Mark Hebblewhite

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.

Background: Hidden Markov models (HMMs) are increasingly used to infer animal behavioural states from GPS tracking data, yet their interpretation often remains uncertain in the absence of empirical validation. Misinterpretation of statistical states as biologically meaningful behaviours can undermine scientific understanding and conservation decisions. Our objective was to evaluate how well HMM-inferred states correspond to directly observed behaviours and to test how the temporal resolution of GPS sampling influences behavioural inference. Methods: We used GPS collars equipped with video cameras to validate HMM-inferred behavioural states in 81 female migratory caribou ( Rangifer tarandus ) from two herds. We compared states derived from two- and three-state HMMs to behaviours observed in short collar video clips. To assess the effect of temporal scale, we fit HMMs to GPS data resampled at 20-, 60-, and 120-minute relocation intervals. Results: HMM-inferred behavioural states frequently diverged from video-observed behaviours at the start of observed GPS steps, especially at longer relocation intervals. These mismatches appeared to result from overlapping movement metrics among caribou behaviours (e.g., foraging vs. resting or traveling) and the inability of coarser GPS data to capture behavioural switches occurring at finer temporal scales than the fix rate. Videos of eating were the most misaligned with HMM-inferred states, likely due to high variation in caribou movement while foraging that is often characteristic of mixed-feeding large herbivores. Inferred states for a given location were often inconsistent across temporal scales, indicating that HMM outputs must be interpreted cautiously with respect to the GPS sampling frequency. Conclusions: The predicted HMM state can differ substantially from true behaviour at the start of each step, in particular at coarse temporal scales. Our results serve as a reminder to interpret HMM states over whole steps rather than at observed positions, validate movement-derived states where possible, and align sampling resolution with species-specific behavioural patterns.

Integrating complex geospatial data into research and applications for wildlife ecology remains a challenge. For example, animations of wildlife tracking data can be useful for developing hypotheses, communicating with stakeholders and infrastructure planning. Conveying an effective message often requires visualizing movements in relation to custom background layers, such as dynamic weather conditions or local transportation features. However, animations are commonly made using software that is easy to use but offers few options for input layers, thus limiting their impact. Alternatively, bespoke solutions require advanced programming skills that are not readily available for many ecologists. We developed ECODATA, a suite of open‐source tools to support exploration, analysis and visualization of animal movements and dynamic geospatial data layers. The tools do not require programming skills and guide users through the process of manipulating vector, raster and tabular data files to prepare inputs to custom animations or further analyses. The software was developed by a team of remote sensing experts, quantitative ecologists, wildlife managers and conservation practitioners. We demonstrate the use of ECODATA through two examples. The first describes the use of the software to animate movements of elk (Cervus elaphus) and wolves (Canis lupus) in relation to roads, wildlife crossing structures and seasonal vegetation green‐up near Banff National Park in Canada. The second illustrates the impact of the software on wildlife management, with an animation of caribou (Rangifer tarandus) movements and parturitions during the calving season. Both examples include processed remote sensing data and feature layers that provide relevant local context. ECODATA offers a novel resource to explore and communicate animals' interactions with their environment, informing management decisions and conservation strategies. The flexible tools for geospatial data manipulation can be used for data visualization, as described here, or to create predictor variables for inclusion in habitat selection or other ecological models.

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.

Rapid climate warming has contributed to significant changes in Arctic and boreal vegetation over the past half century. Changes in vegetation can impact wildlife by altering habitat and forage availability, which can affect behavior and range use. However, animals can also influence vegetation through foraging and trampling and therefore play an important role in determining ecosystem responses to climate change. As wildlife populations grow, density‐dependent processes can prompt range expansion or shifts. One mechanism for this is density‐dependent forage reduction, which can contribute to nutritional stress and population declines, and can also alter vegetation change trajectories. We assessed the range characteristics of a migratory caribou (Rangifer tarandus) herd in east‐central Alaska and west‐central Yukon Territory as it grew (1992–2017) then declined (2017–2020). Furthermore, we analyzed the correlation between caribou relative spatial density and vegetation change over this period using remotely sensed models of plant functional type cover. Over this period, caribou population density increased in all seasonal ranges. This was most acute in the calving range where density increased 8‐fold, from 1.5 to 12.0 animals km⁻². Concurrent with increasing density, we documented range shifts and expansion across summer, post‐calving and winter ranges. In particular, summer range size doubled (12,000 km² increase) and overlap with core range (areas with repeated year‐round use) was halved. Meanwhile, lichen cover, a key forage item, declined more in areas with high caribou density (2.4% absolute, 22% relative decline in cover) compared to areas where caribou were mostly absent (0.3% absolute, 1.9% relative decline). Conversely, deciduous shrub cover increased more in high caribou density areas. However, increases were dominated by less palatable shrubs whereas more palatable shrubs (i.e., willow [Salix spp.]) were stable or declined slightly. These changes in vegetation cover were small relative to uncertainty in the map products used to calculate change. Nonetheless, correlations between vegetation change and caribou range characteristics, along with concerning demographic trends reported over this same period, suggest changing forage conditions may have played a role in the herd's subsequent population decline. Our research highlights the potential of remotely sensed metrics of vegetation change for assessing the impacts of herbivory and trampling and stresses the importance of in situ data such as exclosures for validating such findings.

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.

Population density is a valuable metric used to manage wildlife populations. In the Russian Far East, managers use the Formozov‐ Malyushev‐Pereleshin (FMP) snow tracking method to estimate densities of ungulates for hunting management. The FMP also informs Amur tiger (Panthera tigris altaica) conservation since estimates of prey density and biomass help inform conservation interventions. Yet, climate change and challenges with survey design call into question the reliability of the FMP. Camera traps offer a promising alternative, but they remain unexplored for monitoring tiger prey density. Over three years (2020‐2022), we used the FMP and camera‐based methods to estimate densities of four prey species of the Amur tiger in the Sikhote‐ Alin Biosphere Reserve, Russian Far East: wild boar (Sus scrofa), red deer (Cervus canadensis), roe deer (Capreolus pygargus), and sika deer (Cervus nippon). We compared FMP results from snow track survey routes either along trails, or along routes representative of the study area, and estimates derived from camera data using the random encounter model (REM), space‐to‐event model (STE), and time‐to‐event model (TTE). We found that density estimates from representative routes were typically lower than routes along trails and indicated different relative densities of prey. Density estimates from camera traps and representative track surveys were generally similar with no significant relative bias, but precision was poor for all methods. Differences between estimates were amplified when converted to prey biomass, particularly with larger, more abundant prey, which poses a challenge for their utility for tiger managers. We conclude camera traps can offer an alternative to snow track surveys when monitoring unmarked prey, but we caution that they require considerably more resources to implement. Tiger managers should be especially cautious when extrapolating density to estimates of prey biomass, and we encourage future research to develop more robust methods for doing so.

Animals within social groups respond to costs and benefits of sociality by adjusting the proportion of time they spend in close proximity to other individuals in the group (cohesion). Variation in cohesion between individuals, in turn, shapes important group‐level processes such as subgroup formation and fission–fusion dynamics. Although critical to animal sociality, a comprehensive understanding of the factors influencing cohesion remains a gap in our knowledge of cooperative behavior in animals. We tracked 574 individuals from six species within the genus Canis in 15 countries on four continents with GPS telemetry to estimate the time that pairs of individuals within social groups spent in close proximity and test hypotheses regarding drivers of cohesion. Pairs of social canids (Canis spp.) varied widely in the proportion of time they spent together (5%–100%) during seasonal monitoring periods relative to both intrinsic characteristics and environmental conditions. The majority of our data came from three species of wolves (gray wolves, eastern wolves, and red wolves) and coyotes. For these species, cohesion within social groups was greatest between breeding pairs and varied seasonally as the nature of cooperative activities changed relative to annual life history patterns. Across species, wolves were more cohesive than coyotes. For wolves, pairs were less cohesive in larger groups, and when suitable, small prey was present reflecting the constraints of food resources and intragroup competition on social associations. Pair cohesion in wolves declined with increased anthropogenic modification of the landscape and greater climatic variability, underscoring challenges for conserving social top predators in a changing world. We show that pairwise cohesion in social groups varies strongly both within and across Canis species, as individuals respond to changing ecological context defined by resources, competition, and anthropogenic disturbance. Our work highlights that cohesion is a highly plastic component of animal sociality that holds significant promise for elucidating ecological and evolutionary mechanisms underlying cooperative behavior.

Caring for newborn offspring hampers resource acquisition of mammalian females, curbing their ability to meet the high energy expenditure of early lactation. Newborns are particularly vulnerable, and, among the large herbivores, ungulates have evolved a continuum of neonatal antipredator tactics, ranging from immobile hider (such as roe deer fawns or impala calves) to highly mobile follower offspring (such as reindeer calves or chamois kids). How these tactics constrain female movements around parturition is unknown, particularly within the current context of increasing habitat fragmentation and earlier plant phenology caused by global warming. Here, using a comparative analysis across 54 populations of 23 species of large herbivores from 5 ungulate families (Bovidae, Cervidae, Equidae, Antilocapridae and Giraffidae), we show that mothers adjust their movements to variation in resource productivity and heterogeneity according to their offspring’s neonatal tactic. Mothers with hider offspring are unable to exploit environments where the variability of resources occurs at a broad scale, which might alter resource allocation compared with mothers with follower offspring. Our findings reveal that the overlooked neonatal tactic plays a key role for predicting how species are coping with environmental variation.