%0 Journal Article %1 gillies_application_2006 %A GILLIES, CAMERON S %A HEBBLEWHITE, MARK %A NIELSEN, SCOTT E %A KRAWCHUK, MEG A %A ALDRIDGE, CAMERON L %A FRAIR, JACQUELINE L %A SAHER, D. JOANNE %A STEVENS, CAMERON E %A JERDE, CHRISTOPHER L %D 2006 %J Journal of Animal Ecology %K bear, effects, function functional grizzly habitat random resource response, selection selection, %N 4 %P 887--898 %R 10.1111/j.1365-2656.2006.01106.x %T Application of random effects to the study of resource selection by animals %U http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2656.2006.01106.x/full %V 75 %X 1Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence.2Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability.3Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed.4Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects.5Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection.6We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.

@article{gillies_application_2006, abstract = {1Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence.2Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability.3Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed.4Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects.5Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection.6We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {GILLIES, CAMERON S and HEBBLEWHITE, MARK and NIELSEN, SCOTT E and KRAWCHUK, MEG A and ALDRIDGE, CAMERON L and FRAIR, JACQUELINE L and SAHER, D. JOANNE and STEVENS, CAMERON E and JERDE, CHRISTOPHER L}, biburl = {https://www.bibsonomy.org/bibtex/24eb46697c22e23ebe2b3c1e5084560b7/yourwelcome}, copyright = {© 2006 The Authors. Journal compilation © 2006 British Ecological Society}, doi = {10.1111/j.1365-2656.2006.01106.x}, interhash = {549cbfa6a1a1a6f9a3f536f00e45071f}, intrahash = {4eb46697c22e23ebe2b3c1e5084560b7}, issn = {1365-2656}, journal = {Journal of Animal Ecology}, keywords = {bear, effects, function functional grizzly habitat random resource response, selection selection,}, language = {en}, month = jul, number = 4, pages = {887--898}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Application of random effects to the study of resource selection by animals}, url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2656.2006.01106.x/full}, urldate = {2012-03-08}, volume = 75, year = 2006 }