Development and testing of phenologically driven grizzly bear habitat models

Abstract We developed and compared three habitat models for estimating the relative probability of occurrence, by month, for grizzly bears (Ursus arctos) in Jasper National Park (JNP), Alberta. These models included 1) a habitat map derived from remote sensing Landsat imagery; 2) food-index models generated from the predicted occurrence of bear foods and assigned monthly importance values; and 3) probabilistic food models representing the occurrence of each bear food. Resource selection function (RSF) models for grizzly bears were generated using 3,924 global positioning system (GPS) radiotelemetry locations and the above habitat models. Comparisons were made among RSF models, by month, using Akaike’s Information Criterion (AIC). In all seven months (April to October), food-index models performed poorly. In April and July, the remote-sensing habitat map predicted bears best, while the food-probability models performed best in the remaining five months. Overall, we found substantial improvement by using food-probability models for predicting JNP grizzly bear occurrence. Remote-sensing maps, although predictive, may not reveal underlying mechanisms and fail to recognize the dynamic nature of seasonal grizzly bear habitats. The disconnect between food-index and food-probability models suggests that monthly food importance values require additional parameterization. Development of spatial food models on phenologically important scales more closely matches the resources and temporal scales at which animals perceive and use resources.

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