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Abstract Snow conditions are changing rapidly across our planet, which has important implications for wildlife managers. In Alaska, USA, the later arrival of snow is challenging wildlife managers' ability to conduct aerial fall (autumn) moose (Alces alces) surveys. Complete snow cover is required to reliably detect and count moose using visual observation from an aircraft. With inadequate snow to help generate high‐quality moose survey data, it is difficult for managers to determine if they are effectively meeting population goals and optimizing hunting opportunities. We quantified past relationships and projected future trends between snow conditions and moose survey success across 7 different moose management areas in Alaska using 32 years (1987–2019) of moose survey data and modeled snow data. We found that modeled mean snow depth was 15 cm (SD = 11) when moose surveys were initiated, and snow depths were greater in years when surveys were completed compared to years when surveys were canceled. Further, we found that mean snow depth toward the beginning of the survey season (1 November) was the best predictor of whether a survey was completed in any given year. Based on modeled conditions, the trend in mean snow depth on 1 November declined from 1980 to 2020 in 5 out of 7 survey areas. These findings, coupled with future projections, indicated that by 2055, the delayed onset of adequate snow accumulation in the fall will prevent the completion of moose surveys over roughly 60% of Alaska's managed moose areas at this time of the year. Our findings can be used by wildlife managers to guide decisions related to the future reliability of aerial fall moose surveys and help to identify timelines for development of alternate measurement and monitoring methods.

People who travel on ice-covered rivers to access traditional lands and resources can be profoundly impacted by effects of climate change on river ice seasonality. We used remote sensing, bolstered by citizen science, to assess trends and geospatial patterns of the ice cover in the Copper River Basin of Southcentral Alaska. Our analysis of Landsat imagery from water years (WYs) 1973 to 2021 suggests a severely diminishing season of river ice travel (delayed or incomplete freezeup, early breakup) due to increasing air temperatures. The weekly probability of an adequate ice cover for river crossings declined by an average of 53 percentage points. Ice extent was closely related to accumulated freezing degree days (AFDD). AFDDOct-Apr decreased by 15% since WY 1943, a significant warming trend. We mapped the spatiotemporal variation of ice and open water extent with multispectral and synthetic aperture radar (SAR) imagery (Sentinel-2, Sentinel-1). We identified reaches with more reliable opportunities for winter access and others susceptible to extensive open water, differences related to flow energy and channel form. The results of this study can support local decision making and adaptation in response to rapidly changing river ice conditions, and our approach can be applied elsewhere to document change and improve travel safety.

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.

Abstract Ecosystems across the United States are changing in complex and unpredictable ways and analysis of these changes requires coordinated, long‐term research. This paper is a product of a synthesis effort of the U.S. National Science Foundation funded Long‐Term Ecological Research (LTER) network addressing the LTER core research area of “populations and communities.” This analysis revealed that each LTER site had at least one compelling “story” about what their site would look like in 50–100 yr. As the stories were prepared, themes emerged, and the stories were group into papers along five themes: state change, connectivity, resilience, time lags, and cascading effects. This paper addresses the cascading effects theme and includes stories from the Bonanza Creek (boreal), Kellogg Biological Station (agricultural and freshwater), Palmer (Antarctica), and Harvard Forest (temperate forest) LTER sites. We define cascading effects very broadly to include a wide array of unforeseen chains of events that result from a variety of actions or changes in a system. While climate change is having important direct effects on boreal forests, indirect effects mediated by fire activity—severity, size, and return interval—have large cascading effects over the long term. In northeastern temperate forests, legacies of human management and disturbance affect the composition of current forests, which creates a cascade of effects that interact with the climate‐facilitated invasion of an exotic pest. In Antarctica, declining sea ice creates a cascade of effects including declines in Adèlie and increases in Gentoo penguins, changes in phytoplankton, and consequent changes in zooplankton populations. An invasion of an exotic species of lady beetle is likely to have important future effects on pest control and conservation of native species in agricultural landscapes. New studies of zebra mussels, a well‐studied invader, have established links between climate, the heat tolerance of the mussels, and harmful algal blooms. Collectively, these stories highlight the need for long‐term studies to sort out the complexities of different types of ecological cascades. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about trophic interactions as an important driver of ecosystem structure, function, services, and futures.

Abstract Industrial off‐road activity in winter overlaps denning habitat of polar bear (Ursus maritimus) and grizzly bear (Ursus arctos) in the North Slope oilfields of Alaska (United States). To prevent disturbance of dens, managers have used forward‐looking infrared (FLIR) cameras to detect dens, but the effectiveness of FLIR under different environmental conditions is unresolved. Our objective was to evaluate the effects of environmental variables on FLIR‐based techniques for arctic bear den detection. Using a FLIR‐equipped unmanned aircraft system (UAS), we conducted observations of artificial polar bear (APD) and grizzly bear (AGD) dens from horizontal and vertical perspectives between December 2016 and April 2017. We recorded physical characteristics of artificial dens and weather conditions present during each observation. We captured 291 images and classified each as detection or nondetection based on the number of pixels representative of a den “hot spot.” We used logistic regression to model the effects of four weather variables on the odds of detection (detection). We found that UAS‐FLIR detects APDs two times better than AGDs, and that for both species detections are four times more likely from the vertical than horizontal perspective. Lower air temperature and wind speed, and the absence of precipitation and sunlight increased detection for APDs. A 1°C increase in air temperature lowered detection by 12% for APDs and by 8% for AGDs. We recommend that UAS‐FLIR surveys be conducted early in the denning season, on cold, clear days, with calm winds, in the absence of sunlight (e.g., civil twilight). Our study further refines the application of FLIR techniques for arctic bear den detection and offers practical recommendations for optimizing detection. Putative den locations should be confirmed by a secondary method to minimize disturbance as anthropogenic activity continues in the Arctic.

Alaska Native people and rural Alaskans rely on subsistence harvesting of wild resources for their well-being. This study integrates publicly-available data from >30 Interior Alaskan communities to examine the geospatial patterns of subsistence and develop model-based maps of subsistence land use for the region. Through analysis and identification of lands important for subsistence, this study provides a tool to inform sound decision-making on land use and facilitate communication among land users. We found that most contemporary subsistence land use (approx. 70%) occurred within areas that have been traditionally used for generations. The size of subsistence use areas varied widely among communities (approx. 50–25,000 km²) and was directly related to total population. Subsistence land use varied both by resource type and by season, which reflects differences in resource availability and harvesting strategies. The spatial patterns of subsistence land use were strongly influenced by accessibility, which differed between remote and road-connected communities. Our logistic regression models showed that subsistence land use was largely predictable by distance to communities, distance to main travel corridors (roads and/or rivers), distance to lakes (for remote communities), and population size. Probability maps of subsistence use were generated and classified into used and unused areas with accuracies from 83–86%. Results suggest a large spatial extent (353,771 km²) of subsistence land use in Interior Alaska, comprising >60% of the land area of this sparsely-populated region. The outcomes of this study provide a more comprehensive view of subsistence land use patterns and spatial products that may help reduce conflict and inform decisions affecting lands and resources important for sustaining the subsistence way of life.

Amplified climate warming at high northern latitudes is challenging societies that depend on local provisional and cultural ecosystem services, e.g., subsistence resources, for their livelihoods. Previous qualitative research suggests that climate-induced changes in environmental conditions are affecting rural residents' ability to travel across the land and access local resources, but detailed information on the nature and effect of specific conditions is lacking. Our objectives were to identify climate-related environmental conditions affecting subsistence travel and access, and then estimate rural resident travel and access vulnerability to those environmental conditions. We collaborated with nine Interior Alaskan communities within the Yukon River basin and provided residents with camera-equipped GPS units to document environmental conditions directly affecting subsistence access for 12 consecutive months. We also conducted comprehensive interviews with research participants to incorporate the effects of environmental conditions not documented with GPS units. Environmental conditions reported by rural residents were categorized into seven condition types. We assessed vulnerability to each condition by accounting for both likelihood (number of times a condition was documented) and sensitivity (magnitude of the effect from the condition) information derived from GPS observations and interviews. We also tested for differences in mean vulnerability values among environmental conditions and between community types (road-connected vs. remote) using a one-way analysis of variance. Rural community travel and access were most vulnerable to changes in ice conditions, erosion, vegetative community composition, and water levels. Environmental conditions that impeded natural travel corridors, e.g., waterways, more strongly influenced remote communities than those connected by roads. Increased vulnerability to environmental change puts remote communities at increased risk for food-security issues. Our study used a novel community-based approach to integrate local knowledge with scientific analysis to document and estimate the relative effects that specific environmental conditions are having on access to subsistence resources across Interior Alaska.

ABSTRACT One tool used for wildlife management is the deployment of radiocollars to gain knowledge of animal populations. Understanding the influence of individual factors (e.g., species, collar characteristics) and landscape characteristics (e.g., forested cover, shrubs, and fencing) on retention of expandable radiocollars for ungulates is important for obtaining empirical data on factors influencing ecology of young‐of‐the‐year ungulates. During 2001–2009, we captured and radiocollared 198 white‐tailed deer (Odocoileus virginianus) fawns, 142 pronghorn (Antilocapra americana) fawns, and 73 mule deer (O. hemionus) fawns in South Dakota, Minnesota, and California, USA. We documented 72 (36.4%), 8 (5.6%), and 7 (9.6%) premature (

Abstract Despite widespread use of fecal pellet‐group counts as an index of ungulate density, techniques used to convert pellet‐group numbers to ungulate numbers rarely are based on counts of known individuals, seldom evaluated across spatial and temporal scales, and precision is infrequently quantified. Using DNA from fecal pellets to identify individual deer, we evaluated the relationship between pellet‐group count and count of Sitka black‐tailed deer (Odocoileus hemionus sitkensis) during a 3‐year study (2006–2008) in 3 watersheds in southeast Alaska, USA. We surveyed 141,054 m2 of transect, counted 10,569 pellet groups, and identified 737 unique deer. We used a multilevel mixed‐effects generalized linear model to analyze expected deer count as a function of pellet‐group count. Pellet‐group count was a significant predictor of DNA‐based index of deer count, but that relationship varied by transect, watershed, and year, indicating that extrapolation of a single linear relationship across space and time was not possible. More importantly, most of the variation in our models was residual and unexplained. Assuming that our DNA‐based results were a more accurate and precise metric of true deer count, we do not support the use of pellet‐group count to index deer count in southeast Alaska unless confounding factors are accounted for at fine spatial (e.g., habitat patch) scales. Because of the difficulty in routinely evaluating the influence of confounding variables in remote and unmanaged landscapes, we suggest that wildlife programs in these environments consider alternatives, such as DNA‐based methods, for monitoring trends in ungulate populations. © 2013 The Wildlife Society.

We investigated wildfire-related effects on a slow ecological variable, i.e., forage production, and fast social-ecological variables, i.e., seasonal harvest rates, hunter access, and forage offtake, in a moose-hunter system in interior Alaska. In a 1994 burn, average forage production increased slightly (5%) between 2007 and 2013; however, the proportional removal across all sites declined significantly (10%). This suggests that moose are not utilizing the burn as much as they have in the past and that, as the burn has aged, the apparent habitat quality has declined. Areas with a greater proportion of accessible burned area supported both high numbers of hunters and harvested moose. Our results suggest that evaluating ecological variables in conjunction with social variables can provide managers with information to forecast management scenarios. We recommend that wildlife managers monitor fast variables frequently, e.g., annually, to adapt and keep their management responsive as resources fluctuate; whereas slower variables, which require less frequent monitoring, should be actively incorporated into long-term management strategies. Climate-driven increases in wildfire extent and severity and economically driven demographic changes are likely to increase both moose density and hunting pressure. However, the future resilience of this moose-hunter system will depend on integrated management of wildfire, hunter access, and harvest opportunities.

The effects of landscape changes caused by intensive logging on the availability of wild game are important when the harvest of wild game is a critical cultural practice, food source, and recreational activity. We assessed the influence of extensive industrial logging on the availability of wild game by drawing on local knowledge and ecological science to evaluate the relationship between forest change and opportunities to harvest Sitka black-tailed deer (Odocoileus hemionus sitkensis) on Prince of Wales Island, Alaska. We used data collected through interviews with local deer hunters and GIS analysis of land cover to determine relationships among landscape change, hunter access, and habitat for deer hunting over the last 50 yr. We then used these relationships to predict how harvest opportunities may change in the future. Intensive logging from 1950 into the 1990s provided better access to deer and habitat that facilitated deer hunting. However, successional changes in intensively logged forests in combination with a decline in current logging activity have reduced access to deer and increased undesirable habitat for deer hunting. In this new landscape, harvest opportunities in previously logged landscapes have declined, and hunters identify second-growth forest as one of the least popular habitats for hunting. Given the current state of the logging industry in Alaska, it is unlikely that the logging of the remaining old-growth forests or intensive management of second-growth forests will cause hunter opportunities to rebound to historic levels. Instead, hunter opportunities may continue to decline for at least another human generation, even if the long-term impacts of logging activity and deer harvest on deer numbers are minimal. Adapting hunting strategies to focus on naturally open habitats such as alpine and muskeg that are less influenced by external market forces may require considerably more hunting effort but provide the best option for sustaining deer hunting as a local tradition over the long run. We speculate that managing deer habitat in accessible areas may be more important than managing the overall health of deer populations on a regional scale. We further suggest that the level of access to preferred hunting habitat may be just as important as deer densities in determining hunter efficiency.

As Arctic and boreal regions rapidly warm, the frequency and seasonal timing of hazardous driving conditions on all-season Arctic-boreal roads are likely to change. Because these roads link remote Arctic areas to the rest of the North American road system, climate change may substantially affect safety and quality of life for northern residents and commercial enterprises. To gain insight into future hazardous driving conditions, we built Random Forest models that predict the occurrence of hazardous driving conditions by linking snow, ice, and weather simulated by a spatially explicit modeling system (SnowModel) to archived road condition reports from two highly trafficked all-season northern roads: the Dalton Highway (Alaska, USA) and Dempster Highway (Yukon, Canada). We applied these models to downscaled future climate trajectories for the study period of 2006–2100. We estimated future trends in the frequency and timing of icy, wet-icy, and snowy road surfaces, blowing and drifting snow, and high winds. We found that as the climate warms, and the portion of the year when snow and ice occur becomes shorter, overall frequency of snow storms and ice- and snow-related driving hazards decreased. For example, the mean number of days per year when roads are covered in snow or ice decreased by 51 d (−21%) on the Dalton Highway between the 2006–2020 and 2081–2100 time periods. However, the intensity of storms was predicted to increase, resulting in higher mean annual storm wind speeds (Dalton +0.56 m s ^−1 [+17%]) and snowfall totals (Dalton +0.3 cm [+36%]). Our models also predicted increasing frequency of wet-icy driving conditions during November, December, January, and February, when daylength is short and hazardous conditions may be more difficult to perceive. Our findings may help road managers and drivers adapt their expectations and behaviors to minimize accident risk on Arctic-boreal roads in the future.

Wildlife-vehicle collisions imperil humans, wildlife, and property. Collisions with moose (Alces alces ) are especially consequential and there are indications they may increase during severe winters. We tested hypotheses regarding the influence of moose movements and vehicular traffic patterns on collision risk. We first modeled daily snow depth and accumulation across 5.6 million km ^2 of the North American Arctic-Boreal region. Next, we analyzed the movements and road use of 113 GPS-collared moose in response to snow depth. Finally, we examined the influence of these snow properties on vehicular traffic and 7680 moose-vehicle collisions. As winter progressed and the snowpack deepened in each study area, GPS-collared moose migrated to lower elevations, leading them into areas with shallower snow but higher road densities. This elevational migration corresponded with a higher probability of road-use by moose (by up to ten-fold) in winter than in summer. Corresponding to these patterns, moose-vehicle collisions were 2.4–5.7 times more frequent from December through February (compared to early summer). Collision risk was highest when and where snow depth was less than 120 cm, indicating that migration into areas with shallower snow increased collision risk in those areas. Most (82%) moose-vehicle collisions occurred after dark. This pattern was strongest during winter, when nighttime traffic volumes were eight times higher than summer due to longer nights. Overall, our findings suggest that concurrent seasonal changes in human and wildlife behavior increase the frequency of moose-vehicle collisions during winter. Snow depth influences collisions primarily through its impacts on moose movement, while strong seasonal changes in daylight hours cause an increase in nighttime traffic that further contributes to risk. This information may help predict times and places where risk of moose-vehicle collisions are highest and to develop seasonally dynamic mitigation strategies.

Aircraft activity is expanding across Arctic Alaska, potentially changing social-ecological systems. Arctic communities report that aircraft disturb wildlife and negatively influence harvest practices and experiences. Limited data have restricted knowledge about the extent of aircraft activity over traditional harvest areas. Our objective was to use soundscape monitoring methods to document aircraft overflights around the rural subsistence-based community of Nuiqsut to inform impact mitigation processes. This study provides the first quantitative estimates of aircraft activity in rural Arctic Alaska. We deployed 20 acoustic monitoring systems in summer 2016 along travel corridors used to harvest caribou ( Rangifer tarandus ). Sound recordings captured 7465 aircraft events during peak caribou harvest season. Aircraft activity reached a median of 12 overflights per day near human development, approximately six times greater than undeveloped areas. Aircraft noise decreased incrementally with distance from human development. Given that subsistence harvesters report that aircraft startle caribou and prefer to avoid aircraft themselves, this result implies that they will need to travel farther for a successful harvest, incurring higher costs of fuel, equipment, and effort. Such costs could be prohibitive for many harvesters. Our research demonstrates that acoustic data can aid in understanding how human-to-human interactions impact social-ecological dynamics in the Arctic.

Climatic warming has direct implications for fire-dominated disturbance patterns in northern ecosystems. A transforming wildfire regime is altering plant composition and successional patterns, thus affecting the distribution and potentially the abundance of large herbivores. Caribou (Rangifer tarandus) are an important subsistence resource for communities throughout the north and a species that depends on terrestrial lichen in late-successional forests and tundra systems. Projected increases in area burned and reductions in stand ages may reduce lichen availability within caribou winter ranges. Sufficient reductions in lichen abundance could alter the capacity of these areas to support caribou populations. To assess the potential role of a changing fire regime on winter habitat for caribou, we used a simulation modeling platform, two global circulation models (GCMs), and a moderate emissions scenario to project annual fire characteristics and the resulting abundance of lichen-producing vegetation types (i.e., spruce forests and tundra >60 years old) across a modeling domain that encompassed the winter ranges of the Central Arctic and Porcupine caribou herds in the Alaskan-Yukon Arctic. Fires were less numerous and smaller in tundra compared to spruce habitats throughout the 90-year projection for both GCMs. Given the more likely climate trajectory, we projected that the Porcupine caribou herd, which winters primarily in the boreal forest, could be expected to experience a greater reduction in lichen-producing winter habitats (-21%) than the Central Arctic herd that wintered primarily in the arctic tundra (-11%). Our results suggest that caribou herds wintering in boreal forest will undergo fire-driven reductions in lichen-producing habitats that will, at a minimum, alter their distribution. Range shifts of caribou resulting from fire-driven changes to winter habitat may diminish access to caribou for rural communities that reside in fire-prone areas.

New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001-2009, we captured and radiocollared 174 newborn (≤24-hrs old) ungulates: 76 white-tailed deer (Odocoileus virginianus) in Minnesota and South Dakota, 61 mule deer (O. hemionus) in California, and 37 pronghorn (Antilocapra americana) in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age) in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days) for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days) for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i.e., weekly versus daily) for estimating survival.