Generalized estimating equations and generalized linear mixed-effects models for modelling resource selection.
Abstract
Accurate 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. We 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. Statistical 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. Synthesis 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.