Kernel-based utilization distribution (UD) estimates are powerful tools to investigate home range space use and resource selection in many vertebrate species. By ignoring local movement information provided by the serial correlation between successive locations and the constraints to movement imposed by obvious boundaries, the classical kernel method results in loosely estimated UDs that tend to overflow into never-visited areas and eventually in possibly biased estimates of space use and habitat selection. We improved biological relevance of kernel home range space use estimates by incorporating both movement (and activity) information and boundary constraints.
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