Wildlife Habitat Selection on Landscapes with Industrial Disturbance

摘要: Technological advancements in remote sensing and telemetry provide opportunities for assessing the effects of expanding extractive industries on animal populations. Here, we illustrate applicability resource selection functions (RSFs) modelling wildlife habitat industrially-disturbed landscapes. We used grizzly bears (Ursus arctos) from a threatened population Canada surface mining as case study. RSF predictions based GPS radiocollared (nduring = 7; npost 9) showed that males solitary females selected areas primarily outside mineral leases (MSLs) during active mining, conversely inside MSLs after mine closure. However, with cubs within compared to irrespective activity. Individual variability was pronounced, although some environmental- human-related variables were consistent across reproductive classes. For females, regional-scale RSFs yielded comparable results site-specific models, whereas cubs, two scales produced divergent results. While reclamation may afford bear persistence, managing public access will likely decrease risk human-caused mortality. are powerful tools merit widespread use quantitative visual investigations industrially-modified landscapes, using Geographic Information System layers precisely characterize conditions.