Accelerometer-based predictions of behaviour elucidate factors affecting the daily activity patterns of spotted hyenas.

ABSTRACT: Animal activity patterns are highly variable and influenced by internal and external factors, including social processes. Quantifying activity patterns in natural settings can be challenging, as it is difficult to monitor animals over long time periods. Here, we developed and validated a machine-learning-based classifier to identify behavioural states from accelerometer data of wild spotted hyenas (Crocuta crocuta), social carnivores that live in large fission-fusion societies. By combining this classifier with continuous collar-based accelerometer data from five hyenas, we generated a complete record of activity patterns over more than one month. We used these continuous behavioural sequences to investigate how past activity, individual idiosyncrasies, and social synchronization in

SUBMITTER: Minasandra P 

PROVIDER: S-EPMC10645113 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature