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Behaviour Classification on Giraffes (Giraffa camelopardalis) Using Machine Learning Algorithms on Triaxial Acceleration Data of Two Commonly Used GPS Devices and Its Possible Application for Their Management and Conservation

1
Institut für Biochemie und Biologie, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany
2
Leibniz-Institute for Zoo- and Wildlife Research, Alfred-Kowalke-Straße 17, 10315 Berlin, Germany
3
Tierpark Berlin-Friedrichsfelde GmbH, Am Tierpark 125, 10319 Berlin, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Paul Fergus
Sensors 2021, 21(6), 2229; https://doi.org/10.3390/s21062229
Received: 17 February 2021 / Revised: 15 March 2021 / Accepted: 18 March 2021 / Published: 23 March 2021
(This article belongs to the Special Issue Sensors and Artificial Intelligence for Wildlife Conservation)
Averting today’s loss of biodiversity and ecosystem services can be achieved through conservation efforts, especially of keystone species. Giraffes (Giraffa camelopardalis) play an important role in sustaining Africa’s ecosystems, but are ‘vulnerable’ according to the IUCN Red List since 2016. Monitoring an animal’s behavior in the wild helps to develop and assess their conservation management. One mechanism for remote tracking of wildlife behavior is to attach accelerometers to animals to record their body movement. We tested two different commercially available high-resolution accelerometers, e-obs and Africa Wildlife Tracking (AWT), attached to the top of the heads of three captive giraffes and analyzed the accuracy of automatic behavior classifications, focused on the Random Forests algorithm. For both accelerometers, behaviors of lower variety in head and neck movements could be better predicted (i.e., feeding above eye level, mean prediction accuracy e-obs/AWT: 97.6%/99.7%; drinking: 96.7%/97.0%) than those with a higher variety of body postures (such as standing: 90.7–91.0%/75.2–76.7%; rumination: 89.6–91.6%/53.5–86.5%). Nonetheless both devices come with limitations and especially the AWT needs technological adaptations before applying it on animals in the wild. Nevertheless, looking at the prediction results, both are promising accelerometers for behavioral classification of giraffes. Therefore, these devices when applied to free-ranging animals, in combination with GPS tracking, can contribute greatly to the conservation of giraffes. View Full-Text
Keywords: giraffe; triaxial acceleration; machine learning; random forests; behavior classification; giraffe conservation giraffe; triaxial acceleration; machine learning; random forests; behavior classification; giraffe conservation
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MDPI and ACS Style

Brandes, S.; Sicks, F.; Berger, A. Behaviour Classification on Giraffes (Giraffa camelopardalis) Using Machine Learning Algorithms on Triaxial Acceleration Data of Two Commonly Used GPS Devices and Its Possible Application for Their Management and Conservation. Sensors 2021, 21, 2229. https://doi.org/10.3390/s21062229

AMA Style

Brandes S, Sicks F, Berger A. Behaviour Classification on Giraffes (Giraffa camelopardalis) Using Machine Learning Algorithms on Triaxial Acceleration Data of Two Commonly Used GPS Devices and Its Possible Application for Their Management and Conservation. Sensors. 2021; 21(6):2229. https://doi.org/10.3390/s21062229

Chicago/Turabian Style

Brandes, Stefanie; Sicks, Florian; Berger, Anne. 2021. "Behaviour Classification on Giraffes (Giraffa camelopardalis) Using Machine Learning Algorithms on Triaxial Acceleration Data of Two Commonly Used GPS Devices and Its Possible Application for Their Management and Conservation" Sensors 21, no. 6: 2229. https://doi.org/10.3390/s21062229

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