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

By Cameron S. Gillies, Mark Hebblewhite, Scott E. Nielsen, M. Krawchuk, C. Aldridge, Jacqui Frair, D. J. Saher, C. Stevens, C. Jerde. Journal of Animal Ecology, 2006

821 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/5a452b2e263195b11cf713223f3d0768651f8612

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Citations in the corpus

Modelling wildlife–human relationships for social species with mixed‐effects resource selection models (2007, methodology): “ Gillies et al. 2006 ), or may be expected to demonstrate a functional response in resource selection for a limiting resource (Mysterud & Ims 1998).”
Flexible habitat selection by cougars in response to anthropogenic development (2014, background): “Variation in habitat selection patterns among individuals can be important, and is not accounted for in many population-level RSF models (Gillies et al., 2006).”
Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges (2010, background): “2010), with particular emphasis on individual-level variability and processes (Gillies et al. 2006; Forester et al. 2007); (ii) disentangle patterns of trajectories that correspond to behavioural phases or to specific behaviours (Morales et al.”
Multi-level functional responses for wildlife conservation: the case of threatened caribou in managed boreal forests (2012, methodology): “Most functional responses that have been described to date have assessed changes in the selection among individuals as a function of variation in home-range characteristics, that is, at an interHR level (Mysterud & Ims 1998; Gillies et al. 2006; Hebblewhite & Merrill 2008; Herfindal et al. 2009; Hou”
Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus) (2013, positive): “with a varying number of paths per individual, and because response to landscape could vary among individuals (Gillies et al. 2006), we performed a Landscape Ecol (2013) 28:471–486 475”
Shifting perceptions of risk and reward: Dynamic selection for human development by black bears in the western United States (2015, methodology): “We included the random intercept to account for autocorrelation within animal datasets and differences in sample sizes among individuals (Gillies et al., 2006).”
Behavior and nutritional condition buffer a large‐bodied endotherm against direct and indirect effects of climate (2014, methodology): “…for each individual elk, the relative strength of selection for lowcost areas, and for forage quality, during each season and diel period (Gillies et al. 2006, Indermaur et al. 2009, Wagner et al. 2011); accordingly, we used those conditional estimates to evaluate patterns of…”
Establishing the link between habitat selection and animal population dynamics (2015, background): “For this reason, there is a concerted remedial effort to try and improve their shortcomings (Arthur et al. 1996, Mauritzen et al. 2003, Gillies et al. 2006, Hebblewhite and Merrill 2008, Godvik et al. 2009, McLoughlin et al. 2010, Matthiopoulos et al. 2011) and help them fulfill their original…”
Correlation and studies of habitat selection: problem, red herring or opportunity? (2010, methodology): “Although random intercepts are commonly used to account for correlation (assuming an exchangeable within-animal correlation structure), more biologically meaningful models may be constructed by allowing the selection of various habitat features to vary by individual via random coefficients (Gillies ”
Selecting traits that explain species–environment relationships: a generalized linear mixed model approach (2013, neutral): “In applying generalized linear models, such as logistic regression, researchers often ignore the hierarchical structure of the data thereby producing incorrect variance estimates and increasing the likelihood of committing type I error (Gillies et al. 2006, Wagner et al. 2005).”
Black bear adaptation to low productivity in the boreal forest (2008, result): “Contrary to the results of Gillies et al. (2006), models based on mixed effects had a poorer fit (lower R2 for linear regression test, significant chi-square goodness-of-fit test, slope and intercept of the linear regression significantly different from 1 and 0, respectively; Johnson et al., 2006)…”
Know Thy Enemy: Experience Affects Elk Translocation Success in Risky Landscapes (2007, methodology): “We estimated RSF coefficients separately for winter (15 Oct–14 May) and summer (15 May–14 Oct) using a generalized linear mixed model with a logit link, clustering the data for each wolf (Gillies et al. 2006).”
Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation (2018, background): “Although the importance of including random coefficients in regression models of habitat-selection studies has been stressed repeatedly (Gillies et al., 2006; Duchesne et al., 2010), our literature review suggests that random-effects models are often understood as models that merely include a random”