Ellen O. Aikens

Large carnivore populations face numerous threats, yet leopards ( Panthera pardus ) remain understudied in Bhutan, where conservation is primarily focused on tigers ( Panthera tigris ). We evaluated the bottom-up and top-down influences on leopard habitat use, vital rates, and activity patterns, and provide the first nationwide estimate of leopard density in Bhutan. Using nationwide camera trap surveys from 2014–2015 to 2021–2022, we applied spatially explicit capture-recapture (SECR) models to estimate leopard density, dynamic two-species occupancy models to assess leopard-tiger habitat use, colonization, and local extinction, and activity-overlap analyses to evaluate temporal partitioning. Mean leopard density was 1.02 individuals/100 km ² (95% CI 0.89 −1.16), yielding a national population of 318.73 ± 21.73 SE individuals. Leopard density declined with elevation, stream density, and tree cover, while tiger density, human settlement density, and prey availability had weaker context dependent effects. Estimated site use was higher for leopards than tigers and declined with elevation and stream density for both species, while local extinction declined with increasing prey density. Leopards and tigers showed high diel overlap, suggesting little temporal avoidance. Together, these results suggest that leopard persistence in Bhutan’s montane landscapes is structured more strongly by bottom-up habitat and prey conditions than by tiger suppression.

Seasonal migration is a common behaviour seen in many species worldwide. There is evidence that social factors influence various migration decisions, but compared to the well-studied field of social foraging, the study of social animal migration is still underdeveloped. Nevertheless, a few innovative studies have shown the impact of social influences on migrating animals using different tools and techniques. In this review, we systematically examined the literature to identify what makes a strong study in the field of social animal migration. We synthesized existing literature to provide advice that can improve study design and the conclusions drawn for future studies on social migration. Our analysis revealed that social interactions are widely studied among migrating species. We identified methods and approaches that, when combined correctly, provide strong evidence of social influences during migration. Finally, based on the strengths and shortcomings of past studies, we propose a set of guidelines to create more robust studies on social animal migrations.

Rapid developments in animal‐tracking technology have enabled major advances in the field of movement ecology, which seeks to understand the drivers and consequences of movement across scales, taxa, and ecosystems. The field has made ground‐breaking discoveries, yet the majority of studies in movement ecology remain reliant on observational approaches. While important, observational studies are limited compared to experimental methods that can reveal causal relationships and underlying mechanisms. As such, we advocate for a renewed focus on experimental approaches in animal movement ecology. We illustrate a way forward in experimental movement ecology across two fundamental levels of biological organisation: individuals and social groups. We then explore the application of experiments in movement ecology to study anthropogenic influences on wildlife movement, and enhance our mechanistic understanding of conservation interventions. In each of these examples, we draw upon previous research that has effectively employed experimental approaches, while highlighting outstanding questions that could be answered by further experimentation. We conclude by highlighting the ways experimental manipulations in both laboratory and natural settings provide a promising way forward to generate mechanistic understandings of the drivers, consequences, and conservation of animal movement.

Many ungulates migrate between distinct summer and winter ranges, and identifying, mapping, and conserving these migration corridors have become a focus of local, regional, and global conservation efforts. Brownian bridge movement models (BBMMs) are commonly used to empirically identify these seasonal migration corridors; however, they require location data sampled at relatively frequent intervals to obtain a robust estimate of an animal's movement path. Fitting BBMMs to sparse location data violates the assumption of conditional random movement between successive locations, overestimating the area (and width) of a migration corridor when creating individual and population‐level occurrence distributions and precluding the use of low‐frequency, or sparse, data in mapping migration corridors. In an effort to expand the utility of BBMMs to include sparse GPS data, we propose an alternative approach to model migration corridors from sparse GPS data. We demonstrate this method using GPS data collected every 2 h from four mule deer (Odocoileus hemionus) and four elk (Cervus canadensis) herds within Wyoming and Idaho. First, we used BBMMs to estimate a baseline corridor for the 2‐h data. We then subsampled the 2‐h data to one location every 12 h (a proxy for sparse data) and fitted BBMMs to the 12‐h data using a fixed motion variance (FMV) value, instead of estimating the Brownian motion variance empirically. A range of FMV values was tested to identify the value that best approximated the baseline migration corridor. FMV values within a species‐specific range (mule deer: 400–1200 m²; elk: 600–1600 m²) successfully delineated migration corridors similar to the 2‐h baseline corridors; overall, lower values delineated narrower corridors and higher values delineated wider corridors. Optimal FMV values of 800 m² (mule deer) and 1000 m² (elk) decreased the inflation of the 12‐h corridors relative to the 2‐h corridors from traditional BBMMs. This FMV approach thus enables using sparse movement data to approximate realistic migration corridor dimensions, providing an important alternative when movement data are collected infrequently. This approach greatly expands the number of datasets that can be used for migration corridor mapping—a useful tool for management and conservation across the globe.

How animals refine migratory behavior over their lifetime (i.e., the ontogeny of migration) is an enduring question with important implications for predicting the adaptive capacity of migrants in a changing world. Yet, our inability to monitor the movements of individuals from early life onward has limited our understanding of the ontogeny of migration. The exploration–refinement hypothesis posits that learning shapes the ontogeny of migration in long-lived species, resulting in greater exploratory behavior early in life followed by more rapid and direct movement during later life. We test the exploration–refinement hypothesis by examining how white storks ( Ciconia ciconia ) balance energy, time, and information as they develop and refine migratory behavior during the first years of life. Here, we show that young birds reduce energy expenditure during flight while also increasing information gain by exploring new places during migration. As the birds age and gain more experience, older individuals stop exploring new places and instead move more quickly and directly, resulting in greater energy expenditure during migratory flight. During spring migration, individuals innovated novel shortcuts during the transition from early life into adulthood, suggesting a reliance on spatial memory acquired through learning. These incremental refinements in migratory behavior provide support for the importance of individual learning within a lifetime in the ontogeny of long-distance migration.

Human activity and associated landscape modifications alter the movements of animals with consequences for populations and ecosystems worldwide. Species performing long‐distance movements are thought to be particularly sensitive to human impact. Despite the increasing anthropogenic pressure, it remains challenging to understand and predict animals' responses to human activity. Here we address this knowledge gap using 1206 Global Positioning System movement trajectories of 815 individuals from 14 red deer (Cervus elaphus) and 14 elk (Cervus canadensis) populations spanning wide environmental gradients, namely the latitudinal range from the Alps to Scandinavia in Europe, and the Greater Yellowstone Ecosystem in North America. We measured individual‐level movements relative to the environmental context, or movement expression, using the standardized metric Intensity of Use, reflecting both the directionality and extent of movements. We expected movement expression to be affected by resource (Normalized Difference Vegetation Index, NDVI) predictability and topography, but those factors to be superseded by human impact. Red deer and elk movement expression varied along a continuum, from highly segmented trajectories over relatively small areas (high intensity of use), to directed transitions through restricted corridors (low intensity of use). Human activity (Human Footprint Index, HFI) was the strongest driver of movement expression, with a steep increase in Intensity of Use as HFI increased, but only until a threshold was reached. After exceeding this level of impact, the Intensity of Use remained unchanged. These results indicate the overall sensitivity of Cervus movement expression to human activity and suggest a limitation of plastic responses under high human pressure, despite the species also occurring in human‐dominated landscapes. Our work represents the first comparison of metric‐based movement expression across widely distributed populations of a deer genus, contributing to the understanding and prediction of animals' responses to human activity.

Billions of animals migrate to track seasonal pulses in resources. Optimally timing migration is a key strategy, yet the ability of animals to compensate for phenological mismatches en route is largely unknown. Using GPS movement data collected from 72 adult female deer over a 10-year duration, we study a population of mule deer (Odocoileus hemionus) in Wyoming that lack reliable cues on their desert winter range, causing them to start migration 70 days ahead to 52 days behind the wave of spring green-up. We show that individual deer arrive at their summer range within an average 6-day window by adjusting movement speed and stopover use. Late migrants move 2.5 times faster and spend 72% less time on stopovers than early migrants, which allows them to catch the green wave. Our findings suggest that ungulates, and potentially other migratory species, possess cognitive abilities to recognize where they are in space and time relative to key resources. Such behavioral capacity may allow migratory taxa to maintain foraging benefits amid rapidly changing phenology.