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Making better biogeographical predictions of species’ distributions

  1. ANTOINE GUISAN1,
  2. ANTHONY LEHMANN2,
  3. SIMON FERRIER3,
  4. MIKE AUSTIN4,
  5. JACOB MC. C. OVERTON5,
  6. RICHARD ASPINALL6,
  7. TREVOR HASTIE7

Article first published online: 12 MAY 2006

DOI: 10.1111/j.1365-2664.2006.01164.x

Issue

Journal of Applied Ecology

Journal of Applied Ecology

Volume 43, Issue 3, pages 386–392, June 2006

Additional Information

How to Cite

GUISAN, A., LEHMANN, A., FERRIER, S., AUSTIN, M., OVERTON, J. MC. C., ASPINALL, R. and HASTIE, T. (2006), Making better biogeographical predictions of species’ distributions. Journal of Applied Ecology, 43: 386–392. doi: 10.1111/j.1365-2664.2006.01164.x

Author Information

  1. 1

    University of Lausanne, Department of Ecology and Evolution, Biology Building, CH-1015 Lausanne, Switzerland;

  2. 2

    Swiss Center for Faunal Cartography, Terreaux 14, CH-2000 Neuchâtel, Switzerland;

  3. 3

    New South Wales Department of Environment and Conservation, PO Box 402 Armidale, NSW 2350, Australia;

  4. 4

    CSIRO Sustainable Ecosystems, GPO Box 284, Canberra, ACT 2601, Australia;

  5. 5

    Landcare Research, Private Bag 3127, Hamilton, New Zealand;

  6. 6

    Arizona State University, Department of Geography, PO Box 870104, Tempe, AZ 85287–0104, USA; and

  7. 7

    Stanford University, Statistics Department, Sequoia Hall, Stanford, CA 94305, USA

* Antoine Guisan, University of Lausanne, Department of Ecology and Evolution, Biology Building, CH-1015 Lausanne, Switzerland (fax +41 21 692 42 65; e-mail antoine.guisan@unil.ch).

Publication History

  1. Issue published online: 12 MAY 2006
  2. Article first published online: 12 MAY 2006
  3. Received 18 November 2005; final copy received 20 January 2006 Editor: Rob Freckleton

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Keywords:

  • artificial data;
  • autocorrelation;
  • community and diversity modelling;
  • errors and uncertainties;
  • generalized regressions;
  • interactions;
  • niche-based model

Summary

  • 1
    Biogeographical models of species’ distributions are essential tools for assessing impacts of changing environmental conditions on natural communities and ecosystems. Practitioners need more reliable predictions to integrate into conservation planning (e.g. reserve design and management).
  • 2
    Most models still largely ignore or inappropriately take into account important features of species’ distributions, such as spatial autocorrelation, dispersal and migration, biotic and environmental interactions. Whether distributions of natural communities or ecosystems are better modelled by assembling individual species’ predictions in a bottom-up approach or modelled as collective entities is another important issue. An international workshop was organized to address these issues.
  • 3
    We discuss more specifically six issues in a methodological framework for generalized regression: (i) links with ecological theory; (ii) optimal use of existing data and artificially generated data; (iii) incorporating spatial context; (iv) integrating ecological and environmental interactions; (v) assessing prediction errors and uncertainties; and (vi) predicting distributions of communities or collective properties of biodiversity.
  • 4
    Synthesis and applications. Better predictions of the effects of impacts on biological communities and ecosystems can emerge only from more robust species’ distribution models and better documentation of the uncertainty associated with these models. An improved understanding of causes of species’ distributions, especially at their range limits, as well as of ecological assembly rules and ecosystem functioning, is necessary if further progress is to be made. A better collaborative effort between theoretical and functional ecologists, ecological modellers and statisticians is required to reach these goals.
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