Building better wildlife‐habitat models

Building better wildlife‐habitat models Wildlife‐habitat models are an important tool in wildlife management today, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data., and of those that are, few show useful predictive skill. We demonstrate that six critical assumptions underlie distribution based wildlife‐habitat models, all of which must be valid for the model to predict habitat quality. We outline these assumptions in a meta‐model, and discuss methods for their validation. Even where all six assumptions show a high level of validity, there is still a strong likelihood that the model will not predict habitat quality. However, the meta‐model does suggest habitat quality can be predicted more accurately if distributional data are ignored, and variables more indicative of habitat quality are modelled instead. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecography Wiley

Building better wildlife‐habitat models

Ecography, Volume 22 (2) – Apr 1, 1999

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Publisher
Wiley
Copyright
Copyright © 1999 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0906-7590
eISSN
1600-0587
DOI
10.1111/j.1600-0587.1999.tb00471.x
Publisher site
See Article on Publisher Site

Abstract

Wildlife‐habitat models are an important tool in wildlife management today, and by far the majority of these predict aspects of species distribution (abundance or presence) as a proxy measure of habitat quality. Unfortunately, few are tested on independent data., and of those that are, few show useful predictive skill. We demonstrate that six critical assumptions underlie distribution based wildlife‐habitat models, all of which must be valid for the model to predict habitat quality. We outline these assumptions in a meta‐model, and discuss methods for their validation. Even where all six assumptions show a high level of validity, there is still a strong likelihood that the model will not predict habitat quality. However, the meta‐model does suggest habitat quality can be predicted more accurately if distributional data are ignored, and variables more indicative of habitat quality are modelled instead.

Journal

EcographyWiley

Published: Apr 1, 1999

References

  • Modelling habitat quality for arboreal marsupials in the south coastal forest of New South Wales. Australia
    Pausus, Pausus; Braithwaite, Braithwaite; Austin, Austin
  • Multi‐scale approach to species‐habitat relationships: juvenile fish in a large river section
    Poizat, Poizat; Pont, Pont
  • Modelling the abundance of rare species: statistical models for counts with extra zeros

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