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      The shape of success in a turbulent world: wave exposure filtering of coral reef herbivory

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          A distance-based framework for measuring functional diversity from multiple traits

          A new framework for measuring functional diversity (FD) from multiple traits has recently been proposed. This framework was mostly limited to quantitative traits without missing values and to situations in which there are more species than traits, although the authors had suggested a way to extend their framework to other trait types. The main purpose of this note is to further develop this suggestion. We describe a highly flexible distance-based framework to measure different facets of FD in multidimensional trait space from any distance or dissimilarity measure, any number of traits, and from different trait types (i.e., quantitative, semi-quantitative, and qualitative). This new approach allows for missing trait values and the weighting of individual traits. We also present a new multidimensional FD index, called functional dispersion (FDis), which is closely related to Rao's quadratic entropy. FDis is the multivariate analogue of the weighted mean absolute deviation (MAD), in which the weights are species relative abundances. For unweighted presence-absence data, FDis can be used for a formal statistical test of differences in FD. We provide the "FD" R language package to easily implement our distance-based FD framework.
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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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              A functional approach reveals community responses to disturbances.

              Understanding the processes shaping biological communities under multiple disturbances is a core challenge in ecology and conservation science. Traditionally, ecologists have explored linkages between the severity and type of disturbance and the taxonomic structure of communities. Recent advances in the application of species traits, to assess the functional structure of communities, have provided an alternative approach that responds rapidly and consistently across taxa and ecosystems to multiple disturbances. Importantly, trait-based metrics may provide advanced warning of disturbance to ecosystems because they do not need species loss to be reactive. Here, we synthesize empirical evidence and present a theoretical framework, based on species positions in a functional space, as a tool to reveal the complex nature of change in disturbed ecosystems. Copyright © 2012 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Functional Ecology
                Funct Ecol
                Wiley
                02698463
                June 2017
                June 2017
                February 23 2017
                : 31
                : 6
                : 1312-1324
                Affiliations
                [1 ]Department of Ecology; Leibniz Centre for Tropical Marine Research (ZMT); Fahrenheitstraße 6 28359 Bremen Germany
                [2 ]Global Economic Dynamics and the Biosphere Academy Programme; Royal Swedish Academy of Sciences; PO Box 50005 Stockholm 104 05 Sweden
                [3 ]Stockholm Resilience Centre; Stockholm University; Stockholm 106 91 Sweden
                [4 ]Smithsonian Marine Station; Smithsonian Institution; Fort Pierce FL 34949 USA
                [5 ]Department of Life Sciences Silwood Park; Imperial College London; Ascot UK
                [6 ]Marine Spatial Ecology Lab; School of Biological Sciences & ARC Centre of Excellence for Coral Reef Studies; The University of Queensland; St. Lucia Qld 4072 Australia
                [7 ]Department of Marine Science and Fisheries; College of Agricultural and Marine Sciences; Sultan Qaboos University; PO Box 34 Al Khoud 123 Muscat Oman
                [8 ]School of Marine Sciences; University of Maine; Darling Marine Center; Walpole ME 04573 USA
                [9 ]Department of Geography; University of Hawaii Mānoa; 2424 Maile Way Honolulu HI 96822 USA
                Article
                10.1111/1365-2435.12828
                53c46d97-a89a-4739-aa77-bad84be57dd7
                © 2017

                http://doi.wiley.com/10.1002/tdm_license_1

                http://creativecommons.org/licenses/by/4.0/

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