Digital Event Horizon
Researchers from the Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW) have developed an artificial intelligence algorithm that relies on white-backed vultures to detect carcasses across vast landscapes. This innovative approach combines biologging technologies with machine learning to create a high-tech early warning system for environmental changes and critical ecological incidents.
Researchers from Leibniz-IZW developed an AI algorithm that uses white-backed vultures to detect carcasses across vast landscapes. The algorithm combines biologging technologies with machine learning for a high-tech early warning system. Vultures were equipped with GPS and acceleration tags to record data on their movements. The AI algorithm was trained on reference data from 27 wild vultures, video recordings, and zoo observations. The system can detect critical changes or incidents in the environment, such as droughts or disease outbreaks. The technology has potential applications beyond wildlife conservation, including environmental health monitoring.
In a groundbreaking study published in the Journal of Applied Ecology, researchers from the Leibniz Institute for Zoo and Wildlife Research (Leibniz-IZW) have successfully developed an artificial intelligence algorithm that relies on the natural abilities of white-backed vultures to detect carcasses across vast landscapes. This innovative approach combines biologging technologies with machine learning to create a high-tech early warning system for environmental changes and critical ecological incidents.
The GAIA Initiative, an alliance of research institutes, conservation organizations, and enterprises, aimed to develop this system as part of their larger goal to promote sustainable environmental management and conservation practices. By leveraging the impressive knowledge that wildlife has about ecosystems, scientists hope to make a significant impact in the field of wildlife conservation.
Vultures are perfectly adapted by millions of years of evolution to detect carcasses across vast landscapes quickly and reliably. Their exceptional eye-vision and sophisticated communication enable them to monitor large areas of land when many individuals work together. As scavengers, vultures always look for the next carcass, making them an ideal species for this application.
To tap into this natural ability, researchers equipped white-backed vultures in Namibia with animal tags that recorded two groups of data: GPS sensor and acceleration (ACC) sensor. The GPS sensor provided the exact location of the tagged individual at a specific point in time, while the ACC sensor stored detailed movement profiles of the tag – and thus of the animal – along the three spatial axes at the exact same time.
The combination of these two data sets enabled scientists to develop an AI algorithm that could reliably classify behaviors of white-backed vultures using animal tag data. The algorithm was trained on reference data from 27 wild vultures fitted with tags in Namibia, as well as video recordings and observations of the animals in the zoo.
The resulting system has several applications beyond wildlife conservation. For instance, it can be used to quickly recognize critical changes or incidents in the environment such as droughts, disease outbreaks, or the illegal killing of wildlife. This early warning system can provide valuable insights into environmental health and facilitate prompt responses to prevent further damage.
Furthermore, the development of this AI algorithm highlights the potential for combining animal cognition with machine learning to monitor and understand complex ecological systems. By embracing the impressive knowledge that wildlife has about ecosystems, scientists can create innovative solutions to pressing environmental challenges.
In conclusion, the study demonstrates a remarkable example of interdisciplinary research collaboration between zoologists, conservationists, and computer scientists. As researchers continue to explore new frontiers in environmental monitoring and conservation, the potential for innovative technologies like this vulture-based system is vast and exciting.
Related Information:
https://www.sciencedaily.com/releases/2024/11/241119132833.htm
Published: Wed Nov 20 08:37:20 2024 by llama3.2 3B Q4_K_M
