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

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          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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          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.

                Author and article information

                Biol Rev Camb Philos Soc
                Biological reviews of the Cambridge Philosophical Society
                May 2017
                : 92
                : 2
                [1 ] Department of Ecology and Evolutionary Biology, University of Colorado, Box 334, Boulder, CO, 80309-0334, U.S.A.
                [2 ] Biological Sciences, University of Toronto-Scarborough, Scarborough, M1C 1A4, Canada.
                [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, Guangzhou, PR China.
                [4 ] CIBIO/InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto, 4485-661, Vairão, Portugal.
                [5 ] Department of Biology, McGill University, Montréal, H3A 1B1, Canada.
                [6 ] African Centre for DNA Barcoding, University of Johannesburg, PO Box 524, Johannesburg, 2006, South Africa.
                [7 ] CSIRO Ecosystem Sciences, Climate Adaptation Flagship, GPO BOX 1600, Canberra, 2601, Australia.
                [8 ] Biodiversity & Climate Research Centre (BiK-F) and Senckenberg Gesellschaft für Naturforschung, 60325, Frankfurt am Main, Germany.
                [9 ] Institute of Ecology, Evolution and Diversity, Goethe University, 60438, Frankfurt, Germany.
                [10 ] School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, U.K.
                [11 ] Department of Ecological Sciences - Animal Ecology, Vrije Universiteit, Amsterdam, Netherlands.
                [12 ] Center for Biodiversity, Department of Biology, Temple University, Suite 502, Philadelphia, PA, 19122, U.S.A.
                [13 ] Ecology & Evolutionary Biology, University of Toronto, Room 3055, Toronto, M5S 3B2, Canada.
                [14 ] Department of Biology, Simon Fraser University, Burnaby, V5A 1S6, Canada.
                [15 ] Centre of Ecology and Conservation Sciences (UMR 7204 CESCO), Museum National d'Histoire Naturelle, Paris, France.
                [16 ] Department of Zoology, University of Oxford, Oxford, OX1 3QY, UK.
                [17 ] German Centre of Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, DE-04103, Leipzig, Germany.
                [18 ] Geobotany and Botanical Garden, Institute of Biology, Martin Luther University, Halle-Wittenberg, DE-06108, Halle (Saale), Germany.
                [19 ] Department of Computer Science, Martin-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 6BT, U.K.
                [21 ] Research School of Biology, Australian National University, Acton, 2601, Australia.
                [22 ] Laboratoire d'Ecologie Alpine (LECA), CNRS - Université Grenoble Alpes (UMR 5553), BP 53, 38041, Grenoble Cedex 9, France.


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