Application of random effects to the study of resource selection by animals

Abstract

Summary 1.Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate populationlevel 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. 3. Although 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. 4. Here 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. 5. Both 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. 6. We 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.

Living on the edge: Multiscale habitat selection by cheetahs in a human‐wildlife landscape

“…Cheetah ID was included as a random factor to account for differences between individual cheetahs (Gillies et al., 2006). For each analysis, the Akaike Information Criterion (AIC) was used to select the best model, with the lowest AIC‐value representing the best model (Burnham & Anderson, 2002).…”

“…The parameters of the best model were used to create the habitat suitability map using the resource selection function by the exponential form of$wfalse(xfalse)=expfalse({\mathrm{\beta }}_0+{\beta }_1{x}_{1ij}+{\beta }_2{x}_{2ij}+\cdots +{\beta }_n{x}_{\mathrm{italicnij}}+{\gamma }_{0j}false)$whereby w ( x ) is the outcome of the RSF, x n the covariates, β n the fixed regression coefficients for locations i and individuals j , β 0 the mean intercept and γ 0 j the random intercept for individuals j (Manly et al., 2002; Gillies et al., 2006). …”

Prevalence of Antibodies to Canine Parvovirus and Distemper Virus in Wolves in the Canadian Rocky Mountains

“…In the case of model selection uncertainty, we model-averaged across the top 0-4 DAIC (Burnham and Anderson, 1998). We also considered a random effect for each individual pack (including unknown pack) to accommodate potential between-pack heterogeneity in exposure to diseases (Gillies et al, 2006). Despite our best field efforts, however, wolves were not captured randomly or evenly across the study area, which might bias disease exposure results if age, sex, or packs varied over time.…”

Determining habitat quality for species that demonstrate dynamic habitat selection

“…This allows plasticity in foraging behavior to be incorporated into models of systems with variation in the resource base (Gillies et al. 2006, Hebblewhite and Merrill 2008, Godvik et al. 2009).…”