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Habitat suitability modeling from empirical data : application to mule deer in the interior of British Columbia Simons, V. Brock

Abstract

Habitat suitability modeling has both strengths and weaknesses as a land management tool. Its utility is highly dependent on the ecological interactions and spatial and temporal scales that are pertinent to land management concerns and the species of interest. To maximize the usefulness of mathematical habitat suitability models, it is important that they are constructed using all the reliable a priori information available, and selected using a method that consistently selects models of an appropriate level of complexity. Application is exemplified here to produce winter and summer habitat suitability models for mule deer (Odocoileus hemionus) in young, intensively managed lodgepole pine (Pinus contorta) stands in the interior of British Columbia. The building of all models likely to have good explanatory power was informed by a comprehensive literature review of mule deer habitat requirements. After models were built, multivariate correlations between predictor variables and the dependent variable of standardized pellet-group densities were analyzed to ensure that no strong and sensible relationships suggested by the data were left out of the model set. Akaike's Information Criterion (AIC) was used for model selection, as it is currently the best readily available model selection criterion when 'truth' is of near-infinite complexity. To improve robustness of inference and prediction error estimates, final models are produced as AIC weighted averages of the models most strongly supported by the data. Although models should ideally be validated using independent data, error was estimated here based on the same data used for model fitting.

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