| Marcus E. Blum; University of Nevada, Reno; 1664 N Virginia St, MS 186, Reno, NV, 89557; (979) 450-3092; email@example.com; Kelley M. Stewart, Mike Cox, Brian Wakeling|
|Selection of resources that effect the development of a fetus and increase probability of survival for neonates is essential for maintaining viable populations in large ungulates. Therefore, it is crucial that biologists understand how species select resources across gestation to increase their ability to manage recruitment. Desert bighorn sheep (Ovis canadensis nelsoni) populations have dwindled across their range over the last several decades and translocations have been a key management strategy for recolonizing areas. To increase understanding of sheep resource selection during gestation and following parturition, we captured and collared 30 adult, female sheep on Lone Mountain (west of Tonopah, NV). In addition to receiving collars, all individuals were given vaginal implant transmitters to provide parturition timing information. We used a machine learning algorithm, random forest, to identify habitat selection during gestation, following parturition events, and following the mortality of neonates. Our results indicated that adults shifted resource selection from areas with higher nutritional availability to more precipitous terrain immediately following parturition events. In addition, our results indicated that females shifted resource selection to areas with higher quality vegetation and reduced terrain ruggedness following the mortality of a neonate and as neonate age progressed.
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