Making better biogeographical predictions of species’ distributions

Authors


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).

Summary

  • 1Biogeographical 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).
  • 2Most 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.
  • 3We 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.
  • 4Synthesis 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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