A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution.

Snow covers Arctic and boreal regions (ABRs) for approximately 9 months of the year, thus snowscapes dominate the form and function of tundra and boreal ecosystems. In recent decades, Arctic warming has changed the snowcover's spatial extent and distribution, as well as its seasonal timing and duration, while also altering the physical characteristics of the snowpack. Understanding the little studied effects of changing snowscapes on its wildlife communities is critical. The goal of this paper is to demonstrate the urgent need for, and suggest an approach for developing, an improved suite of temporally evolving, spatially distributed snow products to help understand how dynamics in snowscape properties impact wildlife, with a specific focus on Alaska and northwestern Canada. Via consideration of existing knowledge of wildlife-snow interactions, currently available snow products for focus region, and results of three case studies, we conclude that improving snow science in the ABR will be best achieved by focusing efforts on developing data-model fusion approaches to produce fitfor-purpose snow products that include, but are not limited to, wildlife ecology. The relative wealth of coordinated in situ measurements, airborne and satellite remote sensing data, and modeling tools being collected and developed as part of NASA's Arctic Boreal Vulnerability Experiment and SnowEx campaigns, for example, provide a data rich environment for developing and testing new remote sensing algorithms and retrievals of snowscape properties.

Arctic ungulates are experiencing the most rapid climate warming on Earth. While concerns have been raised that more frequent icing events may cause die‐offs, and earlier springs may generate a trophic mismatch in phenology, the effects of warming autumns have been largely neglected. We used 25 years of individual‐based data from a growing population of wild Svalbard reindeer, to test how warmer autumns enhance population growth. Delayed plant senescence had no effect, but a six‐week delay in snow‐onset (the observed data range) was estimated to increase late winter body mass by 10%. Because average late winter body mass explains 90% of the variation in population growth rates, such a delay in winter‐onset would enable a population growth of r = 0.20, sufficient to counteract all but the most extreme icing events. This study provides novel mechanistic insights into the consequences of climate change for Arctic herbivores, highlighting the positive impact of warming autumns on population viability, offsetting the impacts of harsher winters. Thus, the future for Arctic herbivores facing climate change may be brighter than the prevailing view.

Climate change is rapidly altering the composition and availability of snow, with implications for snow-affected ecological processes, including reproduction, predation, habitat selection, and migration. How snowpack changes influence these ecological processes is mediated by physical snowpack properties, such as depth, density, hardness, and strength, each of which is in turn affected by climate change. Despite this, it remains difficult to obtain meaningful snow information relevant to the ecological processes of interest, precluding a mechanistic understanding of these effects. This problem is acute for species that rely on particular attributes of the subnivean space, for example depth, thermal resistance, and structural stability, for key life-history processes like reproduction, thermoregulation, and predation avoidance. We used a spatially explicit snow evolution model to investigate how habitat selection of a species that uses the subnivean space, the wolverine, is related to snow depth, snow density, and snow melt on Arctic tundra. We modeled these snow properties at a 10 m spatial and a daily temporal resolution for 3 years, and used integrated step selection analyses of GPS collar data from 21 wolverines to determine how these snow properties influenced habitat selection and movement. We found that wolverines selected deeper, denser snow, but only when it was not undergoing melt, bolstering the evidence that these snow properties are important to species that use the Arctic snowpack for subnivean resting sites and dens. We discuss the implications of these findings in the context of climate change impacts on subnivean species.

Pronghorn (Antilocapra americana) are an iconic wildlife species of sagebrush (Artemisia spp.) and grassland ecosystems in western North America. Over 50% of pronghorn have historically occurred in Wyoming; however, these populations have declined by nearly 30% in <2 decades, concurrent with expanding energy development and prolonged drought. Research suggests adult female pronghorn, unlike other temperate ungulates, are more likely to die in summer, when body condition is lower from extreme energetic demands of reproduction, which are higher for pronghorn than other ungulates. To evaluate the potential effects of intrinsic, environmental, and anthropogenic factors on summer mortality risk, we monitored 114 adult female pronghorn equipped with global positioning system transmitters in the Red Desert region of south‐central Wyoming, USA between 2013 and 2015. We modeled mortality risk using Cox's proportional hazards regression. Summer mortality risk was influenced by intrinsic and environmental factors; mortality risk increased when individuals were in poorer body condition entering the previous winter and when they experienced greater variation in average daily snow depth during the previous winter. We did not detect an effect of the distance to and density of roads, oil and gas wells, or fences on pronghorn summer mortality. During years of increased winter severity with deep and fluctuating snow depths, managers may observe higher winter mortality and higher mortality the following summer, likely as a consequence of the energetic expense associated with winter survival and spring reproduction for female pronghorn. © 2018 The Wildlife Society.

Background Caribou and reindeer across the Arctic spend more than two thirds of their lives moving in snow. Yet snow-specific mechanisms driving their winter ecology and potentially influencing herd health and movement patterns are not well known. Integrative research coupling snow and wildlife sciences using observations, models, and wildlife tracking technologies can help fill this knowledge void. Methods Here, we quantified the effects of snow depth on caribou winter range selection and movement. We used location data of Central Arctic Herd (CAH) caribou in Arctic Alaska collected from 2014 to 2020 and spatially distributed and temporally evolving snow depth data produced by SnowModel. These landscape-scale (90 m), daily snow depth data reproduced the observed spatial snow-depth variability across typical areal extents occupied by a wintering caribou during a 24-h period. Results We found that fall snow depths encountered by the herd north of the Brooks Range exerted a strong influence on selection of two distinct winter range locations. In winters with relatively shallow fall snow depth (2016/17, 2018/19, and 2019/20), the majority of the CAH wintered on the tundra north of the Brooks Range mountains. In contrast, during the winters with relatively deep fall snow depth (2014/15, 2015/16, and 2017/18), the majority of the CAH caribou wintered in the mountainous boreal forest south of the Brooks Range. Long-term (19 winters; 2001–2020) monitoring of CAH caribou winter distributions confirmed this relationship. Additionally, snow depth affected movement and selection differently within these two habitats: in the mountainous boreal forest, caribou avoided areas with deeper snow, but when on the tundra, snow depth did not trigger significant deep-snow avoidance. In both wintering habitats, CAH caribou selected areas with higher lichen abundance, and they moved significantly slower when encountering deeper snow. Conclusions In general, our findings indicate that regional-scale selection of winter range is influenced by snow depth at or prior to fall migration. During winter, daily decision-making within the winter range is driven largely by snow depth. This integrative approach of coupling snow and wildlife observations with snow-evolution and caribou-movement modeling to quantify the multi-facetted effects of snow on wildlife ecology is applicable to caribou and reindeer herds throughout the Arctic.

Pronghorn (Antilocapra americana) are endemic to western North America where they occupy expanses of grassland and sagebrush (Artemisia spp.) habitats. The Red Desert region in south‐central Wyoming, USA, has historically served as a stronghold for pronghorn populations, but many herds there have experienced declining population trends over the last two decades, concurrent with oil and natural gas development. These demographic changes and the potential for such energy development, its associated infrastructure, and other anthropogenic features including roads and fences to influence pronghorn habitat selection were the impetuses for our study. We sought to evaluate the potential effect of human‐induced disturbance on multi‐scale seasonal resource selection of 142 adult female pronghorn from 2013 to 2016 using 442 unique animal‐season‐year datasets. We utilized a traditional resource selection function to evaluate seasonal home‐range selection and a step‐selection function to assess fine‐scale, patch‐level seasonal selection. We also compared resource selection during daytime and nighttime hours with step‐selection analyses. At the seasonal home‐range scale, pronghorn selected for areas with more sagebrush during both seasons and areas farther from fences during summer. This trend was also apparent at the patch‐scale level, where pronghorn selected sagebrush‐dominant habitats and avoided crossing fences in all seasons during both day and night. Additionally at this scale, pronghorn selected areas farther from fences during daytime in summer. At the broader, home‐range scale, pronghorn selected areas with greater road density during summer, but with lower road densities and farther from wells during winter. Avoidance of anthropogenic features during winter was also observed at the finer, patch‐scale, with pronghorn selecting for increased density of roads and oil and natural gas wells during daytime in summer, but selecting areas farther from these features during daytime in winter. We recommend minimizing fencing and other forms of anthropogenic disturbance in high‐quality seasonal pronghorn habitats with high proportions of sagebrush, particularly during winter when risk‐avoidance responses may be amplified.

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.