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.

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.

Understanding how migratory birds balance energetic costs of movement with wintering benefits requires quantifying their energy expenditure across the full annual cycle. Here, we present a novel approach to reconstruct annual energy budget from multi-sensor geolocator data on atmospheric pressure and activity. We apply this method to tracking data of Eurasian Wrynecks from a European breeding population exhibiting striking variation in migration distance: short-distance migration to southern Europe, medium-distance to northern Africa, and long-distance to sub-Saharan Africa. Long-distance migrants had 21-26% lower annual energy expenditure than shorter-distance migrants, primarily due to reduced thermoregulation costs during boreal winter. However, they faced extreme physiological demands during migration, with daily energy expenditure exceeding 9.5 times basal metabolic rate. Since 1950, climate warming has progressively reduced winter thermoregulation demands disproportionately benefiting and potentially promoting shorter-distance strategies. These results reveal shifting energetic trade-offs under climate change, potentially driving evolution of migration patterns.

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

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.

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.

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.

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.

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.

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

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

O_LICurtailment of wind farms effectively reduces collision mortality in bats. Implementing this measure in offshore wind farms requires knowledge on the spatiotemporal occurrence and environmental predictors of migration over sea. In bats, such information can be obtained through acoustic monitoring and individual tracking. However, these techniques provide seemingly contradictory insights into migration patterns. C_LIO_LIWe used a Bayesian capture-recapture state-space model to investigate how environmental predictors influence spring departure decisions of Nathusius pipistrelle Pipistrellus nathusii migrating over the North Sea. The model was applied to both acoustic and tracking data, enabling comparable analyses across methods and incorporating uncertainty in migration dates of tracked bats. Additionally, we examined nightly offshore bat occurrence to further explore differences in movement patterns detected by the two techniques. C_LIO_LIWind conditions at 200 m above sea level were identified as key driver of Nathusius pipistrelle spring migration. In May-June, most bats migrated from the United Kingdom under westerly and northwesterly tailwinds. Tracked individuals flew in stronger supportive winds than acoustically recorded bats, which were also detected under crosswinds and headwinds. In March-April, acoustic detections occurred mainly during strong southerly winds, suggesting that early-season migrants largely consisted of individuals migrating over the European mainland and drifted northwards onto the North Sea by strong crosswinds. C_LIO_LIAcoustic detectors primarily recorded bats that landed on offshore platforms, likely because they were unable to cross the North Sea in a single flight due to less favorable wind conditions, or because they departed from more inland locations. In contrast, tracking data mainly represented bats that successfully crossed the North Sea in a non-stop flight under moderate supportive tailwinds. C_LIO_LISynthesis and applications: Combining observation techniques improves our understanding of bat migration patterns. Additionally, acoustic monitoring can capture migration from different geographic origins. Current mitigation measures for offshore wind farms at the North Sea rely solely on acoustic data, likely overlooking the part of the population that crosses over sea with optimal wind support. Acoustic and tracking data are therefore complementary rather than contradictory, and both methods should be used together when developing mitigation measures. C_LI

Researching the complete life cycles of migratory animals is essential for understanding conservation and population dynamics. Many studies focus on the breeding season, but surviving winter is equally important. Living in groups during winter can play a vital role as social connections within groups can provide many benefits such as protection from predators and increased access to resources. However, it is often unknown how social connections change across seasons in migratory animals. We focus on social connections in a migratory bird and ask whether social connections in winter continue during breeding. Golden-crowned sparrows have distinct, stable winter communities which include both site and group fidelity across years: birds almost always rejoin the same social community each year. If these birds have social connectivity across migration, we would expect individuals that associate in winter would also associate together on their breeding grounds. Our small-scale GPS tagging study combined with intensive social behavior data revealed that sparrows in the same tightly-knit winter community migrated to highly disparate locations during summer, showing that social connections in winter do not continue in summer. This suggests that golden-crowned sparrows have entirely separate social structures across seasons and that long-term social memories allow them to reform stable groups each winter.

Millions of birds travel every year between Europe and Africa detouring ecological barriers and funnelling through migratory corridors where they face variable weather conditions. Little is known regarding the response of migrating birds to mesoscale meteorological processes during flight. Specifically, sea-breeze has a daily cycle that may directly influence the flight of diurnal migrants. We collected radar tracks of soaring migrants using modified weather radar in Latrun, central Israel, in 7 autumns between 2005 and 2016. We investigated how migrating soaring birds adjusted their flight speed and direction under the effects of daily sea-breeze circulation. We analysed the linear and, uniquely, the non-linear effects of wind on bird ground-, air- and sideways speed as function of time along the day using Generalized Additive Mixed Models and calculated first derivatives to identify when birds adjusted their response to the wind over time. Using data collected during a total of 148 days, we characterised the diel dynamics of horizontal wind flow in its two vectorial components relative to soaring migration goal (South), finding a consistent rotational movement of the wind blowing towards the East (morning) and to the South-East (late afternoon), with highest speed of crosswind component around mid-day and increasing tailwinds towards the late afternoon. We found that the airspeed of radar detected birds decreased consistently with increasing tailwind throughout the day, resulting in a rather stable groundspeed of 16-17 m/s. In addition, birds increased their sideways speed when crosswinds were at their maximum to an extent similar to that of the winds sideways component, meaning a full compensation to wind drift, which decreased after the time of crosswind maximum. Using a simple, novel and broadly applicable statistical method, we studied, for the first time, how wind influences bird flight by highlighting non-linear effects over time, providing new insights regarding the behavioural adjustments in the response of soaring birds to wind conditions. Our work enhances our understanding of how migrating birds respond to changing wind conditions during their journeys in order to exploit migratory corridors.