Movement Ecology

BackgroundThe Interior Population (IP) of trumpeter swans (Cygnus buccinator), formerly extirpated by market hunting, was re-established in eastern North America by releasing individuals from both migratory and non-migratory populations. Their current annual movement patterns are largely unknown. Our goal was to describe their seasonal movements and quantify the proportion of the IP that is migratory, the extent and phenology of seasonal movements, and associations between movement patterns and breeding status and breeding location. MethodsWe deployed 113 GPS-GSM transmitters on IP trumpeter swans in six U.S. states and one Canadian province across the current IP breeding range. Using data from 252 swan-years, we estimated dates of migration events by segmenting the annual cycle using piecewise regression models fit to each yearly time-series of displacement from the breeding site. We fit a latent-state model to characterize population-level associations between breeding latitude and maximum extent of migration, and linear mixed models to quantify associations between individual characteristics (e.g., breeding status, sex) and migration phenology. ResultsAt the individual level, 59% of swans moved to distant non-breeding-period areas (long-distance migration, defined as moving >100 km from the breeding site), 16% exhibited regional migration (25-100 km from breeding site), 19% exhibited non-migratory but local movements (<25 km from breeding site), and 6% exhibited multiple migration strategies. Swans breeding at more-northern latitudes departed their territories earlier in autumn and returned later in the spring than those breeding at more southern latitudes. Breeding swans departed later in the autumn than non-breeders, but breeding status did not have a strong association with arrival in the spring. ConclusionIP trumpeter swans are partial migrants, with a continuum of strategies each year, from local movements to long-distance migration. Much of the variability in movement patterns was related to factors tied to natural history demands (e.g., breeding status) and response to environmental conditions (e.g., through associations with breeding latitude).

High-frequency animal tracks must often be subsampled to allow a simple analysis of the movement on the most meaningful scale for the respective study. One way of achieving this is to identify biologically significant turns, compared to heading changings caused by noise. Many turn identification methods have been developed, but the accuracy and consistency of such methods have rarely been validated against ground truth trajectories with known true turns and noise. We analyze simulated tracks with known parameters as well as two empirical tracks and identify turns with 10 different frequently used resampling methods. We assess the specificity and sensitivity of identifying the location of turns and compare the known mean step length and turn angle of the paths with the resampled trajectories. We found great accuracy differences between, and sometimes within, methods, even on simulated tracks of the same characteristics. Results of some methods were also highly sensitive to the user-set threshold the method requires (e.g. max angle). Overall, the best-performing methods in this study were DP and MRPA, methods used in human mobility research, and TPA, which is mostly used in primate research. We thus advise caution when comparing results of studies using different resampling methods and recommend justifying the use of the resampling method in addition to quantifying the sensitivity of results to the threshold value. This study is also an appeal to authors of novel turn identification methods to consider thorough comparisons in different scenarios with a wide range of previous methods, including those developed outside the movement ecology discipline.

Many young animals must gain independence from parental care by acquiring the necessary skills for survival. Thus, the rate of skill acquisition can predict how long juveniles remain with their parents before emigrating from the natal territory. Yet in some systems, young animals acquire adult-like skills and the capacity to be independent long before they emigrate. This raises the question of why the timing of emigration decouples from the rate of skill acquisition, resulting in extended parental care. We addressed this by leveraging GPS tracking data from juvenile golden eagles (Aquila chrysaetos), which learn to fly and to hunt while remaining with their parents for 61-283 days after fledging. Young golden eagles often go on excursions without their parents outside of their natal territories before emigration. We expected flight performance to predict the timing of excursions better than the timing of emigration, which should be more influenced by external factors. In a set of time-to-event models, we predicted the rate of excursions and of emigration using metrics of soaring flight performance or of daily movement. Contrary to our prediction, we found that soaring performance failed to predict the rate of either excursions or emigration, whereas daily movement predicted both rates, but inconsistently. This was likely because those coarser metrics captured motivation and weather in addition to flight skills. Thus external factors seem more likely to explain the timing of independence and internal development--in this case of flight skills--may be completed quickly. Juveniles may delay emigration not due to lack of skill, but because the costs of leaving parental care outweigh the benefits. The difference we discovered between when individuals were capable of independence and when they committed to it may obscure the more general link between skill acquisition and the end of parental care in other systems as well.

Persistent habitat edges can structure animal movement, yet many analyses treat boundary geometry only implicitly. Advances in aerial platforms, sensor miniaturization, and battery durations now enable high spatial and temporal resolution tracking, creating new opportunities to quantify fine-scale movements. We develop and apply a boundary-referenced framework to quantify movements of white sharks (Carcharodon carcharias) off of Cape Cod, Massachusetts, using [~]50 hours of tracking data. Trajectories were analyzed in a local Cartesian coordinate system and a shoreline-referenced system in which displacement was decomposed into along-shore and cross-shore components. Four complementary metrics quantified path straightness, net and continuous alignment with the shoreline, and unique shoreline traversal. These metrics enabled movement modes to be defined directly from movement relative to the boundary. A parsimonious rules-based classifier recovered visually assigned modes with 88% leave-one-out cross-validated accuracy. Among 90 mode-separated tracks derived from tracks exceeding 15 min, nearly three quarters consisted primarily of sustained along-shore travel, whereas spatially confined movement and nearshore-offshore transitions were less frequent. Boundary-referenced metrics revealed site-level differences in shoreline alignment and space usage across beaches. Findings demonstrate that boundary-referenced metrics recover recurrent, interpretable movement modes in a large predator and provide a general framework for quantifying movements along environmental boundaries.

Understanding juvenile survival is crucial for the population ecology of long-lived species, where parental guidance can significantly influence survival rates of completely naive juveniles. In southern elephant seals (Mirounga leonina), however, offspring receive no knowledge from their parents, only fat reserves. This research focuses on how dispersal direction on their maiden foraging trip and physical traits influence the survival of naive southern elephant seal pups at Macquarie Island. We tracked 44 pups with satellite tags during their post-weaning migration and compared their movements to the post-moult winter migrations of 58 adult females. We found that most pups (61.2%) travelled southeast, in line with the Antarctic Circumpolar Current. Pups travelling with the predominant east-southeast current had a 1.5 times higher survival rate for their first trip than those swimming westward against it. Those that swam with the current and were heavier were more likely to survive their first year. Adult females showed different dispersal patterns, where they travelled southwards towards Antarctic waters, implying that learning from experience influences their direction. Future investigations into the role of the primary eastward current in the sub-Antarctic on circumpolar movement patterns of top marine predators could expand our understanding of Southern Ocean ecology.

Prey availability and predation risk drive animal distribution, movement, and foraging ecology, yet studies rarely analyze multiple predator-prey levels together. Understanding how predators optimize risk-reward tradeoffs is important for species conservation and management, especially in systems facing extreme ecosystem change. We examined how top-down (modelled polar bear habitat selection) and bottom-up (modeled fish diversity) processes influence the habitat selection, movement, and foraging behavior of 26 ringed seals (greater than 70,000 dives and 10,000 locations over 877 seal days). Our results suggest that polar bears spatially restrict seal movements and reduce the time seals spend foraging, potentially decreasing foraging success. Seals were more likely to be present and dive longer in high-predation risk areas when prey diversity was high. Further, seal habitat selection models excluding polar bears overestimated core space use. These findings illustrate the dynamic tradeoffs that mesopredators make when balancing predation risk and resource acquisition.

BackgroundUnderstanding animal movement across broad ecological contexts is essential for guiding management decisions in natural resource and animal disease management. However, most movement studies collect data over small spatial extents, which challenges using the data for landscape-scale management applications. MethodsWe collated relocation data from 564 wild pigs spanning 25 studies across 10 states in the United States. We derived net and daily displacement distributions for individuals in each of 4 seasons. We fit gamma distributions to displacement distributions and derived mean and dispersion parameters as metrics of displacement magnitude and variation. We modeled mean and dispersion variables using gradient-boosted regression, including landscape and weather predictors, and predicted daily and net displacement distributions across the species range. We used integrated step selection analysis to estimate habitat selection for wild pig groups sharing a common composition of habitat availability across the studied populations. ResultsFemale average ({+/-} 95% confidence interval) net displacement means varied by season, peaking in Jul-Sept at 1,152 {+/-} 140 m along with daily displacement mean distances of 650 {+/-} 41 m. Average net displacement means for males peaked in Oct.-Dec. at 1,651 {+/-} 160 m with daily displacement mean distances of 1,092 {+/-} 95 m. Extreme net displacement values ranged up to 9,649 m and 4,080 m for male and female, respectively, and extreme daily displacement distribution means of 6,616 m for males, and 3,855 m for females. Almost half of the individuals included a similar composition of habitat availability, regardless of study site location. Herbaceous wetland was the most consistently favored land type and was available for more than 70% of all individual pigs across all studies. Preference for other land types depended strongly on habitat availability. ConclusionsBy comparing movement and habitat selection across a large geographic extent, our work provides new insights about the environmental drivers and limits of wild pig movement behavior. Our workflow digests variation wild pig movement distributions and habitat selection into parameters that can be used to predict movement on heterogeneous landscapes for downstream applications such as invasive species elimination campaigns or disease management.

Animals navigating in fluid environments often face lateral forces from wind or water currents that challenge travel efficiency and route accuracy. We investigated how 27 Magellanic penguins (Spheniscus magellanicus) adapt their navigation strategies to return to their colony amid regional tidal ocean currents. Using GPS-enhanced dead-reckoning loggers and high-resolution ocean current data, we reconstructed penguin travel vectors during foraging trips to assess their responses to variable currents during their colony-bound movements. By integrating estimates of energy costs and prey pursuits, we found that birds balanced direct navigation with current-driven drift: in calm currents, they maintained precise line-of-sight headings to their colony. In stronger currents, they aligned their return with lateral flows, which increased travel distance, but at minimal energy costs, and provided them with increased foraging opportunities. Since the lateral tidal currents always reversed direction over the course of return paths, the penguins return paths were consistently S-shaped but still resulted in the birds returning efficiently to their colonies. These findings suggest that Magellanic penguins can sense current drift and use it to optimize energy expenditure by maintaining overall directional accuracy while capitalizing on foraging opportunities.

O_LIHabitat-selection analyses allow researchers to link animals to their environment via habitat-selection or step-selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat-selection analyses that incorporate movement characteristics, referred to as integrated step-selection analyses, are particularly appealing because they allow modeling of both movement and habitat-selection processes. C_LIO_LIDespite their popularity, many users struggle with interpreting parameters in habitat-selection and step-selection functions. Integrated step-selection analyses also require several additional steps to translate model parameters into a full-fledged movement model, and the mathematics supporting this approach can be challenging for many to understand. C_LIO_LIUsing simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point-process can facilitate parameter interpretation in habitat-selection analyses. Further, we provide a "how to" guide illustrating the steps required to implement integrated step-selection analyses using the amt package. C_LIO_LIBy providing clear examples with open-source code, we hope to make habitat-selection analyses more understandable and accessible to end users. 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.

Flying over the open sea is energetically costly for terrestrial birds. Despite this, over-water journeys of many birds, sometimes hundreds of kilometers long, are uncovered by bio-logging technology. To understand how these birds afford their flights over the open sea, we investigated the role of atmospheric conditions, specifically wind and uplift, in subsidizing over-water flight at the global scale. We first established that {Delta}T, the temperature difference between sea surface and air, is a meaningful proxy for uplift over water. Using this proxy, we showed that the spatio-temporal patterns of sea-crossing in terrestrial migratory birds is associated with favorable uplift conditions. We then analyzed route selection over the open sea for five facultative soaring species, representing all major migratory flyways. The birds maximized wind support when selecting their sea-crossing routes and selected higher uplift when suitable wind support was available. They also preferred routes with low long-term uncertainty in wind conditions. Our findings suggest that, in addition to wind, uplift may play a key role in the energy seascape for bird migration that in turn determines strategies and associated costs for birds crossing ecological barriers such as the open sea.

BackgroundAs GPS tags and data loggers have become lighter, cheaper, and longer-lasting, there has been a growing influx of data on animal movement. Simultaneously, methods of analyses and software to apply such methods to movement data have expanded dramatically. Even so, for many interdisciplinary researchers and managers without familiarity with the field of movement ecology and the open-source tools that have been developed, the analysis of movement data has remained an overwhelming challenge.\n\nDescriptionHere we present stmove, an R package designed to take individual relocation data and generate a visually rich report containing a set of preliminary results that ecologists and managers can use to guide further exploration of their data. Not only does this package make report building and exploratory data analysis (EDA) simple for users who may not be familiar with the extent of available analytical tools, but it sets forth a framework of best practice analyses, which offers a common starting point for the interpretation of terrestrial movement data.\n\nResultsUsing data from African elephants (Loxodonta africana) collected in southern Africa, we demonstrate stmoves report building function through the main analyses included: path visualization, primary statistic calculation, summary in space and time, and space-use construction.\n\nConclusionsThe stmove package provides consistency and increased accessibility to managers and researchers who are interested in movement analysis but who may be unfamiliar with the full scope of movement packages and analytical tools. If widely adopted, the package will promote comparability of results across movement ecology studies.

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.

Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements. We designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by changes in step length and turning angle distributions. To this end, we can also use the model to estimate how an animals movement changes when it begins migrating. We tested our model on three datasets: migratory ferruginous hawks (Buteo regalis) in the Great Plains and barren-ground caribou (Rangifer tarandus groenlandicus) in northern Canada, and non-migratory brown bears (Ursus arctos) from the Canadian Arctic. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bear population. The flexibility of our modelling framework allowed us to assess intricacies associated with each dataset: long-term stopover behaviour in ferruginous hawks and a priori knowledge of caribou calving areas and behaviour. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how the two populations migrated; ferruginous hawks achieved efficient migrations by increasing their movement rates while caribou migration was achieved through significant increases in directional persistence. Our approach is broadly applicable to many animal movement studies. We anticipate that rigorous assessment of migration metrics will aid understanding of both how and why animals move.

Underwater receiver networks (passive acoustic telemetry systems) are deployed to track animals in aquatic habitats all over the world, but remarkably limited attention has been given over to how we can strengthen the value of these networks through statistical and computational advances. Here, we upscale state-of-the-art methods of Bayesian inference to big animal-tracking datasets from acoustic telemetry, with the largest geolocation analysis in a sparse passive acoustic telemetry system (with non-overlapping receivers) to date. Using four years of data from 93 lake trout (Salvelinus namaycush) in North Americas Lake Champlain (657,360 timesteps per individual), we formulate and fit state-space models to reconstruct animal movements through time. Uniquely, we directly embed biological expertise and detailed complementary datasets from fine-scale positioning systems, accelerometry, swim-tunnel experiments and field range tests in our analysis. Using simulated and real-world datasets, we map movement patterns and estimate residency in distinct management zones. We quantify array precision and deliver maps and residency estimates with a median error and precision (standard error) below 1 %. These results strengthen the evidence base for management. This work takes us a step towards robust inference of movement patterns at scale in acoustic telemetry systems across the world. We can, and should, build on prior scientific progress and extend the value of hard-earned data beyond individual studies to refine inferences for ecology and management. Significance statementAcoustic receivers are deployed across the globe to track aquatic animals, but reconstructing detailed movement patterns from detections at receivers remains a considerable challenge. Here, we upscale state-of-the-art methods of Bayesian inference by two orders of magnitude to analyse big, real-world datasets, using an extensive case study of lake trout (Salvelinus namaycush) in Lake Champlain. By directly integrating diverse complementary datasets from animal-borne tags, swim-tunnel experiments, field studies and close-kin mark-recapture in our analysis, we resolve detailed movement patterns over a four-year period, with broad implications for ecology and management. This work provides a powerful framework for acoustic telemetry studies that strives to meet the challenges of big, real-world datasets from telemetry networks across the world.

Citizen science programs can be effective at collecting information at large temporal and spatial scales. However, sampling bias is a concern in citizen science datasets and can lead to unreliable estimates. We address this issue with a novel approach in a first-of-its-kind citizen science survey of Weddell seals for the entire coast of Antarctica. Our citizen scientists inspected very high-resolution satellite images to tag any presumptive seals hauled out on the fast ice during the pupping period. To assess and reduce the error in counts in term of bias and other factors, we ranked surveyors on how well they agreed with each other in tagging a particular feature (presumptive seal), and then ranked these features based on the ranking of surveyors placing tags on them. We assumed that features with higher rankings, as determined by "the crowd wisdom," were likely to be seals. By comparing citizen science feature ranks with an experts determination, we found that non-seal features were often highly ranked. Conversely, some seals were ranked low or not tagged at all. Ranking surveyors relative to their peers was not a reliable means to filter out erroneous or missed tags; therefore, we developed an effective correction factor for both sources of error by comparing surveyors tags to those by the expert. Furthermore, counts may underestimate true abundance due to seals not being present on the ice when the image was taken. Based on available on-the-ground haul-out location counts in Erebus Bay, the Ross Sea, we were able to correct for the proportion of seals not detected through satellite images after accounting for year, time-of-day, location (islet vs. mainland locations), and satellite sensor effects. We show that a prospective model performed well at estimating seal abundances at appropriate spatial scales, providing a suitable methodology for continent-wide Weddell Seal population estimates.

BackgroundGeomorphologically complex landscapes constrain animal movement and, therefore, the spatial spread of host-associated pathogens. Monitoring animal movement in such landscapes is required to understand these processes but remains challenging because structural barriers to animal movement (forests, mountains) also disrupt the visibility and signal quality of satellites used by animal-borne Global Positioning System (GPS) tags. In Latin America, rabies transmitted by vampire bats (Desmodus rotundus) epitomises this challenge: viral spread is strongly constrained by mountains and may be exacerbated by bat dispersal responses to human disturbance. MethodsWe used GPS tags to track the post-disturbance (i.e., capture, handling and tagging) movement of 93 vampire bats from five roosts in the highly complex landscape of inter-Andean valleys in Peru, where signal degradation reduced usable data by 89%. To overcome this data sparsity, the information gap needed to be filled by modelling and data imputation. Therefore, we developed a probabilistic Bayesian state-space model to infer the movement dynamics of vampire bats and to identify correlates of departure time and distance from the roost, with a focus on disturbance effects. ResultsImmediately following the disturbance of capture, 43% of bats, particularly males and stronger flyers, disappeared from their roosts for the remainder of the study period, suggesting an acute disturbance response. For the 55 remaining GPS-tagged vampire bats, we observed longer flight distances and high individual heterogeneity on the night of disturbance, with bats gradually remaining closer to the roost over the 9-day study period. ConclusionsRoost abandonment and expanded foraging areas are expected to exacerbate rabies spread, demonstrating how human disturbances that alter animal movement may have unintended consequences for disease transmission. More generally, our Bayesian model accounting for GPS uncertainty and missing data extends the utility of animal-borne tracking to species living in complex landscapes with poor satellite visibility.

Understanding how animals respond to vessels is a fundamental question in conservation, since interactions between marine animals and vessels can expose wildlife to threats such as fisheries bycatch and vessel collisions. Although animal-borne tags and vessel-tracking systems now allow both components to be monitored at fine spatiotemporal scales, distinguishing behavioural interactions from incidental co-occurrence remains challenging, particularly in areas of dense marine traffic. Here, we developed a simulation-based framework to determine whether an animal is attracted to or follows a vessel, or instead moves independently. We applied the framework to concurrent GPS data from 2,705 foraging trips by Scopolis shearwaters (Calonectris diomedea) and Automatic Identification System (AIS) trajectories from fishing and nonfishing vessels in the northwestern Mediterranean. Candidate association events (n = 1,567) were first identified using spatiotemporal proximity and subsequently tested against simulated seabird trajectories representing movement in the absence of a vessel response. The framework classified apparent associations arising when vessels approached stationary birds or when independently moving trajectories crossed by chance as independent movement. Overall, 53.5% of association events showed evidence of vessel attraction, following, or both, whereas the remainder were consistent with independent movement. Although only approximately 3% of the vessels recorded by AIS were fishing vessels, they accounted for 69% of attraction events and 74% of following events, whereas most associations with nonfishing vessels were consistent with incidental proximity. Attraction and following became less likely as birds spent more time stationary and were more likely during daytime, while following was less likely for nonfishing than fishing vessels. These ecologically coherent patterns support the biological relevance of the interactions identified by the simulation-based approach. Sensitivity analyses showed that the number of associations detected by conventional threshold methods increased markedly with broader proximity criteria, whereas inference of attraction and, particularly, following remained comparatively stable. Implemented in the R package intersimR, the framework provides an accessible, reproducible, and scalable approach for separating behavioural responses from incidental co-occurrence at the event level and identifying the animal-vessel interactions most relevant to conservation and management.

O_LIUnderstanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioral states to help understand why animals move across the landscape. While there are a variety of methods that make behavioral inferences from biotelemetry data, some features of these methods (e.g., analysis of a single data stream, use of parametric distributions) may limit their generality to reliably discriminate among behavioral states. C_LIO_LITo address some of the limitations of existing behavioral state estimation models, we introduce a non-parametric Bayesian framework called the mixed-membership method for movement (M4), which is available within the open-source bayesmove R package. This framework can analyze multiple data streams (e.g., step length, turning angle, acceleration) without relying on parametric distributions, which may capture complex behaviors more successfully than current methods. We tested our Bayesian framework using simulated trajectories and compared model performance against two segmentation methods (behavioral change point analysis (BCPA) and segclust2d), one machine learning method (expectation-maximization binary clustering (EMbC)), and one type of state-space model (hidden Markov model (HMM)). We also illustrated this Bayesian framework using movements of juvenile snail kites (Rostrhamus sociabilis) in Florida, USA. C_LIO_LIThe Bayesian framework estimated breakpoints more accurately than the other segmentation methods for tracks of different lengths. Likewise, the Bayesian framework provided more accurate estimates of behavior than the other state estimation methods when simulations were generated from less frequently considered distributions (e.g., truncated normal, beta, uniform). Three behavioral states were estimated from snail kite movements, which were labeled as encamped, area-restricted search, and transit. Changes in these behaviors over time were associated with known dispersal events from the nest site, as well as movements to and from possible breeding locations. C_LIO_LIOur non-parametric Bayesian framework estimated behavioral states with comparable or superior accuracy compared to the other methods when step lengths and turning angles of simulations were generated from less frequently considered distributions. Since the most appropriate parametric distributions may not be obvious a priori, methods (such as M4) that are agnostic to the underlying distributions can provide powerful alternatives to address questions in movement ecology. C_LI

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.