Generalized estimating equations and generalized linear mixed-effects models for modelling resource selection
Article first published online: 27 MAR 2009
© 2009 The Authors. Journal compilation © 2009 British Ecological Society
Journal of Applied Ecology
Volume 46, Issue 3, pages 590–599, June 2009
How to Cite
Koper, N. and Manseau, M. (2009), Generalized estimating equations and generalized linear mixed-effects models for modelling resource selection. Journal of Applied Ecology, 46: 590–599. doi: 10.1111/j.1365-2664.2009.01642.x
- Issue published online: 28 APR 2009
- Article first published online: 27 MAR 2009
- Received 14 October 2008; accepted 24 February 2009Handling Editor: Mark Hebblewhite
- correlated data;
- k-fold cross-validation;
- resource selection function;
- woodland caribou
- 1Accurate resource selection functions (RSFs) are important for managing animal populations. Developing RSFs using data from GPS telemetry can be problematic due to serial autocorrelation, but modern analytical techniques can help to compensate for this correlation.
- 2We used telemetry locations from 18 woodland caribou Rangifer tarandus caribou in Saskatchewan, Canada, to compare marginal (population-specific) generalized estimating equations (GEEs), and conditional (subject-specific) generalized linear mixed-effects models (GLMMs), for developing resource selection functions at two spatial scales. We evaluated the use of empirical standard errors, which are robust to misspecification of the correlation structure. We compared these approaches with destructive sampling.
- 3Statistical significance was strongly influenced by the use of empirical vs. model-based standard errors, and marginal (GEE) and conditional (GLMM) results differed. Destructive sampling reduced apparent habitat selection. k-fold cross-validation results differed for GEE and GLMM, as it must be applied differently for each model.
- 4Synthesis and applications. Due to their different interpretations, marginal models (e.g. generalized estimating equations, GEEs) may be better for landscape and population management, while conditional models (e.g. generalized linear mixed-effects models, GLMMs) may be better for management of endangered species and individuals. Destructive sampling may lead to inaccurate resource selection functions (RSFs), but GEEs and GLMMs can be used for developing RSFs when used with empirical standard errors.