Modelling species distributions using regression quantiles

Authors

  • Sandrine Vaz,

    1. Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER), Laboratoire Ressources Halieutiques, 150 quai Gambetta, BP699, F-62321, Boulogne/mer, France;
    Search for more papers by this author
      Correspondence author. E-mail: sandrine.vaz@ifremer.fr
  • Corinne S. Martin,

    1. Department of Geographical and Life Sciences, Canterbury Christ Church University, Canterbury CT1 1QU, Kent, UK;
    Search for more papers by this author
  • Paul D. Eastwood,

    1. Centre for Environment, Fisheries, and Aquaculture Science, Lowestoft Laboratory, Lowestoft, Suffolk NR33 0HT, UK;
    Search for more papers by this author
  • Bruno Ernande,

    1. Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER), Laboratoire Ressources Halieutiques, avenue du Général de Gaulle, F-14520 Port-en-Bessin, France; and
    2. Evolution and Ecology Program, International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
    Search for more papers by this author
  • Andre Carpentier,

    1. Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER), Laboratoire Ressources Halieutiques, 150 quai Gambetta, BP699, F-62321, Boulogne/mer, France;
    Search for more papers by this author
  • Geoff J. Meaden,

    1. Department of Geographical and Life Sciences, Canterbury Christ Church University, Canterbury CT1 1QU, Kent, UK;
    Search for more papers by this author
  • Frank Coppin

    1. Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER), Laboratoire Ressources Halieutiques, 150 quai Gambetta, BP699, F-62321, Boulogne/mer, France;
    Search for more papers by this author

Correspondence author. E-mail: sandrine.vaz@ifremer.fr

Summary

  • 1Species distribution modelling is an important and well-established tool for conservation planning and resource management. Modelling techniques based on central estimates of species responses to environmental factors do not always provide ecologically meaningful estimates of species–environment relationships and are being increasingly questioned.
  • 2Regression quantiles (RQ) can be used to model the upper bounds of species–environment relationships and thus estimate how the environment is limiting the distribution of a species. The resulting models tend to describe potential rather than actual patterns of species distributions.
  • 3Model selection based on null hypothesis testing and backward elimination, followed by validation procedures, are proposed here as a general approach for constructing RQ limiting effect models for multiple species.
  • 4This approach was applied successfully to 16 of the most abundant marine fish and cephalopods in the eastern English Channel. Most models were validated successfully and null hypothesis testing for model selection proved effective for RQ modelling.
  • 5 Synthesis and applications. Modelling the upper bounds of species-habitat relationships enables the detection of the effects of limiting factors on species’ responses. Maps showing potential species distributions are also less likely to underestimate species responses’ to the environment, and therefore have subsequent benefits for precautionary management.

Ancillary