Canadian Forest Service Publications
An evaluation of alternative algorithms for fitting species distribution models using logistic regression. 2000. Pearce, J.L.; Ferrier, S. Ecological Modelling 128: 127-147.
Issued by: Great Lakes Forestry Centre
Catalog ID: 18714
Availability: PDF (request by e-mail)
Logistic regression is being used increasingly to develop regional-scale predictive models of species distributions for use in regional conservation planning. These models are usually developed using automated stepwise procedures to select the explanatory variable to include in each model and to fit the functions relating each of these variables to the probability of species occurrence. Available procedures for fitting logistic regression models differ in terms of number of factors, including the basic modelling technique employed (generalised linear or generalised additive modelling), the strategy used to select explanatory variables and to detemine the complexity of fitted functions, and the approach used to correct for multiple testing. This study evaluates the effect that each of these factors has on the predictive accuracy of fitted models, using fauna and flora survey data from north-east New South Wales. The results suggest that predictive accuracy is maximised by employing variable selection procedures that stringently guard against the inclusion of extraneous variables in a model, such as forwards selection with a 5% significance level and removal of insignificant variables at each stage of the selection process. Models fitted using generalised additive modelling were more accurate than those derived using generalised linear modelling. The best approach to controlling the complexity of fitted models was less clear, as it tended to vary between the biological groups examined. Small reptile species were best modelled by complex relationships (3 or 4 df), and vascular plants and diurnal birds by simple relationships (1 or 2df). Correction for multiple testing using Bonferroni correction factor did not improve the accuracy of models.