Canadian Journal of Zoology · 2008 · 32 citations · 62 references
Life SciencesEcologyZoologyLandscape ModelingPopulation EcologyWildlife ManagementAnimal EcologyHabitat SelectionSeasonal EcologyWildlife ConservationWildlife EcologyWildlife-habitat RelationshipsGeographic Information System DataLogistic RegressionRaster-based Geographic ProfilingResource Selection AnalysisResource Selection FunctionsSatellite Imagery AnalysisSeasonal Habitat Use AssessmentSubsistence Resource Use
Conservation planning for species of concern or importance can be aided by resource selection functions (RSFs) that identify important areas or attributes. Models that can be interpreted biologically and provide reasonable predictive capacity may best be based on data from individuals grouped into seasonal selection strategies for particular geographical areas or similarities in topographical and vegetative associations. We used logistic regression, the information–theoretic approach, satellite imagery, and locational data (n = 31 females; 16 803 locations) from global positioning system (GPS) collared woodland caribou ( Rangifer tarandus caribou (Gmelin, 1788)) to model resource selection by animals during calving, summer, fall, breeding, winter, and late-winter seasons. Higher variation in resource use corresponded to times when caribou and their young were most susceptible to predation or when food resources were limited. Even with multiple selection strategies, caribou followed a general progression from higher to lower elevation habitats from calving and summer to late winter. Caribou selected against or completely avoided the burned–disturbed vegetation class in every season except summer. We incorporated RSFs with a raster geographic information system to create selection landscapes. We validated selection landscapes using withheld GPS data (n = 6077), 50 known calving sites, and Spearman’s rank correlation coefficient. Selection models and final selection landscapes performed well in validating use locations of woodland caribou in all seasons (all P < 0.003) and in predicting known calving sites (P < 0.001). When seasonal selection strategies are identified and models are coupled with validation, RSFs are effective tools to assist in conservation planning.
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Applied Logistic Regression Analysis
Technometrics · 1996 · 3.6K citations
Evaluating resource selection functions
Mark S. Boyce, Pierre Vernier, Scott E. Nielsen et al. · Ecological Modelling · 2002 · 2.6K citations