Community ecology in the age of multivariate multiscale spatial analysis

Authors

  • S. Dray,

    Corresponding author
    1. Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France
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  • R. Pélissier,

    1. Institut de Recherche pour le Développement (IRD), Université Montpellier 2, UMR Botanique et Bioinformatique de l'Architecture des Plantes (AMAP), Boulevard de la Lironde, TA A-51/PS2, F-34398 Montpellier cedex 5, France
    2. Institut Français de Pondichéry, UMIFRE 21 CNRS-MAEE, 11 St Louis Street, Puducherry 605 001 India
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  • P. Couteron,

    1. Institut de Recherche pour le Développement (IRD), Université Montpellier 2, UMR Botanique et Bioinformatique de l'Architecture des Plantes (AMAP), Boulevard de la Lironde, TA A-51/PS2, F-34398 Montpellier cedex 5, France
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  • M.-J. Fortin,

    1. Department of Ecology and Evolutionary Biology, University of Toronto, 25 Harbord Street, Toronto, Ontario M5S 3G5 Canada
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  • P. Legendre,

    1. Département de Sciences Biologiques, Université de Montréal, C.P. 6128, Succursale Centre-ville, Montréal, Québec H3C 3J7 Canada
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  • P. R. Peres-Neto,

    1. Département des Sciences Biologiques, Université du Québec à Montréal, C.P. 8888, Succursale Centre-ville, Montréal, Québec H3C 3P8 Canada
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  • E. Bellier,

    1. Unité Biostatistique et Processus Spatiaux, INRA Avignon, Domaine Saint-Paul, Site Agroparc 84914 Avignon cedex 9, France
    2. Norwegian Institute for Nature Research, NINA-NO-7485, Trondheim, Norway
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  • R. Bivand,

    1. Department of Economics, NHH Norwegian School of Economics, Helleveien 30, N-5045 Bergen, Norway
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  • F. G. Blanchet,

    1. Department of Renewable Resources, University of Alberta, 751 General Services Building, Edmonton, Alberta T6G 2H1 Canada
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  • M. De Cáceres,

    1. Biodiversity and Landscape Ecology Laboratory, Forest Science Center of Catalonia, Ctra. Antiga St Llorenç km 2, E-25280, Solsona, Catalonia, Spain
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  • A.-B. Dufour,

    1. Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France
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  • E. Heegaard,

    1. Norwegian Forest and Landscape Institute, Fanaflaten 4, N-5244 Fana, Norway
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  • T. Jombart,

    1. Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France
    2. Department of Infectious Disease Epidemiology, Imperial College London, London W21PG United Kingdom
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  • F. Munoz,

    1. Institut de Recherche pour le Développement (IRD), Université Montpellier 2, UMR Botanique et Bioinformatique de l'Architecture des Plantes (AMAP), Boulevard de la Lironde, TA A-51/PS2, F-34398 Montpellier cedex 5, France
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  • J. Oksanen,

    1. Department of Biology, University of Oulu, FI-90014 Oulu, Finland
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  • J. Thioulouse,

    1. Université de Lyon, F-69000, Lyon; Université Lyon 1; CNRS, UMR5558, Laboratoire de Biométrie et Biologie Evolutive, F-69622, Villeurbanne, France
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  • H. H. Wagner

    1. Department of Ecology and Evolutionary Biology, University of Toronto, 3359 Mississauga Road, Mississauga L5L 1C6 Canada
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Abstract

Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes.

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