White-tailed deer population dynamics in a multipredator landscape shaped by humans.

Large terrestrial mammals increasingly rely on human-modified landscapes as anthropogenic footprints expand. Land management activities such as timber harvest, agriculture, and roads can influence prey population dynamics by altering forage resources and predation risk via changes in habitat, but these effects are not well understood in regions with diverse and changing predator guilds. In northeastern Washington state, USA, white-tailed deer (Odocoileus virginianus) are vulnerable to multiple carnivores, including recently returned gray wolves (Canis lupus), within a highly human-modified landscape. To understand the factors governing predator-prey dynamics in a human context, we radio-collared 280 white-tailed deer, 33 bobcats (Lynx rufus), 50 cougars (Puma concolor), 28 coyotes (C. latrans), and 14 wolves between 2016 and 2021. We first estimated deer vital rates and used a stage-structured matrix model to estimate their population growth rate. During the study, we observed a stable to declining deer population (lambda = 0.97, 95% confidence interval: 0.88, 1.05), with 74% of Monte Carlo simulations indicating population decrease and 26% of simulations indicating population increase. We then fit Cox proportional hazard models to evaluate how predator exposure, use of human-modified landscapes, and winter severity influenced deer survival and used these relationships to evaluate impacts on overall population growth. We found that the population growth rate was dually influenced by a negative direct effect of apex predators and a positive effect of timber harvest and agricultural areas. Cougars had a stronger effect on deer population dynamics than wolves, and mesopredators had little influence on the deer population growth rate. Areas of recent timber harvest had 55% more forage biomass than older forests, but horizontal visibility did not differ, suggesting that timber harvest did not influence predation risk. Although proximity to roads did not affect the overall population growth rate, vehicle collisions caused a substantial proportion of deer mortalities, and reducing these collisions could be a win-win for deer and humans. The influence of apex predators and forage indicates a dual limitation by top-down and bottom-up factors in this highly human-modified system, suggesting that a reduction in apex predators would intensify density-dependent regulation of the deer population owing to limited forage availability.

Cougars, wolves, and humans drive a dynamic landscape of fear for elk.

To manage predation risk, prey navigate a dynamic landscape of fear, or spatiotemporal variation in risk perception, reflecting predator distributions, traits, and activity cycles. Prey may seek to reduce risk across this landscape using habitat at times and in places when predators are less active. In multipredator landscapes, avoiding one predator could increase vulnerability to another, making the landscape of fear difficult to predict and navigate. Additionally, humans may shape interactions between predators and prey, and induce new sources of risk. Humans can function as a shield, providing a refuge for prey from human-averse carnivores, and as a predator, causing mortality through hunting and vehicle collisions and eliciting a fear response that can exceed that of carnivores. We used telemetry data collected between 2017 and 2021 from 63 Global Positioning System-collared elk (Cervus canadensis), 42 cougars (Puma concolor), and 16 wolves (Canis lupus) to examine how elk habitat selection changed in relation to carnivores and humans in northeastern Washington, USA. Using step selection functions, we evaluated elk habitat use in relation to cougars, wolves, and humans, diel period (daytime vs. nighttime), season (summer calving season vs. fall hunting season), and habitat structure (open vs. closed habitat). The diel cycle was critical to understanding elk movement, allowing elk to reduce encounters with predators where and when they would be the largest threat. Elk strongly avoided cougars at night but had a near-neutral response to cougars during the day, whereas elk avoided wolves at all times of day. Elk generally used more open habitats where cougars and wolves were most active, rather than altering the use of habitat structure depending on the predator species. Elk avoided humans during the day and ~80% of adult female mortality was human caused, suggesting that humans functioned as a "super predator" in this system. Simultaneously, elk leveraged the human shield against wolves but not cougars at night, and no elk were confirmed to have been killed by wolves. Our results add to the mounting evidence that humans profoundly affect predator-prey interactions, highlighting the importance of studying these dynamics in anthropogenic areas.

Fear of large carnivores amplifies human-caused mortality for mesopredators.

The challenge that large carnivores face in coexisting with humans calls into question their ability to carry out critical ecosystem functions such as mesopredator suppression outside protected areas. In this study, we examined the movements and fates of mesopredators and large carnivores across rural landscapes characterized by substantial human influences. Mesopredators shifted their movements toward areas with twofold-greater human influence in regions occupied by large carnivores, indicating that they perceived humans to be less of a threat. However, rather than shielding mesopredators, human-caused mortality was more than three times higher than large carnivore-caused mortality. Mesopredator suppression by apex predators may thus be amplified, rather than dampened, outside protected areas, because fear of large carnivores drives mesopredators into areas of even greater risk from human super predators.

Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring.

Estimating habitat and spatial associations for wildlife is common across ecological studies and it is well known that individual traits can drive population dynamics and vice versa. Thus, it is commonly assumed that individual- and population-level data should represent the same underlying processes, but few studies have directly compared contemporaneous data representing these different perspectives. We evaluated the circumstances under which data collected from Lagrangian (individual-level) and Eulerian (population-level) perspectives could yield comparable inference to understand how scalable information is from the individual to the population. We used Global Positioning System (GPS) collar (Lagrangian) and camera trap (Eulerian) data for seven species collected simultaneously in eastern Washington (2018-2020) to compare inferences made from different survey perspectives. We fit the respective data streams to resource selection functions (RSFs) and occupancy models and compared estimated habitat- and space-use patterns for each species. Although previous studies have considered whether individual- and population-level data generated comparable information, ours is the first to make this comparison for multiple species simultaneously and to specifically ask whether inferences from the two perspectives differed depending on the focal species. We found general agreement between the predicted spatial distributions for most paired analyses, although specific habitat relationships differed. We hypothesize the discrepancies arose due to differences in statistical power associated with camera and GPS-collar sampling, as well as spatial mismatches in the data. Our research suggests data collected from individual-based sampling methods can capture coarse population-wide patterns for a diversity of species, but results differ when interpreting specific wildlife-habitat relationships.

Permanent daylight saving time would reduce deer-vehicle collisions.

Overlap between wildlife and human activity is key to causing wildlife-vehicle collisions, a globally pervasive and growing source of wildlife mortality.1,2 Policies regarding clock time often involve abrupt seasonal shifts in human activity, potentially influencing rates of human-wildlife conflict. Here, we harness the biannual shift between standard and daylight saving time as a natural experiment to reveal how the timing of human activity influences deer-vehicle collisions. Based on 1,012,465 deer-vehicle collisions and 96 million hourly traffic observations across the United States, we show that collisions are 14 times more frequent 2 hours after sunset than before sunset, highlighting the importance of traffic during dark hours as a key determinant of deer-vehicle collision risk. The switch from daylight saving to standard time in autumn causes peak traffic volumes to shift from before sunset to after sunset, leading to a 16% spike in deer-vehicle collisions. By reducing traffic after dark, our model predicts that year-round daylight saving time would prevent 36,550 deer (Odocoileus sp.) deaths, 33 human deaths, 2,054 human injuries, and US$1.19 billion in collision costs annually. In contrast, permanent standard time is predicted to increase collisions by an even larger magnitude, incurring an additional US$2.39 billion in costs. By targeting the temporal dimension of wildlife-vehicle collisions, strategies such as year-round daylight saving time that reduce traffic during dark hours, especially during the breeding season of abundant ungulates, would yield substantial benefits for wildlife conservation and reduce the social and economic costs of deer-vehicle collisions.

Interactive effects of wildfires, season and predator activity shape mule deer movements.

Wildfires are increasing in size, frequency and severity due to climate change and fire suppression, but the direct and indirect effects on wildlife remain largely unresolved. Fire removes forest canopy, which can improve forage for ungulates but also reduce snow interception, leading to a deeper snowpack and potentially increased vulnerability to predation in winter. If ungulates exhibit predator-mediated foraging, burns should generally be selected for in summer to access high-quality forage and avoided in winter to reduce predation risk in deep snow. Fires also typically increase the amount of deadfall and initiate the growth of dense understory vegetation, creating obstacles that may confer a hunting advantage to stalking predators and a disadvantage to coursing predators. To minimize risk, ungulates may therefore avoid burns when and where stalking predators are most active, and use burns when and where coursing predators are most active. We used telemetry data from GPS-collared mule deer (Odocoileus hemionus), cougars (Puma concolor) and wolves (Canis lupus) to develop step selection functions to examine how mule deer navigated species-specific predation risk across a landscape in northern Washington, USA, that has experienced substantial wildfire activity during the past several decades. We considered a diverse array of wildfire impacts, accounting for both the severity of the fire and time since the burn (1-35 years) in our analyses. We observed support for the predator mediating foraging hypothesis: mule deer generally selected for burned areas in summer and avoided burns in winter. In addition, deer increased use of burned areas when and where wolf activity was high and avoided burns when and where cougar use was high in winter, suggesting the hunting mode of resident predators mediated the seasonal response of deer to burns. Deer were not more likely to die by predation in burned than in unburned areas, indicating that they adequately manage fire-induced changes to predation risk. As fire activity increases with climate change, our findings indicate the impact on ungulates will depend on trade-offs between enhanced summer forage and functionally reduced winter range, mediated by characteristics of the predator community.