to test the null hypothesis of no pattern of similarity among relatives. The test demonstrates correct Type I error rate at a nominal alpha = 0.05 and good power (0.8) for simulated datasets with 20 or more species. Second, we derive a descriptive statistic, K, which allows valid comparisons of the amount of phylogenetic signal across traits and trees. Third, we provide two biologically motivated branch-length transformations, one based on the Ornstein-Uhlenbeck (OU) model of stabilizing selection, the other based on a new model in which character evolution can accelerate or decelerate (ACDC) in rate (e.g., as may occur during or after an adaptive radiation). Maximum likelihood estimation of the OU (d) and ACDC (g) parameters can serve as tests for phylogenetic signal because an estimate of d or g near zero implies that a phylogeny with little hierarchical structure (a star) offers a good fit to the data. Transformations that improve the fit of a tree to comparative data will increase power to detect phylogenetic signal and may also be preferable for further comparative analyses, such as of correlated character evolution. Application of the methods to data from the literature revealed that, for trees with 20 or more species, 92% of traits exhibited significant phylogenetic signal (randomization test), including behavioral and ecological ones that are thought to be relatively evolutionarily malleable (e.g., highly adaptive) and/or subject to relatively strong environmental (nongenetic) effects or high levels of measurement error. Irrespective of sample size, most traits (but not body size, on average) showed less signal than expected given the topology, branch lengths, and a Brownian motion model of evolution (i.e., K was less than one), which may be attributed to adaptation and/or measurement error in the broad sense (including errors in estimates of phenotypes, branch lengths, and topology). Analysis of variance of log K for all 121 traits (from 35 trees) indicated that behavioral traits exhibit lower signal than body size, morphological, life-history, or physiological traits. In addition, physiological traits (corrected for body size) showed less signal than did body size itself. For trees with 20 or more species, the estimated OU (25% of traits) and/or ACDC (40%) transformation parameter differed significantly from both zero and unity, indicating that a hierarchical tree with less (or occasionally more) structure than the original better fit the data and so could be preferred for comparative analyses.
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            New multidimensional functional diversity indices for a multifaceted framework in functional ecology.

            Functional diversity is increasingly identified as an important driver of ecosystem functioning. Various indices have been proposed to measure the functional diversity of a community, but there is still no consensus on which are most suitable. Indeed, none of the existing indices meets all the criteria required for general use. The main criteria are that they must be designed to deal with several traits, take into account abundances, and measure all the facets of functional diversity. Here we propose three indices to quantify each facet of functional diversity for a community with species distributed in a multidimensional functional space: functional richness (volume of the functional space occupied by the community), functional evenness (regularity of the distribution of abundance in this volume), and functional divergence (divergence in the distribution of abundance in this volume). Functional richness is estimated using the existing convex hull volume index. The new functional evenness index is based on the minimum spanning tree which links all the species in the multidimensional functional space. Then this new index quantifies the regularity with which species abundances are distributed along the spanning tree. Functional divergence is measured using a novel index which quantifies how species diverge in their distances (weighted by their abundance) from the center of gravity in the functional space. We show that none of the indices meets all the criteria required for a functional diversity index, but instead we show that the set of three complementary indices meets these criteria. Through simulations of artificial data sets, we demonstrate that functional divergence and functional evenness are independent of species richness and that the three functional diversity indices are independent of each other. Overall, our study suggests that decomposition of functional diversity into its three primary components provides a meaningful framework for its quantification and for the classification of existing functional diversity indices. This decomposition has the potential to shed light on the role of biodiversity on ecosystem functioning and on the influence of biotic and abiotic filters on the structure of species communities. Finally, we propose a general framework for applying these three functional diversity indices.
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              Opposing effects of competitive exclusion on the phylogenetic structure of communities.

              Though many processes are involved in determining which species coexist and assemble into communities, competition is among the best studied. One hypothesis about competition's contribution to community assembly is that more closely related species are less likely to coexist. Though empirical evidence for this hypothesis is mixed, it remains a common assumption in certain phylogenetic approaches for inferring the effects of environmental filtering and competitive exclusion. Here, we relate modern coexistence theory to phylogenetic community assembly approaches to refine expectations for how species relatedness influences the outcome of competition. We argue that two types of species differences determine competitive exclusion with opposing effects on relatedness patterns. Importantly, this means that competition can sometimes eliminate more different and less related taxa, even when the traits underlying the relevant species differences are phylogenetically conserved. Our argument leads to a reinterpretation of the assembly processes inferred from community phylogenetic structure.
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                Author and article information

                Contributors
                caroline.tucker@colorado.edu
                Journal
                Biol Rev Camb Philos Soc
                Biol Rev Camb Philos Soc
                10.1111/(ISSN)1469-185X
                BRV
                Biological Reviews of the Cambridge Philosophical Society
                Blackwell Publishing Ltd (Oxford, UK )
                1464-7931
                1469-185X
                20 January 2016
                May 2017
                : 92
                : 2 ( doiID: 10.1111/brv.2017.92.issue-2 )
                : 698-715
                Affiliations
                [ 1 ] Department of Ecology and Evolutionary BiologyUniversity of Colorado Box 334 Boulder CO 80309‐0334U.S.A.
                [ 2 ] Biological SciencesUniversity of Toronto‐Scarborough Scarborough M1C 1A4Canada
                [ 3 ] Stake Key Laboratory of Biocontrol, Key Laboratory of Biodiversity Dynamics and Conservation of Guangdong, Higher Education Institutes, College of Ecology and Evolution Sun Yat‐sen University GuangzhouPR China
                [ 4 ]CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto 4485‐661 VairãoPortugal
                [ 5 ] Department of BiologyMcGill University Montréal H3A 1B1Canada
                [ 6 ] African Centre for DNA BarcodingUniversity of Johannesburg PO Box 524 Johannesburg 2006South Africa
                [ 7 ]CSIRO Ecosystem Sciences, Climate Adaptation Flagship GPO BOX 1600 Canberra 2601Australia
                [ 8 ]Biodiversity & Climate Research Centre (BiK‐F) and Senckenberg Gesellschaft für Naturforschung 60325 Frankfurt am MainGermany
                [ 9 ] Institute of Ecology, Evolution and DiversityGoethe University 60438 FrankfurtGermany
                [ 10 ] School of Geography and the EnvironmentUniversity of Oxford Oxford OX1 3QYU.K.
                [ 11 ] Department of Ecological Sciences ‐ Animal EcologyVrije Universiteit AmsterdamNetherlands
                [ 12 ] Center for Biodiversity, Department of BiologyTemple University Suite 502 Philadelphia PA 19122U.S.A.
                [ 13 ] Ecology & Evolutionary BiologyUniversity of Toronto Room 3055 Toronto M5S 3B2Canada
                [ 14 ] Department of BiologySimon Fraser University Burnaby V5A 1S6Canada
                [ 15 ] Centre of Ecology and Conservation Sciences (UMR 7204 CESCO)Museum National d'Histoire Naturelle ParisFrance
                [ 16 ] Department of ZoologyUniversity of Oxford Oxford OX1 3QYUK
                [ 17 ]German Centre of Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Deutscher Platz 5e DE‐04103 LeipzigGermany
                [ 18 ] Geobotany and Botanical Garden, Institute of BiologyMartin Luther University, Halle‐Wittenberg DE‐06108 Halle (Saale)Germany
                [ 19 ] Department of Computer ScienceMartin‐Luther‐University, Halle‐Wittenberg DE‐06120 Halle (Saale)Germany
                [ 20 ] Centre for Biodiversity and Environmental Research, Department of Genetics, Evolution and Environment University College London London WC1E 6BTU.K.
                [ 21 ] Research School of BiologyAustralian National University Acton 2601Australia
                [ 22 ]Laboratoire d'Ecologie Alpine (LECA), CNRS ‐ Université Grenoble Alpes (UMR 5553) BP 53 38041 Grenoble Cedex 9France
                Author notes
                [* ]Address for correspondence (Tel: (303) 492‐8961; E‐mail: caroline.tucker@ 123456colorado.edu ).
                Article
                BRV12252
                10.1111/brv.12252
                5096690
                26785932
                © 2016 The Authors. Biological Reviews published by John Wiley © Sons Ltd on behalf of Cambridge Philosophical Society.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                Page count
                Figures: 0, Tables: 0, Pages: 18, Words: 3000
                Product
                Funding
                Funded by: >Fundacão para a Ciência e Tecnologia (FCT)
                Award ID: SFRH/BPD/74423/2010
                Funded by: FEDER
                Funded by: National Funds
                Funded by: European Research Council under the European Community's Seven Framework Programme
                Award ID: 281422
                Funded by: LOEWE funding program
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                brv12252
                May 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.0.9 mode:remove_FC converted:02.05.2017

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