Winter severity for white‐tailed deer in Alberta, Canada

Abstract

Winter Severity Indices ( WSI s) are especially important for white‐tailed deer ( Odocoileus virginianus ), a species for which population dynamics often are tied to winter conditions throughout much of their range. However, existing WSI s often oversimplify environmental variability, limiting their ability to support effective management decisions. To address this, we developed a data‐driven WSI _Vaughan that uses fine‐scale weather data to better reflect the spatial and temporal variability of winter conditions across Alberta. Our model generates region‐specific indices that can support tailored management strategies within Alberta's distinct ecoregions. Using publicly available temperature and precipitation data, we modeled winter severity for each of 5 ecoregions in Alberta and compared multiple WSI formulations using information‐theoretic methods. We found that temperature patterns were broadly consistent across large areas, whereas snow‐related variables were more spatially localized. We used route regression with the wildlife management unit (WMU) as the unit of replication, estimating autoregressive models to include the previous year's maternal effects. We found significant regional variation in winter severity, e.g., with the Northern Boreal ecoregion experiencing harsher conditions than the Prairie region. Among all models tested, our WSI _Vaughan best explained year‐over‐year changes in hunter success, a proxy for deer abundance, outperforming widely used threshold‐based indices. These findings support the value of localized, ecologically‐relevant models for informing adaptive wildlife management.