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

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Conners_2021_Hidden_Markov_models_identify_ME_9_7.pdf (4.097Mb)
Date
22/02/2021
Author
Conners, Melinda G.
Michelot, Théo
Heywood, Eleanor I.
Orben, Rachael A.
Phillips, Richard A.
Vyssotski, Alexei L.
Shaffer, Scott A.
Thorne, Lesley H.
Keywords
Research
Accelerometer
Albatross
Animal movement
Behavioral classification
Dynamic soaring
Hidden Markov models
Inertial measurement unit
Magnetometer
QA Mathematics
QH301 Biology
DAS
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Abstract
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.
Citation
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 , vol. 9 , 7 . https://doi.org/10.1186/s40462-021-00243-z
Publication
Movement Ecology
Status
Peer reviewed
DOI
https://doi.org/10.1186/s40462-021-00243-z
ISSN
2051-3933
Type
Journal article
Rights
Copyrigh © The Author(s). 2021. 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. 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.
Description
Funding was provided by an NSF CAREER award to L. Thorne under award number 79804, and by a Minghua Zhang Early Career Faculty Innovation award to L Thorne.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/21507

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