For wildlife inhabiting snowy environments, snow properties such as onsetdate, depth, strength, and distribution can influence many aspects of ecology,including movement, community dynamics, energy expenditure, and forageaccessibility. As a result, snow plays a considerable role in individual fitnessand ultimately population dynamics, and its evaluation is, therefore, importantfor comprehensive understanding of ecosystem processes in regions experienc-ing snow. Such understanding, and particularly study of how wildlife–snowrelationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. How-ever, studying and monitoring wildlife–snow relationships continue to be chal-lenging because characterizing snow, an inherently complex and constantlychanging environmental feature, and identifying, accessing, and applying rele-vant snow information at appropriate spatial and temporal scales, often require adetailed understanding of physical snow science and technologies that typically lieoutside the expertise of wildlife researchers and managers. We argue that thor-oughly assessing the role of snow in wildlife ecology requires substantive collabora-tion between researchers with expertise in each of these two fields, leveraging thediscipline-specific knowledge brought by both wildlife and snow professionals. Tofacilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five-step protocol: (1) identify relevant snow propertyinformation; (2) specify spatial, temporal, and informational requirements; (3) buildthe necessary datasets; (4) implement quality control procedures; and (5) incorpo-rate snow information into wildlife analyses. Additionally, we explore the types ofsnow information that can be used within this collaborative framework. We illus-trate, in the context of two examples, field observations, remote-sensing datasets,and four example modeling tools that simulate spatiotemporal snow property distri-butions and, in some cases, evolutions. For each type of snow data, we highlightthe collaborative opportunities for wildlife and snow professionals when designingsnow data collection efforts, processingsnow remote sensing products, producingtailored snow datasets, and applying the resulting snow information in wildlife ana-lyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best availablesnow science through collaboration with snow professionals.
