Analysis of animal accelerometer data using hidden Markov models
Abstract
Summary: Use of accelerometers is now widespread within animal biologging as they provide a means of measuring an animal's activity in a meaningful and quantitative way where direct observation is not possible. In sequential acceleration data, there is a natural dependence between observations of behaviour, a fact that has been largely ignored in most analyses. Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models ( HMM s). Depending on the aim of an analysis, an HMM can be used for state prediction or to make inferences about drivers of behaviour. For state prediction, a supervised learning approach can be applied. That is, an HMM is trained to classify unlabelled acceleration data into a finite set of pre‐specified categories. An unsupervised learning approach can be used to infer new aspects of animal behaviour when biologically meaningful response variables are used, with the caveat that the states may not map to specific behaviours. We provide the details necessary to implement and assess an HMM in both the supervised and unsupervised learning context and discuss the data requirements of each case. We outline two applications to marine and aerial systems (shark and eagle) taking the unsupervised learning approach, which is more readily applicable to animal activity measured in the field. HMM s were used to infer the effects of temporal, atmospheric and tidal inputs on animal behaviour. Animal accelerometer data allow ecologists to identify important correlates and drivers of animal activity (and hence behaviour). The HMM framework is well suited to deal with the main features commonly observed in accelerometer data and can easily be extended to suit a wide range of types of animal activity data. The ability to combine direct observations of animal activity with statistical models, which account for the features of accelerometer data, offers a new way to quantify animal behaviour and energetic expenditure and to deepen our insights into individual behaviour as a constituent of populations and ecosystems.
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An animal's time-activity budget can be estimated using a hidden Markov model (HMM) to decode behavioral state from acceleration.
“Furthermore, an animal's time-activity budget can be estimated using a hidden Markov model (HMM) to decode behavioral state from acceleration [42,43].”
From Seasonal activity levels of a farm-island population of striated caracaras (Phalcoboenus australis) in the Falkland Islands (2020) · cites this paper as [42]
Hidden Markov models allow identification of relevant changes in behavioral intensity (activity versus rest) while taking into account the serial dependence between observations.
“We used hidden Markov models (HMMs) to infer behavioral states from the 10-Hz acceleration data as they allow identification of relevant changes in behavioral intensity, i.e., activity versus rest, taking into account the serial dependence between observations [42,70].”
From Seasonal activity levels of a farm-island population of striated caracaras (Phalcoboenus australis) in the Falkland Islands (2020) · cites this paper as [42]
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- Authors
- Leos‐Barajas, V., Photopoulou, T., Langrock, R., Patterson, T. A., Watanabe, Y. Y., Murgatroyd, M., & Papastamatiou, Y. P.
- Affiliations
- Department of Statistics Iowa State University Snedecor Hall Ames IA 50011 USA
- University of Cape Town (ZA)
- Bielefeld University (DE)
- CSIRO Oceans and Atmosphere (AU)
- The Graduate University for Advanced Studies, SOKENDAI (JP)
- University of St Andrews (GB)
- ORCID
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2016-10-24
- Volume
- 8
- Issue
- 2
- Pages
- 161-173
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2278434462
- Cited by
- 187
- References
- 54
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 27 · 2023: 21 · 2022: 25 · 2021: 29 · 2020: 14 · 2019: 17 · 2018: 11 · 2017: 13 · 2016: 1
- FWCI
- 12.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Methods in Ecology and Evolution (publishedVersion)
- Florida International University Digital Commons (Florida International University) (submittedVersion)
- Nelson Mandela University Research Repository (submittedVersion)
- St Andrews Research Repository (St Andrews Research Repository) (submittedVersion)
- Funders
- Japan Society for the Promotion of Science (25850138)
- Scottish Funding Council (HR09011)
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- hidden Markov models, animal behavior, serial dependence, supervised learning, unsupervised learning, shark behavior, tidal effects, atmospheric effects, time effects, energy expenditure, marine fauna
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