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Cited by 154Wildlife Ecology and ConservationHuman Mobility and Location-Based AnalysisAnimal Ecology and Behavior Studies

Machine learning for inferring animal behavior from location and movement data

Open access
Wang, Guiming·2019·Ecological Informatics
DOI 10.1016/j.ecoinf.2018.12.002
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Abstract

Movement ecology has rapidly advanced owing to recent developments of animal-attached devices and wide applications of sophisticated statistical and machine learning techniques in analysis of animal movement data. Global Positioning System (GPS) transmitters used for estimating animal locations and tri-axial accelerometers used for measuring the 3-dimensional accelerations of animal's motion aid researchers in collecting location and locomotion data at fine spatial and temporal scales. Machine learning and other advanced statistical methods bridge conceptual models to data, providing insights into ecological and physiological mechanisms underlying animal behavior and movements. This study reviews the general principles and applications of state space models, hidden Markov models, random forests, and support vector machines in the inference of animal behavior from movement data. Unsupervised learning algorithms, including Bayesian state space models implemented for robust correlated random walk models and hidden Markov models, help infer different behavioral modes using GPS location data. State space models can account for measurement error in GPS locations and estimate the true locations of animals; however, without including movement-state switching, state space models do not infer behavioral modes directly. On the contrary, hidden Markov models estimate the probabilities that animals switch between different behavioral modes. Nevertheless, hidden Markov models neither directly estimate animal locations nor account for measurement error explicitly. Supervised learning algorithms integrate data on locations and directional accelerations with synchronized behavioral observations (i.e., labels) to classify behaviors to pre-defined behavioral categories. Unlike unsupervised learning, supervised learning requires behavioral observations to label locations and accelerometer data to train the learning algorithms. However, behavioral observations synchronized with relocations and acceleration records are often missing or unattainable in many species, hindering the applications of supervised learning, making unsupervised learning a suitable tool for behavioral annotation of movement paths in secretive (cryptic) or less studied species. Environmental and behavioral annotations of animal movement paths by machine learning improve understanding the effects of environmental conditions on animal movements and behavioral decisions.

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  • Various supervised machine learning methods have shown potential in automating behavior analysis from accelerometer data, identifying behavioral state from trajectories, and predicting animal movement.

    “Various supervised ML methods have shown their potential in automating behavior analysis from accelerometer data68,69, identifying behavioral state from trajectories70, and predicting animal movement71.”

    From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [70]

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Authors
Wang, G.
Affiliations
Mississippi State University (US)
Corresponding author
Wang, Guiming
ORCID
Wang, Guiming
Venue
Ecological Informatics
Publisher
Elsevier BV
Published
2019-01
Volume
49
Pages
69-76
ISSN
1574-9541 (ISSN-L)
DOI
10.1016/j.ecoinf.2018.12.002
OpenAlex
W2905502390
Cited by
154
References
72
Citations by year
2026: 18 · 2025: 19 · 2024: 25 · 2023: 24 · 2022: 20 · 2021: 21 · 2020: 20 · 2019: 7
FWCI
9.04
Type
journal article
Language
en
Open access
Licence
Locations
Ecological Informatics (publishedVersion, cc-by-nc-nd)
Funders
Mississippi State University
Topics
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Physical Sciences › Environmental Science › Ecology › Animal Ecology and Behavior Studies
Keywords
animal movement data, GPS tracking, triaxial accelerometer, state-space model, hidden Markov models, random forest, support vector machine, correlated random walk, measurement error correction, unsupervised learning, supervised learning, cryptic species

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