Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment

Authors

  • Simon Ferrier,

    Corresponding author
    1. New South Wales Department of Environment and Conservation, PO Box 402, Armidale, New South Wales 2350, Australia;
      *Correspondence: Simon Ferrier, New South Wales Department of Environment and Conservation, PO Box 402, Armidale, New South Wales 2350, Australia. E-mail: simon.ferrier@environment.nsw.gov.au
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  • Glenn Manion,

    1. New South Wales Department of Environment and Conservation, PO Box 402, Armidale, New South Wales 2350, Australia;
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  • Jane Elith,

    1. School of Botany, University of Melbourne, Parkville, Victoria 3010, Australia; and
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  • Karen Richardson

    1. Department of Geography, McGill University, Montreal, Quebec H3A 2K6, Canada
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*Correspondence: Simon Ferrier, New South Wales Department of Environment and Conservation, PO Box 402, Armidale, New South Wales 2350, Australia. E-mail: simon.ferrier@environment.nsw.gov.au

ABSTRACT

Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach is an extension of matrix regression, designed specifically to accommodate two types of nonlinearity commonly encountered in large-scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and observed compositional dissimilarity, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients. GDM can be further adapted to accommodate special types of biological and environmental data including, for example, information on phylogenetic relationships between species and information on barriers to dispersal between geographical locations. The approach can be applied to a wide range of assessment activities including visualization of spatial patterns in community composition, constrained environmental classification, distributional modelling of species or community types, survey gap analysis, conservation assessment, and climate-change impact assessment.

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