The road to standardisation: Defining behavioural categories in camera-trap research for standardising automated behaviour studies

Abstract

Understanding animal behaviour forms an essential cornerstone of modern conservation biology. Behavioural processes determine how animals interact with their environments, respond to threats, and contribute to ecosystem functioning. Within this context, conservation behaviour – the application of behavioural knowledge to conservational challenges – has become an increasingly important component within conservation science. Recognising the value of such behavioural indicators makes it increasingly important to monitor them effectively. Indirect observation methods address many of the limitations of direct observation by detecting or recording animals without direct human presence. More recently, advances in remote sensing have substantially expanded the potential of indirect observations, particularly through the use of camera traps. These autonomous, motion- or heat-triggered devices capture images or videos of animals, creating major visual datasets suitable for behavioural analysis. Despite the growing application of camera-trap-derived behavioural data in ecology and wildlife management, their broader comparability and scalability remain limited by how behaviours are defined and annotated. At a conceptual level, ethograms continue to play an essential role providing frameworks for both manual and automated classification. At the same time, camera-trap studies introduce new analytical challenges associated with the volume and unstructured nature of visual data. This review evaluates the extent to which wildlife camera traps can be used to classify animal behaviour accurately and consistently. It combines existing methodological frameworks for behavioural annotation and analysis. Specifically, it examines behavioural classification and recommends the standardisation of behavioural definitions and shared analytical frameworks to improve reproducibility, comparability, and scalability in CT-based behavioural research.