Quantifying finer-scale behaviours using self-organising maps (SOMs) to link accelerometery signatures with behavioural patterns in free-roaming terrestrial animals

Nicole Galea; Fern Murphy; Joshua Gaschk; David Schoeman; Christofer Clemente

doi:10.1038/s41598-021-92896-4

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Quantifying finer-scale behaviours using self-organising maps (SOMs) to link accelerometery signatures with behavioural patterns in free-roaming terrestrial animals

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Quantifying finer-scale behaviours using self-organising maps (SOMs) to link accelerometery signatures with behavioural patterns in free-roaming terrestrial animals

Nicole Galea, Fern Murphy, Joshua Gaschk, David Schoeman and Christofer Clemente

Scientific Reports, Vol.11(1), pp.1-10

2021

DOI: https://doi.org/10.1038/s41598-021-92896-4

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Quantifying Finer-scale Behaviours Using Selforganising Maps (SOMs) to Link Acce...

Abstract

Behavioural Ecology

Ecology

Control of Pests, Diseases and Exotic Species in Urban and Industrial Environments

cat

machine learning

Collecting quantitative information on animal behaviours is difficult, especially from cryptic species or species that alter natural behaviours under observation. Using harness-mounted tri-axial accelerometers free-roaming domestic cats (Felis Catus) we developed a methodology that can precisely classify finer-scale behaviours. We further tested the effect of a prey–protector device designed to reduce prey capture. We aligned accelerometer traces collected at 50 Hz with video files (60 fps) and labelled 12 individual behaviours, then trained a supervised machine-learning algorithm using Kohonen super self-organising maps (SOM). The SOM was able to predict individual behaviours with a ~ 99.6% overall accuracy, which was slightly better than for random forest estimates using the same dataset (98.9%). There was a significant effect of sample size, with precision and sensitivity decreasing rapidly below 2000 1-s observations. We were also able to detect a behaviour specific reduction in the predictability when cats were fitted with the prey–protector device indicating it altered biomechanical gait. Our results can be applied in movement ecology, zoology and conservation, where habitat specific movement performance between predators or prey may be critical to managing species of conservation significance, or in veterinary and agricultural fields, where early detection of movement pathologies can improve animal welfare.

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Title: Quantifying finer-scale behaviours using self-organising maps (SOMs) to link accelerometery signatures with behavioural patterns in free-roaming terrestrial animals
Authors: Nicole Galea (Author) - University of the Sunshine Coast, Queensland, School of Science and Engineering - Legacy
Fern Murphy (Author) - University of the Sunshine Coast, Queensland, School of Science and Engineering - Legacy
Joshua Gaschk (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
David Schoeman (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Christofer Clemente (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Publication details: Scientific Reports, Vol.11(1), pp.1-10
Publisher: Nature Publishing Group
Date published: 2021
DOI: 10.1038/s41598-021-92896-4
ISSN: 2045-2322
Copyright note: © 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. Te 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/.
Grants: Using performance to predict the survival of threatened mammals, DP180103134, Australian Research Council (Australia, Canberra) - ARC
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
Language: English
Record Identifier: 99548508102621
Output Type: Journal article

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