Skip to content

Research at St Andrews

Analysis of animal accelerometer data using hidden Markov models

Research output: Contribution to journalArticlepeer-review

Author(s)

Vianey Leos-Barajas, Theoni Photopoulou, Roland Langrock, Toby A. Patterson, Yuuki Y. Watanabe, Megan Murgatroyd, Yannis P. Papastamatiou

School/Research organisations

Abstract

1. 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.

2.  Analyses of acceleration data where serial dependence has been explicitly modelled have largely relied on hidden Markov models (HMMs). 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.

3.  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. HMMs were used to infer the effects of temporal, atmospheric and tidal inputs on animal behaviour.

4.  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.

Close

Details

Original languageEnglish
Pages (from-to)161-173
Number of pages13
JournalMethods in Ecology and Evolution
Volume8
Issue number2
Early online date30 Sep 2016
DOIs
Publication statusPublished - Feb 2017

    Research areas

  • Activity recognition, Animal behaviour, Latent states, Serial correlation, Time series

Discover related content
Find related publications, people, projects and more using interactive charts.

View graph of relations

Related by author

  1. Sex-specific variation in the use of vertical habitat by a resident Antarctic top predator

    Photopoulou, T., Heerah, K., Pohle, J. & Boehme, L., 28 Oct 2020, In: Proceedings of the Royal Society B: Biological Sciences. 287, 1937, 10 p., 20201447.

    Research output: Contribution to journalArticlepeer-review

  2. Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries

    Mendo, T., Smout, S. C., Photopoulou, T. & James, M., 2 Oct 2019, In: Royal Society Open Science. 6, 10, 12 p., 191161.

    Research output: Contribution to journalArticlepeer-review

  3. Where eagles soar: fine-resolution tracking reveals the spatiotemporal use of differential soaring modes in a large raptor

    Murgatroyd, M., Photopoulou, T., Underhill, L., Bouten, W. & Amar, A., 11 Jun 2018, In: Ecology and Evolution. Early View, 12 p.

    Research output: Contribution to journalArticlepeer-review

  4. Summer at the beach: spatio-temporal patterns of white shark occurrence along the inshore areas of False Bay, South Africa

    Kock, A. A., Photopoulou, T., Durbach, I., Mauff, K., Meÿer, M., Kotze, D., Griffiths, C. & O'Riain, M. J., 22 May 2018, In: Movement Ecology. 6, 13 p., 7.

    Research output: Contribution to journalArticlepeer-review

Related by journal

  1. Methods in Ecology and Evolution (Journal)

    Michael Blair Morrissey (Member of editorial board)

    1 Jan 20171 Jan 2020

    Activity: Publication peer-review and editorial work typesEditor of research journal

  2. Methods in Ecology and Evolution (Journal)

    Theoni Photopoulou (Editor)

    2017 → …

    Activity: Publication peer-review and editorial work typesEditor of research journal

  3. Methods in Ecology and Evolution (Journal)

    Oscar Eduardo Gaggiotti (Member of editorial board)

    1 Sep 2014 → …

    Activity: Publication peer-review and editorial work typesEditor of research journal

Related by journal

  1. A spatial capture-recapture model to estimate call rate and population density from passive acoustic surveys

    Stevenson, B. C., van Dam-Bates, P., Young, C. K. Y. & Measey, J., Mar 2021, In: Methods in Ecology and Evolution. 12, 3, p. 432-442

    Research output: Contribution to journalArticlepeer-review

  2. A standardisation framework for bio-logging data to advance ecological research and conservation

    Sequeira, A. M. M., O’Toole, M., Keates, T. R., McDonnell, L. H., Braun, C. D., Hoenner, X., Jaine, F. R. A., Jonsen, I. D., Newman, P., Pye, J., Bograd, S. J., Hays, G. C., Hazen, E. L., Holland, M., Tsontos, V., Blight, C., Cagnacci, F., Davidson, S. C., Dettki, H., Duarte, C. M. & 22 others, Dunn, D. C., Eguíluz, V. M., Fedak, M., Gleiss, A. C., Hammerschlag, N., Hindell, M. A., Holland, K., Janekovic, I., McKinzie, M. K., Muelbert, M. M. C., Pattiaratchi, C., Rutz, C., Sims, D. W., Simmons, S. E., Townsend, B., Whoriskey, F., Woodward, B., Costa, D. P., Heupel, M. R., McMahon, C. R., Harcourt, R. & Weise, M., 1 Apr 2021, In: Methods in Ecology and Evolution. Early View

    Research output: Contribution to journalArticlepeer-review

  3. Fast, flexible alternatives to regular grid designs for spatial capture-recapture

    Durbach, I. N., Borchers, D. L., Sutherland, C. & Sharma, K., Feb 2021, In: Methods in Ecology and Evolution. 12, 2, p. 298-310

    Research output: Contribution to journalArticlepeer-review

  4. A field and video-annotation guide for baited remote underwater stereo-video surveys of demersal fish assemblages

    Langlois, T., Goetze, J., Bond, T., Monk, J., Abesamis, R. A., Asher, J., Barrett, N., Bernard, A. T. F., Bouchet, P. J., Birt, M. J., Cappo, M., Currey-Randall, L. M., Driessen, D., Fairclough, D. V., Fullwood, L. A. F., Gibbons, B. A., Harasti, D., Heupel, M. R., Hicks, J., Holmes, T. H. & 21 others, Huveneers, C., Ierodiaconou, D., Jordan, A., Knott, N. A., Lindfield, S., Malcolm, H. A., McLean, D., Meekan, M., Miller, D., Mitchell, P. J., Newman, S. J., Radford, B., Rolim, F. A., Saunders, B. J., Stowar, M., Smith, A. N. H., Travers, M. J., Wakefield, C. B., Whitmarsh, S. K., Williams, J. & Harvey, E. S., Nov 2020, In: Methods in Ecology and Evolution. 11, 11, p. 1401-1409

    Research output: Contribution to journalArticlepeer-review

  5. Listening and watching: do camera traps or acoustic sensors more efficiently detect wild chimpanzees in an open habitat?

    Crunchant, A-S., Borchers, D., Kühl, H. & Piel, A., Apr 2020, In: Methods in Ecology and Evolution. 11, 4, p. 542-552

    Research output: Contribution to journalArticlepeer-review

ID: 252783232

Top