Animal-borne video systems provide insight into the reproductive behavior of the Asian black bear

Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision

Methods for collecting animal behaviour data in natural environments, such as direct observation and biologging, are typically limited in spatiotemporal resolution, the number of animals that can be observed and information about animals' social and physical environments. Video imagery can capture rich information about animals and their environments, but image-based approaches are often impractical due to the challenges of processing large and complex multi-image datasets and transforming resulting data, such as animals' locations, into geographical coordinates. We demonstrate a new system for studying behaviour in the wild that uses drone-recorded videos and computer vision approaches to automatically track the location and body posture of free-roaming animals in georeferenced coordinates with high spatiotemporal resolution embedded in contemporaneous 3D landscape models of the surrounding area. We provide two worked examples in which we apply this approach to videos of gelada monkeys and multiple species of group-living African ungulates. We demonstrate how to track multiple animals simultaneously, classify individuals by species and age-sex class, estimate individuals' body postures (poses) and extract environmental features, including topography of the landscape and animal trails. By quantifying animal movement and posture while reconstructing a detailed 3D model of the landscape, our approach opens the door to studying the sensory ecology and decision-making of animals within their natural physical and social environments.

Comparing information derived on food habits of a terrestrial carnivore between animal-borne video systems and fecal analyses methods

In recent years, animal-borne video cameras have been used to identify the food habits of many species. However, the usefulness and difficulties of identifying food habits from animal-borne video cameras have not been sufficiently discussed in terrestrial mammals, especially large omnivores. The aim of this study is to compare the video analysis of foraging behavior by Asian black bears (Ursus thibetanus) acquired by camera collars with estimates from fecal analysis. We attached GPS collars equipped with video cameras to four adult Asian black bears in the Okutama mountains in central Japan from May to July 2018 and analyzed video clips for foraging behavior. Simultaneously, we collected bear feces in the same area to determine food habits. We found that using video analyses was advantageous to recognize foods, such as leaves or mammals, that were physically crushed or destroyed while bears chewed and digested foods, which are difficult to identify to species using fecal analyses. On the other hand, we found that camera collars are less likely to record food items that are infrequently or quickly ingested. Additionally, food items with a low frequency of occurrence and short foraging time per feeding were less likely to be detected when we increased the time between recorded clips. As one of the first applications of the video analysis method for bears, our study shows that video analysis can be an important method for revealing individual differences in diet. Although video analysis may have limitations for understanding the general foraging behavior of Asian black bears at the present stage, the accuracy of food habit data from camera collars can be improved by using it in combination with established techniques such as microscale behavior analyses.