Abstract
Resource 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.
Highlights
This document provides a practical guide for developing resource selection functions from telemetry data, using generalized estimating equations and generalized linear mixed models, and outlines how to validate these models using k-fold cross validation
For more detailed explanations and to better understand the theory and mathematics behind these methods, readers should refer to Koper & Manseau (2009), in which we cover most of the topics within the present manuscript in more detail; Gillies et al (2006) and Bolker et al (2009) regarding generalized linear mixed models (GLMM); and Boyce et al (2002) regarding k-fold cross validation, as well as numerous excellent sources and textbooks referred to in those works
We note that this paper discusses the development of resource selection functions (RSF), which estimate the relative probability of use of different habitat types, rather than resource selection probability functions (RSPF), which estimate the actual probability of a habitat being used; for more information on the additional assumptions and issues associated with RSPFs, see Lele & Keim (2006)
Summary
Introduction and rationaleThis document provides a practical guide for developing resource selection functions from telemetry data, using generalized estimating equations and generalized linear mixed models, and outlines how to validate these models using k-fold cross validation. While generally serial autocorrelation has relatively little effect on the parameter estimates that are derived from statistical models, they can affect any associated statistical comparisons, or any analysis that uses standard errors or confidence intervals.
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