Movement Ecology

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.

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

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.

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

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

Spatial and spatiotemporal models are increasingly critical for understanding species distributions, tracking population change, and informing conservation decisions. As biological processes are influenced by increasing external pressures, including human disturbance or environmental change, accurate model predictions become essential for adaptive management. However, the reliability of spatial predictions depends on often-overlooked modelling choices, including the spatial resolution used to approximate underlying processes. Using long term monitoring data from a large-scale groundfish survey in the California Current ecosystem, we investigated how spatial model complexity affects the quality of ecological predictions and derived indices used for management. We fit spatial and spatiotemporal models of ocean temperature and fish biomass density for 27 commercially important species using varying levels of spatial resolution. We evaluated both in-sample and out-of-sample prediction, and effects on area-weighted biomass indices. Counter to common assumptions, increasing spatial approximation resolution did not universally improve predictions. Our case studies demonstrate that for many datasets, out-of-sample prediction quality peaked at intermediate spatial resolutions and declined at the finest scales. Through simulation testing, we found this pattern was strongest when spatial patterning had a small range and high spatial variance, and observation error was low. For most species, spatial resolution had a minimal effect on biomass trend estimates used in management, but for several commercially important rockfish species, resolution choices substantially affected both the scale and uncertainty of population indices. Our findings demonstrate that spatial model specification can substantially affect ecological inference, with direct implications for management and conservation planning. We provide practical guidance for ecologists on selecting appropriate spatial complexity through cross-validation. When out-of-sample prediction is a focus, appropriate approximation complexity should improve both parameter estimation accuracy and derived quantities.

Understanding how fish navigate complex natural environments requires bridging fine-scale biomechanics with ecological behavior. We investigated the volitional movement and energetics of wild red drum (Sciaenops ocellatus) across laboratory, mesocosm, and field settings. Using flow-respirometry, we quantified metabolic costs and swimming kinematics under ecologically relevant flow conditions shaped by bluff bodies mimicking mangrove roots and oyster mounds. Fish swimming in turbulent wakes exhibited reduced oxygen consumption and altered tailbeat dynamics, especially at high flow speeds. In a large outdoor mesocosm, dual accelerometers revealed a rich behavioral repertoire, including maneuvering and rest, which is not easily observable in confined lab settings. Spectral analysis and clustering identified eight distinct locomotory states, highlighting the limitations of summed acceleration metrics. Field telemetry tracked wild red drum across a 54 km estuarine corridor for a three-year period through an array of 36 acoustic receivers, revealing movement patterns shaped by tidal flow and physical habitats. Hydrodynamic modeling revealed that while laboratory trials demonstrated substantial energetic savings at high flows (approaching 100 cm/s), wild fish were detected predominantly in low-velocity microhabitats (<30 cm/s) near structurally complex features. This mismatch suggests that habitat selection is an adaptive strategy driven by ecological factors such as foraging opportunities, predation refuge, and site fidelity, rather than hydrodynamic efficiency alone. Our multi-scalar approach demonstrates that while flow-structure interactions can reduce locomotor costs for fish, habitat use in the wild reflects broader ecological constraints, offering a framework for integrating biomechanics, physiology, and ecology in conservation-relevant contexts.

Movement is costly, and animals are under strong selective pressure to move efficiently, yet, in patchy, dynamic landscapes, decision-making is inherently uncertain. We quantify the energetic savings achieved by using up-to-date information presented within social cues for reducing movement costs. We use an agent-based model, founded on realistic aeronautical rules and parametrised on the Andean condor (Vultur gryphus), to study movement in patchy landscapes. By explicitly considering altitude, flight results in a sequence of soaring and gliding in the 3D space. We investigate how the cost of movement to an overall goal varies when birds use social information from others that are either fixed in space or moving collectively to the common goal, and under different risk-taking speed strategies, from slow and cautious to fast and risky. The value of social information is operationalised as energetic savings in units of basal metabolic rate. Under low predictability, agents with intermediate risk and high social-information use exhibit lowest movement costs, with up to 41% energy savings over asocial movement. By extending classical aeronautical theory to social and variable environments we demonstrate the adaptive value of social information for efficient movement in patchy, unpredictable landscapes.

Radar aeroecology is dedicated to making ecological inference about aerial wildlife from radar-derived information. While producing unique, large-scale datasets describing biological activity in the sky, radar methodologies are largely incapable of relating these to specific species and are thus taxonomically limited. I describe a computational method to increase taxonomic resolution in vertical looking radar data by dividing detected organisms into morphology and movement-based aerial morphotypes. Using the Birdscan MR1 radar target classifier, wing flapping frequency calculation and target size estimation, I demonstrate a nearly 8 fold increase in classification resolution of bird radar data from the Hula Valley Research station, Israel. Furthermore, by relating each species in the regions species pool to its relevant morphotype, I show that most of these newly separated classes are related to small numbers of species (1-10), providing realistic opurtunities to bridge the taxonomy gap in radar data. By using the morphotype approach, radar aeroecologists can start observing and discussing the concept of "Aerodiversity", analogues to widely used biodiversity, a fundamental measure in ecology and conservation sciences. By analitically adressing taxonomy in radar-aeroecology, practitioners will increase the impact and dissemintation of their work and contribute to a better, more complete understanding of the aerial habitat.

Migration is widely recognized as a strategy for animals to track seasonally shifting resources. Yet, seasonal and spatial dynamics of migration are challenging to study, particularly for difficult-to-track insects. Among insects, monarch butterflies (Danaus plexippus) have a well-documented fall migration, but spring breeding recolonization remains poorly understood, particularly for the western population. We conducted multi-year surveys across six regions in the western United States to characterize monarch breeding phenology and evaluate three related hypotheses: (i) the successive broods model, with discrete generations shifting activity across the breeding range, (ii) a diffusion-like expansion model with overlapping breeding periods, and (iii) a mid-summer lull model with temporary summer declines in breeding for areas near the overwintering habitat. Monarch immature presence served as an indicator of local breeding activity. Our results do not support the successive broods or mid-summer lull hypotheses. Breeding onset occurred earlier near overwintering areas and gradually expanded north-and eastward, with sustained activity in many regions throughout the season. Termination of breeding also occurred earlier at more distant sites, resulting in longer breeding activity nearer to overwintering habitat. Immature monarch density declined with distance from overwintering areas at onset and termination, suggesting delayed colonization of peripheral regions. Together, these results support a diffusion-like expansion of breeding rather than sequential generational replacement. Western monarchs also do not initiate or terminate breeding in close synchrony with host plant availability, contrary to predictions from resource-tracking theory. These findings highlight fundamental differences between western monarch breeding dynamics and paradigms for eastern monarchs, demonstrating that a single species can employ fundamentally different spatial strategies for recolonizing its breeding range in different regions. More generally, these results distinguish insect migration from systems with direct movements between wintering and breeding habitats, and underscore the value of long-term, landscape-scale monitoring for resolving habitat use across heterogeneous environments.

Collective decision-making in animal groups is often studied using short, trial-based mazes experimental setups that restrict observations to isolated choice events. However, how leadership and decision dynamics unfold over extended periods in symmetric environments remains poorly understood. Here we introduce a novel cyclic three-room Y-shaped environment that enables continuous, and autonomous sequences of collective decisions without experimental reset. We tracked the positions and identities of 20 groups of five AB-strand zebrafish (Danio rerio) during one-hour sessions in which animals freely transitioned between three identical rooms connected by visually isolated identical corridors. We show that this symmetric Y-maze enables the collection of large amounts of data to study decision-making with a few replicates, because habituation occurs after 45 minutes of exploration. After an initial exploration phase, groups reached a stable behavioural regime, generating thousands of decision events per replicate. Collective dynamics were consistent across spatial contexts, indicating that the symmetric architecture does not bias movement patterns, as opposed to traditional mazes. We show that zebrafish leadership is typically shared among shoal members, with leaders often acting as decision-makers. By transforming a classical maze into a self-renewing decision system, this approach enables the study of long-term collective dynamics and spontaneous leadership in controlled yet ecologically relevant conditions. Author summarypresentation

Humans are profoundly reshaping the natural world. These changes are giving rise to complex and mutually risky dynamics between people and large carnivores. In protected areas across North America, bears (Ursus sp.) face rapidly rising recreation pressures that can alter their use of the landscape, either displacing them from high-quality habitats or drawing them into human-wildlife conflicts through habituation or attraction to anthropogenic resources. However, disentangling responses to recreation from other drivers can be difficult because human activity covaries with environmental and seasonal processes that also shape bear activity. We leveraged the partial closure of the popular Berg Lake Trail in Mount Robson Park, British Columbia, Canada, to investigate whether black (Ursus americanus) and grizzly bears (Ursus arctos) showed fear, attraction or neutral behavioural responses to varying recreation levels across multiple spatiotemporal scales. To understand both anticipatory responses to predictable patterns of human activity, and reactive responses to hiker events, we used detections from 43 camera traps over two years (July 2023-June 2025). We compared weekly habitat use, daily activity patterns, and direct responses to hikers (using Avoidance-Attraction Ratios; AARs) among camera sites and between open and closed sections of the trail. Our results revealed that both bear species exhibited patterns consistent with fear responses, while some black bear behaviours were also consistent with attraction responses. Both kinds of responses reflect anticipatory strategies rather than reactionary behaviours (i.e., no AAR effect). Neither species avoided recreation spatially at the weekly scale: black bears were detected more at site-weeks with greater recreation intensity, while grizzly bears were consistently detected more at sites closer to hiking trails. However, both species used daily temporal partitioning to avoid direct encounters with humans. These findings demonstrate scope for human-bear coexistence when recreation levels are managed to be moderate and predictable, and bears have sufficient space to segregate from humans during peak times. Thus, successful coexistence will hinge on co-adaptation by both bears and people. Understanding how recreation influences bear behaviour, and the spatiotemporal scale at which that occurs, is critical for guiding effective adaptive management aimed at fostering human-bear coexistence in high-traffic protected areas.

Many animals communicate using sequences of signals, but identifying recurrent, non-random signal combinations remains methodologically challenging. Collocation analyses are increasingly popular approaches for detecting which signals animals combine at rates greater than expected by chance. However, existing methods for animal collocation analysis face several limitations that reduce their statistical rigour: they lack uncertainty estimates, fail to control for non-independence in sampled data, and do not account for inflated family-wise error rates when identifying attraction among many different signal types. These limitations restrict the broader applicability of animal collocation analysis, including preventing robust comparisons of signal combination strength between cohorts (e.g. populations, sexes or age classes). We adapt a novel form of Multiple Distinctive Collocation Analysis using Pearson residuals (MDCA-Pr) that addresses these statistical limitations, and validate its use in animal communication research in three ways: first, using numerous simulated datasets of different sizes and levels of signal recombination; second, using simulated data to evaluate the performance of MDCA-Pr in intercohort comparisons, and third, by demonstrating how MDCA-Pr can be applied to compare the vocal sequences produced by male and female captive-living common marmosets (Callithrix jacchus). MDCA-Pr shows high sensitivity, including at small sample sizes, and generally low false-positive rates, which we further reduce by applying additional criteria for identifying attraction between signals. During intercohort comparisons, MDCA-Pr is conservative, with low false-positive rates, and statistical power increases with sample size. MDCA-Pr is a robust method for evaluating signal attraction in animal communication and enables accurate intercohort comparison of animal signal combinations. Significance StatementBy assessing the performance of MDCA-Pr on simulated animal-like data, we demonstrate that this method reliably detects signal combinations within and across animal cohorts, while overcoming statistical limitations of previous collocation analyses. We present an analytical pipeline for applying MDCA-Pr to animal signal data, including for intercohort comparisons, enabling identification and comparison of combinatorial strategies across entire signal repertoires. We illustrate this approach by comparing call combination strategies of male and female common marmosets when presented with food under experimental conditions, finding similar combinatorial strategies between sexes. MDCA-Pr therefore permits rigorous characterization of animal signal combinatoriality and opens avenues for investigating how demographic, social, and group-level factors influence combinatorial patterns.

Sleeping and feeding are crucial for survival of any animal. In case of large predators, knowing where these activities occur can help us understand their behavioural adaptations for coexisting with people and could help mitigating human-carnivore conflicts. Leopard (Panthera pardus) is an elusive and highly adaptable large felid that mostly lives outside protected areas and can survive also in close proximity to humans. However, most leopard research in Africa has been conducted in protected areas and we poorly understand leopards habitat selection while resting and hunting. To shed light on their coexistence with humans, we investigated habitat features influencing leopard selection of resting and kill sites on farmlands in central Namibia, using generalized linear mixed models (GLMM) under a use-availability study design and blinded field-sampling. Leopards primarily selected resting sites that were located in mountainous, steep, rugged terrain and sites with good concealment while kill sites were selected in mountainous habitats. Human infrastructure did not affect leopard resting and kill site selection. Thus, the capacity of leopards to perform essential life-supporting behaviours while coexisting with people appears to be primarily driven by their ability to remain concealed, rather than spatially avoiding humans.

Accurate estimation of post-release survival is fundamental to fisheries stock assessment and effective management. Conventional tag-return studies and acoustic telemetry are commonly used to estimate this probability, yet each approach has limitations when applied independently. Using gag (Mycteroperca microlepis) as a case study, we integrated data from a large-scale conventional tagging program and an acoustic telemetry experiment within a discrete-time statistical modeling framework that links relative recapture risk with telemetry-derived fate. This approach enabled estimation of post-release survival across a broad gradient of capture depths representative of recreational fishing conditions. Estimated survival was high in shallow waters ({approx}97%) but declined with increasing capture depth, consistent with depth-related barotrauma. Applying model predictions to depth distributions from the recreational fishery yielded annual and monthly post-release survival probabilities. Annual estimates were consistent with values assumed in recent stock assessments, while monthly values highlighted seasonal patterns potentially relevant for management. This integrated framework advances post-release survival estimation by combining the extensive sample sizes and environmental coverage characteristic of conventional tagging data with the direct fate observations provided by acoustic telemetry, and offers a transferable approach for other highly targeted fisheries.

Reliable freshwater access drives terrestrial wildlife movements and habitat use globally. The small, rain-fed seasonal pools critical for dryland wildlife persistence are vulnerable to rising temperatures and unstable precipitation regimes projected under climate change. In southern Africa, which is expected to warm rapidly by 2100, the drying and disappearance of surface water may cause a breakdown in seasonal migrations of large, area-sensitive, and water-dependent wildlife species. Furthermore, the disappearance of ephemeral water may concentrate wildlife around remaining surface water, increasing resource competition and human-wildlife conflict. An accurate understanding of the dynamics and drivers of seasonal surface water will therefore be critical to wildlife and human health as climate change intensifies. Here, we present a framework and empirical analysis of fine-scale surface water mapping in the 520,000km2 Kavango Zambezi Transfrontier Conservation Area (KAZA), the worlds largest terrestrial conservation area. From 2019-2025, we implemented Otsu thresholding on median Automated Water Extraction Index imagery from 10m Sentinel-2 MSI, leveraging high wet season contrast between vegetation and water as a dry season positive mask. We created >35 quasi-monthly KAZA-wide Ephemeral Surface Water (ESW) rasters (mean classification accuracy 87%, compared to 50% accuracy for existing water products), and found wet season precipitation drivers of non-riparian water fill levels did not extend into the dry season. Then, using GPS data from 27 African savanna elephants (Loxodonta africana), which typically visit water every 48 hours, we compared elephant water visitation rates based on ESW to existing 30m Global Surface Water (GSW) maps. Models using ESW estimated 99% of elephant data came within a 48-hour window, compared to 42% for GSW, suggesting that ESW is a better proxy for actual wildlife water use in animal movement modeling. As aridification threatens to diminish surface water resources, we must model the drivers of wildlife movements at the scale of wildlife needs. With ESW, we provide fine scale accessible surface water data and a straightforward coding architecture for applications beyond KAZA.

The long-standing misconception that the Galapagos petrel (Pterodroma phaeopygia) and the Hawaiian petrel (Pterodroma sandwichensis) were conspecific masked the severe vulnerability of the Galapagos population. By the time its distinct status was recognized, the Galapagos petrel was already in marked decline, primarily due to invasive predators. Consequently, sustained rodent control programs have been implemented on Santa Cruz Island. An unintentional one-year failure in rodent control provided a rare quasi-experimental opportunity to quantify the demographic consequences of the invasive black rat predator. During this year, hatching success declined by [~]35% and breeding success by [~]40% relative to long-term means (66% and 62%, respectively), representing a substantial reproductive collapse. Fledging success exhibited a comparatively modest decline (from a long-term mean of 94% to 86% in 2017), suggesting stage-specific vulnerability. These results support the hypothesis that invasive black rats primarily affect early reproductive stages through egg predation and predation on small chicks, while older chicks surpass a critical size threshold that reduces susceptibility. Across the remaining managed years, reproductive metrics exhibited great stability, demonstrating the petrels resilience against other environmental or climatic stressors. Our findings provide robust empirical evidence that invasive rodent control is the dominant driver of reproductive success in this endangered seabird. The quasi-experimental failure underscored both the effectiveness and the necessity of continuous predator management, highlighting the severe and immediate consequences of even short-term lapses.

Urbanization and human-induced environmental changes create unique and unprecedented thermal landscapes, yet the extent to which species respond to these changes remains poorly understood. One major challenge in studying these responses is the spatial mismatch between the small scale at which organisms experience their environment and the broader scale at which climate data are typically collected. We use Infrared Thermography (IRT) to quantify the fine scale microclimate in urban and rural habitats used by two tropical agamid lizards, Calotes versicolor and Psammophilus dorsalis. By combining field-based body temperatures and lab-based measures of thermal limits (CTmax, CTmin)and preferences (Tpref), we assess how the thermal heterogeneity of these fine mosaics of microhabitats influence the degree of thermoregulation (k) of these species. We find that thermal responses to urbanization are shaped by species-specific thermal traits and patterns of microhabitat use. Between the species, urban individuals did not differ markedly in habitat thermal heterogeneity, substrate temperature used or degree of thermoconformity. However, within species, P. dorsalis experiences warmer and more heterogeneous conditions in rural habitats, whereas C. versicolor experiences similar thermal conditions across habitats. Calotes versicolor also exhibits broader thermal tolerance and preferred temperature ranges than P. dorsalis. Collectively, our results suggest that P. dorsalis may be more susceptible to the thermal constraints imposed by human-modified landscapes. Overall, we demonstrate the critical need to account for microclimatic conditions and species-specific thermal traits when determining how animals respond to changes in the thermal environment expected from climate change.

With bird populations across the world being impacted by ever-growing anthropogenic pressures, reliable monitoring is essential to help halt or reverse declines. Existing visual bird monitoring approaches, which employ cameras or radars to deliver automated and large-scale monitoring data, face a variety of issues. Image-based species classification is only possible if the fine-scale features of a bird are clear, which can be difficult to achieve in real monitoring contexts without expensive, high-resolution cameras due to occlusion and lighting. Radar and video-based approaches which analyse longer-term flight behaviour over the course of seconds can achieve more reliable results in real monitoring contexts, particularly from greater distances, but still require expensive equipment and do not account for all the possible types of flight patterns. Here we present a novel approach to track a wide range of bird flight patterns using inexpensive equipment. As a proof-of-concept, we demonstrate how our approach can be used to classify birds between four species, Red Kite, Kestrel, Black-Headed Gull and Sparrowhawk, which represent four different types of flight patterns. The balanced accuracy of the classification is 0.5583, with a recall and precision per species that range from 0.2640-0.7750 and 0.4583-0.5962, respectively. Our proof-of-concept study demonstrates how new and existing visual bird monitoring systems can leverage flight patterns to deliver species-level insights at lower costs and on larger scales than before.

Body orientation is a key variable in the analysis of insect flight behavior, yet it remains difficult to measure across the full extent of a trajectory in most experimental settings. Although modern tracking systems reliably capture the position and velocity of the center of mass, resolving body yaw orientation typically requires dedicated hardware confined to a small, purpose-built volume, and is impractical for large-scale or long-duration studies. Here, we develop a data-driven estimator that predicts body yaw orientation directly from translational flight trajectory data. We trained a fully connected feed-forward artificial neural network (ANN) on a dataset in which both flight trajectory and body orientation were recorded simultaneously in freely flying Drosophila, using a time-delay embedding of ground velocity, air velocity, and inferred thrust vectors as input features. To improve generalization across arbitrary coordinate frames, we augmented the training data with random rotational transformations. Evaluated on a withheld test set of 3,313 trajectories (101,576 frames), the rotation-augmented model achieved a median mean absolute angular error of 10.51{degrees}, with accurate heading recovery across the full [-{pi}, {pi}) range. The estimator provides a practical tool for recovering body orientation information from existing trajectory datasets in which only center- of-mass motion was recorded, extending the behavioral and computational analysis of insect navigation to previously inaccessible data.