Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

Conners, M.G.; Michelot, T.; Heywood, E.I.; Orben, R.A.; Phillips, R.A.; Vyssotski, A.L.; Shaffer, S.A.; Thorne, L.H.. 2021 Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species. Movement Ecology, 9, 7. 16, pp. 10.1186/s40462-021-00243-z

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Text (Open Access) © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. s40462-021-00243-z.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (4MB) | Preview

Abstract/Summary

Background Inertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors. Methods We deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data. Results HMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale. Conclusions The use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1186/s40462-021-00243-z
ISSN:	2051-3933
Additional Keywords:	Accelerometer, Albatross, Animal movement, Behavioral classification, Dynamic soaring, Hidden Markov models, Inertial measurement unit, Magnetometer
Date made live:	25 Feb 2021 09:02 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/529742

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