An evaluation of alternative algorithms for fitting species distribution models using logistic regression

An evaluation of alternative algorithms for fitting species distribution models using logistic... 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 variables 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 a number of factors, including the basic modelling technique employed (generalised linear or generalised additive modelling), the strategy used to select explanatory variables and to determine 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 2 df). Correction for multiple testing using the Bonferroni correction factor did not improve the accuracy of models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

An evaluation of alternative algorithms for fitting species distribution models using logistic regression

Ecological Modelling, Volume 128 (2) – Apr 20, 2000

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Publisher
Elsevier
Copyright
Copyright © 2000 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
D.O.I.
10.1016/S0304-3800(99)00227-6
Publisher site
See Article on Publisher Site

Abstract

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 variables 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 a number of factors, including the basic modelling technique employed (generalised linear or generalised additive modelling), the strategy used to select explanatory variables and to determine 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 2 df). Correction for multiple testing using the Bonferroni correction factor did not improve the accuracy of models.

Journal

Ecological ModellingElsevier

Published: Apr 20, 2000

References

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