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Minasandra P, Jensen FH, Gersick AS, Holekamp KE, Strauss ED, Strandburg-Peshkin A. Accelerometer-based predictions of behaviour elucidate factors affecting the daily activity patterns of spotted hyenas. R Soc Open Sci 2023; 10:230750. [PMID: 38026018 PMCID: PMC10645113 DOI: 10.1098/rsos.230750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023]
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.
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Affiliation(s)
- Pranav Minasandra
- Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany
- Biology Department, University of Konstanz, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
- International Max Planck Research School for Organismal Biology, Konstanz, Germany
| | - Frants H. Jensen
- Department of Ecoscience, Aarhus University, Roskilde, Denmark
- Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA
- Biology Department, Syracuse University, Syracuse, NY, USA
| | - Andrew S. Gersick
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Department of Computer Science, San Diego State University, San Diego, CA, USA
| | - Kay E. Holekamp
- Department of Integrative Biology, Michigan State University, East Lansing, MI, USA
- Program in Ecology, Evolution, and Behavior, Michigan State University, East Lansing, MI, USA
| | - Eli D. Strauss
- Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany
- Biology Department, University of Konstanz, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
| | - Ariana Strandburg-Peshkin
- Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany
- Biology Department, University of Konstanz, Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
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