Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision

Temporal insights into ecological community: Advancing waterbird monitoring with dome camera and deep learning

Biodiversity monitoring is critical for conservation and management. However, efficient species monitoring is often hindered by the complexities of ecological dynamics and the constraints of conventional techniques. This study presents an automated observation system by integrating dome camera with cascade neural networks (CNNs) to map the dynamics of waterbird communities across a semi-enclosed wetland in Dianchi Lake, southwestern China, a well-known important bird habitat. The trained model achieved performance with a Top-1 accuracy of 96.83 %, a Top-5 accuracy of 99.55 %, an F1 score of 93.54 %, a recall rate of 93.38 % and precision of 93.44 %, demonstrating its reliability for precise and well-balanced classification performance. Automatic and manual monitoring performed simultaneously showed highly significant correlations for community abundance (R2 = 0.89, n = 68, p < 0.0001), underscoring the value of this system as a tool for waterbird communty monitoring. Analysis of the monitoring results showed significant differences in species richness (n = 595, p < 0.0001) and community abundance (n = 595, p < 0.05) between morning and afternoon sessions, suggesting the need to consider ecological community changes across different time scales when conducting biodiversity surveys. In 2023, the system identified 17 species of birds in 5 orders, 6 families. By analyzing the data obtained from this system, the community composition, diversity changes, the arrival and departure times of waterbirds were revealed. Enabling high-frequency, continuous, and long-term monitoring at a lower cost, this system provides a reliable, alternative tool for developing species conservation and habitat management strategies.

Tracking spatial patterns and daily modulation of behavior in a natural population of the pulse-type weakly electric fish, Gymnotus omarorum

Tracking individual spatial and activity-rest patterns in natural populations is challenging because it is seldom possible to monitor individual-specific traits continuously. The continuous emission of electric signals (EODs) by weakly electric fish provides a unique opportunity to do this. We present a cutting-edge technique, arrays of electrodes connected to low-cost amplifiers and tracking algorithm, to provide the individual identification of pulse-type weakly electric fish in the wild. Based only on EOD recordings of individuals of Gymnotus omarorum, we show that (1) there are more fish in core than in edge zones; (2) transitions into and out of the recording sites were more frequent at night, and (3) resident fish show robust nocturnal increases of EOD rate likely associated with daily variations of water temperature. This experimental approach can be extended to other species to improve our understanding of the behavior, ecology, and well-being of electric fish in natural environments.

A new data-driven paradigm for the study of avian migratory navigation

Avian navigation has fascinated researchers for many years. Yet, despite a vast amount of literature on the topic it remains a mystery how birds are able to find their way across long distances while relying only on cues available locally and reacting to those cues on the fly. Navigation is multi-modal, in that birds may use different cues at different times as a response to environmental conditions they find themselves in. It also operates at different spatial and temporal scales, where different strategies may be used at different parts of the journey. This multi-modal and multi-scale nature of navigation has however been challenging to study, since it would require long-term tracking data along with contemporaneous and co-located information on environmental cues. In this paper we propose a new alternative data-driven paradigm to the study of avian navigation. That is, instead of taking a traditional theory-based approach based on posing a research question and then collecting data to study navigation, we propose a data-driven approach, where large amounts of data, not purposedly collected for a specific question, are analysed to identify as-yet-unknown patterns in behaviour. Current technological developments have led to large data collections of both animal tracking data and environmental data, which are openly available to scientists. These open data, combined with a data-driven exploratory approach using data mining, machine learning and artificial intelligence methods, can support identification of unexpected patterns during migration, and lead to a better understanding of multi-modal navigational decision-making across different spatial and temporal scales.

WildPose: a long-range 3D wildlife motion capture system

Understanding and monitoring wildlife behavior is crucial in ecology and biomechanics, yet challenging because of the limitations of current methods. To address this issue, we introduce WildPose, a novel long-range motion capture system specifically tailored for free-ranging wildlife observation. This system combines an electronically controllable zoom-lens camera with a LiDAR to capture both 2D videos and 3D point cloud data, thereby allowing researchers to observe high-fidelity animal morphometrics, behavior and interactions in a completely remote manner. Field trials conducted in Kgalagadi Transfrontier Park (South Africa) have successfully demonstrated WildPose's ability to quantify morphological features of different species, accurately track the 3D movements of a springbok herd over time, and observe the respiratory patterns of a distant lion. By facilitating non-intrusive, long-range 3D data collection, WildPose marks a significant complementary technique in ecological and biomechanical studies, offering new possibilities for conservation efforts and animal welfare, and enriching the prospects for interdisciplinary research.

Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies

The collective dynamics of self-organised systems emerge from the decision rules agents use to respond to each other and to external forces. This is evident in groups of animals under attack from predators, where understanding collective escape patterns requires evaluating the risks and rewards associated with particular social rules, prey escape behaviour, and predator attack strategies. Here, we find that the emergence of the 'fountain effect', a common collective pattern observed when animal groups evade predators, is the outcome of rules designed to maximise individual survival chances given predator hunting decisions. Using drone-based empirical observations of schooling sardine prey (Sardinops sagax caerulea) attacked by striped marlin (Kajikia audax), we first find the majority of attacks produce fountain effects, with the dynamics of these escapes dependent on the predator's attack direction. Then, using a spatially-explicit agent-based model of predator-prey dynamics, we show that fountain manoeuvres can emerge from combining an optimal individual prey escape angle with social interactions. The escape rule appears to prioritise maximising the distance to the predator and creates conflict in the effectiveness of predators' attacks and the prey's avoidance, explaining the empirically observed predators' attack strategies and the fountain evasions produced by prey. Overall, we identify the proximate and ultimate explanations for fountain effects and more generally highlight that the collective patterns of self-organised predatory-prey systems can be understood by considering both social escape rules and attack strategies.

Development of the calf grimace scale for pain and stress assessment in castrated Angus beef calves

Grimace scales have been used to assess pain in various animal species. This study aimed to develop the calf grimace scale (CGS), evaluate its responsiveness and the effect of external factors (change of environment and dam separation, and restraint) on CGS. Sixty-nine Angus calves, 6-8 weeks old, were randomly allocated into castrated (n = 34) and sham castrated (n = 35) groups. Images were extracted from videos pre- (M1-M4), during- (M5), and post-castration/sham castration (M6, M7). Six facial action units (FAUs) were identified: ear position, orbital tightening, tension above the eye, nostril dilation, straining of chewing muscle, and mouth opening. Final CGS median scores increased after castration (P < 0.001) for both non-restrained (M7 versus M2) and restrained (M6 versus M3) calves, indicating scale responsiveness. Final CGS median scores increased (P < 0.001) when calves were subjected to external factors before castration (M1 [baseline] versus M2 and M3). However, there was no difference (P > 0.05) in CGS median scores before and after sham castration, regardless of restraint (M3 versus M6, and M2 versus M7), indicating that the external factors may have reached a maximum effect. The CGS is composed of six FAUs, responsive to acute pain and can identify stress unrelated to pain.

Understanding collective behavior through neurobiology

A variety of organisms exhibit collective movement, including schooling fish and flocking birds, where coordinated behavior emerges from the interactions between group members. Despite the prevalence of collective movement in nature, little is known about the neural mechanisms producing each individual's behavior within the group. Here we discuss how a neurobiological approach can enrich our understanding of collective behavior by determining the mechanisms by which individuals interact. We provide examples of sensory systems for social communication during collective movement, highlight recent discoveries about neural systems for detecting the position and actions of social partners, and discuss opportunities for future research. Understanding the neurobiology of collective behavior can provide insight into how nervous systems function in a dynamic social world.

Modelling animal social networks: New solutions and future directions

Research Highlight: Ross, C. T., McElreath, R., & Redhead, D. (2023). Modelling animal network data in R using STRAND. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14021. One of the most important insights in ecology over the past decade has been that the social connections among animals affect a wide range of ecological and evolutionary processes. However, despite over 20 years of study effort on this topic, generating knowledge from data on social associations and interactions remains fraught with problems. Redhead et al. present an R package-STRAND-that extends the current animal social network analysis toolbox in two ways. First, they provide a simple R interfaces to implement generative network models, which are an alternative to regression approaches that draw inference by simulating the data-generating process. Second, they implement these models in a Bayesian framework, allowing uncertainty in the observation process to be carried through to hypothesis testing. STRAND therefore fills an important gap for hypothesis testing using network data. However, major challenges remain, and while STRAND represents an important advance, generating robust results continues to require careful study design, considerations in terms of statistical methods and a plurality of approaches.

Fantastic beasts and how to study them: rethinking experimental animal behavior

Humans have been trying to understand animal behavior at least since recorded history. Recent rapid development of new technologies has allowed us to make significant progress in understanding the physiological and molecular mechanisms underlying behavior, a key goal of neuroethology. However, there is a tradeoff when studying animal behavior and its underlying biological mechanisms: common behavior protocols in the laboratory are designed to be replicable and controlled, but they often fail to encompass the variability and breadth of natural behavior. This Commentary proposes a framework of 10 key questions that aim to guide researchers in incorporating a rich natural context into their experimental design or in choosing a new animal study system. The 10 questions cover overarching experimental considerations that can provide a template for interspecies comparisons, enable us to develop studies in new model organisms and unlock new experiments in our quest to understand behavior.

Advances in biologging can identify nuanced energetic costs and gains in predators

Foraging is a key driver of animal movement patterns, with specific challenges for predators which must search for mobile prey. These patterns are increasingly impacted by global changes, principally in land use and climate. Understanding the degree of flexibility in predator foraging and social strategies is pertinent to wildlife conservation under global change, including potential top-down effects on wider ecosystems. Here we propose key future research directions to better understand foraging strategies and social flexibility in predators. In particular, rapid continued advances in biologging technology are helping to record and understand dynamic behavioural and movement responses of animals to environmental changes, and their energetic consequences. Data collection can be optimised by calibrating behavioural interpretation methods in captive settings and strategic tagging decisions within and between social groups. Importantly, many species' social systems are increasingly being found to be more flexible than originally described in the literature, which may be more readily detectable through biologging approaches than behavioural observation. Integrating the effects of the physical landscape and biotic interactions will be key to explaining and predicting animal movements and energetic balance in a changing world.

replicAnt: a pipeline for generating annotated images of animals in complex environments using Unreal Engine

Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To help overcome these limitations, we developed replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation. We also show that it increases the subject-specificity and domain-invariance of the trained networks, thereby conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.

Elephants and algorithms: a review of the current and future role of AI in elephant monitoring

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.

The role of hydrodynamics in collective motions of fish schools and bioinspired underwater robots

Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces, which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy, which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.

Mechanisms of group-hunting in vertebrates

Group-hunting is ubiquitous across animal taxa and has received considerable attention in the context of its functions. By contrast much less is known about the mechanisms by which grouping predators hunt their prey. This is primarily due to a lack of experimental manipulation alongside logistical difficulties quantifying the behaviour of multiple predators at high spatiotemporal resolution as they search, select, and capture wild prey. However, the use of new remote-sensing technologies and a broadening of the focal taxa beyond apex predators provides researchers with a great opportunity to discern accurately how multiple predators hunt together and not just whether doing so provides hunters with a per capita benefit. We incorporate many ideas from collective behaviour and locomotion throughout this review to make testable predictions for future researchers and pay particular attention to the role that computer simulation can play in a feedback loop with empirical data collection. Our review of the literature showed that the breadth of predator:prey size ratios among the taxa that can be considered to hunt as a group is very large (<100 to >102 ). We therefore synthesised the literature with respect to these predator:prey ratios and found that they promoted different hunting mechanisms. Additionally, these different hunting mechanisms are also related to particular stages of the hunt (search, selection, capture) and thus we structure our review in accordance with these two factors (stage of the hunt and predator:prey size ratio). We identify several novel group-hunting mechanisms which are largely untested, particularly under field conditions, and we also highlight a range of potential study organisms that are amenable to experimental testing of these mechanisms in connection with tracking technology. We believe that a combination of new hypotheses, study systems and methodological approaches should help push the field of group-hunting in new directions.

Fine-scale collective movements reveal present, past and future dynamics of a multilevel society in Przewalski's horses

Studying animal societies needs detailed observation of many individuals, but technological advances offer new opportunities in this field. Here, we present a state-of-the-art drone observation of a multilevel herd of Przewalski's horses, consisting of harems (one-male, multifemale groups). We track, in high spatio-temporal resolution, the movements of 238 individually identified horses on drone videos, and combine movement analyses with demographic data from two decades of population monitoring. Analysis of collective movements reveals how the structure of the herd's social network is related to kinship and familiarity of individuals. The network centrality of harems is related to their age and how long the harem stallions have kept harems previously. Harems of genetically related stallions are closer to each other in the network, and female exchange is more frequent between closer harems. High movement similarity of females from different harems predicts becoming harem mates in the future. Our results show that only a few minutes of fine-scale movement tracking combined with high throughput data driven analysis can reveal the structure of a society, reconstruct past group dynamics and predict future ones.

Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings

Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn't require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat-wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.

Camera Trap Methods and Drone Thermal Surveillance Provide Reliable, Comparable Density Estimates of Large, Free-Ranging Ungulates

Camera traps and drone surveys both leverage advancing technologies to study dynamic wildlife populations with little disturbance. Both techniques entail strengths and weaknesses, and common camera trap methods can be confounded by unrealistic assumptions and prerequisite conditions. We compared three methods to estimate the population density of white-tailed deer (Odocoileus virgnianus) in a section of Pilot Mountain State Park, NC, USA: (1) camera trapping using mark-resight ratios or (2) N-mixture modeling and (3) aerial thermal videography from a drone platform. All three methods yielded similar density estimates, suggesting that they converged on an accurate estimate. We also included environmental covariates in the N-mixture modeling to explore spatial habitat use, and we fit models for each season to understand temporal changes in population density. Deer occurred in greater densities on warmer, south-facing slopes in the autumn and winter and on cooler north-facing slopes and in areas with flatter terrain in the summer. Seasonal density estimates over two years suggested an annual cycle of higher densities in autumn and winter than in summer, indicating that the region may function as a refuge during the hunting season.

ViewShedR: a new open-source tool for cumulative, subtractive and elevated line-of-sight analysis

Many environmental and ecological studies require line of sight (LOS) and/or viewshed analyses. While tools for performing these analyses from digital elevation models (DEMs) are widespread, they are either too restrictive, inaccessible or pricey and difficult to use. This methodological gap is potentially imperative for scholars using solutions like telemetry tracking systems or spatial ecology landscape mapping. Here we present ViewShedR-a free, open-source and intuitive graphical user-interface application for performing LOS calculations, including cumulative, subtractive (areas covered by towers A + B or by A but not by B, respectively), and elevated-target analyses. ViewShedR is implemented in the widely used R environment, thus facilitating usage and further modification by end-users. We provide two working examples for ViewShedR in the context of permanent animal-tracking systems requiring simultaneous tag-detection by multiple towers (receivers): first, the ATLAS system for terrestrial animals in the Harod Valley, Israel; and second, an acoustic telemetry array for marine animals in the Dry Tortugas, Florida. ViewShedR allowed effective tower deployment and finding partially detected tagged animals in the ATLAS system. Similarly, it allowed us to identify reception shadows cast by islands in the marine array. We hope ViewShedR will facilitate deployment of tower arrays for tracking, communication networks and other ecological applications.