Open Access

Jennifer Gübert

• The study of animal behavior is essential for gaining a better understanding of the behavior, patterns, and needs of animals. A better understanding not only serves scientific progress, but also plays an important role in improving husbandry conditions in zoos, which can help to improve animal welfare (Berger, 2010; Brando and Buchanan-Smith, 2018; Walsh et al., 2019; Rose and Riley, 2021). The behavior of large herbivores differs significantly between day and night, and most ungulates are diurnal or crepuscular (Bennie et al., 2014; Gravett et al., 2017; Davimes et al., 2018; Wu et al., 2018). In contrast, many studies examine animal behavior during the day, and unfortunately there is little information on nocturnal behavior, including sleep behavior (Berger, 2010; Rose and Robert, 2013). However, sleep behavior, especially the proportion of REM sleep, is of great importance for the well-being of an individual (Hänninen et al., 2008; Fukasawa et al., 2018; Northeast et al., 2020). To gain more insight into the behavior of ungulates in general, studies based on large samples of different species with a long recording period are useful. This is difficult to achieve with manual data analysis, as data collection and analysis in behavioral biology is time consuming and costly. Therefore, modern methods such as automated analysis are helpful in the field of behavioral biology (Norouzzadeh et al., 2018; Beery et al., 2020; Lürig et al., 2021). Hence, the development of a software tool for the automated assessment of nocturnal behavior of ungulates in zoos is part of this dissertation. The resulting software tool is called BOVIDS (Behavioral Observations by Videos and Images using Deep-Learning Software) and allows the automatic evaluation of video material in three steps. In the first step, object detection, the individuals on the images are recognized and cut out in order to classify the behavior in the following step, action classification. In the final step, post-processing, errors of the automated analysis are corrected and the data is prepared for further use (Hahn-Klimroth et al., 2021; Gübert et al., 2022). To create such a system, it must first be trained. Typically, two nights per individual were manually annotated, resulting in a total of 594 manually annotated nights. In addition, 224,922 images were used to evaluate whether the system was already correctly recognizing the animals' behavior. Bounding boxes were either manually drawn or evaluated on a total of 201,827 images to train the object detection network. The software package BOVIDS was used to analyze data from a total of 196 individuals from 19 different ungulate species over a period of 101,629 hours of video material from 9,239 nights. A night is defined as the period from 7 pm to 6 am. The species studied belong to the two orders of odd-toed ungulates (Perissodactyla) and even-toed ungulates (Artiodactyla). The focus is on the behavioral categories of standing, moving, lying – head up, and lying – head down, the latter corresponding to the typical REM sleep position of ungulates. Based on the analyzed data, several biological questions were discussed in this thesis. In addition to the activity budgets and rhythms underlying the night, factors influencing behavior are also investigated. In addition, the enclosure use by the animals is evaluated. Zebras as representatives of the Perissodactyla spend about 25% of the night lying, while the average for the Artiodactyla studied is 77%. All species studied spend an average of 8.8% of the night in REM sleep (Gübert et al., 2023a), with a typical REM sleep phase lasting between 2.2 and 7.6 minutes (Gübert et al., 2023b). Only 0.7% of time during the night is spent with movement by the animals (Gübert and Dierkes). While the number of lying phases within the Artiodactyla is very constant with an average of five phases, the number of phases in the REM sleep position varies. Age, average species size and taxonomy were found to be influencing factors (Gübert et al., 2023a). With regard to rhythmicity, it is striking that most of the species studied show an increase in lying during the night and that a strong rhythmicity of behavior can be observed. The time between two lying events is very constant and is about two hours for most animals (Gübert et al., 2023b). With regard to enclosure use, it is striking that only a small part of the enclosure is used regularly. All individuals prefer to lie down on the bedding and most individuals prefer one or two different resting places (Gübert and Dierkes). The data created as part of this thesis can contribute to a better overall understanding of ungulate behavior. The newly developed software package BOVIDS makes it relatively easy to analyze further data on this topic. Long-term studies can now be carried out more cost-effectively, making it easier to answer many questions in the future, such as investigating other influencing factors or responses to changes in husbandry conditions.