Movement Ethology: Body Kinematics Meets Relocation Data
Abstract
Animal movement is studied by two different communities. Movement ecologists use GPS location point sequences to track where whole animals range over extended periods of time. Computational ethologists use video and machine learning to measure how body parts move over short periods of time, mostly in the laboratory. We propose Movement Ethology to link the study of body configurations and actions in relation to local surroundings with the ecological functions of animal movement over landscapes. Pose and accelerometer data reveal locomotor cycles invisible to relocation data, and information measures test, using Shannon information theory, whether body codes add behavioral information beyond path codes. Synchronized observations and independent validation test how body data improve models of space use and energetics, and evaluate movement decisions and individual health.
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BibTeX
@misc{getz2026movement,
title = {{Movement Ethology: Body Kinematics Meets Relocation Data}},
author = {Wayne Marcus Getz and Joris Komen},
howpublished = {EcoEvoRxiv},
year = {2026},
month = oct,
doi = {10.32942/x2pm5x},
url = {https://ecoevorxiv.org/repository/view/15780/},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Movement Ethology: Body Kinematics Meets Relocation Data
AU - Wayne Marcus Getz
AU - Joris Komen
PY - 2026
DA - 2026/10/27
PB - EcoEvoRxiv
DO - 10.32942/x2pm5x
UR - https://ecoevorxiv.org/repository/view/15780/
AB - Animal movement is studied by two different communities. Movement ecologists use GPS location point sequences to track where whole animals range over extended periods of time. Computational ethologists use video and machine learning to measure how body parts move over short periods of time, mostly in the laboratory. We propose Movement Ethology to link the study of body configurations and actions in relation to local surroundings with the ecological functions of animal movement over landscapes. Pose and accelerometer data reveal locomotor cycles invisible to relocation data, and information measures test, using Shannon information theory, whether body codes add behavioral information beyond path codes. Synchronized observations and independent validation test how body data improve models of space use and energetics, and evaluate movement decisions and individual health.
ER -