Creating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species

Campbell, Hamish A., Gao, Lianli, Bidder, Owen R., Hunter, Jane and Franklin, Craig E. (2013) Creating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species. Journal of Experimental Biology, 216 24: 4501-4506. doi:10.1242/jeb.089805

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Author Campbell, Hamish A.
Gao, Lianli
Bidder, Owen R.
Hunter, Jane
Franklin, Craig E.
Title Creating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species
Journal name Journal of Experimental Biology   Check publisher's open access policy
ISSN 0022-0949
1477-9145
Publication date 2013-12-01
Year available 2013
Sub-type Article (original research)
DOI 10.1242/jeb.089805
Open Access Status File (Publisher version)
Volume 216
Issue 24
Start page 4501
End page 4506
Total pages 6
Place of publication Cambridge, United Kingdom
Publisher The Company of Biologists Ltd.
Language eng
Formatted abstract
Distinguishing specific behavioural modes from data collected by animal-borne tri-axial accelerometers can be a time-consuming and subjective process. Data synthesis can be further inhibited when the tri-axial acceleration data cannot be paired with the corresponding behavioural mode through direct observation. Here, we explored the use of a tame surrogate (domestic dog) to build a behavioural classification module, and then used that module to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species. Tri-axial acceleration data were recorded from a domestic dog whilst it was commanded to walk, run, sit, stand and lie-down. Through video synchronisation, each tri-axial acceleration sample was annotated with its associated behavioural mode; the feature vectors were extracted and used to build the classification module through the application of support vector machines (SVMs). This behavioural classification module was then used to identify and quantify the same behavioural modes in acceleration collected from a range of other species (alligator, badger, cheetah, dingo, echidna, kangaroo and wombat). Evaluation of the module performance, using a binary classification system, showed there was a high capacity (90%) for behaviour recognition between individuals of the same species. Furthermore, a positive correlation existed between SVM capacity and the similarity of the individual's spinal length-to-height above the ground ratio (SL:SH) to that of the surrogate. The study describes how to build a behavioural classification module and highlights the value of using a surrogate for studying cryptic, rare or endangered species.
Keyword Accelerometry
Biotelemetry
Support vector machines (SVMs)
Movement ecology
Endangered species
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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