Understanding animal behaviour is central to predicting, understanding, and miti-gating impacts of natural and anthropogenic changes on animal populations andecosystems. However, the challenges of acquiring and processing long-term, eco-logically relevant data in wild settings have constrained the scope of behaviouralresearch. The increasing availability of Unmanned Aerial Vehicles (UAVs), cou-pled with advances in machine learning, has opened new opportunities for wildlifemonitoring using aerial tracking. However, the limited availability of datasets with wildanimals in natural habitats has hindered progress in automated computer visionsolutions for long-term animal tracking. Here, we introduce the first large-scaleUAV dataset designed to solve multi-object tracking (MOT) and re-identification(Re-ID) problem in wild animals, specifically the mating behaviour (or lekking) ofblackbuck antelopes. Collected in collaboration with biologists, the MOT datasetincludes over 1.2 million annotations including 680 tracks across 12 high-resolution(5.4K)

Blanketed by frost in the Mongolian wilderness, a Pallas's cat endures the aftermath of a snowstorm at -35 C. Perfectly adapted to its frozen world, this elusive feline's dense fur, flattened ears, and high-set eyes allow it to vanish into the landscape. Breakthroughs, discoveries, and DIY tips sent every weekday. A herd of musk ox protect their young from a hungry arctic wolf, a flock of flamingoes chill near Dubai's imposing skyline, and a flying squirrel pops out for a quick hello. These are just a few of the stunning wildlife scenes captured in the 2025 Nature inFocus Photography Awards . This year, photographers from 38 countries submitted nearly 16,000 images.

Collective vigilance describes how animals in groups benefit from the predator detection efforts of others. Empirical observations typically find either a many-eyes strategy with all (or many) group members maintaining a low level of individual vigilance, or a sentinel strategy with one (or a few) individuals maintaining a high level of individual vigilance while others do not. With a general analytical treatment that makes minimal assumptions, we show that these two strategies are alternate solutions to the same adaptive problem of balancing the costs of predation and vigilance. Which strategy is preferred depends on how costs scale with the level of individual vigilance: many-eyes strategies are preferred where costs of vigilance rise gently at low levels but become steeper at higher levels (convex; e.g. an open field); sentinel strategies are preferred where costs of vigilance rise steeply at low levels and then flatten out (concave; e.g. environments with vantage points). This same dichotomy emerges whether individuals act selfishly to optimise their own fitness or cooperatively to optimise group fitness. The model is extended to explain discrete behavioural switching between strategies and differential levels of vigilance such as edge effects.

This review provides an in-depth exploration of the field of animal action recognition, focusing on coarse-grained (CG) and fine-grained (FG) techniques. The primary aim is to examine the current state of research in animal behaviour recognition and to elucidate the unique challenges associated with recognising subtle animal actions in outdoor environments. These challenges differ significantly from those encountered in human action recognition due to factors such as non-rigid body structures, frequent occlusions, and the lack of large-scale, annotated datasets. The review begins by discussing the evolution of human action recognition, a more established field, highlighting how it progressed from broad, coarse actions in controlled settings to the demand for fine-grained recognition in dynamic environments. This shift is particularly relevant for animal action recognition, where behavioural variability and environmental complexity present unique challenges that human-centric models cannot fully address. The review then underscores the critical differences between human and animal action recognition, with an emphasis on high intra-species variability, unstructured datasets, and the natural complexity of animal habitats. Techniques like spatio-temporal deep learning frameworks (e.g., SlowFast) are evaluated for their effectiveness in animal behaviour analysis, along with the limitations of existing datasets. By assessing the strengths and weaknesses of current methodologies and introducing a recently-published dataset, the review outlines future directions for advancing fine-grained action recognition, aiming to improve accuracy and generalisability in behaviour analysis across species.

Understanding animal behaviour is central to predicting, understanding, and miti-gating impacts of natural and anthropogenic changes on animal populations andecosystems. However, the challenges of acquiring and processing long-term, eco-logically relevant data in wild settings have constrained the scope of behaviouralresearch. The increasing availability of Unmanned Aerial Vehicles (UAVs), cou-pled with advances in machine learning, has opened new opportunities for wildlifemonitoring using aerial tracking. However, the limited availability of datasets with wildanimals in natural habitats has hindered progress in automated computer visionsolutions for long-term animal tracking. Here, we introduce the first large-scaleUAV dataset designed to solve multi-object tracking (MOT) and re-identification(Re-ID) problem in wild animals, specifically the mating behaviour (or lekking) ofblackbuck antelopes.

Bio-loggers, electronic devices used to track animal behaviour through various sensors, have become essential in wildlife research. Despite continuous improvements in their capabilities, bio-loggers still face significant limitations in storage, processing, and data transmission due to the constraints of size and weight, which are necessary to avoid disturbing the animals. This study aims to explore how selective data transmission, guided by machine learning, can reduce the energy consumption of bio-loggers, thereby extending their operational lifespan without requiring hardware modifications.

Better understanding the natural world is a crucial task with a wide range of applications. In environments with close proximity between humans and animals, such as zoos, it is essential to better understand the causes behind animal behaviour and what interventions are responsible for changes in their behaviours. This can help to predict unusual behaviours, mitigate detrimental effects and increase the well-being of animals. There has been work on modelling the dynamics behind swarms of birds and insects but the complex social behaviours of mammalian groups remain less explored. In this work, we propose a method to build behavioural models using causal structure discovery and graph neural networks for time series. We apply this method to a mob of meerkats in a zoo environment and study its ability to predict future actions and model the behaviour distribution at an individual-level and at a group level. We show that our method can match and outperform standard deep learning architectures and generate more realistic data, while using fewer parameters and providing increased interpretability.

An animal behaviour lab built inside a converted barn uses motion-capture cameras to track the movements and behaviours of entire flocks of birds or swarms of insects. The so-called SMART-BARN resembles a Hollywood motion-capture studio with 30 infrared cameras capable of tracking up to 500 individual markers attached to animal's bodies. All of this takes place within an area one quarter the size of a standard basketball court, and which can include feeding stations and animal perches. "We have a very high precision and controllable environment, but with large enough volume for the animals to move and interact much as they do in nature", says Máté Nagy at Eeötvös Loránd University in Hungary. Nagy and his colleagues showed that their SMART-BARN lab can also track animals without any markers by using six video cameras and computer vision software based on artificial intelligence. The space also has 30 microphones to record animal sounds and even pinpoint animal locations based on sound.

Animals communicating with each other might seem simplistic at first glance. Compared to human communication, animals do not appear to be using any particular language but merely noises to communicate with each other. Several noises that animals make are less of a conversation in the present, and more of a call for predicting natural changes such as rain, water, or signals for food some distance away. When it comes to artificial intelligence, plenty of progress has been made in the development of AGI using machine learning and neural networks on animals and through the understanding of animal behaviour. However, understanding the language of animals and communicating with them is one of the longest-running fields of study in technology and biological sciences alike.

HANSON: They've had a chequered past, I'll give you that. The Egyptians seemed to be confusing them for some kind of Gods at some point.