Modeling grizzly bear habitats in the Yellowhead Ecosystem of Alberta: Taking autocorrelation seriously

Authored by SE Nielson, MS Boyce, GB Stenhouse, RHM Munro

Date Published: 2002

Sponsors: National Science and Engineering Research Council of Canada (NSERC) Alberta Conservation Association (ACA) Ainsworth Lumber Alberta Energy Company Alberta Sustainable Resource Development Alberta Newsprint Anderson Resources Ltd. AVID Canada BC Oil and Gas Commission Environmental Fund Blue Ridge Lumber Mountain Equipment Coop Petroleum Technology Alliance of Canada (PTAC) The Centre for Wildlife Conservation (USA) World Wildlife Fund

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

We used resource selection functions (RSF) to estimate relative probability ofgrizzly bear (Ursus arctos) use for habitats, landscape features, and areas of varying human access density across a 5,342-km(2) study area in west-central Alberta, Canada. Models were developed based on 1999 data at both the population and individual levels for the spring and summer-autumn seasons. Individual-based RSF models revealed strong differences in selection among animals. Models developed for the summer-autumn season fit better than models of the spring season. High greenness values, derived from Landsat imagery, corresponded well with grizzly bear habitat use. Significance of parameters was frequently overestimated when using logistic regression models that were unadjustedfor autocorrelation or pseudoreplication, both in individual-based models andin population-based models. Although not affecting predictability of bears atthe individual-level, such biases may lead to inappropriate conclusions without adjustment. Population-based models further showed bias without correction for pseudo-replication within individuals (unit of replication). Considerationof variance inflation factors and nesting of telemetry points on the individual enhances the reliability of habitat modeling. We found problems predicting grizzly bear habitat use when local habitat index models were used. The RSF models presented here improve such models while also generating information on the contribution of particular environmental variables.

Tags