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1. WildWing: An open‐source, autonomous and affordable UAS for animal behaviour video monitoring

   https://doi.org/10.1111/2041-210X.70018  

   Kline, Jenna; Zhong, Alison; Irizarry, Kevyn; Stewart, Charles V; Stewart, Christopher; Rubenstein, Daniel I; Berger‐Wolf, Tanya (March 2025, Methods in Ecology and Evolution)

   Abstract Drones have become invaluable tools for studying animal behaviour in the wild, enabling researchers to collect aerial video data of group‐living animals. However, manually piloting drones to track animal groups consistently is challenging due to complex factors such as terrain, vegetation, group spread and movement patterns. The variability in manual piloting can result in unusable data for downstream behavioural analysis, making it difficult to collect standardized datasets for studying collective animal behaviour.To address these challenges, we present WildWing, a complete hardware and software open‐source unmanned aerial system (UAS) for autonomously collecting behavioural video data of group‐living animals. The system's main goal is to automate and standardize the collection of high‐quality aerial footage suitable for computer vision‐based behaviour analysis. We provide a novel navigation policy to autonomously track animal groups while maintaining optimal camera angles and distances for behavioural analysis, reducing the inconsistencies inherent in manual piloting.The complete WildWing system costs only $650 and incorporates drone hardware with custom software that integrates ecological knowledge into autonomous navigation decisions. The system produces 4 K resolution video at 30 fps while automatically maintaining appropriate distances and angles for behaviour analysis. We validate the system through field deployments tracking groups of Grevy's zebras, giraffes and Przewalski's horses at The Wilds conservation centre, demonstrating its ability to collect usable behavioural data consistently.By automating the data collection process, WildWing helps ensure consistent, high‐quality video data suitable for computer vision analysis of animal behaviour. This standardization is crucial for developing robust automated behaviour recognition systems to help researchers study and monitor wildlife populations at scale. The open‐source nature of WildWing makes autonomous behavioural data collection more accessible to researchers, enabling wider application of drone‐based behavioural monitoring in conservation and ecological research. 

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2. NAPS : Integrating pose estimation and tag‐based tracking

   https://doi.org/10.1111/2041-210X.14201  

   Wolf, Scott W.; Ruttenberg, Dee M.; Knapp, Daniel Y.; Webb, Andrew E.; Traniello, Ian M.; McKenzie‐Smith, Grace C.; Leheny, Sophie A.; Shaevitz, Joshua W.; Kocher, Sarah D. (August 2023, Methods in Ecology and Evolution)

   Abstract Significant advances in computational ethology have allowed the quantification of behaviour in unprecedented detail. Tracking animals in social groups, however, remains challenging as most existing methods can either capture pose or robustly retain individual identity over time but not both.To capture finely resolved behaviours while maintaining individual identity, we built NAPS (NAPS is ArUco Plus SLEAP), a hybrid tracking framework that combines state‐of‐the‐art, deep learning‐based methods for pose estimation (SLEAP) with unique markers for identity persistence (ArUco). We show that this framework allows the exploration of the social dynamics of the common eastern bumblebee (Bombus impatiens).We provide a stand‐alone Python package for implementing this framework along with detailed documentation to allow for easy utilization and expansion. We show that NAPS can scale to long timescale experiments at a high frame rate and that it enables the investigation of detailed behavioural variation within individuals in a group.Expanding the toolkit for capturing the constituent behaviours of social groups is essential for understanding the structure and dynamics of social networks. NAPS provides a key tool for capturing these behaviours and can provide critical data for understanding how individual variation influences collective dynamics. 

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3. The Movebank system for studying global animal movement and demography

   https://doi.org/10.1111/2041-210X.13767  

   Kays, Roland; Davidson, Sarah C.; Berger, Matthias; Bohrer, Gil; Fiedler, Wolfgang; Flack, Andrea; Hirt, Julian; Hahn, Clemens; Gauggel, Dominik; Russell, Benedict; et al (December 2021, Methods in Ecology and Evolution)

   Abstract Quantifying movement and demographic events of free‐ranging animals is fundamental to studying their ecology, evolution and conservation. Technological advances have led to an explosion in sensor‐based methods for remotely observing these phenomena. This transition to big data creates new challenges for data management, analysis and collaboration.We present the Movebank ecosystem of tools used by thousands of researchers to collect, manage, share, visualize, analyse and archive their animal tracking and other animal‐borne sensor data. Users add sensor data through file uploads or live data streams and further organize and complete quality control within the Movebank system. All data are harmonized to a data model and vocabulary. The public can discover, view and download data for which they have been given access to through the website, the Animal Tracker mobile app or by API. Advanced analysis tools are available through the EnvDATA System, the MoveApps platform and a variety of user‐developed applications. Data owners can share studies with select users or the public, with options for embargos, licenses and formal archiving in a data repository.Movebank is used by over 3,100 data owners globally, who manage over 6 billion animal location and sensor measurements across more than 6,500 studies, with thousands of active tags sending over 3 million new data records daily. These data underlie >700 published papers and reports. We present a case study demonstrating the use of Movebank to assess life‐history events and demography, and engage with citizen scientists to identify mortalities and causes of death for a migratory bird.A growing number of researchers, government agencies and conservation organizations use Movebank to manage research and conservation projects and to meet legislative requirements. The combination of historic and new data with collaboration tools enables broad comparative analyses and data acquisition and mapping efforts. Movebank offers an integrated system for real‐time monitoring of animals at a global scale and represents a digital museum of animal movement and behaviour. Resources and coordination across countries and organizations are needed to ensure that these data, including those that cannot be made public, remain accessible to future generations. 

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4. Environmental DNA ‐based detection of pathogens in trade and captive settings: Best practices and validation for Batrachochytrium salamandrivorans

   https://doi.org/10.1111/2041-210X.14217  

   Brunner, Jesse L.; Yarber, Christian M.; Pearhill, Robert A. I.; Goldberg, Caren S. (September 2023, Methods in Ecology and Evolution)

   Abstract Detecting pathogens in the live animal trade is critical for tracking and preventing their movement, introduction and spillover into susceptible fauna. However, the scale of the live animal trade makes individually testing animals infeasible for all but the most economically important taxa. For instance, while the fungal pathogen,Batrachochytrium salamandrivorans(Bsal), threatens amphibian, particularly caudate diversity, in Europe and the Americas, screening even a fraction of the millions of live amphibians imported into the United States, alone, is impractically laborious and expensive. A promising alternative to individual‐level sampling (e.g. swabbing the skin of salamanders) is to instead collect DNA from the animals' environment (e.g. housing container or water) which allows us to screen a whole group of animals at a time.We used a series of experiments withBsal‐spiked water and substrates and experimentally infected rough‐skinned newts (Taricha granulosa) to determine which methods yield the mostBsalenvironmental DNA (eDNA) and evaluate the capacity of these methods to detectBsal‐infected animals in conditions found in captive settings and trade.We found that filtering water housing infected animals for even an hour can consistently recover detectable levels ofBsaleDNA, that there is little evidence ofBsaleDNA being clumped in housing containers or swamped or inhibited by dirty housing containers, and that eDNA‐based methods achieves an equivalent or higher chance of detectingBsalinfections in a (virtual) population of co‐housed newts with fewer samples than individual swabs.By sampling the genetic materials accumulated from a whole group of animals, eDNA‐based methods are a powerful means of detecting pathogens, such asBsal, in shipments and captive populations. These methods bring routine pathogen surveillance into reach in many more contexts and can thus be an important tool in conservation and disease control. 

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5. Studying collective animal behaviour with drones and computer vision

   https://doi.org/10.1111/2041-210X.70128  

   Kline, Jenna; Afridi, Saadia; Rolland, Edouard_G_A; Maalouf, Guy; Laporte‐Devylder, Lucie; Stewart, Christopher; Crofoot, Margaret; Stewart, Charles_V; Rubenstein, Daniel_I; Berger‐Wolf, Tanya (August 2025, Methods in Ecology and Evolution)

   Abstract Drones are increasingly popular for collecting behaviour data of group‐living animals, offering inexpensive and minimally disruptive observation methods. Imagery collected by drones can be rapidly analysed using computer vision techniques to extract information, including behaviour classification, habitat analysis and identification of individual animals. While computer vision techniques can rapidly analyse drone‐collected data, the success of these analyses often depends on careful mission planning that considers downstream computational requirements—a critical factor frequently overlooked in current studies.We present a comprehensive summary of research in the growing AI‐driven animal ecology (ADAE) field, which integrates data collection with automated computational analysis focused on aerial imagery for collective animal behaviour studies. We systematically analyse current methodologies, technical challenges and emerging solutions in this field, from drone mission planning to behavioural inference. We illustrate computer vision pipelines that infer behaviour from drone imagery and present the computer vision tasks used for each step. We map specific computational tasks to their ecological applications, providing a framework for future research design.Our analysis reveals AI‐driven animal ecology studies for collective animal behaviour using drone imagery focus on detection and classification computer vision tasks. While convolutional neural networks (CNNs) remain dominant for detection and classification tasks, newer architectures like transformer‐based models and specialized video analysis networks (e.g. X3D, I3D, SlowFast) designed for temporal pattern recognition are gaining traction for pose estimation and behaviour inference. However, reported model accuracy varies widely by computer vision task, species, habitats and evaluation metrics, complicating meaningful comparisons between studies.Based on current trends, we conclude semi‐autonomous drone missions will be increasingly used to study collective animal behaviour. While manual drone operation remains prevalent, autonomous drone manoeuvrers, powered by edge AI, can scale and standardise collective animal behavioural studies while reducing the risk of disturbance and improving data quality. We propose guidelines for AI‐driven animal ecology drone studies adaptable to various computer vision tasks, species and habitats. This approach aims to collect high‐quality behaviour data while minimising disruption to the ecosystem. 

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