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

Movement Ecology

Richard A. Phillips¹  Théo Michelot²  Scott A. Shaffer³  Rachael A. Orben⁴  Alexei L. Vyssotski⁵  Melinda G. Conners⁶  Lesley H. Thorne⁶  Eleanor I. Heywood⁶ 

[1] British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, CB3 0ET, Cambridge, UK;Centre for Research into Ecological and Environmental Modelling, University of St Andrews, KY169LZ, St Andrews, UK;Department of Biological Sciences, San Jose State University, 95192-0100, San Jose, CA, USA;Department of Fisheries and Wildlife, Oregon State University, Hatfield Marine Science Center, 2030 SE Marine Science Dr., 97365, Newport, OR, USA;Institute of Neuroinformatics, University of Zurich and Swiss Federal Institute of Technology (ETH), 8057, Zurich, Switzerland;School of Marine and Atmospheric Sciences, Stony Brook University, 11794, Stony Brook, NY, USA;

关键词: Accelerometer;    Albatross;    Animal movement;    Behavioral classification;    Dynamic soaring;    Hidden Markov models;    Inertial measurement unit;    Magnetometer;   

DOI  :  10.1186/s40462-021-00243-z

来源: Springer

【 摘 要 】

BackgroundInertial 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.MethodsWe 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.ResultsHMMs 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.ConclusionsThe 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.

【 授权许可】

CC BY