Shedding light into the dark: Age and light shape nocturnal activity and sleep behaviour of giraffe

What’s Black and White and Pink All Over? Lesser Flamingo Nocturnal Behaviour Captured by Remote Cameras

The study of animal behaviour is important for the development of husbandry and management practices for zoo-housed species. Yet, data are typically only collected during daylight hours, aligning with human work schedules rather than animal activity patterns. To remedy this, 24 h data collection is needed. This study investigated the behaviour of a captive flock of lesser flamingos to understand temporal changes in their time-activity patterns. Two remote camera traps were placed around the birds’ outdoor enclosure and one within the indoor house. Counts of birds visible within specific enclosure zones were recorded from photographic data. Behaviour was defined as active or inactive, and modified Spread of Participation Index (SPI) was used to calculate enclosure zone occupancy. Results indicated that lesser flamingos are active overnight, and to a similar amount as in the daytime. Proportions of birds observed as active were significantly higher at later times of the day (i.e., dusk) when compared to the number of active birds in the morning. Enclosure usage was diverse and indoor and outdoor zones could be used by different numbers of birds at different times of the day. Variation in enclosure usage may indicate the changing needs of the flamingos when housed indoors overnight and when they have night-time access to an outdoor enclosure. This research has identified the need for further research into the nocturnal behaviour and space use of lesser flamingos and suggests the need for 24 h research in captive birds, and other zoo-held species, especially when species are locked indoors or face behavioural restriction overnight due to biosecurity measures surrounding zoonoses outbreaks, e.g., Avian Influenza.

Diurnal and Nocturnal Behaviour of Cheetahs (Acinonyx jubatus) and Lions (Panthera leo) in Zoos

Mammals are constantly exposed to exogenous and endogenous influences that affect their behaviour and daily activity. Light and temperature, as well as anthropogenic factors such as husbandry routines, visitors, and feeding schedules are potential influences on animals in zoological gardens. In order to investigate the effects of some of these factors on animal behaviour, observational studies based on the analyses of activity budgets can be used. In this study, the daily and nightly activity budgets of six lions (Panthera leo) and five cheetahs (Acinonyx jubatus) from four EAZA institutions were investigated. Focused on the influencing factor light and feeding, we analysed these activity budgets descriptively. Behaviour was recorded and analysed during the winter months over an observation period of 14 days and 14 nights using infrared-sensitive cameras. Our results show that lions and cheetahs exhibit activity peaks at crepuscular and feeding times, regardless of husbandry. Thus, lions in captivity shift nocturnal behaviour familiar from the wild to crepuscular and diurnal times. In cheetahs, in contrast, captive and wild individuals show similar 24 h behavioural rhythms. The resting behaviour of both species is more pronounced at night, with cheetahs having a shorter overall sleep duration than lions. This study describes the results of the examined animals and is not predictive. Nevertheless, the results of this study make an important contribution to gaining knowledge about possible factors influencing the behaviour of lions and cheetahs in zoos and offer implications that could be useful for improving husbandry and management.

Deep learning-based pose estimation for African ungulates in zoos

The description and analysis of animal behavior over long periods of time is one of the most important challenges in ecology. However, most of these studies are limited due to the time and cost required by human observers. The collection of data via video recordings allows observation periods to be extended. However, their evaluation by human observers is very time-consuming. Progress in automated evaluation, using suitable deep learning methods, seems to be a forward-looking approach to analyze even large amounts of video data in an adequate time frame.In this study, we present a multistep convolutional neural network system for detecting three typical stances of African ungulates in zoo enclosures which works with high accuracy. An important aspect of our approach is the introduction of model averaging and postprocessing rules to make the system robust to outliers.Our trained system achieves an in-domain classification accuracy of >0.92, which is improved to >0.96 by a postprocessing step. In addition, the whole system performs even well in an out-of-domain classification task with two unknown types, achieving an average accuracy of 0.93. We provide our system at https://github.com/Klimroth/Video-Action-Classifier-for-African-Ungulates-in-Zoos/tree/main/mrcnn_based so that interested users can train their own models to classify images and conduct behavioral studies of wildlife.The use of a multistep convolutional neural network for fast and accurate classification of wildlife behavior facilitates the evaluation of large amounts of image data in ecological studies and reduces the effort of manual analysis of images to a high degree. Our system also shows that postprocessing rules are a suitable way to make species-specific adjustments and substantially increase the accuracy of the description of single behavioral phases (number, duration). The results in the out-of-domain classification strongly suggest that our system is robust and achieves a high degree of accuracy even for new species, so that other settings (e.g., field studies) can be considered.

Biological and environmental factors as sources of variation in nocturnal behavior of giraffe

Upon a drastic decline of the giraffe population in the wild, conservation efforts and therefore the role of zoos have become more important than ever. With their unique opportunities, zoos provide excellent conditions to study animal behavior, expanding the knowledge about the giraffe's behavior repertoire and their ability to adapt. This study therefore examined the nocturnal behavior of 63 giraffe living in 13 different EAZA zoos across Germany and the Netherlands. Giraffe were observed and videos recorded via infrared sensitive cameras during the winter seasons 2015-2018. The observation period spanned nightly from 17:00 to 7:00. Thus, 198 nights, with a total of 2772 h were recorded and analyzed. Linear mixed models were then used to assess potential biological and environmental factors influencing behavior during the dark phase. Results show that individual variables such as age, subspecies and motherhood determined nocturnal activity and sleep behavior most. Among the variables studied, husbandry conditions and environmental factors complying with EAZA standards had no influence on the giraffe's nocturnal behavior. By combining nocturnal activity analyses and an assessment of potential influencing factors, our findings present a holistic approach to a better understanding of captive giraffe behavior and allow for management implications.