BOVIDS: A deep learning-based software 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, this database needs to be expanded.

Zoo animals offer a good opportunity to lay the corresponding

foundations. The results 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. To tackle this open question,

we developed a stand-alone open-source software based on deep learning

techniques, named BOVIDS (Behavioral Observations by Videos and Images

using a Deep-Learning Software). This software is used to identify

ungulates in their enclosure and to determine crucial behavioral poses

on video material with an accuracy of 99.4%. A case study on 25 Common

Elands (Tragelaphus oryx) out of 5 EAZA zoos with a total of 11,411

hours video material out of 822 nights is conducted, 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. Finally, the

results suggest the existence of species-specific rhythms that open

future research directions.