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Abstract:
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The delineation of animal home ranges is a long studied topic where kernel density based methods have been widely employed. Moreover, certain methods have been traditionally utilized for characterizing animal core areas, though a robust method, spanning species and environments, is still needed. We present a new method for statistical core area estimation when available data are limited to temporally independent animal locations with continuous spatial support on a bounded region. A key concept to this method is the optimal partitioning of a clustered spatial point process into a set of two complete spatially random point processes such that the union of their support is equal to the home range. Additionally, we account for uncertainty in the core area delineation by allowing it to be a random set that is parameterized and estimated with Bayesian methods.
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