Extending the functionality of behavioural change-point analysis with k-means clustering: A case study with the little penguin (Eudyptula minor)
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We present a simple framework for classifying mutually exclusive behavioural states within the geospatial lifelines of animals. This method involves use of three sequentially applied statistical procedures: (1) behavioural change point analysis to partition movement trajectories into discrete bouts of same-state behaviours, based on abrupt changes in the spatio-temporal autocorrelation structure of movement parameters; (2) hierarchical multivariate cluster analysis to determine the number of different behavioural states; and (3) k-means clustering to classify inferred bouts of same-state location observations into behavioural modes. We demonstrate application of the method by analysing synthetic trajectories of known 'artificial behaviours' comprised of different correlated random walks, as well as real foraging trajectories of little penguins (Eudyptula minor) obtained by global-positioning-system telemetry. Our results show that the modelling procedure correctly classified 92.5% of all individual location observations in the synthetic trajectories, demonstrating reasonable ability to successfully discriminate behavioural modes. Most individual little penguins were found to exhibit three unique behavioural states (resting, commuting/active searching, area-restricted foraging), with variation in the timing and locations of observations apparently related to ambient light, bathymetry, and proximity to coastlines and river mouths. Addition of k-means clustering extends the utility of behavioural change point analysis, by providing a simple means through which the behaviours inferred for the location observations comprising individual movement trajectories can be objectively classified.
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Machine learning can tackle a wide range of tasks, including classifying observations into predefined sets, clustering data into groups that share an underlying process, and regressing an outcome against multiple factors and elucidating their contributory effect.
“ML can tackle a wide range of tasks, including classifying observations into predefined sets (Kabra, Robie, Rivera-Alba, Branson, & Branson, 2013), clustering data into groups that share an underlying process (Zhang, O'Reilly, Perry, Taylor, & Dennis, 2015) and regressing an outcome of interest against multiple factors and elucidating their contributory effect (Chesler, Wilson, Lariviere, Rodrig”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Zhang et al., 2015
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Applications of machine learning in animal behaviour studies
Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2016.12.005 · Cited by 505 · see citing works
“…we present three popular algorithms of each type, followed by a brief discussion on the problem of estimating the correct number of clusters in the data. k-means Arguably the most widely used partitioning clustering method (e.g. to group behavioural modes in little penguins; Zhang et al., 2015).”
- Authors
- Zhang, J., O’Reilly, K. M., Perry, G. L. W., Taylor, G. A., & Dennis, T. E.
- Affiliations
- University of Auckland (NZ)
- University of Portland (US)
- Department of Conservation (NZ)
- Corresponding author
- Dennis, Todd E.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-04-29
- Volume
- 10
- Issue
- 4
- Pages
- e0122811
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4414459
- OpenAlex
- W2034868571
- Cited by
- 44
- References
- 47
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 5 · 2019: 6 · 2018: 4 · 2017: 6 · 2016: 5
- FWCI
- 4.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- University of Auckland
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Keywords
- K-means clustering, animal movement trajectories, little penguin, Eudyptula minor, foraging behavior, resting behavior, spatiotemporal autocorrelation, GPS tracking, correlated random walk, ambient light, bathymetry, seabird behavior
- MeSH terms
- Animals, Behavior, Animal, Movement, Telemetry, Cluster Analysis, Geographic Information Systems, Spheniscidae, Animal Distribution
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