Briana Abrahms

Intraguild competition is a key force structuring predator communities, with dominant species impacting subordinate species' space use and access to resources. Climate change may intensify or reshape these processes, yet how climate conditions interact with dominance to influence predator space use and resource selection remains poorly understood. In particular, it is well‐established that droughts impact carnivores by altering predator‐prey dynamics, but less is known about how it affects intraguild competition and risk‐dependent habitat selection. Here, we leverage a 12‐year movement dataset for four sympatric species within the African large carnivore guild – lions Panthera leo , leopards Panthera pardus , cheetahs Acinonyx jubatus and African wild dogs Lycaon pictus – to examine how drought affects behavioral tradeoffs between competition avoidance and selection for a key resource, surface water. We found that lions, the most dominant species of the guild, consistently selected for areas closer to water, and selection intensified during times of drought. For subordinate species, selection for water was mediated by both drought conditions and lion risk, but the dynamics of this tradeoff varied based on the relative dominance of each species. During times of drought, leopards and cheetahs avoided water in areas where lion risk was high, but selected water where lion risk was low. In contrast, African wild dogs, the smallest species of the guild, exhibited a similar risk‐sensitive strategy during wetter conditions, but during times of drought avoided areas closer to water across all levels of lion risk, likely due to amplified intraguild competition during drought conditions. These findings suggest that drought can intensify the tradeoffs between resource acquisition and competitor avoidance amongst top predators. In semiarid systems, surface water, around which prey aggregate, is likely a dynamic axis across which intraguild competition unfolds, with implications for intensifying costs for subordinate species under projected increases in drought frequency.

Wildlife is increasingly forced to share space with humans, facing disturbances that operate across different spatial and temporal scales. The press–pulse framework, originally developed in the disturbance ecology literature, distinguishes between long-term sustained ‘presses’ and more acute ‘pulses’. Because pulses occur during ongoing press conditions, their ecological effects depend on how they interact with that background, helping explain why certain disturbances result in transient, localized changes, while others lead to lasting, widespread impacts. Here, we expand this framework by applying it to regimes of human disturbances, and incorporating ‘pauses’ as a third category. Pulses and pauses (e.g. episodes of extreme weather, or drastic changes in human mobility) can substantially affect wildlife behaviour, yet their effects are often modulated by background ‘press’ conditions. We offer a conceptual framework for disentangling effects across space and time and highlight how plasticity in wildlife movement—particularly in terms of navigating risks and tracking resources—can lead to both adaptive and nonadaptive responses to human disturbances. This enables predictions about how shifts in animal movement can influence human wildlife interactions and conflict with humans under different scenarios. Applying the press–pulse–pause framework to movement ecology allows researchers to improve our understanding of how wildlife is affected by different disturbance regimes, advancing efforts to foster sustainable human–wildlife coexistence in an era of mounting pressures.

Animal‐worn sensors have revolutionised the study of animal behaviour and ecology. Accelerometers, which measure changes in acceleration across planes of movement, are increasingly being used in conjunction with machine learning models to classify animal behaviours across taxa and research questions. However, the widespread adoption of these methods faces challenges from imbalanced training data, unquantified uncertainties in model outputs, shifts in model performance across contexts and noisy classifications in continuous data streams, where predicted behaviours change abruptly within a sequence. To address these challenges, we introduce an open‐source approach for classifying animal behaviour from raw acceleration data. Our approach integrates machine learning and statistical inference techniques to evaluate and mitigate class imbalances, changes in model performance across ecological settings and noisy classifications. Importantly, we extend predictions from single behaviour classifications to prediction sets: sets of behaviour labels guaranteed to contain the true behaviour with a pre‐specified probability, in a framework analogous to the use of prediction intervals in statistical analyses. We evaluate our approach via simulation and highlight its utility using data collected from a free‐ranging large carnivore, African wild dogs ( Lycaon pictus ), in the Okavango Delta, Botswana. We demonstrate significantly improved predictions along with associated uncertainty metrics in African wild dog behaviour classification, particularly for rare and ecologically important behaviours such as feeding, where correct classifications more than doubled following quality checks and data rebalancing introduced in our pipeline. Our approach is applicable across taxa and represents a key step towards advancing the burgeoning use of machine learning to remotely observe around‐the‐clock behaviours of free‐ranging animals. Future work could include the integration of multiple data streams, such as accelerometer, audio and GPS data, for model training and could be incorporated directly into our pipeline.

Marine megafauna species are affected by a wide range of anthropogenic threats. To evaluate the risk of such threats, species’ vulnerability to each threat must first be determined. We build on the existing threats classification scheme and ranking system of the International Union for Conservation of Nature (IUCN) Red List of Threatened Species by assessing the vulnerability of 256 marine megafauna species to 23 at‐sea threats. The threats we considered included individual fishing gear types, climate‐change‐related subthreats not previously assessed, and threats associated with coastal impacts and maritime disturbances. Our ratings resulted in 70 species having high vulnerability ( v > 0.778 out of 1) to at least 1 threat, primarily drifting longlines, temperature extremes, or fixed gear. These 3 threats were also considered to have the most severe effects (i.e., steepest population declines). Overall, temperature extremes and plastics and other solid waste were rated as affecting the largest proportion of populations. Penguins, pinnipeds, and polar bears had the highest vulnerability to temperature extremes. Bony fishes had the highest vulnerability to drifting longlines and plastics and other solid waste; pelagic cetaceans to 4 maritime disturbance threats; elasmobranchs to 5 fishing threats; and flying birds to drifting longlines and 2 maritime disturbance threats. Sirenians and turtles had the highest vulnerability to at least one threat from all 4 categories. Despite not necessarily having severe effects for most taxonomic groups, temperature extremes were rated among the top threats for all taxa except bony fishes. The vulnerability scores we provide are an important first step in estimating the risk of threats to marine megafauna. Importantly, they help differentiate scope from severity, which is key to identifying threats that should be prioritized for mitigation.

Diverse factors, including environmental features and cognitive processes, can drive animals’ movements and space use, with far-reaching implications. For example, repeated use of individual-level travel routeways (directionally constrained but imperfectly aligned routes), which results in spatial concentration of activity, can shape encounter-based processes including predation, mate finding, and disease transmission. However, how much variation in routeway usage exists across species remains unknown. By analyzing GPS movement tracks for 1,239 range-resident mammalian carnivores—representing 16 canid and 18 felid species from six continents—we found strong evidence of a clade-level difference in species’ reliance on repeatedly used travel routeways. Across the global dataset, tracked canids had a 15% (±7 CI) greater density of routeways within their home ranges than did felids, rising to 33% (±16 CI) greater in landscapes shared with tracked felids. Moreover, comparisons within species across landscapes revealed broadly similar home range routeway densities despite habitat differences. On average, canids also reused their travel routeways more intensively than did felids, with hunting strategies and spatial contexts also contributing to the intensity of routeway usage. Collectively, our results suggest that key aspects of carnivore routeway-usage have an evolutionary component. Striking interspecific and clade-level differences in carnivores’ reliance on reused travel routeways within home ranges identify important ways in which the movement patterns of real-world predators depart from classical assumptions of predator-prey theory. Because such departures can drive key aspects of human-wildlife interactions and other encounter-based processes, continued investigations of the relationships between movement mechanisms and space use are critical.

Responses of natural populations to climate change are driven by how multiple climatic and biotic factors affect survival and reproduction, and ultimately shape population dynamics. Yet, despite substantial progress in synthesizing the sensitivity of populations to climatic variation, comparative studies still overlook such complex interactions among drivers that generate variation in population-level metrics. Here, we use a common framework to synthesize how the joint effects of climate and biotic drivers on different vital rates impact population change, using unique long-term data from 41 species, ranging from trees to primates. We show that simultaneous effects of multiple climatic drivers exacerbate population responses to climate change, especially for fast-lived species. However, accounting for density feedbacks under climate variation buffers the effects of climate change on population dynamics. In all species considered in our analyses, such interactions between climate and density had starkly different effects depending on the age, size, or life-cycle stage of individuals, regardless of the life history of species. Our work provides the first general framework to assess how covarying effects of climate and density across a wide range of population models can impact populations of plants and animals under climate change.

Who conducts biological research, where they do it and how results are disseminated vary among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical step towards addressing them. We documented perceived and observed biases in movement ecology, a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. We surveyed attendees before an international conference to assess a baseline within-discipline perceived bias (uninformed perceived bias). We analysed geographic patterns in Movement Ecology articles, finding discrepancies between the country of the authors’ affiliation and study site location, related to national economics. We analysed race-gender identities of USA biology researchers (the closest to our sub-discipline with data available), finding that they differed from national demographics. Finally, we discussed the quantitatively observed bias at the conference, to assess within-discipline perceived bias informed with observational data (informed perceived bias). Although the survey indicated most conference participants as bias-aware, conversations only covered a subset of biases. We discuss potential causes of bias (parachute-science, fieldwork accessibility), solutions and the need to evaluate mitigatory action effectiveness. Undertaking data-driven analysis of bias within sub-disciplines can help identify specific barriers and move towards the inclusion of a greater diversity of participants in the scientific process.