Applied AI in Animal Behavior & Welfare

Beyond Individual Animals: How AI and Next-Generation Sensors Are Reshaping Collective Behaviour Research

March 1, 2026

In recent years, AI’s role in animal studies has expanded from identifying species and individuals to addressing more complex questions: how groups of animals behave as coordinated units. A growing body of research shows that machine learning and advanced sensing tools are now being applied not just to monitor individual animals, but to analyze collective behaviour — the ways in which animals move, communicate, and coordinate as a group. These advances promise to deepen our scientific understanding of social dynamics and ecosystem responses, and could have significant implications for conservation, behavior science, and even robotics.

What Is Collective Behaviour — and Why AI?

Collective behaviour refers to how groups of animals interact and coordinate their actions at the group level. Examples include flocking in birds, schooling in fish, herding in ungulates, and swarming in insects. These patterns emerge from repeated interactions among individuals, and they are often sensitive to environmental conditions or internal group states. Studying collective behaviour has historically required painstaking manual observation and coding, especially for large groups or prolonged activity.

Today, AI — especially computer vision, sensing networks, and machine learning — enables researchers to track and analyze every individual in a group over time while also deriving metrics that capture group-level dynamics. This is vital because collective behaviours often contain signals of stress, resource availability, or environmental change that simply aren’t visible when focusing solely on individuals.

AI + Sensors: The Technological Backbone

Advances in both hardware and software are making these new approaches possible. High-resolution cameras, lidar, and next-generation environmental sensors can capture rich data across space and time. AI models then process this data to extract behavioral features such as movement trajectories, spatial organization, and synchronization patterns. For example:

• • Vision transformers and motion-aware tracking algorithms can reconstruct individual positions within a group and maintain consistent tracking even when animals occlude one another.
• • Multimodal architectures are emerging that integrate visual, audio, and contextual data to better resolve behaviour in complex environments (e.g., farms or mixed habitats), improving beyond simple video analysis.

Together, these tools allow researchers to build computational models of behaviour that describe both what individuals are doing and how groups self-organize.

Why This Matters for Welfare and Ecology

While individual behaviour is essential to welfare assessment — for example, tracking a cow’s feeding patterns — collective behaviour can offer insights that individual metrics miss. For instance:

• • Early detection of stress or disease: Changes in group cohesion or movement patterns can signal discomfort or outbreaks before individual animals show obvious signs.
• • Habitat disturbance monitoring: Variations in collective movement patterns may reflect disruptions due to human activity or environmental stressors, serving as early indicators of ecosystem change.
• • Behavioral flexibility and adaptation: Some species shift collective strategies (e.g., tighter grouping in response to predation or environmental stress), and AI allows quantification of these shifts in ways not previously possible.

Challenges and Ethical Considerations

Despite its promise, this field also faces significant challenges. Processing collective behaviour data requires enormous computational capacity and sophisticated models that can handle high-dimensional inputs. Additionally, AI systems trained on one species or environment often don’t generalize well to others without substantial retraining.

From an ethical perspective, researchers also need to consider how these technologies are deployed. Automated surveillance of large animal groups can inadvertently collect sensitive data (e.g., on endangered populations or domesticated animals), and frameworks for data stewardship and responsible usage are still evolving. These concerns intersect with broader debates about animals as stakeholders in AI ethics. A recent article argues that animals themselves are largely absent from ethical discourses around AI, even though technologies increasingly shape their lives and welfare. Integrating animal interests into AI development isn’t just good practice — it’s necessary for responsible innovation.

My Perspective: A New Frontier in Behaviour Science

One of the most exciting aspects of this trend is how it bridges individual and group behaviour. Traditional ethology often isolates individuals for careful study, but many species’ most important behaviours occur in social contexts. AI’s ability to process and interpret group dynamics at scale means researchers can now observe the forest and the trees simultaneously — something that was previously impractical or impossible.

This shift has profound implications not only for basic science, but for applied welfare work: it allows us to detect patterns and early warning signs that emerge only at the group level. Understanding these interactions could lead to more humane livestock management practices, better wildlife protection strategies, and richer ecological insights than ever before.

Sources:

• “AI and new sensing tools are reshaping collective animal behavior research,” The Royal Society Interface.
• “Seen but overlooked: animals as stakeholders in AI ethics discourses,” Animal Frontiers.