Movement Ecology

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.

O_LIHow early-life exploration shapes the adult annual cycle routines of migratory animals remains challenging to study, especially in long-lived species. Delayed recruitment often observed in long-lived migratory birds suggests that the first breeding attempt may be constrained by a protracted learning process in which individuals develop their annual itinerary, which may involve extensive exploration of the winter and breeding sites, potentially causing inexperienced young to wander beyond traditional population areas. C_LIO_LIUsing greater flamingo Phoenicopterus roseus GPS tracking data from 83 individuals tagged as nestlings in the Mediterranean, we analyzed ontogenetic changes in mobility and space use for up to 8 years of age. We segmented autumn-winter and spring-summer tracks (n = 223 device-bird-year combinations) into staging events (n = 1914). We then computed seasonal staging metrics and analysed how they changed with age. We map the traditional congregation sites of flamingos to investigate age-related changes in space use between males and females. C_LIO_LIFlamingos were more exploratory during their early years, gradually transitioning to a more sedentary lifestyle as they grew older. With each additional year of age, the number of staging events decreased by 16%, while the average duration of staging events increased by 8 days. C_LIO_LIBirds remained faithful to the non-breeding staging sites where they spent most of their time during the first year of life. Over time, they returned progressively closer to their natal sites, even after years of exploration at non-traditional locations. Breeding was rare (8%) and occurred at a relatively late age ([≥]5 years), highlighting an extended exploratory phase, a pattern observed in other long-lived species. C_LIO_LIOlder birds were more likely to use traditional sites and spent more time at these sites, particularly after age 5. Our study reveals the individual exploration process underlying natal and non-breeding dispersal patterns previously laid bare by ring-resighting studies and shows that flamingos retain long-term memory of sites learned in early life. C_LI

Accurate information on the population-level movements of migratory animals is highly valuable for migration research and critical for designing effective conservation strategies for a changing world. However, population-wide movement information is lacking for most migratory species due to the effort and expense needed to collect data across species ranges. BirdFlow is a probabilistic modeling framework that infers population-level movements from weekly species distribution maps produced by the participatory science project eBird. Producing accurate species-specific BirdFlow models has required model tuning using high-resolution individual tracking data, which is not available for most migratory species. Here, we introduce a new model tuning framework that eliminates this reliance on tracking data and generalizes BirdFlow to hundreds of migratory species. This framework allows us to tune and validate BirdFlow models using a combination of data sources, including GPS tracks, banding recoveries, and radio telemetry data from the Motus Wildlife Tracking System. We investigate the generalizability of this approach by (1) investigating predictive performance compared to null models; (2) validating the biological plausibility of BirdFlow models by comparing movement properties such as route straightness, number of stopovers, and migration speed between model-generated routes and real movement tracks; and (3) comparing the performance of models tuned on species-specific movement data to models tuned using hyperparameters transferred from other species. Our results show that the BirdFlow modeling framework achieves biologically realistic performance, even for prediction horizons of thousands of kilometers and several months. When species-specific data are unavailable, models can still be tuned using data from other phylogenetically adjacent species to achieve improved performance. Alongside this study, we release 153 tuned BirdFlow models, representing the first collection of large-scale population-level movement forecasting models and offering a foundation for more accurate predictions for applications in conservation, disease surveillance, aviation, and public outreach.

Direct encounters, in which two or more individuals are physically close to one another, are a topic of increasing interest as more and better movement data become available. Recent progress, including the development of statistical tools for estimating robust measures of changes in animals space use over time, facilitates opportunities to link direct encounters between individuals with the long-term consequences of those encounters. Working with movement data for coyotes (Canis latrans) and grizzly bears (Ursus arctos horribilis), we investigate whether close intraspecific encounters were associated with spatial shifts in the animals range distributions, as might be expected if one or both of the individuals involved in an encounter were seeking to reduce or avoid conflict over space. We analyze the movement data of a pair of coyotes in detail, identifying how a shift in home range location resulting from altered movement behavior was apparently a consequence of a close intraspecific encounter. With grizzly bear movement data, we approach the problem from the perspective of a set of encounter pairs within a population. We find support for the hypotheses that 1) close intraspecific encounters between bears are, on average, associated with subsequent shifts in range distributions and 2) encounters defined at finer spatial scales are followed by greater changes in space use. Our results suggest that animals can undertake long-term, large-scale spatial shifts in response to close intraspecific encounters that have the potential for conflict. These results lend support for existing theory on the evolution of territories and space use (e.g., Maynard-Smiths bourgeois strategy regarding low-conflict coexistence). Overall, we find that analyses of movement data in a pairwise context can 1) identify distances at which individuals proximity to one another may alter behavior and 2) facilitate testing of population-level hypotheses concerning the potential for direct encounters to alter individuals space use. Open Research StatementMovement data for the coyotes and grizzly bears are posted on Movebank.org as datasets 1614661371 and 1044288582, respectively. Statistical tools for estimating, manipulating, and comparing home ranges from movement data are implemented in the open-source R package ctmm. R scripts used to carry out specific analyses for this study are openly available on GitHub at https://github.com/anagkrish/encounter_homerangeshift.

O_LIPredicting animal space use could greatly improve our understanding and forecasting of ecological processes. Despite growing interest, the development of predictive space use models amenable to the integration of spatial processes into ecological frameworks have yet to reach their full potential. C_LIO_LIUsing high-resolution tracking data collected at 4-minute intervals from 26 Arctic foxes over five years, we developed a predictive space use model based on a step-selection approach. We assessed fine-scale habitat selection in relation to prey distribution, landscape features, and ecological constraints such as central place foraging and territoriality. We then used these results to build an agent-based model simulating fox space use and evaluated its ability to reproduce observed space use patterns. C_LIO_LIStep-selection analyses confirmed that fox movements were driven by habitat type, goose nest density, distance to den, and avoidance of distance to the home range boundary. Agent-based simulations closely matched empirical tracking data and accurately forecasted fox space use, even for individuals excluded from model parameterization. C_LIO_LIBy developing a predictive model of predator space-use, our study provides a foundation for incorporating additional components of the predation sequence and contributes to more spatially informed approaches in predator-prey ecology. C_LI

There is strong incentive to model behaviour-dependent habitat selection, as this can help delineate critical habitats for important life processes and reduce bias in model parameters. For this purpose, a two-stage modelling approach is often taken: (i) classify behaviours with a hidden Markov model (HMM), and (ii) fit a step selection function (SSF) to each subset of data. However, this approach does not properly account for the uncertainty in behavioural classification, nor does it allow states to depend on habitat selection. An alternative approach is to estimate both state switching and habitat selection in a single, integrated model called an HMM-SSF. We build on this recent methodological work to make the HMM-SSF approach more efficient and general. We focus on writing the model as an HMM where the observation process is defined by an SSF, such that well-known inferential methods for HMMs can be used directly for parameter estimation and state classification. We extend the model to include covariates on the HMM transition probabilities, allowing for inferences into the temporal and individual-specific drivers of state switching. We demonstrate the method through an illustrative example of African zebra (Equus quagga), including state estimation, and simulations to estimate a utilisation distribution. In the zebra analysis, we identified two behavioural states, with clearly distinct patterns of movement and habitat selection ("encamped" and "exploratory"). In particular, although the zebra tended to prefer areas higher in grassland across both behavioural states, this selection was much stronger in the fast, directed exploratory state. We also found a clear diel cycle in behaviour, which indicated that zebras were more likely to be exploring in the morning and encamped in the evening. This method can be used to analyse behaviour-specific habitat selection in a wide range of species and systems. A large suite of statistical extensions and tools developed for HMMs and SSFs can be applied directly to this integrated model, making it a very versatile framework to jointly learn about animal behaviour, habitat selection, and space use.

O_LIIn alpine habitats, fluctuating early-season weather conditions and short breeding seasons limit reproductive opportunities, such that arriving and breeding earlier or later than the optimum may be particularly costly for migratory species. Given early-season energy limitations, the influence of environmental conditions across the annual cycle on breeding phenology may have pronounced fitness consequences, yet our understanding of cross-seasonal dynamics in alpine breeding organisms is severely limited. C_LIO_LIFor an alpine-breeding, migratory population of horned lark (Eremophila alpestris) in northern British Columbia, Canada (54.8{degrees}N latitude) we assessed how spatially explicit weather conditions from across the annual cycle influenced clutch initiation date and offspring development. We also addressed how cross-seasonal effects on breeding parameters interact to influence reproductive fitness. C_LIO_LIWith 12 years of intensive breeding data and 3 years of migration data from archival light-level geolocators, we used a sliding window approach to identify critical points during the annual cycle where weather events most influenced breeding phenology and offspring development. Consequences for reproductive success were assessed using nest survival simulations. C_LIO_LIAverage clutch initiation varied up to 11 days among years but did not advance from 2003 to 2019. Colder temperatures with greater precipitation at wintering habitats, as well as colder temperatures upon arrival at the breeding site delayed clutch initiation, independent of arrival time. Extreme cold (sub-zero temperatures) within a staging area just prior to arrival at the breeding site carried over to prolong offspring development rate, potentially by influencing parental investment. Nest survival decreased with both later clutch initiation and prolonged offspring development, such that females that nested earlier and fledged offspring at a younger age were up to 45% more likely to reproduce successfully. C_LIO_LIWe demonstrate pronounced carry-over effects acting through mechanisms that influence breeding phenology and offspring development independently. We also highlight the potential importance of staging areas for alpine songbirds, particularly given that environmental conditions are becoming increasingly decoupled across seasons. Understanding the cross-seasonal mechanisms shaping breeding decisions in stochastic environments like the alpine enables more accurate predictions of future individual- and population-level responses to climate change. C_LI

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.

O_LIForecasting encounters between humans and large carnivores has largely relied on mechanistic models driven by causal factors such as food resources and weather. However, for short-term forecasting these approaches implicitly require unrealistically detailed real-time data on many covariates and an almost complete understanding of the underlying causal pathways. As a result, they offer little practical support for short-term, operational decision-making. C_LIO_LIWe developed a short-term forecasting system that predicts end-of-month cumulative bear sightings from the beginning of each month by exploiting temporal autocorrelation without mechanistic assumptions, using an ensemble of multiple components: (i) sequential estimation of daily sighting rates via a non-stationary Poisson process, (ii) seasonal baselines with ratio-based corrections from previous months, and (iii) rule-based transitions among components as daily sightings accumulate. C_LIO_LIApplied to Asiatic black bear (Ursus thibetanus) sighting records from two Japanese regions differing 18-fold in encounter frequency (maximum monthly counts: 83 vs. 1490) and with contrasting seasonal peaks, the ensemble achieved correlations of [≥]0.8 between predicted and observed month-end totals from day 1, increasing to [≥]0.98 by day 20 and substantially outperforming a null model that assumed no seasonal or interannual variation ({Delta}AIC: 477-652). C_LIO_LIAfter controlling for baseline spatial risk and for the region-wide daily bear-forecast level (temporal risk) provided by our ensemble, we detected strongly localized short-term recurrence in bear encounters: prior sightings increased encounter probability within 500 m for up to 3 days, with rapid decay in space and time. C_LIO_LISynthesis and applications. This observation-based ensemble demonstrates that temporal dynamics alone can approach the practical limits of short-term predictability of wildlife encounter rates, without relying on detailed environmental covariates or extensive new data collection. By quantifying both when (daily risk levels) and where (localized hotspots around recent sightings) encounters are most likely, the system offers wildlife agencies and residents an immediately implementable tool for issuing targeted warnings, adjusting outdoor activities, and reducing human injuries in regions experiencing increasing human-carnivore conflict. C_LI

Global increases in human activity threaten connectivity of animal populations. Protection and restoration of animal movement corridors requires robust models to forecast the effects of human activity on connectivity. Recent advances in the field of animal movement ecology and step selection functions offer new approaches for estimating connectivity. We show how a combination of hidden Markov movement models and step selection functions can be used to simulate realistic movement paths with multiple behavioral states. Simulated paths can be used to generate utilization distributions and estimate changes in connectivity for multiple land use scenarios. We applied movement models to 20 years of grizzly bear (Ursus arctos) and gray wolf (Canis lupus) data collected in and around Banff National Park, Canada. These carnivores avoided areas near towns in all seasons, avoided areas of high trail density in most seasons, and campgrounds during summer and fall. We simulated movement paths for three landscape scenarios: reference conditions with no anthropogenic development, current conditions, and future conditions with expanded town footprints and trail networks. We counted the number of paths that crossed valley-wide, digital transects through mountain tourist towns of Banff and Canmore, Alberta. We divided current and future crossing rates by the reference crossing rates to estimate connectivity. Current connectivity rates ranged between 7 and 45% of reference values with an average of 21% for grizzly bears and 25% for wolves. Potential town expansion and increased development of trails further decreased connectivity an average of 6% in future scenarios. Anthropogenic developments reduced the amount of available high quality large carnivore habitat in the Bow Valley by an average of 14% under current conditions and 16% under future conditions. Our approach for estimating connectivity provides a robust and flexible method for combining movement models with step selection analyses to estimate connectivity for a variety of species.

Step selection analysis is used to jointly describe animal movement patterns and habitat preferences. Recent work has extended this framework to model inter-individual differences, account for unexplained structure in animals space use, and capture temporally-varying patterns of movement and habitat selection.In this paper, we formulate step selection functions with penalised smooths (similar to generalised additive models) to unify new and existing extensions, and conveniently implement the models in the popular, open-source mgcv R package. We explore non-linear patterns of movement and habitat selection, and use the equivalence between penalised smoothing splines and random effects to implement individual-level and spatial random effects. This framework can also be used to fit varying-coefficient models to account for temporally or spatially-heterogeneous patterns of selection (e.g., resulting from behavioural variation), or any other non-linear interactions between drivers of the animals movement decisions. We provide the necessary technical details to understand several key special cases of smooths and their implementation in mgcv, showcase the ecological relevance using two illustrative examples, and provide R code (available at https://github.com/NJKlappstein/smoothSSF) to facilitate the adoption of these methods. This paper is a broad overview of how smooth effects can be applied to increase the flexibility and biological realism of step selection analysis.

Animal movement paths are represented by point-location time series called relocation data. How well such paths can be simulated, when the rules governing movement depend on the internal state of individuals and environmental factors (both local and, when memory is involved, global) remains an open question. To answer this, we formulate and test models able to capture the essential statistics of multiphase versions of such paths (viz., movement-phase-specific step-length and turning-angle means, variances, auto-correlation, and cross correlation values), as well as broad scale movement patterns. The latter may include patchy coverage of the landscape, as well as the existence of home-range boundaries and gravitational centers-of-movement (e.g., centered around nests). Here we present a Numerus Model Builder implementation of two kinds of models: a high-frequency, multi-mode, biased, correlated random walk designed to simulate real movement data at a scale that permits simulation and identification of path segments that range from minutes to days; and a model that uses statistics extracted from relocation data--either empirical or simulated--to construct movement modes and phases at subhourly to daily scales. We evaluate how well our derived statistical movement model captures patterns produced by our more detailed simulation model as a way to evaluate how well derived statistical movement models may capture patterns occurring in empirical data.

Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While GPS location error can often be ignored in coarsely sampled data, fine-scale data require more care, and tools to do this have not kept pace. Current approaches to dealing with location error largely fall into two categories--either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. In some cases these approaches can provide a level of correction, but they have known limitations, and in some cases they can be worse than doing nothing. Here, we provide a general framework to account for location error in the analysis of triangulated and trilatcralizcd animal tracking data, which includes GPS, Argos Doppler-shift, triangulated VHF, trilatcralized acoustic and cellular location data. We apply our error-modelselection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices were used to track a wide range of taxa comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. In almost half of the tested device models, error-model selection was necessary to obtain the best performing error model, and in almost a quarter of tested device models, the reported DOP values were actually misinformative. Then, using empirical tracking data from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed, and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. Because error-induced biases depend on many factors--sampling schedule, movement characteristics, tracking device, habitat, etc.--differential bias can easily confound biological inference and lead researchers to draw false conclusions. We demonstrate how error-informed analyses on calibrated tracking data can provide more accurate estimates are that are insensitive to location error, and allow researchers to use all of their data.

O_LIAvian elevational migration is a common but often overlooked form of short-distance migration. Numerous hypotheses have been proposed to explain this behavior, yet few studies have tested those comprehensively. We examine the climatic constraint hypothesis - a key hypothesis explaining elevational migration - to evaluate its predictions for avian elevational migration in the monsoon-dominated Western Ghats of India. C_LIO_LIWe used citizen science data from eBird to quantify elevational migration patterns of resident birds of the region. Using robust data curation protocols and new bias correction methods, we modeled migration patterns of 164 species across summer, monsoon and winter seasons. To formally test the climatic constraint hypothesis, we modeled the likelihood and distance of migration against body size and environmental niche breadths, using phylogenetically informed logistic regression and Bayesian generalized linear mixed models. C_LIO_LIOur results show that 43% of resident birds exhibited elevational migration in at least one season. Most species migrated between winter and summer (57), followed by migrations between summer and monsoon (39) and then monsoon and winter (38). Species predominantly moved upslope in summer (82%) and downslope in monsoon (87%), with no discernible pattern of direction in winter. Species with broader temperature tolerances were more likely to migrate across all seasons. Additionally, species with broader precipitation niches and larger body sizes moved further downslope during the monsoon and winter, respectively. C_LIO_LIWe found that species with broader tolerances to environmental conditions were more likely to be migratory and moved further along the elevational gradient, contradictory to the expectations of the climatic constraint hypothesis. Our analyses suggest that elevational migrants of the Western Ghats are not climatically limited but likely possess flexibility to track ecological resources that vary with season and elevation. C_LI

O_LIQuantifying the space requirements of a population is a fundamental problem in spatial ecology, particularly as it relates to the identification of important utilization areas and the designation of protected areas for conservation and wildlife management. C_LIO_LITraditionally, population space use estimation techniques scale up from the individual to the population level by aggregating individual animal tracks and then using a single pooled distribution estimator like minimum convex polygons (MCP) or kernel density estimation (KDE). These techniques fail to account for the high levels of temporal autocorrelation in modern tracking datasets, and estimates are often sensitive to the number of individuals sampled. C_LIO_LIWe introduce a new population kernel density estimator (PKDE) that accounts for temporal autocorrelation in tracking data, propagates uncertainty from the individual to the population level, accounts for inter-individual variation when scaling up to the population level, and is not highly sensitive to the number of individuals tracked. Through a combination of simulated data and empirical GPS tracking datasets from three species: (a) grizzly bear (Ursus arctos horribilis); (b) lowland tapir (Tapirus terrestris); and (c) bobcat (Lynx rufus), we demonstrate that PKDE produces minimally biased estimates of population-level space use compared to conventional methods like MCP and KDE. C_LIO_LIThe use of conventional estimators can lead to substantial underestimation of population space usage, making them unsuitable for area-based conservation planning. The statistically efficient PKDE estimator provides relatively unbiased estimates of space use with fewer individuals sampled. This method has been made available as a function in the ctmm R package. C_LI

Seasonal migrations allow to access temporally varying resources and individuals may show fidelity to specific locations. Polar bears (Ursus maritimus) are a sea ice dependent species that migrate between marine and terrestrial habitats, the latter being important for parturition and early cub rearing. However, fidelity to maternity den sites is poorly understood. We assessed polar bear maternal den site fidelity of the Western Hudson Bay subpopulation in Manitoba, Canada. Using capture and telemetry data collected between 1979 - 2018, we examined site fidelity from 188 maternity den locations from 78 individuals. We calculated within-individual inter-year distances between dens, and compared these to between-individual distances via non-parametric bootstrapping. We used generalised additive models to assess how maternal age, years between denning events, and sea ice conditions affected site fidelity. We found some evidence of site fidelity, as within-individual inter-year distances were smaller than between-individual den distances by approximately 18.5 km. As time between captures increased, inter-den distances also increased (ranging from approximately 25 km to 55 km), but no other variables significantly affected fidelity. Our findings suggest that western Hudson Bay polar bears show a moderate amount of fidelity to denning areas, but not necessarily to specific sites.

Our understanding of habitat selection in wild vertebrates has been heavily influenced by observations of preferred foraging areas. However, foraging is only one of many ways animals interact with their environment, and preferences for habitat features that support resting, breeding, and safety, along with trade-offs between these needs, remain underexplored. These trade-offs are likely to be particularly acute in complex environments where these needs are met in different locations. Using a long-term dataset of movements and life history, we examine how preferences for foraging areas and burrow sites shape space use in Kalahari meerkats (Suricata suricatta). Meerkats cannot dig new sleeping and breeding burrows and must use those abandoned by other species, potentially generating trade-offs in daily space use if overnight refuges are far from optimal foraging areas. We find that space use is strongly anchored by burrow location, with burrows in calcareous pans and dry riverbed ("white sand") habitat being preferred year-round and showing lower burrow switching rates. However, white sand areas were not preferred for foraging, and yielded lower weight gains, particularly during the dry season. As a result, meerkats faced a trade-off between optimal burrow locations and productive foraging grounds, as indicated by faster, longer, and more energetically costly travel when moving through or waking up in white sands. Our results suggest that changes in the distribution or abundance of key burrow-constructing species in desert environments may have cascading effects on the many secondary burrow-using species that depend on them for survival and reproduction. More broadly, our results highlight that limited refuge availability across the landscape can impose strong ecological constraints on animals and may restrict behavioural plasticity under environmental change, particularly if productive foraging areas shift.

Animals often use consistent areas. Some are territorial, restricting their space use within territorial boundaries, whereas others at not territorial animals but still restrict their space use despite not being constrained by surrounding conspecifics. Staying within a familiar area can provide a range of benefits, such as using previous knowledge (i.e. memory) to efficiently exploit resources or because they can consistently return to key locations (such as a nest or sleeping site). In group-living animals, consistent space use could reduce the complexity of decision-making time (e.g. by choosing among known foraging sites), facilitating group cohesion. However, to date, little research has explicitly asked what factors determine whether groups use consistent areas. Here we used repeated movements by groups of vulturine guineafowl (Acryllium vulturinum)--leaving and returning back to the same areas in response to seasonal conditions--to examine and disentangle social processes from spatial and ecological factors that might shape the distribution of animals over space. Specifically, we quantified (i) how groups distribute themselves over the landscape, (ii) if their space use is consistent across seasons with similar environmental conditions, (iii) how different social and spatial factors shape the consistency of space use by groups over time, and (iv) how social and spatial factors affect home range overlap between groups. We found that groups were highly consistent in their space use over time and that home ranges were distinct across groups. Fidelity to the core home range area was higher when group composition was more stable, while overall home range fidelity was higher when groups recently experienced milder ecological conditions. Overlap in core areas and the overall home ranges among groups were greater among groups that shared roosts and groups that were fused in the previous season. Home range overlap was also lowest during long intermediate seasons (i.e. a sampling period that immediately follows intermediate season conditions, as opposed to sampling periods that followed dry or wet conditions), suggesting that extended intermediate conditions allow groups to increasingly partition their overall space use. These results provide insights into how the movement decisions by groups, the distribution of animals, and group-level space use emerge, and the role of social and ecological conditions as potential precursors to territoriality.

Alaskas boreal birds face a rapidly changing environment, but we know little about shifts in migratory timing, particularly in autumn. We used quantile regression to quantify long-term changes in autumn capture date in 21 boreal passerines using 22+year datasets from two banding stations in central Alaska. We also quantified differences between sites and explored whether select climate indices during three periods of the annual cycle (breeding, post-fledge, and migration) could predict long-term changes in median capture. Long-term changes in autumn migration were detected in 86% of taxa, 76% of which exhibited advances in capture date ([~]2-3 days/decade), particularly long-distance migrants at one field site. However, site-specific differences unexpectedly highlight the need for caution before extrapolating long-term timing patterns over broad spatial extents. Warmer conditions during the breeding period (using the AO climate index) were associated with advances in autumn capture date in the greatest number of species (9). Collectively, we hypothesize that Alaskas immense size and spatially-variable climate regions impact reproductive timing, often resulting in long-term advances (with warming) and occasionally delays (with cooling). Carry-over effects of reproductive timing may therefore influence the autumn passage of different breeding populations, causing site-specific patterns, such as a species showing long-term advances at one location, but delays at another. Finally, as part of the broader effort to anticipate and reduce declines in boreal migratory birds, our study underscores the conservation value of banding station data in quantifying avian responses to and investigating drivers associated with varied climate indices.

O_LIDemographic data are essential to construct mechanistic models to understand how populations change over time and in response to global threats like climate change. Existing demographic data are either lacking or insufficient for many species, particularly those that are challenging to study, such as marine mammals. A pipeline for collecting accurate demographic data to construct robust demographic models at scale would fill this knowledge gap for many species, including marine mammals like pinnipeds (seals, sea lions, and walruses). C_LIO_LIWe introduce a non-invasive pipeline to estimate the 3D body size (volume) of species that will allow monitoring at high spatial and temporal scales. Our pipeline integrates 3D structure-from-motion photogrammetry data collected via planned flight missions using off-the-shelf, multirotor unmanned aerial vehicles (UAVs). We apply and validate this pipeline on the grey seal Halichoerus grypus, a marine species that spends much of its time at sea but is predictably observable during its annual breeding season. We investigate the optimal ground sampling distance (GSD) for surveys by calculating the success rates and accuracy of volume estimates of individuals at different elevations. C_LIO_LIWe establish an optimal GSD of 0.8 cm px-1 for animals similar in size to UK grey seals ([~]1.4 - 2.5 m length), making our pipeline reproducible and applicable to a broad range of organisms. Volume estimates were accurate and could be made for up to 68% of hauled-out seals in the study areas. Finally, we highlight six key traits that make a species well-suited to estimating body volume following this pipeline. Good candidates include large reptiles like crocodiles, large mammals such as hippopotamus, and shrubs or bushes in deserts and Mediterranean habitats. C_LIO_LIOur pipeline accurately estimates individual body volume of marine macrovertebrates in a time-and cost-effective manner whilst minimising disturbance. Whilst the approach is applied to pinnipeds here, the pipeline is adaptable to many different taxa that are otherwise challenging to study. Our proposed approach therefore opens up previously inaccessible areas of the Tree of Life to demographic studies, which will improve our ability to protect and conserve these species into the future. C_LI