Movement Ecology

In Baltic grey seals (Halichoerus grypus), interannual variation in sea-ice availability can alter the proportion of females breeding on land, generating substantial variation in local breeding density among years. We investigated preweaning survival, post-weaning residence, and weaning body condition at a Baltic grey seal land-breeding colony over two years with contrasting colony densities. Drone imagery provided repeated counts of pups in distinct developmental stages and estimates of moulting pup mass as an index of weaning body condition. A Bayesian state-space population model estimated substantially greater pup production in the high- (282.5 [95% CrI = 252.8 to 320.9] pups) than the low-density (188.3 [95% CrI =168.9 to 210.8] pups) year, but similar preweaning survival (0.66 [95% CrI = 0.53 to 0.81] and 0.67 [95% CrI = 0.56 to 0.79], respectively). In contrast, post-weaning residence differed substantially between the high- (11.8 [95% CrI = 9.2 to 15.7] days) and low-density (30.3 [95% CrI = 23.1 to 39.1] days) years. Greater exposure to other pups during early life was associated with lower weaning body mass ({beta} = -0.45 [-0.89 to -0.01], P[{beta} < 0] = 0.98). Together, these results suggest that the consequences of higher breeding density may emerge through poorer weaning condition and altered post-weaning residence even when effects on preweaning survival are not detectable.

From the iconic V-formations of geese to the spectacular murmurations of starlings and communal roosting of millions of bramblings, bird flocks keep sparking wonder and scientific curiosity. In science, common themes include how flocking formation impacts the energy expenditure during migration, how it affects risks of predation, how it relates to information spread on food abundance, and how birds manoeuvre in dense flocks. Here, we report on observations of a very different type of flock formation: migrating terns over open sea flying side by side in a search party manner -- strikingly orthogonal to their heading -- with interbird distances up towards 300 m and flocks of around ten birds covering widths up to one kilometer. Such sparse formations of migrating birds stand in great contrast to the classes of bird flocks previously described in the literature. We present simultaneous field observations and ship-based radar measurements of flock characteristics and argue that this is a form of fly-and-forage migration that may be a common, but overlooked, network foraging strategy of surface feeding birds: while most migrants cannot combine migration and foraging, the situation is different for surface feeders like terns.

1. Common dolphins (Delphinus delphis) are the most abundant cetaceans off mainland Portugal and a species of conservation and bycatch concern, so reliable abundance estimates are a management priority. Yet no single survey provides adequate spatiotemporal coverage: dedicated programmes offer sparse snapshots, while opportunistic programmes are spatially constrained by their routes. Integrating them is methodologically demanding, and the existing methods that do so in a single model estimate the density of animal groups, not of the individual animals that management often requires. 2. We develop a spatiotemporal marked log-Gaussian Cox process that integrates four structurally distinct surveys through a shared intensity surface, comprising environmental covariates, a latent spatial field, and survey-specific detection functions. Group size enters as a zero-truncated negative binomial mark with its own spatiotemporal field. We apply it to a dedicated aerial survey and three opportunistic ship-based programmes off mainland Portugal, comprising 162,514 km of on-effort transects and 1,756 sightings of 17,000 animals. We fit the model in a Bayesian framework with integrated nested Laplace approximations, yielding seasonally resolved group and animal density surfaces across the Portuguese Exclusive Economic Zone, and abundance estimates with fully propagated uncertainty. 3. Predicted animal density concentrated over the shelf and upper slope in every season, peaking northwest, and increased in shallower, cooler, more productive waters and over steeper seabed. Abundance nearly doubled from summer (77,000 [95% credible interval: 60,000, 98,000]) to winter (146,000 [99,000, 208,000]). Seasons shared 56% of the spatial field's variance, so effort from any programme in any season informed all others, and the seasonal contrast remained estimable despite uneven coverage. 4. Synthesis and applications. Our framework allows managers to combine dedicated surveys with existing platforms of opportunity to estimate animal density by season. Because group size is modelled explicitly, densities are estimated in animals rather than groups, as required for bycatch risk assessment and marine spatial planning. Because each survey enters through its own observation model, the approach accommodates programmes that differ in platform and protocol and transfers to any species or region where several surveys each cover part of a population.

Protecting deep-sea sessile communities as Marine Protected Areas (MPAs) requires accurate estimates of their spatial distribution and abundance. Existing computer-vision detection methods require careful calibration to avoid counting errors; multi-object tracking (MOT) theoretically offers a more robust alternative. In practice, however, poor lighting and turbidity limit the efficacy of purely kinematic MOT algorithms in deep-sea environments. At the same time, the intra-species visual homogeneity typical of deep-sea scenes means traditional per-frame appearance features are often too weak to maintain stable identities in deep-association MOT algorithms. In this paper we introduce JEPA-SORT, an MOT algorithm that injects V-JEPA 2.1 embeddings into detection-to-track association, to show that video world-model embeddings - which capture not only physical appearance but spatiotemporal trajectory across viewpoints - can help maintain stable identities on visually homogeneous specimens. We provide a benchmark of twelve ROV transects surveying deep-sea sessile communities and evaluate JEPA-SORT against standard MOT algorithms across a range of simulated optical environments. We also propose the Biodensity Utility Metric (BDU), which measures tracker performance in the context of downstream ecological modeling. JEPA-SORT outperforms all benchmarked trackers on IDF1, MOTA, and BDU and is competitive on specimen-count accuracy. Ablations indicate its advantage stems primarily from the discriminative power of world-model embeddings on visually homogeneous footage.

Translocations are commonly used to reduce the risk of extinction of threatened species, and often to reintroduce animals to their former habitats. If extirpation occurred a long time ago, individuals may be placed into novel environments outside of conditions they have previously experienced, which can affect translocation success. To measure success of translocations, particularly those to novel habitats, it is important to monitor post-release spatial behaviour. In 2023, ten critically endangered k[a]k[a]p[o] (Strigops habroptilus) were released to form a new population at the fenced mainland island of Sanctuary Mountain Maungatautari (hereafter Maungatautari) in the North Island of New Zealand, an area from which they were extirpated approximately a century earlier. The spatial behaviour and habitat selection of these k[a]k[a]p[o] was analysed using dynamic space use and step selection functions with temporally dynamic responses applied to GPS logger data recorded over a seven month period. These showed three unexpected results: k[a]k[a]p[o] were highly mobile and used larger areas compared to other sites; there was synchronicity of movement, with most individuals simultaneously moving toward the perimeter fence in spring; and when near the fence k[a]k[a]p[o] selected for areas of pasture and exotic vegetation more than anticipated. While direct comparisons are difficult due to varying data availability, this unexpected spatial behaviour and habitat selection suggests that k[a]k[a]p[o] behavioural responses were different at Maungatautari compared to other sites. This may indicate that k[a]k[a]p[o] released at Maungatautari had a longer exploratory stage than elsewhere; that there were behavioural effects influenced by conspecific density or the availability of novel and exotic vegetation; or that there were specific breeding habitat requirements not met at the site during the study period. These results highlight the importance of high-resolution spatial monitoring to evaluate the success of translocations, particularly those to novel environments.

Recent work on the design of spatial capture-recapture (SCR) studies has largely focused on settings with a single set of movement and detection parameters. In many applied contexts, however, populations are naturally stratified-for example, when different sexes exhibit markedly different movement scales and encounter processes, or when more than one focal species is monitored. Designs that perform well for one group may be inefficient or suboptimal for others, despite the substantial logistical effort typically required to implement an SCR survey. We describe several potential SCR design strategies for surveying multiple groups, and compare these using simulation. Designs considered include regular and optimised grids, clustered and lacework designs, and designs generated using genetic algorithms, including new approaches that optimise information from both groups and a recently published two-stage design. Using proximity detectors such as camera traps, we evaluate design performance in terms of precision, bias, and coverage, and examine how performance changes with the number of available resources. Our results show that multi-group SCR surveys can perform well even when focal groups differ greatly in movement scale and encounter rate, provided that some simple guidelines are followed. Systematic layouts accommodating more than one characteristic spacing e.g. cluster designs, performed consistently well and are a reliable default option. Regular grid designs with a single spacing between detectors are feasible when differences between groups are moderate but performed poorly as differences increased. Establishing when these designs become problematic adds unnecessary risk to the design process. Algorithmic designs did not outperform systematic designs even in highly-skewed scenarios expected to favour them. In practice, when there is more than one focal group we recommend identifying the smallest and largest plausible movement scales and rejecting potentially pathological designs, primarily those that cannot generate recaptures at the smaller scale or cover a substantial area at the larger scale.

Motion-activated camera traps are an effective tool for monitoring animals, but a single study can produce millions of images, which must be analyzed before they yield data. The burden of analyzing them has led many teams to automate the work with computer vision. These models lose accuracy on images unlike those they were trained on, however, and a general model may not use the categories a project needs. Adapting a model to a project usually requires at least one of the following: many labeled images per species, programming expertise, a graphics processing unit (GPU), or uploading images to a remote server. I describe WildObserve, a desktop application that adapts to each project without any of these requirements. SpeciesNet suggests a species for each animal that MegaDetector finds, and WildObserve keeps SpeciesNet's feature vector for each one. A project model, a logistic regression on those features, then retrains in about a second as the reviewer labels, learning the project's own species and names. From the reviewed records, and optionally the suggestions, WildObserve also fits occupancy and abundance models. Images stay on the user's computer, and every record says which model suggested its species and who confirmed it. WildObserve processed 8.1 images/s on an Apple M1 Pro laptop. On Oregon Critters, a dataset released after SpeciesNet was trained, the project model's accuracy passed that of SpeciesNet with ten labels per species (balanced accuracy 0.783 against 0.751). I also tested occupancy on 63 cameras in Malawi that SpeciesNet had not seen. With 5% of the images reviewed and the project model's suggestions counted for the rest, species' occupancy estimates differed from those of a full review by 0.11 on average. From the reviewed images alone, they differed by 0.41. WildObserve takes a project from images to species records in its own categories and on to occupancy estimates, without writing code, needing a GPU, or sending images elsewhere.

Reliable demographic data are critical for seal population assessment and management, yet remain challenging to obtain from drone surveys without automated tools. Using data from harbor seal (Phoca vitulina) haulout surveys conducted in British Columbia, Canada, in 2023-2024, we developed and tested an automated pipeline that integrates detection, morphometric extraction, and size-class classification of seals into a single system. The pipeline combines cascaded DeepLabV3+ segmentation (water removal, colony-level seal detection, and individual body-contour refinement), principal component analysis (PCA)-based extraction of body length and maximum width, and three-class size-class determination based on body length (pups, juveniles, subadults/adults). We evaluated the pipeline on 2,483 manually annotated seals across four drone platforms (DJI Mavic 3 Thermal, Mavic 2 Pro, Mini 3 Pro and Mini 2), five substrate types, and two sensor modalities (RGB, thermal infrared). Aggregate detection F1 was 0.914. Orthomosaic geometric accuracy, quantified as the RMSE of calibration-rod length on the orthomosaic, ranged from 1.0 cm to 18.0 cm depending on the platform-altitude-substrate combination, a spread that aggregate metrics would mask. Relative to manual annotation on the same orthomosaics, automated RGB body lengths showed near-zero bias (-0.6 cm; RMSE = 12.5 cm), whereas automated thermal lengths were systematically shorter (bias -19.9 cm, RMSE = 24.1 cm), establishing the unsuitability of thermal imagery for automated quantitative pinniped morphometry. For RGB imagery, threshold classification by body length reached an overall accuracy (OA) of 78.7%; for individuals outside the uncertainty zones (defined as {+/-}2 x the geometric RMSE on calibration rods around each class boundary), OA reached 90.9%. Within the RGB subset, OA was 78.9% at 20 m altitude, declining to 75.0% at 40 m where geometric error dominated. The pipeline enables automated demographic monitoring of pinniped haulouts with quantitatively characterized uncertainty across survey conditions.

Heavy snowpack can be a severe constraint for alpine herbivores, yet adult survival in long-lived temperate ungulates is typically canalized against environmental variation. Using a 45-year individual-based dataset on bighorn sheep (Ovis canadensis) and historical snow reconstructions at Ram Mountain, Canada, we evaluated the direct and indirect effects of snow conditions on age- and sex-specific survival. For both sexes, survival of lambs and senescent individuals decreased with increasing average snow depth, whereas non-senescent adults (aged [&ge;] 1 year and < 8 years) showed high survival under mean conditions. However, this resilience broke down once specific thresholds of snow depth were crossed, with sharp survival declines beyond 90 cm of snow depth for females and 100 cm for males. Lamb survival, already reduced by increasing average snow depth, declined further once snow depth exceeded 90 cm. Crucially, the annual probability of snow depth exceeding 90 cm increased markedly over the study period, along with a density-independent temporal decline in survival across all demographic groups. Lamb survival declined by 0.37, and adult female survival, the most critical vital rate for population growth, dropped by 0.13 over the study period. Our findings challenge the established paradigm of demographic buffering in prime-aged adults, demonstrating that their resilience to environmental variability is fundamentally non-linear and breaks down once a changing climate regime breaches adult physiological boundaries, severely compromising population viability.

Quantifying how individuals adjust the seasonal timings of life-history events (i.e. their phenology) in response to seasonal environmental conditions is fundamental to predicting impacts of changing seasonality. However, the current standard approach of inferring phenological responses by directly analysing observed event dates relies on foundational assumptions that could be invalidated by changing seasonality, and excludes individuals that do not express focal events, potentially generating misleading inferences. To overcome these limitations, we devised a Phenological Threshold Model (PTM) that conceptualizes phenological events as switches between discrete life-history states and primarily estimates variation in underlying latent dynamic responses to seasonal progression, from which event dates secondarily arise. We demonstrate that the PTM can accurately retrieve simulated parameter values, including given incomplete seasonal field observations. Application to multi-year data on annual breeding in black-legged kittiwakes (Rissa tridactyla) demonstrated substantial among-individual variation in latent responses to seasonally varying environmental drivers, and associated daily probabilities that individuals will initiate breeding, revealing how annual distributions of breeding dates and proportions of non-breeders jointly arise. We thereby formalise a new conceptual and statistical framework for quantifying the potential for, and constraints on, rapid plastic and micro-evolutionary changes in phenology in changing seasonal climatic and environmental regimes.

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

Data curation-the process of collecting, cleaning, and joining raw datasets for unified analysis-is a time-consuming yet crucial part of any data science project. With the emergence of powerful agentic coding tools, it is tempting to "vibe curate"-i.e., to prompt AI agents to perform data curation operations and blindly trust their outputs in order to finish work more quickly. However, this can exacerbate two key challenges that already plague manual data curation workflows: reproducibility-the ability to trace the exact set of modifications applied to raw datasets-and verifiability-the ability to audit changes and confirm that data is processed properly. To address these issues, we introduce Scrub Data, a framework that enables the use of AI agents in data curation workflows while transparently maintaining both reproducibility and verifiability. The framework involves an interactive data curation loop between a user and an AI agent, backed by a data provenance graph that tracks and versions all dataset updates. The data graph requires that all updates are formatted as self-contained executable steps, allowing any version of a dataset to be reconstructed from raw data by playing forward the transformations stored in the graph. This workflow takes place within a lightweight web application that is designed to be modified by users and agents in real-time to create custom visualization tools for identifying data curation needs and verifying outcomes. We demonstrate the usefulness of the framework with a real data curation use case from animal movement ecology. Starting with 89M raw GPS coordinates, we use the framework to curate a benchmark dataset of 2.5M coordinates to be used for machine learning or statistical analysis. The framework is available for use and extension at https://github.com/justinkay/scrubdata.

1. Group foraging reduces the risks associated with foraging: larger groups dilute individual predation risk, allow group members to share the costs of vigilance, and provide a social structure for exchanging information about predators. The benefits of shared social information over simple safety in numbers should depend on the relationships among group members. Consequently, group composition should affect the selection of risky and safe habitats in social species. 2. We asked whether relationships among elk (Cervus canadensis) group members moving together influence their selection of risky habitats more than grouping alone. We tested three hypotheses. The familiarity hypothesis predicts that elk with close social ties are most likely to select risky habitats together. The inclusive fitness hypothesis predicts that the closest genetic relatives are most likely to select risky habitats together. The dilution hypothesis predicts that familiarity and genetic relationships do not influence habitat selection, with all group foraging benefits derived from proximity to others. 3. We used integrated Step Selection Analysis to test whether the identity of the closest group members influenced habitat selection. We tested whether the relationship of the nearest elk--either genetic or social--incentivized selection of risky habitat more than proximity to the nearest elk. 4. Group foraging clearly reduced risk, with elk more likely to select risky habitat when near others. Elk were also more likely to select risky habitats when near closer relatives, albeit with considerable variation among individuals. However, familiarity had a large and consistent effect: elk were far more likely to select risky habitats when near socially familiar group members. 5. Our results suggest that information shared within foraging groups, especially between socially familiar individuals, may lower the risks associated with habitat selection. Similarly, the benefits of reliable social information for reducing risk likely incentivizes the formation of stronger social ties within foraging groups. This positive feedback between social familiarity and habitat selection could thereby increase access to profitable habitats that are otherwise too risky, cementing what we term the social information landscape as a key driver of movement and habitat selection.

(1) Kelp forests support ecologically, commercially, and culturally important species, yet their benthic communities remain difficult to monitor consistently across space and time. Scientific SCUBA surveys provide valuable observations but can be constrained by safety, bottom time, depth, and the spatial extent that divers can efficiently cover, motivating complementary approaches for collecting standardized benthic imagery. (2) We developed and field-tested a standardized, open-source framework for benthic visual-imaging surveys using a small, modular remotely operated vehicle (ROV). Across four cost-scalable hardware configurations, we integrated navigation sensors, custom BlueOS Extensions, and our surftrak contribution to ArduSub, which enables fixed-altitude flight above the seafloor, and developed two open-source desktop applications that streamline field operations (rov_flight_ops) and imagery processing (rov_imagery_processing). We also evaluated power supplies, benthic lighting, camera settings, field protocols, and telemetry-processing workflows over 303 transects conducted on 64 survey days from 2022 to 2026. (3) The ROV operated across soft-sediment and rocky habitats, currents of 0-3 kt, and underwater visibility of 0-10 m. surftrak held the ROV within 0.2 m of its target altitude for 96% of 21 h of survey transects, producing an approximately 1 m^2 standardized image footprint. Sufficient illumination and optimized camera settings yielded high-resolution imagery in visibility below 2 m, while topside power increased endurance and operational flexibility. Combining Doppler velocity log and underwater acoustic positioning data through the vehicle's extended Kalman filter improved navigation and post hoc transect reconstruction. During 2026 surveys, rov_flight_ops and rov_imagery_processing automated flight logging, log retrieval, fault diagnosis, and the sorting, development, and telemetry annotation of survey imagery. Dense kelp canopies nevertheless constrained vehicle operation, and downward-facing imagery provided limited observations of cryptic or obscured organisms. (4) We provide configuration-specific bills of materials, hardware and wiring documentation, software and firmware inventories, camera settings, open-source field and imagery-processing software, step-by-step field protocols, and pre- and post-dive checklists to support replication and adaptation of the framework. Together, these resources provide a field-tested starting point for expanding standardized benthic imaging while complementing diver surveys across kelp forests and other shallow coastal ecosystems.

The mechanisms by which organisms extract relevant information from complex sensory signals and use it for decision-making constitute a fundamental issue in biological and cognitive sciences. In social animal societies, individual decision-making is profoundly shaped by information provided by conspecifics. Collective patch-foraging experiments in the laboratory provide a controlled setting in which social information use can be quantified. Here, we consider a go/no-go task in which groups choose between two patches differing in food reward probability. We use agent-based simulations, underpinned by an augmented collective drift-diffusion model, to investigate alternative mechanisms for representing and integrating social information. We consider two representations, continuous (counting representation) and discrete (pulsatile representation), and two integration mechanisms, biasing the decision threshold (threshold modulation) and modifying the accumulated belief (belief modulation), yielding four distinct social models, in addition to a non-interacting model. We first characterize the collective dynamics generated by these models across cognitive parameters and experimental conditions. The temporal dynamics of group accuracy provide informative signatures of the underlying mechanisms. We then assess model selection and parameter identifiability using Bayesian inference and Wasserstein distance minimization, applied to group-accuracy and departure-time distributions. Bayesian inference using distributions of group accuracy provides the most reliable identification across the conditions considered. Importantly, model selection and parameter identifiability are associated with different aspects of collective behavior: model selection is closely linked to the presence of oscillations in the temporal dynamics, whereas parameter identifiability is more closely related to the global accuracy value. Thus, the information available for distinguishing the underlying cognitive mechanisms is not necessarily the same as that required to recover their parameters. Our results further show that identification depends not only on model structure but also on the experimental conditions. Overall, this study provides practical guidelines for identifying socio-cognitive mechanisms in collective foraging experiments.

1. Adaptive behavioural strategies require animals to simultaneously navigate social and ecological domains across multiple spatial and temporal scales. Although drones and computer vision have recently transformed the study of wild animal societies, many nocturnal species and those occupying structurally complex habitats remain inaccessible to these approaches, limiting our understanding of behaviour in natural settings. 2. We aimed to determine how behavioural strategies are organised across nested spatial and temporal scales within a wild communication network. 3. We used three-dimensional acoustic localisation and source separation to track individual male Hyperolius sp. A, a nocturnal African reed frog, within a natural rainforest chorus and quantify patterns of site fidelity, movement, spatial organisation, and call-timing interactions. 4. Males exhibited significant site fidelity across nights, while chorus spatial structure varied with local caller density. Within nights, individuals followed a stereotyped behavioural sequence, descending from elevated arboreal refugia before settling into lower calling positions near breeding sites. At finer temporal scales, call-timing interactions varied according to both local competitor density and the proximity of neighbouring rivals. 5. These findings demonstrate that behavioural strategies emerge across nested spatial and temporal scales and that long-term spatial positioning, short-term movement decisions, and moment-to-moment signalling interactions are tightly linked within natural communication networks. More broadly, acoustic localisation provides a powerful framework for studying behaviour in species and habitats that remain difficult to observe using conventional approaches.

Early-life phenotypes frequently predict adult phenotypes through developmental 'silver-spoon' carry-over effects, but how strongly and under what circumstances? Using 40 years of individual-based data from a great tit (Parus major) population, we used covariance reaction norms to understand how the natal-to-adult body mass association shifts across temperature, rainfall, and population density gradients during both life stages. We show that the natal-adult mass correlation is substantial (r {approx} 0.7) but strongly environment-dependent. Reaction norm shapes across climatic and density axes point to distinct mechanisms, including compensatory growth under benign adult conditions and density-dependent modulation of early-life differences. We further show that the conditions experienced in adulthood largely drive these patterns and determine whether early-life advantages are retained, even when explicitly tested within an environmental-matching paradigm. Decomposing the correlation using 38 generations of pedigree data revealed no evidence of gene-by-environment interactions. Instead, the observed phenotypic correlation reaction norms could be attributed almost entirely to environmentally-sensitive non-genetic individual differences. Overall, our findings show that climatic and demographic shifts can substantially reshape developmental carry-over without altering the genetic architecture tying body mass across life stages, with implications for how reliably early-life mass can predict individual quality and adult traits in wild animal populations.

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

Researcher disturbance can alter the behavior, survival, and breeding success of wildlife, with effects that are especially well-documented in waterbirds. Anecdotal reports suggest that disturbance can even cause cavity-nesting species to alter the structure of their nests. We used a long-term study of a burrowing seabird, the Leach's Storm-Petrel (Hydrobates leucorhous), to ask whether storm-petrels renovate existing burrows and whether such renovation is related to a history of researcher disturbance. Our study was conducted at the Bowdoin Scientific Station on Kent Island, New Brunswick, Canada, where in 2024 we measured the burrow architecture of 363 burrows, including length and structure for occupied and unoccupied burrows both inside and outside a long-term study site with consistent research activity. Of these, 150 burrows had been measured in 2006, allowing for direct longitudinal comparison of burrow architecture. Nearly all burrows changed in length over the 18-year study period (mean absolute change = 20.8 cm +- 18.2 SD). A linear model revealed two contrasting processes of burrow length change: more frequently occupied burrows grew longer, whereas initially longer burrows shortened towards mean burrow length (~50 cm) through collapse. Occupied burrows inside the long-term study site were on average 34% longer and significantly more complex than burrows adjacent to the site, suggesting that researcher disturbance directly or indirectly reshapes burrows. Overall, these results show that storm-petrel burrows are shaped by cycles of renovation and subsequent collapse that may be exacerbated by human disturbance. Given ongoing population declines in storm-petrels and other cavity-nesting seabirds, this study emphasizes the need to balance scientific research with minimizing disturbance, particularly in long-term studies.

Spatial expansion is a common feature of many ecological processes, including biological invasions, range shifts, population recovery, and the spread of pathogens and human activities. Although such expansion often arises from diffusive processes, there remain few general statistical methods to quantify these dynamics. Here, we present a diffusion-enhanced spatio-temporal model (DESTM) that approximates diffusive process using Gaussian Markov random fields (GMRF). The model works by incorporating the statistical interaction between spatial diffusion and temporal lags into a joint precision structure of the latent field. To demonstrate, we simulate data from a diffusion movement process using a continuous-time Markov chain (CTMC), and then approximate this mechanistic process using the DESTM. We show that separable spatio-temporal models can lead to large bias in estimates of the local density. In contrast, the DESTM did not appear to bias inferences, and reverts to the separable model when diffusion is not apparent. Finally, we apply the model to two large-scale examples of spatial expansion: (1) the historical spread of the Japanese distant-water longline fleet across the Pacific, representing anthropogenic spatial expansion in the ocean, and (2) the recovery and geographic expansion of Bald Eagle populations across North America. Estimates from both case studies, supported by model comparisons and ecological plausibility, suggest that the DESTM provides a better description of the underlying expansion dynamics, although improvements in forecast performance were more apparent for the Japanese longline fleet. We highlight that the model provides a fast and efficient framework for modeling spatial expansion dynamics, with the potential to represent increasingly complex ecological dynamics through future extensions. We propose that future research explore this framework as a quantitative tool to explain how spatial expansion occurs.