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Mixed conditional logistic regression for habitat selection studies
Article first published online: 25 FEB 2010
DOI: 10.1111/j.1365-2656.2010.01670.x
© 2010 The Authors. Journal compilation © 2010 British Ecological Society
Additional Information
How to Cite
Duchesne, T., Fortin, D. and Courbin, N. (2010), Mixed conditional logistic regression for habitat selection studies. Journal of Animal Ecology, 79: 548–555. doi: 10.1111/j.1365-2656.2010.01670.x
Publication History
- Issue published online: 22 MAR 2010
- Article first published online: 25 FEB 2010
- Received 26 October 2009; accepted 15 January 2010 Handling Editor: Fanie Pelletier
Keywords:
- case–control location sampling;
- farmland;
- Global Positioning System;
- likelihood-ratio test;
- mixed multinomial logit model;
- Prince Albert National Park;
- Spatially Explicit Landscape Event Simulator
Summary
1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unconditional (standard) and conditional logistic regressions. While the advantage of mixed-effects models is recognized for standard logistic regression, mixed conditional logistic regression remains largely overlooked in ecological studies.
2. We demonstrate the significance of mixed conditional logistic regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison.
3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects regressions, and fixed-effects models could even provide faulty conclusions.
4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands.
5. Conditional logistic regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects conditional logistic regression should become a valuable tool for ecological research.