Modeling grizzly bear habitats in the Yellowhead ecosystem of Alberta: taking autocorrelation seriously

By S. Nielson, Boyce, Gordon B. Stenhouse, Robin Munro. 2002

187 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/986f665c0b660e1c4f446bdd8ff30fd222da9a25

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Citations in the corpus

Scales of movement by elk (Cervus elaphus) in response to heterogeneity in forage resources and predation risk (2005, methodology): “When estimating standard errors for coefficients, we employed a clustering algorithm to account for the individual elk (N = 18) rather than the GPS location (N = 17,692) as the unit of replication (Pendergast et al. 1996; Nielsen et al. 2002).”
A habitat-based framework for grizzly bear conservation in Alberta (2006, background): “Maps of safe-harbour habitats differed from traditional radiotelemetry-based maps of grizzly bear occurrence in Alberta (e.g., Nielsen et al., 2002, 2003; Theberge, 2002), because they also consider security (low mortality risk), similar to concepts of habitat effectiveness and security (i.e., Gib”
Human Activity Differentially Redistributes Large Mammals in the Canadian Rockies National Parks (2011, methodology): “Matched case-control logistic regression was performed using robust variance estimates (Huber-White sandwich estimator) to account for autocorrelation in GPS data (Nielson et al. 2002).”
Modelling distribution and abundance with presence‐only data (2006, methodology): “To avoid committing a Type I error, adjustments for autocorrelation can be achieved using post-hoc methods of variance inflation ( Nielsen et al. 2002 ) such as the Newey‐West method (Newey & West 1987), or autocorrelation can be modelled more explicitly using mixed models (Laidre et al. 2004).”
Flexible habitat selection by cougars in response to anthropogenic development (2014, background): “However, population models based on average individual responses will poorly predict behavior of individual animals if variation among individuals is high (Nielsen et al., 2002).”
Modelling wildlife–human relationships for social species with mixed‐effects resource selection models (2007, background): “Statistical advances in RSF models especially have overcome earlier methodological deficiencies (Garshelis 2000) by accommodating categorical and continuous covariates, nonlinear functions, autocorrelation ( Nielsen et al . 2002 ), and flexible definitions of availability (Compton, Rhymer & McCollou”
Correlation and studies of habitat selection: problem, red herring or opportunity? (2010, methodology): “the buffer) (Rhodes et al. 2005). Rather than assume ga to be known, Hjermann (2000) estimated ga using observed step lengths (r) and a model for log(r) as a linear function of the log(time between observations) and environmental variables (with w subsequently estimated using ĝa and the MANOVA appro”
Evaluating resource selection functions (2002, methodology): “A more satisfactory approach is to use variance inflators to obtain robust standard errors ( Nielsen et al., 2002; Vernier et al. 2002; also see Section 2.4.2).”
Grizzly bears and forestry: II: Grizzly bear habitat selection and conflicts with road placement (2008, background): “Individualmodelswere chosen because bears are highly variable in their behaviour and selection (Nielsen et al., 2002).”
The pitfalls of ignoring behaviour when quantifying habitat selection (2014, methodology): “To reduce the probability of a Type I error resulting from serially correlated telemetry data, we used Newey–West variance inflation when estimating standard errors (Newey & West, 1987; Nielsen et al., 2002).”