Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management

Abstract

For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.

Spatiotemporal risk factors predict landscape‐scale survivorship for a northern ungulate

“…SWE is the vertical depth of water that would be produced if the snow was instantaneously melted on a horizontal surface (USDA, 2022). Thus, in the absence of actual snow depth, we presented SWE depth (in centimeters) based on a unit conversion (1 kg/m 2 = 0.1 cm) to aid interpretation (Reinking et al, 2022). However, if spatial snow densities ($$$ {\uprho}_s $$$) were available, we could translate estimates of SWE depth to snow depth (HS) via the following equation: $$$$ \mathrm{SWE}=\mathrm{HS}\times \frac{\uprho_s}{\uprho_w}, $$$$ where $$$ {\uprho}_w $$$ is the density of water (Reinking et al, 2022).…”

“…Thus, in the absence of actual snow depth, we presented SWE depth (in centimeters) based on a unit conversion (1 kg/m 2 = 0.1 cm) to aid interpretation (Reinking et al, 2022). However, if spatial snow densities ($$$ {\uprho}_s $$$) were available, we could translate estimates of SWE depth to snow depth (HS) via the following equation: $$$$ \mathrm{SWE}=\mathrm{HS}\times \frac{\uprho_s}{\uprho_w}, $$$$ where $$$ {\uprho}_w $$$ is the density of water (Reinking et al, 2022). In our study area, average snowpack densities were only available at a single location each year, so we relied on SWE depth and used the R packages daymetr (Hufkens et al, 2018) and FedData (Bocinsky, 2019) to mosaic the daily tiles.…”

Virtual snow stakes: a new method for snow depth measurement at remote camera stations

“…Fine‐scale snow data could be useful in many wildlife studies (Boelman et al 2019, Reinking et al 2022). Remotely sensed, satellite‐based, and modelled snow data products are coarse scale (typically hundreds of meters to several kilometers), frequently prone to error in complex forested terrain (Sirén et al 2018), and do not capture the considerable spatial variability of snow depth under forest canopies (Jost et al 2007).…”

“…Fine-scale snow data could be useful in many wildlife studies (Boelman et al 2019, Reinking et al 2022.…”

Defining the danger zone: critical snow properties for predator–prey interactions

“…Seasonal snow cover shapes a suite of ecological processes across nearly half of all land in the Northern Hemisphere (Robinson et al 2014, Niittynen et al 2018). The dynamic nature of snow throughout the landscape and throughout the year presents a challenge Defining the danger zone: critical snow properties for predatorprey interactions for evaluating wildlife-snow relationships (Reinking et al 2022). Furthermore, climate change is rapidly altering seasonal snowpacks globally, with the greatest effects observed across northern Eurasia and North America (IPCC 2022).…”