A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed 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 eq...
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ftdoajarticles:oai:doaj.org/article:82823b41e3634634a944913a1aa6baa9 2023-05-15T15:53:32+02:00 A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models Nicola Koper Micheline Manseau 2012-03-01T00:00:00Z https://doi.org/10.7557/2.32.2.2269 https://doaj.org/article/82823b41e3634634a944913a1aa6baa9 EN eng Septentrio Academic Publishing https://septentrio.uit.no/index.php/rangifer/article/view/2269 https://doaj.org/toc/1890-6729 doi:10.7557/2.32.2.2269 1890-6729 https://doaj.org/article/82823b41e3634634a944913a1aa6baa9 Rangifer, Vol 32, Iss 2 (2012) autocorrelation conditional models empirical standard errors GEE generalized estimating equations generalized linear mixed-effects models Animal culture SF1-1100 article 2012 ftdoajarticles https://doi.org/10.7557/2.32.2.2269 2022-12-31T14:36:55Z 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. Article in Journal/Newspaper caribou Rangifer Directory of Open Access Journals: DOAJ Articles Rangifer 32 2 195 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
autocorrelation conditional models empirical standard errors GEE generalized estimating equations generalized linear mixed-effects models Animal culture SF1-1100 |
spellingShingle |
autocorrelation conditional models empirical standard errors GEE generalized estimating equations generalized linear mixed-effects models Animal culture SF1-1100 Nicola Koper Micheline Manseau A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models |
topic_facet |
autocorrelation conditional models empirical standard errors GEE generalized estimating equations generalized linear mixed-effects models Animal culture SF1-1100 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Nicola Koper Micheline Manseau |
author_facet |
Nicola Koper Micheline Manseau |
author_sort |
Nicola Koper |
title |
A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models |
title_short |
A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models |
title_full |
A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models |
title_fullStr |
A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models |
title_full_unstemmed |
A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models |
title_sort |
guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models |
publisher |
Septentrio Academic Publishing |
publishDate |
2012 |
url |
https://doi.org/10.7557/2.32.2.2269 https://doaj.org/article/82823b41e3634634a944913a1aa6baa9 |
genre |
caribou Rangifer |
genre_facet |
caribou Rangifer |
op_source |
Rangifer, Vol 32, Iss 2 (2012) |
op_relation |
https://septentrio.uit.no/index.php/rangifer/article/view/2269 https://doaj.org/toc/1890-6729 doi:10.7557/2.32.2.2269 1890-6729 https://doaj.org/article/82823b41e3634634a944913a1aa6baa9 |
op_doi |
https://doi.org/10.7557/2.32.2.2269 |
container_title |
Rangifer |
container_volume |
32 |
container_issue |
2 |
container_start_page |
195 |
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1766388640574865408 |