Movement Ecology

O_LIClassifying animal movement strategies from GPS tracking data is essential for understanding space use, population dynamics and conservation planning. However, existing approaches either require strong parametric assumptions about trajectory shape, large labelled datasets (i.e. expert-annotated) for machine learning, or lack formal uncertainty quantification. These limitations create barriers for researchers working with novel species or limited sample sizes. C_LIO_LIWe present a profile-based classification framework consisting of three steps. First, trajectories are segmented using breakpoint detection applied to Net Squared Displacement (NSD) time series. Movement metrics are then extracted from each segment and classified by comparing them to empirically derived behavioural profiles via Z-score distances transformed to softmax probabilities. Bootstrap resampling quantifies uncertainty in the resulting classifications from both training and test data. We validated the framework through simulation experiments and applied it to GPS tracking data from two ecologically contrasting species: gray wolf (Canis lupus;43 individuals) and northern lapwing (Vanellus vanellus;15 individuals). C_LIO_LISimulations showed that 5-10 training segments per movement strategy suffice for reliable classification, with overall accuracy of 91.1%across residential, floating and dispersal strategies. Segment duration of 30-60 days was required for confident discrimination of residential and floating behaviour. For wolves, the framework clearly distinguished residency, floating or dispersal (91.2%of segments classified with >50%probability). For lapwings, migration was identified with high confidence, while residential-floating discrimination reflected genuine ecological ambiguity confirmed by domain experts, with bootstrap confidence intervals transparently flagging uncertain cases. C_LIO_LIThe profile-based framework provides an accessible, interpretable alternative to parametric NSD fitting and machine learning approach, requiring modest training data while delivering probabilistic classifications with honest uncertainty estimates. An R package (moveprofile) implementing the complete workflow is freely available. The framework is applicable to any tracked species where distinct movement strategies can be identified by experts knowledge. C_LI

Migrating bats alternate between stopover periods and directed flights. When departing from a stopover site, bats select the night, the specific time within the night, and the flight direction to resume migration. Despite their ecological importance, the factors shaping these stopover departure decisions remain poorly understood. To identify the intrinsic and environmental factors driving departure decisions and movement patterns, we tagged Nathusius pipistrelles Pipistrellus nathusii at three coastal locations in the Netherlands and tracked 178 individuals during autumn migration, using the MOTUS Wildlife Tracking System. We examined movement patterns and analysed departure probability in relation to a set of individual and environmental covariates in a Bayesian capture-recapture model in state-space formulation. Additionally, we modelled within-night variation in departure timing. Seasonal patterns were strongly influenced by reproductive behaviour, with decreased migration probability during the mating period. Regardless of their seasonal timing, bats departed under moderate tailwinds and dry conditions, optimizing energy efficiency, while avoiding crosswinds and cloud cover, enhancing navigational safety. Most individuals departed shortly after sunset, whereas headwinds delayed nocturnal departure. Movement patterns were diverse, including migration towards lower latitudes, coastal barrier movements, and long-distance roundtrips, suggesting the use of multiple movement strategies. Our study demonstrates that migration patterns in bats emerge from the interaction between intrinsic factors and external conditions, and highlights the importance of both energy efficiency and safety in shaping stopover departure decisions. The presence of multiple movement strategies complicates predictions of spatiotemporal occurrence, emphasising the need to account for behavioural variability in conservation planning, for example in the context of wind energy developments.

BackgroundIn hibernating mammals, the timing of den entry and exit reflects complex interactions among environment, physiology, and energetic constraints, with consequences for fitness. Consequently, shifts in denning phenology can affect population dynamics, particularly under climate change. Reliable estimation of denning timing is therefore critical, yet current methods often rely on GPS-derived movement data, limited by coarse sampling intervals, detection issues, and the inability to distinguish true inactivity from active presence at the den site. In this study, we developed and apply a method to estimate denning phenology in a brown bear population in south-central Sweden using accelerometer-derived activity data. Our approach employs adaptive, individual-specific thresholds to account for variation in baseline activity across bears, focusing on day-to-day changes to identify the start and end of inactivity periods. This method allows flexible and reproducible detection of den entry and exit dates, overcoming limitations associated with fixed thresholds and small sample sizes. ResultsWe compared activity-based estimates with GPS-derived den occupancy and examined variation in denning behavior across demographic groups. Analyzing 388 bear-winters, the method successfully identified inactivity periods in 360 cases. The method failed to identify clear start and end dates of hibernation for 28 (7%) bear-winters, which were characterized by unusually high or low daily activity levels at the boundaries of the inactivity period. Den site occupancy ranged from September 5 to June 2, with durations of 112-260 days, whereas inactivity periods detected from activity data extended from September 6 to May 13, lasting 83-217 days. Our comparison of activity-based and GPS-based methods indicates that bears may arrive at the den site several weeks before the onset of inactivity, with timing varying among demographic groups. ConclusionWe show that activity-based analysis provides a robust framework for estimating denning phenology, distinguishing actual inactivity from site presence, and improving understanding of the timing and variability of bear denning behavior. Applying an individual-level activity-based method improves accuracy in assessing ecological mechanisms underlying hibernation in bears and other hibernators, while also enhancing interpretation of environmental drivers and providing a reliable tool to monitor phenological shifts in response to climate change.

BackgroundThe Andean bear (Tremarctos ornatus), South Americas only ursid, is one of the worlds most elusive large mammals, making movement data collection exceptionally rare. Addressing this gap, we present the largest telemetry dataset ever assembled, spanning 19 individuals tracked across three Ecuadorian National Parks over two decades, paired with a novel analytical approach. MethodsWe integrated Continuous-Time Movement Models (CTMM), Auto-correlated Kernel Density Estimators (AKDEs), Hidden Markov Models (HMM) and a diel niche theoretical framework to mitigate biases previously unaccounted for the species in telemetry studies. Fine-scale AKDEs and non-linear movement metrics were calculated to understand seasonal space use and movement behaviors. Speed and diffusion from CTMM and behavioral states from HMM were modelled with environmental covariates to investigate which conditions shape diel and seasonal activity. ResultsPopulation mean home range was 138.2 km2 (95% Confidence Intervals 78.7-225.5), with males (239.8 km2; 182.8-307.5), significantly exceeding females (58.5 km2; 35.5-90.3). Notably, three females exhibited ranges comparable to some males. Weekly and monthly AKDEs uncovered cyclic home range dynamics potentially driven by resource availability, with contractions around corn harvests, mortino and achupalla fruiting, and expansions during paramo transitions. Decoupling speed from diffusion rates showed region-specific behaviors: intensive patch exploitation in Llanganates, broad exploratory ranging in Cayambe-Coca, and suppressed female locomotion in Cotacachi-Cayapas. Statistical analyses identified temperature as a key diel modulator and precipitation as the seasonal driver. Foraging probability increased between 2:00-6:00, large displacements between 7:00-14:00, and nocturnal movement rose significantly under colder conditions. Across diel hypothesis frameworks, bears were classified as cathemeral rather than strictly diurnal, corroborated by camera-trap records from Colombia, Ecuador, and Peru. ConclusionsWe propose a cathemeral diel phenotype that responds to thermal fluctuations and situates Andean bears within a broader ursid context of thermoregulatory niche plasticity. This dataset reveals unprecedented resolution of regional and sex specific behaviors that will facilitate and accelerate comparative studies in rapidly changing Andean landscapes. By releasing this long-term dataset as an open resource, we provide a foundation for climate-resilient conservation strategies. More broadly, we advocate for data democratization and invite collaboration.

Winter can affect animal population dynamics by limiting resource availability and increasing energetic costs of movement caused by deep snow. Given the rapid alteration of snowpack properties due to climate change, quantifying how snow characteristics influence reproduction and physical condition is critical. We evaluated how snow cover duration, depth, and density affect spring body mass, reproduction probability, and subsequent autumn body mass of bighorn sheep (Ovis canadensis) using 45 years of individual-based data at Ram Mountain, Alberta, Canada, along with historical snow records reconstructed via the SNOWPACK model. Using Bayesian structural equation modeling, we quantified the direct and indirect effects of snow across different sex and age classes. Long and deep snow covers reduced spring body mass across all demographic groups, with yearlings, especially males, losing up to 0.12 kg per additional cm of snow depth. Harsh snow conditions reduced the probability of reproduction for adult females and generated a compensatory indirect effect on mass by avoiding the energetic costs of reproduction. In contrast, yearlings showed no compensatory responses and entered the following autumn in poor condition (up to 14% lighter for males and 8% for females following the deepest snow years). The impact of snow density on autumn mass of adult males was density-dependent, shifting from beneficial at low density (+0.09 kg per kg/m3) to detrimental at high density (-0.04 kg per kg/m3). The effects of snow conditions generate persistent, context-dependent carry-over effects across seasons. Our study suggests that distinct demographic groups rely on different mechanisms to cope with environmental constraints, highlighting complex, time-lagged consequences of changing winter climate on alpine herbivore populations.

O_LIDemographic processes in populations are inherently heterogeneous across both space and time. Many ecological models explicitly account for temporal heterogeneity in the demographic rates that govern these processes, but assume spatial homogeneity. Ignoring spatial heterogeneity can bias inference, limit predictive performance, and obscure key spatial structure in demographic rates. Integrated population models (IPMs) offer a powerful framework to estimate spatio-temporal demographic rates by combining diverse ecological data sources collected from multiple sampling locations. However, to accomplish this, IPMs face significant statistical and computational hurdles, including misalignment between different data sources and the need to efficiently account for residual spatial autocorrelation. C_LIO_LIWe present a novel Bayesian spatially explicit integrated population model (sIPM) which integrates population count and capture-recapture data from multiple sampling locations to estimate and predict continuous spatio-temporal demographic rates, such as survival, recruitment and population growth rate, across large geographic domains. This framework employs a joint likelihood approach with change of support to flexibly accommodate spatial and spatio-temporal data misalignment, and incorporates a nearest-neighbor Gaussian process to efficiently model residual spatial autocorrelation and generate spatial predictions. C_LIO_LIWe assess the performance of our sIPM through an extensive simulation study. Results show that our approach provides unbiased and precise estimates and predictions of spatio-temporal demographic rates, even in the presence of significant data misalignment and residual spatial autocorrelation. We demonstrate the utility of our method by analyzing data on Gray Catbirds (Dumetella carolinensis) from the North American Breeding Bird Survey and the Monitoring Avian Productivity and Survivorship program across the eastern coast of the United States from 2004-2014. This analysis results in maps of apparent survival, recruitment and population growth rate, thereby revealing important spatio-temporal variations in demographic rates that would have been obscured by traditional, spatially homogeneous IPMs. C_LIO_LIOur sIPM offers a robust and computationally efficient method for studying spatio-temporal variation in demographic processes across large areas, even in the presence of data misalignment and residual spatial autocorrelation. Ultimately, this framework, applicable to many ecological monitoring programs, facilitates the development of spatially targeted strategies necessary for effective conservation and management. C_LI

BackgroundWintering migratory birds must balance energetic requirements, resource availability, and disturbance in increasingly human-modified landscapes. However, individual-level variability in daily movement and winter space use remains poorly understood in South Asian populations of the common crane. We investigated how seasonal dynamics, landscape composition, and individual differences structure winter movement ecology in a semi-arid agro-wetland system in western India. MethodsWe analysed high-resolution GPS telemetry data from multiple tagged cranes tracked across three consecutive winters. Daily movement distances were modelled using mixed-effects approaches to partition variance within and among individuals and among winters. Daily movement trajectories were evaluated using non-linear temporal terms. Landscape predictors, including cropland proportion, built-up area, and habitat heterogeneity, were incorporated to assess environmental drivers. Winter range distributions were estimated using autocorrelation-informed kernel density estimation within a continuous-time movement modelling framework. ResultsMost variation in daily movement occurred within individuals rather than among them, indicating strong behavioural flexibility. Interannual differences explained substantial variance, suggesting sensitivity to changing environmental conditions. Daily movement distance followed a non-linear seasonal pattern consistent with shifts in the profitability of agricultural resources over winter. Cropland proportion and landscape evenness were negatively associated with movement distance, whereas a high proportion of built-up areas increased daily movement distance, reflecting a trade-off between resource concentration and anthropogenic disturbance. Winter range distribution size varied markedly both within individuals across years and among individuals within seasons. ConclusionWinter movement and space use in common cranes are predominantly context-dependent and environmentally driven. Seasonal dynamics, agricultural landscapes, and human disturbance jointly structure movement patterns, with limited but consistent individual differences. Multi-year, individual-based telemetry provides a comprehensive understanding of winter spatial strategies in dynamic semi-arid agro-wetland systems.

Population density can influence individual predation risk in colonial breeders through shared vigilance and predator deterrence. We investigated how predator-prey interactions are shaped by population density at two Antarctic fur seal (Arctocephalus gazella) breeding colonies at Bird Island, South Georgia, which differ four-fold in seal density. By deploying autonomous time-lapse cameras, we captured high-resolution images at one-minute intervals throughout the breeding season. Using a YOLOv8 neural network, we identified fur seal adult males, females and pups, as well as three predator-scavenger bird species: giant petrels (Macronectes spp.), brown skuas (Stercorarius antarcticus) and snowy sheathbills (Chionis alba). Abundance patterns corresponded to the known foraging and breeding behaviours of these species. Differences in seal density between the colonies were mainly driven by adult females and their pups, but not adult males. The ratios of predatory birds to pups were markedly lower at the high-density colony, while scavenger to pup ratios remained similar. Spatial analyses revealed that predators were largely excluded from areas of high seal density, whereas scavengers overlapped extensively with pups in both colonies. This study demonstrates the value of remote observation in resolving predator-prey interactions and illustrates how density can shape predation risk in a colonial breeder.

O_LIThe management of invasive species often emphasizes removals to manage populations. However, evaluating the success of this management technique remains challenging, especially at large scales. Understanding the relationship between removal intensity and population growth is essential for determining when management achieves desired outcomes. C_LIO_LIWe used management removal data (removal resources [e.g. trapping] and relative effort [trap nights]) to estimate population density, demographic structure, and growth rates of invasive wild pigs (Sus scrofax domesticus) across a large landscape. From the management data and population estimates, we inferred population trajectories in the absence of removals and quantified the proportion of the population removed by the most widely used methods to control wild pigs. We then compared observed removal intensities and population growth rates to predict expected population trajectories immediately after management occurs. C_LIO_LIResults suggest substantial spatial and temporal variation in wild pig growth rates and variation in the effectiveness of removal efforts. Additionally, removing wild pigs at higher densities had a greater effect on limiting population growth than removals conducted at lower densities, though both are important. However, on large properties, removal intensity was often insufficient to offset population growth, indicating that management effort does not scale to large areas. C_LIO_LIThese results demonstrate how removal data and population modeling can provide robust inference on population dynamics and management effectiveness, offering a scalable framework for evaluating and improving invasive species control programs. We also discuss the current limitation of how effort is defined for different large-mammal removal techniques, and offer potential solutions for a more complete definition, such as going beyond trap nights and including constraints on personnel, equipment, and logistics. C_LI

Encounter rate models are important tools for evaluating and estimating trophic interactions between species. While encounter rate parameters have been measured for many freshwater pelagic fishes, most benthic fishes remain mostly unstudied. Those few efforts to generate encounter rate models for benthic fishes often hold mathematical assumptions based on visual foraging, despite the many cases in which benthic fishes employ the lateral line to forage. Furthermore, encounter rate models are rarely compared, despite the many cases in which prey animals face predation risk from multiple types of predators. For example, the macroinvertebrate Mysis is exposed to both benthic and pelagic predation risk during diel vertical migration (DVM). Comparing the risks between habitats could help evaluate predation risk as an ultimate cause of their DVM behavior. We created a novel encounter rate model based on lateral line ("tactile") foraging by sculpins (Cottidae) given the saltatory (stop-and-go) nature of their movement. The tactile model demonstrated variation in behavior and peak encounter rate with detection distance, movement velocity, and rest durations. We then directly compared predation risk for Mysis by parameterizing both our tactile benthic (2D) encounter rate model for sculpin and a visual pelagic (3D) for rainbow smelt (Osmerus mordax). Tactile encounter rates were generally lower than visual rates for individual predators. However, population level encounter rates at night were greater in the benthic habitat than the pelagic habitat. Overall, our model estimates of encounter rates were consistent with the long-standing hypothesis that predation is an ultimate driver of DVM behavior.

Density is a major determinant of population dynamics, with high densities exacerbating intraspecific competition and disease transmission, while low densities increase predation risk. To investigate spatiotemporal density patterns and predator-prey interactions in a breeding colony of Antarctic fur seals (Arctocephalus gazella), we deployed an autonomous camera capturing minute-by-minute, high-resolution images throughout a breeding season. Using a YOLO-based neural network, we identified adult males, females and pups, as well as three avian predator-scavengers: giant petrels (Macronectes spp.), brown skuas (Stercorarius antarcticus) and snowy sheathbills (Chionis alba). Analysis of 4.1 million automated detections from over 10,000 high-quality images revealed spatiotemporal abundance patterns corresponding with the known breeding and foraging behaviours of these species. Strong temporal associations emerged between the abundance of pups and two avian species, while fine-scale spatial analyses showed that pups grouped together with other pups and adult females but avoided avian predators and territorial males. Notably, proximity to adult fur seals of both sexes reduced pup predation risk, defined as the distance between the pup and the nearest bird, whereas proximity to other pups did not. Overall, this study provides a framework for quantifying density-dependent interactions in wild populations and emphasises the value of remote observation in ecological research.

Changing climates are reshaping animal populations, but our understanding of how demographic trends are shaped by individual responses to local environmental conditions is often limited to long-term studies with restricted spatial scales. Increasing evidence suggests that climatic extremes exert differential selection pressures across environments, often leading to nonstationary biological responses among populations. Participatory science (i.e. citizen science) observations can detect this variation at large geographic scales, but analyses of these data often lack insight into the individual-level responses that are required to explain the origins of such variation. Here we present a new research framework that uses long-term data to validate, then inform analyses of participatory science data to measure reproductive responses to environmental variation across large geographic scales. We use this approach to investigate how reproduction in a montane-adapted songbird, the mountain chickadee (Poecile gambeli), varies across elevations and latitudes in response to extreme scarcity and extreme accumulation of snow throughout the Sierra Nevada Mountains in North America. Chickadee reproduction in lower and higher elevation populations was often differentially impacted by drought and deluge snowfall extremes, but these relationships varied across latitudes. Reproductive performance in the northern Sierra Nevada was negatively affected by snow deluge conditions at high elevations, whereas snow drought conditions reduced reproductive output at low elevations. These relationships changed in the central Sierras where drought conditions negatively impacted both elevations, but deluge conditions improved reproductive performance at both low and high elevations. Reproduction in the southern Sierra Nevada was less affected by spring snow levels, likely due to the lower snow accumulation and earlier snowmelt in this region. These results emphasize the power of long-term studies to inform and interpret participatory science data in order to better understand how animal responses to environmental extremes vary across large geographic scales.

O_LINitrogen (N) is limiting for terrestrial herbivores, particularly over winter. Caribou (Rangifer tarandus) have adapted to seasonal scarcity of N by accruing muscle mass during the growing season when N is more abundant. C_LIO_LINitrogen stored in muscle tissue is then relied upon during winter to compensate for dietary deficits. Once their diet shifts from N-rich vascular plants to N-poor lichen over winter, caribou can lose [~]30% of their muscle mass. As catabolized N is shed in urine on wintering grounds, caribou could act as elemental transport across seasons and landscapes. Furthermore, if deposited N is taken up by lichen or other winter forage, it might enrich the nitrogen-poor winter diet of caribou in the future. C_LIO_LIWe tested this potential transport via three steps. We analysed Cladonia spp. lichen and vascular plants upon which caribou forage across Fogo Island, Newfoundland, using %N content as our metric of forage quality. We then compared seasonal habitat selection responses to forage quality by caribou using integrated step selection analyses. In summer, caribou selected areas with higher vascular plant %N but did not select nor avoid Cladonia quality. In contrast, caribou selected sites with higher quality Cladonia in winter but responded neutrally to vascular plant quality. C_LIO_LIWe compared seasonal distributions of caribou to determine whether nitrogen consumed in summer and deposited in winter would occur in spatially discrete locations. Population-level kernel density estimates for summer and winter in this island herd were mostly non-overlapping, lending credence to the potential landscape effects of N transport. C_LIO_LIWhen viewed together with established seasonal changes in woodland caribou physiology, sociality, and forage preferences, the shifts in habitat selection and seasonal ranges we observe here could serve as an adaptive strategy for caribou to recycle N and mitigate winter nutrient scarcity. C_LI

Recent progress in mathematical kinship modelling has allowed one to predict the probable numbers of kin for a typical population member. In the models, kin may be structured by age and sex, both in static or time-variant demographies. Knowing the probable numbers of kin in different stages - such as parity, health status, or geographic location - however, remains an open challenge in Kinship Demography. Knowing how population structure delimits kin to distinct stages is an advance - for instance, the probability of having one sister at home and one sister away has different social implications from the probability of having two sisters. We present a novel analytical framework, grounded in branching process theory, that provides kin-number distributions jointly structured by age and stage. Using recursive compositions of probability generating functions (PGFs), we derive the joint age, stage, and age x stage kin-number distributions. All marginal distributions over either dimension naturally emerge. Simple extensions of the PGF approach additionally yield: the joint distribution of an individuals own stage and their kins stage; the probable numbers of kin deaths, both in total and by generation number; and the probabilities of being kinless and/or orphaned. We demonstrate the framework through novel results in an application using UK parity-specific fertility and mortality data. HighlightsO_LIA new method calculates probability generating functions for the number of kin structured by age and stage C_LIO_LIThe model allows predicting the probable numbers of kin organised by age and stage C_LIO_LIRecursive nesting of probability generating functions in branching processes is used C_LIO_LIAn application is presented highlighting the novel results C_LI

Larval dispersal models are central to mapping and predicting ichthyoplankton dynamics in the ocean, yet despite decades of refinement they remain fundamentally limited by their ability to represent adaptive behaviors, relying instead on static trait parameterizations. This deficiency constrains our capacity to design effective restoration and mitigation strategies in an increasingly stressed ocean. SWARM (Simulating Waterborne Agent Routes for Marine connectivity) overcomes this barrier by integrating Large Language Model (LLM)-based behavioral agents with a standard biophysical model to simulate active decision-making during the pelagic larval stage. In both idealized and realistic conditions focusing on Red Snapper larvae in the Gulf of Mexico, agents develop adaptive behaviors that improve settlement and generate explainable vertical distribution patterns. SWARM demonstrates that LLMs can overcome long-standing limitations in dispersal modelling by explicitly representing behavioral drivers of movement, opening new pathways for predicting connectivity and designing effective marine-ecosystem restoration.

Wildlife crossing infrastructure is promoted to restore connectivity for fragmented populations, but its effectiveness at enabling natural recolonisation remains untested. We tested this using a spatially explicit agent-based model parameterised with GPS telemetry data from bobcats (Lynx rufus) in New Jersey, USA. By integrating movement behaviour, stochastic demography, habitat suitability, and traffic-dependent mortality risk, we simulated 50-year recolonisation dynamics across a highly urbanised landscape. Despite extensive unoccupied suitable habitat, natural recolonisation completely failed across all scenarios, with vehicle-induced mortality during dispersal acting as the primary limiting factor and turning the matrix into a demographic sink. Even an idealised mitigation scenario in which mortality at high-mortality crossings was reduced to zero failed to produce a self-sustaining population. Although dispersal increased, individuals at the recolonisation front remained too sparse to overcome the mate-finding Allee effect. Sensitivity analysis confirmed that the recolonisation-failure result is robust to {+/-}50% variation in per-crossing mortality and {+/-}25% variation in disperser survival. Restoring structural connectivity is not, in itself, a sufficient intervention for recovering low-density carnivore populations facing a high-mortality matrix. Instead disperser survival and local density at the recolonisation front are the rate-limiting determinants. In such systems translocation rather than crossing-structure investment is more likely to result in recolonisation success.

Migration is a well-described behavioural strategy that allows species to track variation in resources and climatic conditions by moving in response to seasonality. A common form is elevational migration, an annual short-distance movement undertaken by many mountain bird species globally. While studies show that the timing of migration may relate to food availability, the mechanisms determining which species migrate remain unclear. Our study investigated if the degree of dietary specialization explains why some high-elevation bird species in seasonal environments migrate downslope for the winter while others remain resident at high altitudes despite the apparent scarcity of their preferred food resources. We mist-netted birds along a 2300-m elevational gradient in the Eastern Himalaya and collected blood and faecal samples from 261 individual birds belonging to 18 species of high-elevation residents (ten) and elevational migrants (eight) in their breeding and wintering ranges. Using stable isotope ratios of carbon and nitrogen in whole blood and faecal DNA metabarcoding, we compared their seasonal trophic levels and dietary niches. Nitrogen isotope ratios showed that residents had a substantially lower trophic position in the winter compared to summer (-0.35 [-0.52, -0.17]), whereas migrants had a slightly higher trophic position in the winter (0.15 [-0.02, 0.32]). This trophic shift in residents was likely due to a decrease in insectivory and an increase in frugivory in the winter. The frequency of key insect orders (Lepidoptera, Hemiptera, and Coleoptera) declined by 20-35% in their winter diets alongside an increase in fruit, particularly from the family Polygonaceae (0.33 [0.18, 0.46]). Additionally, compared with residents, migrants showed greater overlap in their dietary niches between summer and winter (98% vs 80%). Because arthropod abundances in the Himalayas peak at high elevations in the summer and decline in the winter, we suggest that elevational migrants are likely dietary specialists that track resources, while high-elevation residents are dietary generalists that supplement their winter diet with fruit and nectar because of the scarcity of arthropods. These findings indicate that a species dietary specialization is linked to its migratory behaviour, providing a potential mechanistic explanation for how different species solve the challenge of seasonal resource limitation.

Habitat partitioning supports the coexistence of sympatric species and shapes their ecological roles across coastal seascapes. Understanding how sympatric species move through and use coastal habitats therefore provides fundamental ecological insight. Aerial drones provide new opportunities to monitor fine-scale movement and habitat utilisation of elasmobranchs in shallow waters. Here, we use drones to investigate fine-scale habitat partitioning and foraging behaviour among stingrays in a coastal lagoon in the central Red Sea. We conducted 30 aerial transect surveys (~17 ha each) and tracked 40 rays and 1 shark (total tracking time > 23 h). Using a double-observer protocol (manual + AI-assisted), 1,468 rays (6 species) and 4 sharks (2 species) were recorded from the transect surveys. Transect detections were dominated by bluespotted ribbontail rays (Taeniura lymma; n = 1,221) and larger-bodied whiprays (predominantly Himantura uarnak; n = 187). AI-assisted image analysis outperformed human analysts detecting 97% of these observations, compared to 76% for human analysts. We found pronounced habitat partitioning at sub-kilometre scales: bluespotted rays occupied the shallowest (< 0.4 m deep) lagoonal areas, away from open water, with foraging-related digging concentrated along the mangrove edge, identifying this zone as a key feeding ground and bioturbation hotspot. Whiprays predominated on macroalgal reef flat habitats and appeared to forage non-disruptively on epifaunal prey. Both taxa aggregated with conspecifics. Together, our results demonstrate that contrasting micro-habitat preferences and foraging strategies structure the spatial ecology of sympatric stingrays and highlight how drone-based monitoring coupled with AI can scale ecological inference in nearshore ecosystems. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/710512v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1cefdd9org.highwire.dtl.DTLVardef@7bc807org.highwire.dtl.DTLVardef@895540org.highwire.dtl.DTLVardef@3c146b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Parental care can shape post-fledging behaviour through provisioning, guidance and social information, yet its absence may alter how young birds establish space use and habitat preferences. We tested the consequences of absent parental care by comparing, hand-reared juvenile herring gulls released without parents with wild, parent-reared conspecifics, focusing on the first two months after fledging. Wild juveniles frequently revisited their natal nest during the first month, whereas hand-reared birds rarely returned to the release site; revisits declined in both groups by the second month but remained more common in wild birds. Wild juveniles used smaller ranges that subsequently expanded, while hand-reared birds began with larger ranges that later contracted, leading to convergence. Contrary to expectation, wild juveniles occurred in areas with higher human population density than hand-reared birds. Habitat use also differed between groups and changed over time. Early on, wild juveniles concentrated activity in anthropogenic and marine habitats, whereas hand-reared birds used rural green habitats more. Later, both groups shifted away from marine areas towards rural green habitats, reducing but not eliminating between-group differences. Short-term survival, did not differ between hand-reared and wild juveniles, indicating that parental care primarily reshaped early space use and habitat choice rather than immediate survival.

Understanding how the fear of predation acts as a driver of spatial distribution is fundamental to animal behaviour research, yet this relationship is not wholly understood in primates such as baboons. Olive baboons (Papio anubis) have evolved a diverse range of antipredator strategies that reduce, but do not eliminate, predation risk from the large carnivores they encounter across their broad geographic range. This raises a critical question: does the need to access essential resources outweigh the risk of predation when determining habitat selection? We addressed this question by examining the relative influence of three environmental factors and relative predator abundance on olive baboon occupancy patterns and detection probability in Serengeti National Park, Tanzania. Using data from 225 camera traps deployed by the Snapshot Safari program, we fitted three separate Bayesian occupancy models, each incorporating the same three environmental covariates (terrain ruggedness index, distance to nearest river, and Normalized Difference Vegetation Index, NDVI), together with the relative abundance of one of three principal predators (lion, leopard, or spotted hyena). This approach allowed us to assess whether environmental covariates associated with baboon occupancy remained consistent across different predator contexts. Baboon occupancy strongly increased with terrain ruggedness in all three models and consistently decreased with a greater distance to rivers. Vegetation greenness (NDVI) showed a positive association with baboon occupancy, though credible intervals narrowly overlapped zero. NDVI also showed a strong positive relationship with baboon detection probability. Associations between predator relative abundance and baboon occupancy varied between models: the relative abundance of lions and spotted hyenas showed no strong association with baboon occupancy, whereas the relative abundance of leopards was strongly correlated with baboon occupancy, consistent with shared habitat preferences. Our findings demonstrate that, independent of predator presence, olive baboon spatial distribution in the Serengeti is primarily and consistently associated with resource-related environmental features. This study expands our knowledge on the ecological factors that influence primate occupancy by showing that, for a behaviourally flexible species with diverse antipredator strategies, access to essential resources can outweigh spatial avoidance of predators in a multi-predator landscape.