A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models
Keywords:autocorrelation, conditional models, empirical standard errors, GEE, generalized estimating equations, generalized linear mixed-effects models, GLMM, k-fold cross validation, marginal models
AbstractResource selection functions (RSF) are often developed using satellite (ARGOS) or Global Positioning System (GPS) telemetry datasets, which provide a large amount of highly correlated data. We discuss and compare the use of generalized linear mixed-effects models (GLMM) and generalized estimating equations (GEE) for using this type of data to develop RSFs. GLMMs directly model differences among caribou, while GEEs depend on an adjustment of the standard error to compensate for correlation of data points within individuals. Empirical standard errors, rather than model-based standard errors, must be used with either GLMMs or GEEs when developing RSFs. There are several important differences between these approaches; in particular, GLMMs are best for producing parameter estimates that predict how management might influence individuals, while GEEs are best for predicting how management might influence populations. As the interpretation, value, and statistical significance of both types of parameter estimates differ, it is important that users select the appropriate analytical method. We also outline the use of k-fold cross validation to assess fit of these models. Both GLMMs and GEEs hold promise for developing RSFs as long as they are used appropriately.
How to Cite
Authors retain copyright and grant Rangifer irrevocable and non-exclusive right of publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY). This means, among other things, that anyone is free to copy and distribute the content, as long as they give proper credit to the author(s) and the journal. For further information, see Creative Commons website for human readable or lawyer readable versions.