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Revolution in animal research: Mobile cameras reveal bird behavior!
Researchers at the University of Konstanz are developing an innovative camera system for behavioral research on wild birds using 3D-SOCS.
Revolution in animal research: Mobile cameras reveal bird behavior!
Researchers from the Konstanz Cluster of Excellence “Collective Behavior” have developed an innovative mobile camera system called “3D-SOCS”. This system is designed to capture detailed data about the behavior of animals in their natural habitat and enables precise, markerless 3D tracking of the postures and movements of multiple birds simultaneously. To date, 3D tracking has mostly focused on indoor spaces or animals in captivity. The new system was published in the renowned journal Methods in Ecology and Evolution and represents an advance in animal behavior research.
The use of 3D-SOCS took place in a forest near the Max Planck Institute for Behavioral Biology in Möggingen. During a field experiment, visual stimuli such as mealworms and stuffed birds were presented to observe the birds' responses. The data obtained allows conclusions to be drawn about the use of the visual field and the lateralization of the birds. The system can also be used to estimate the body volume of the animals, which serves as an approximation for their weight. What is particularly noteworthy is that data collection is non-invasive, meaning that the animals do not have to be captured.
Technological innovations and ecological monitoring
The 3D SOCS system represents an open platform. Hardware plans and software pipelines are publicly available, allowing a broad scientific community to use the technology. This system promotes synergies between laboratory and field research and closes the gap between controlled studies and ecologically valid field observations. It is funded by the German Research Foundation (DFG) and the Swiss State Secretariat for Education, Research and Innovation (SERI) and is battery-operated, so it is designed for use in the field.
The authors of the underlying study, including Michael Chimento and Alex Hoi Hang Chan, aim to collect large behavioral data sets on wild animals in natural habitats. Using state-of-the-art sensor technologies such as GPS and passive transponder tags, the quality and scope of behavioral data is significantly improved. Advances in machine learning and computer vision enable particularly precise measurements that can compete with controlled laboratory conditions.
The role of image analysis algorithms
In addition to the developments in Konstanz, researchers from the University's Institute for Neuroinformatics and ETH Zurich have created an image analysis algorithm to automate the analysis of video recordings in behavioral studies. This algorithm uses computer vision and machine learning to distinguish individual animals and detect behaviors such as curiosity and fear. The algorithm's particular strengths lie in the rapid and automated evaluation of video recordings, which increases the reproducibility and validity of research results.
The method was trained using video recordings of both mice and macaques, but is universally applicable. From monitoring abnormal behavior in animal husbandry to analyzing complex social interactions in animal communities, this algorithm has wide application. Together with Zurich Zoo, this initiative aims to improve animal husbandry and establish automated behavioral research. ETH Professor Yanik plans to use this technique in his research on imitation learning.
The advances made by these technologies could significantly contribute to deepening our understanding of animal behavior in the wild and thus also improving the basis for the protection and conservation of endangered species.