BOVIDS: A deep learning-based software package for pose estimation to evaluate nightly behavior and its application to common elands (Tragelaphus oryx) in zoos

  • Only a few studies on the nocturnal behavior of African ungulates exist so far, with mostly small sample sizes. For a comprehensive understanding of nocturnal behavior, the data basis needs to be expanded. Results obtained by observing zoo animals can provide clues for the study of wild animals and furthermore contribute to a better understanding of animal welfare and better husbandry conditions in zoos. The current contribution reduces the lack of data in two ways. First, we present a stand-alone open-source software package based on deep learning techniques, named Behavioral Observations by Videos and Images using Deep-Learning Software (BOVIDS). It can be used to identify ungulates in their enclosure and to determine the three behavioral poses “Standing,” “Lying—head up,” and “Lying—head down” on 11,411 h of video material with an accuracy of 99.4%. Second, BOVIDS is used to conduct a case study on 25 common elands (Tragelaphus oryx) out of 5 EAZA zoos with a total of 822 nights, yielding the first detailed description of the nightly behavior of common elands. Our results indicate that age and sex are influencing factors on the nocturnal activity budget, the length of behavioral phases as well as the number of phases per behavioral state during the night while the keeping zoo has no significant influence. It is found that males spend more time in REM sleep posture than females while young animals spend more time in this position than adult ones. Finally, the results suggest a rhythm between the Standing and Lying phases among common elands that opens future research directions.

Download full text files

Export metadata

Author:Jennifer GübertORCiD, Maximilian Grischa Hahn-KlimrothORCiDGND, Paul W. DierkesORCiD
Parent Title (English):Ecology and evolution
Publisher:John Wiley & Sons, Inc.
Place of publication:[Erscheinungsort nicht ermittelbar]
Document Type:Article
Date of Publication (online):2022/03/14
Date of first Publication:2022/03/14
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2023/11/06
Tag:REM sleep; Tragelaphus oryx; deep learning; nightly behavior; posture estimation; video action classification
Issue:3, art. e8701
Article Number:e8701
Page Number:23
First Page:1
Last Page:23
We thank the Opel-Zoo Foundation Professorship in Zoo Biology of the von Opel Hessische Zoostiftung who funded the research leading to the results.
Open Access funding enabled and organized by Projekt DEAL.
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International