A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models

Nicola Koper, Micheline Manseau

Full Text:


DOI: https://doi.org/10.7557/


autocorrelation; conditional models; empirical standard errors; GEE; generalized estimating equations; generalized linear mixed-effects models; GLMM; k-fold cross validation; marginal models


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.


  • There are currently no refbacks.

Copyright (c) 2015 Nicola Koper, Micheline Manseau

License URL: http://creativecommons.org/licenses/by/3.0/