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1000 Titel
  • 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
1000 Autor/in
  1. Brandes, Stefanie |
  2. Sicks, Florian |
  3. Berger, Anne |
1000 Erscheinungsjahr 2021
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-03-23
1000 Erschienen in
1000 Quellenangabe
  • 21(6):2229
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.3390/s21062229 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8005050 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Sacherschließung
lokal machine learning
lokal behavior classification
lokal random forests
lokal giraffe
lokal giraffe conservation
lokal triaxial acceleration
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/QnJhbmRlcywgU3RlZmFuaWU=|https://frl.publisso.de/adhoc/uri/U2lja3MsIEZsb3JpYW4=|https://orcid.org/0000-0001-5765-8039
1000 Label
1000 Förderer
  1. Leibniz-Gemeinschaft |
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. Open Access Fund
1000 Dateien
1000 Förderung
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    1000 Förderer Leibniz-Gemeinschaft |
    1000 Förderprogramm Open Access Fund
    1000 Fördernummer -
1000 Objektart article
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1000 @id frl:6432996.rdf
1000 Erstellt am 2022-04-12T17:28:16.738+0200
1000 Erstellt von 218
1000 beschreibt frl:6432996
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet Mon May 09 12:56:31 CEST 2022
1000 Objekt bearb. Mon May 09 12:56:20 CEST 2022
1000 Vgl. frl:6432996
1000 Oai Id
  1. oai:frl.publisso.de:frl:6432996 |
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