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 influence hyena activity patterns. We found that hyenas exhibit characteristic crepuscular-nocturnal daily activity patterns. Time spent active was independent of activity level on previous days, suggesting that hyenas do not show activity compensation. We also found limited evidence for an effect of individual identity on activity, and showed that pairs of hyenas who synchronized their activity patterns must have spent more time together. This study sheds light on the patterns and drivers of activity in spotted hyena societies, and also provides a useful tool for quantifying behavioural sequences from accelerometer data.

Behavioral sequences across multiple animal species in the wild share common structural features

“…Data were collected from five female hyenas residing in the Talek clan in the Maasai Mara National Reserve in southwestern Kenya [70]. These individuals wore Tellus Medium collars (Followit, Sweden) fitted with custom-designed tags (DTAG; Mark Johnson) that collected continuous 24 h tri-axial accelerometer and magnetometer data at 1000 Hz from January 1 until mid-February 2017 [71]. Accelerometry data were down-sampled to 25 Hz for further analysis.…”

“…Hyena behavioral labels were lying (head down), lying (head up), standing, walking, and running. Additional information about training these classifiers is found in [74] and (see SI Appendix, Text ‘Primary data collection and behavioral classifier design’)…”