Navigating snowscapes: scale‐dependent responses of mountain sheep to snowpack properties

摘要: Winters are limiting for many terrestrial animals due to energy deficits brought on by resource scarcity and the increased metabolic costs of thermoregulation traveling through snow. A better understanding how respond snow conditions is needed predict impacts climate change wildlife. We compared performance remotely sensed modeled products as predictors winter movements at multiple spatial temporal scales using a data set 20,544 locations from 30 GPS-collared Dall sheep (Ovis dalli dalli) in Lake Clark National Park Preserve, Alaska, USA 2005 2008. used daily 500-m MODIS normalized difference index (NDSI), multi-resolution depth density outputs snowpack evolution model (SnowModel), covariates step selection functions. predicted that would perform best across all more informative spatiotemporal variation relevance animal movement. Our results indicated adding any evaluated metrics substantially improved helped characterize movements. As expected, SnowModel-simulated outperformed NDSI fine-to-moderate (step 300 kg/m3 ) depths above chest height, which likely further reduced expenditure hoof penetration deeper snows. At moderate-to-coarse (112-896 h scales), however, was best-performing covariate. Thus, use publicly available, sensed, cover can improve models movement, particularly cases where movement distances exceed grid threshold. However, remote sensing may require substantial thinning cloud cover, potentially its power complex necessary. Snowpack such SnowModel offer users flexibility expense added complexity, but provide critical insights into fine-scale responses rapidly changing properties.