Jesse Whittington

Anthropogenic land conversion is putting increasing pressure on wildlife populations around the world. To mitigate impacts, it is necessary to develop a detailed mechanistic understanding of how animals are affected by different types of human activity. A key challenge is to disentangle the effects of static infrastructure, like roads or buildings, and the presence of humans in the landscape. To address this question, we examined if terrestrial mammals altered their movement behaviour around buildings in response to reduced human mobility during COVID‐19 lockdowns. We compiled GPS tracking data from 35 study sites across five continents, for 10 carnivore species and 13 herbivore species, totalling >1 million location records from 586 individuals. For each study, we used integrated step selection analysis to test the extent to which animals changed their avoidance of buildings as lockdown took effect, leveraging the recently released Microsoft MLBuildings dataset of global building locations. Analysis of population‐level effects revealed that, in areas with high Human Footprint Index (HFI), animals tended to show a significant reduction in their avoidance of buildings during lockdown, but not in low HFI areas. No such trend was detected during equivalent periods in years other than 2020, indicating that behavioural changes were a result of reduced human mobility during lockdowns. Overall, our findings suggest that animals living alongside humans exhibit greater plasticity when people change their behaviour, likely indicating the combined effects of environmental filtering and habituation. More generally, our study provides a critical first step towards developing evidence‐based tools for forecasting how wildlife movement behaviour may change in response to different land‐use strategies, human activities, conservation interventions or environmental perturbations.

Recreation in protected areas (PAs) is growing worldwide, potentially conflicting with wildlife and ecosystem protection. Efficiently estimating human activity in PAs is crucial for balancing a dual mandate of supporting visitor access and biodiversity, but managers lack clear recommendations about the conditions under which specific tools are more effective for monitoring spatial and temporal trends in human activity associated with recreation. Through two case studies, we assessed key tools commonly used for measuring human activity related to non‐consumptive outdoor recreation in PAs: camera traps, day passes, trail counters and user‐contributed geographical data from Strava and AllTrails. We measured human activity across multiple scales and compared estimates of spatial and temporal activity within and between PAs. Correlations among the results from the different tools were strong across PAs and suggested that a combination of tools may be best suited to understanding finer scale trends within parks. Individual tools, and their combination, can be tailored to specific research and management goals. Synthesis and applications: Our case studies provide insights into the effectiveness of tools for measuring human activity in PAs and can inform practitioners and researchers about how the tools can be used to address management decisions. As tools varied in their strengths and weaknesses, the widespread adoption of multiple, integrated measures of human activity is needed to develop evidence‐based park management strategies, benefitting both humans and nature. Read the free Plain Language Summary for this article on the Journal blog.

The number and distribution of animals in space form the basis of many wildlife studies. Yet, reliable estimation of population abundance remains challenging. Generalized spatial mark‐resight (gSMR) models are widely applicable abundance estimators that do not require all individuals be uniquely identifiable. Despite their flexibility, gSMR models assume independence in distribution and detections of individuals throughout space. Group‐living species that aggregate (i.e. share activity centres) and move cohesively (i.e. share detections) violate these assumptions, limiting the applicability of gSMR models to solitary species with limited home range overlap. We developed a gSMR model that accounts for group associations to estimate spatial abundance. We treated groups as the measurable ‘units’ and included a submodel that uses Poisson processes to estimate group size. We conducted a simulation study to compare our novel group‐based gSMR to a gSMR that ignores group associations. We generated data across varying levels of aggregation and cohesion parameters. We then quantified and compared the precision and bias of parameter estimates between the two models. The group‐based gSMR model outperformed the individual‐based gSMR under all aggregation and cohesion scenarios. Group‐based 95% Bayesian Credible Intervals (BCI) overlapped the true value of abundance in 91% of simulations, compared to only 15% of simulations in the individual‐based gSMR. Both the group‐based and individual‐based models became less precise and more biased as aggregation and group size increased. However, the group‐based gSMR model was less biased in estimating all parameters when compared to the individual‐based gSMR model. We applied our group‐based gSMR model to grey wolf ( Canis lupus ) monitoring data within a Canadian national park complex (Banff, Yoho and Kootenay National Parks, and Ya Ha Tinda). We used 12 years (2012–2023) of remote camera data, along with information from GPS radio‐collared wolves ( N = 16) to estimate spatiotemporal variation in abundance. Over a 12‐year period, wolf abundance averaged 73 (95% BCI = 20–207) individuals, and a density of 6.69 (95% BCI = 1.83–19.0) wolves per 1000 km ² within the park complex. Our novel group‐based gSMR model extends the applicability of spatial mark‐resight to species with group associations.

Forage availability is a key factor regulating large herbivore populations. Global changes in land use and climate may affect the spatiotemporal distribution of forage across the ranges of large herbivores, especially in mountain ecosystems. We test two synergistic hypotheses for how landscape and climate changes from 2001 to 2023 have affected forb and graminoid biomass at the peak of the growing season within and across ecoregions of the eastern slopes of the Rocky Mountains in Alberta, Canada. The successional change hypothesis posits that the encroachment of woody vegetation into previously herbaceous communities has reduced forage biomass. The disturbance hypothesis proposes that abrupt community shifts caused by fire and timber harvesting have increased forage biomass. Using remote sensing, we quantify temporal changes in land cover and disturbances, NDVI greenness and phenology indices, and spring climate. We then used in situ vegetation data to parameterize generalized linear and gradient boosted regression tree models of forb and graminoid biomass to predict annual peak forb and graminoid biomass. Herbaceous land cover declined while shrub and forest area increased, and the percent of annual biomass within herbaceous areas declined from woody encroachment. Disturbance effects varied, with rising forage biomass in conjunction with increased area of logged forests in the foothills ecoregion, while burned areas declined and had a reduced contribution to the percent of annual biomass. Additionally, spring became warmer across the study area and ended earlier in the alpine, suggesting the effects of long‐term climatic shifts may be strongest at higher ecoregions. Disturbance frequency, succession, and climate together shaped forage biomass in space and time. Increased prescribed fire and other ecological restoration actions may be needed to ensure that shifts in forage biomass do not threaten large herbivore persistence in the face of global change.

Understanding wildlife responses to human disturbance is essential for developing effective conservation and management strategies. Grizzly bears in the southern Canadian Rocky Mountains face increasing habitat alteration from roads, forest harvest, human settlements, and mining, which can alter the way animals move through the landscape. Deleterious effects on genetic exchange, demographic connectivity, and access to key resources can occur if movements are dramatically altered. We used integrated step‐selection functions (iSSF) to model movement and habitat selection for 109 GPS‐collared grizzly bears across an 85,000 km ² multi‐use landscape in southeastern British Columbia and southwestern Alberta. We then simulated individual grizzly bear movements from fitted iSSFs to predict changes in population‐level space use and functional connectivity under the following scenarios: (1) without current levels of human disturbance, (2) under current conditions, and (3) with a defined increase in human disturbance. Bears avoided crossing highways but were attracted to areas alongside highways in areas with relatively low forage availability at a broad spatial scale, such as in Banff National Park and the Kananaskis region. Females generally avoided moving through towns in spring and summer, while males were more likely to do so. Additional footprints of proposed mines and expanded human settlements in a potential future scenario were predicted to further decrease functional connectivity for grizzly bears on top of prior connectivity losses from existing human disturbance. Our study builds upon existing work simulating animal space use from fitted iSSFs by incorporating individual‐level variation into population‐level simulations and by fitting functional responses that help capture broad‐scale variation in behavior and improve model transferability to new areas. Our results provide insights into grizzly bear movement and connectivity in an area of high conservation importance, and our predictive maps can be used to directly inform transboundary management actions and conservation planning.

Wildlife ecologists throughout the world strive to monitor trends in population abundance to help manage wildlife populations and conserve species at risk. Spatial capture–recapture studies are the gold standard for monitoring density, yet they can be difficult to apply because researchers must be able to distinguish all detected individuals. Spatial mark–resight (SMR) models only require a subset of the population to be marked and identifiable. Recent advances in SMR models with radio‐collared animals required a two‐staged analysis. We developed a one‐stage generalized SMR (gSMR) model that used detection histories of marked and unmarked animals in a single analysis. We used simulations to assess the performance of one‐ and two‐stage gSMR models. We then applied the one‐stage gSMR with telemetry and remote camera data to estimate grizzly bear (Ursus arctos) abundance from 2012 to 2023 within the Canadian Rocky Mountains. We estimated abundance trends for the population and reproductive females (females with cubs of the year). Simulations suggest that one‐ and two‐stage models performed equally well. One‐stage models are more dependable as they use exact likelihoods, whereas two‐stage models have shorter computation times for large data sets. Both methods had >95% credible interval coverage and minimal bias. Increasing the number of marked animals increased the accuracy and precision of abundance estimates, and ≥10 marked animals were required to obtain coefficients of variation mean = 1.02) to a 2023 density of 10.4 grizzly bears/1000 km². Reproductive female abundance had high interannual variability and increased to 1.0 bears/1000 km². Population density was highest within protected areas, within high‐quality habitat and far from paved roads. The density of activity centers declined near paved roads over time. Mechanisms of decline may have included direct mortality and shifting activity centers to avoid human activity. Our study demonstrates the influence of human activity on localized density and the importance of protected areas for carnivore conservation. Finally, our study highlights the widespread utility of remote camera and telemetry‐based SMR models for monitoring spatiotemporal trends in abundance.

Coexistence between humans and wildlife is necessary for many conservation goals but is difficult to achieve in landscapes with increasing human populations and species that are often wary of people and may also threaten human safety. In these contexts, coexistence may be enhanced by identifying geographic areas where animal movement is particularly important and changes to human use via trail design could support both wildlife conservation and human safety. We used camera trap data to monitor the spatial distribution of grizzly bears (Ursus arctos), grey wolves (Canis lupus), and humans within the central Canadian Rocky Mountains, where anthropogenic development and human activity have gradually encroached on limited wildlife habitat. We quantified spatial variation in human use and then incorporated this output into models for the detection rates of bears and wolves. We interpolated metrics of human use throughout the study area using inverse distance weighted averages of human detection rates from cameras. This approach supported a novel estimate of the cumulative effects of human use at all nearby trails on animal space use. We used our models to estimate the zone of influence of human use on bears and wolves, determining the distance at which human use on nearby trails no longer exhibited a measurable change in detection rates for each of grizzly bears and wolves. The negative effects of human use on wildlife declined steeply with distance such that 50% of the decrease in detection rates immediately adjacent to trails would be expected to occur at 267 m for grizzly bears and 576 m for wolves. Weak effects, 5% as strong as the effect adjacent to trails, extended up to 1.8 and 6.1 km for grizzly bears and wolves, revealing the importance of cumulative measures of human use. Synthesis and applications. Our work shows how human activity over entire landscapes can alter wildlife detection rates. Our results identify target buffer distances for protected areas near trails, and the modelling framework could be used by land managers to predict how altering trail networks and modifying human activity could affect wary wildlife species and advance coexistence.

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.

Fences are an important tool for anchoring reintroduced species to a target area, and there is a need to understand their effect on other wildlife species. However, little is understood about the response of wildlife to newly constructed fences over time. We evaluated fences used in the reintroduction of plains bison Bison bison to Banff National Park, Canada. These fences were designed to contain reintroduced bison while allowing for the free passage of other wildlife. In 2020, we provided an assessment of the permeability of several fence designs. Here, we investigated longer‐term fence effects and addressed the emerging question of whether wildlife adapt their behaviours to navigate fences more effectively over time. We used an expanded array of remote cameras and a before‐after‐control‐impact design to evaluate changes in detection probability for 12 species. Next, we tested for changes in crossing rates and travel speeds of migratory elk Cervus canadensis using 22 years of GPS collar data. Finally, we examined whether species detections or elk movements changed over time after fences were constructed. Changes in detection probability near fences were inconsistent between species. Elk fence crossing rates decreased after fence construction, and travel speeds slowed by a negligible amount. However, these effects were temporary – wildlife learned to cross fences more efficiently over time. Elk movement metrics followed a non‐linear pattern after the appearance of fences and began returning to pre‐fence states after approximately two years. Our study provides new information on the implementation of fences for conservation objectives while minimizing impacts on sympatric wildlife.