Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents

Authors

  • W. D. Kissling,

    Corresponding author
    1. Ecoinformatics & Biodiversity Group, Department of Bioscience, Aarhus University, DK-8000 Aarhus C, Denmark
      W. Daniel Kissling, Ecoinformatics & Biodiversity Group, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark.
      E-mail: danielkissling@web.de.
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  • Carsten F. Dormann,

    1. Biometry and Environmental System Analysis, Faculty of Forest and Environmental Sciences, University of Freiburg, 79106 Freiburg, Germany
    2. Helmholtz Centre for Environmental Research – UFZ, Department of Computational Landscape Ecology, 04318 Leipzig, Germany
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  • Jürgen Groeneveld,

    1. Helmholtz Centre for Environmental Research – UFZ, Department of Ecological Modelling, 04318 Leipzig, Germany
    2. School of Environment, The University of Auckland, Auckland, New Zealand
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  • Thomas Hickler,

    1. Biodiversity and Climate Research Centre (BiK-F), 60325 Frankfurt am Main, Germany
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  • Ingolf Kühn,

    1. Helmholtz Centre for Environmental Research – UFZ, Department of Community Ecology, 06120 Halle, Germany
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  • Greg J. McInerny,

    1. Computational Ecology and Environmental Science Group, Computational Science Laboratory, Microsoft Research, Cambridge CB3 0FB, UK
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  • José M. Montoya,

    1. Instituto de Ciencias del Mar, Consejo Superior de Investigaciones Científicas, E-08003 Barcelona, Spain
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  • Christine Römermann,

    1. Institute for Physical Geography, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
    2. Theoretical Ecology, Faculty of Biology and Preclinical Medicine, University of Regensburg, 93040 Regensburg, Germany
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  • Katja Schiffers,

    1. Laboratoire d’Ecologie Alpine, UMR-CNRS 5553, Université J. Fourier, 38041 Grenoble Cedex 9, France
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  • Frank M. Schurr,

    1. Institute for Physical Geography, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
    2. Plant Ecology and Nature Conservation, Institute of Biochemistry and Biology, University of Potsdam, 14469 Potsdam, Germany
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  • Alexander Singer,

    1. Helmholtz Centre for Environmental Research – UFZ, Department of Ecological Modelling, 04318 Leipzig, Germany
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  • Jens-Christian Svenning,

    1. Ecoinformatics & Biodiversity Group, Department of Bioscience, Aarhus University, DK-8000 Aarhus C, Denmark
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  • Niklaus E. Zimmermann,

    1. Landscape Dynamics, Swiss Federal Research Institute WSL, CH-8903 Birmensdorf, Switzerland
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  • Robert B. O’Hara

    1. Biodiversity and Climate Research Centre (BiK-F), 60325 Frankfurt am Main, Germany
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W. Daniel Kissling, Ecoinformatics & Biodiversity Group, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark.
E-mail: danielkissling@web.de.

Abstract

Aim  Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices.

Location  Local to global.

Methods  We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions.

Results  Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits.

Main conclusions  There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.

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