Perspectives in machine learning for wildlife conservation

Devis Tuia (), Benjamin Kellenberger, Sara Beery, Blair R. Costelloe, Silvia Zuffi, Benjamin Risse, Alexander Mathis, Mackenzie W. Mathis, Frank Langevelde, Tilo Burghardt, Roland Kays, Holger Klinck, Martin Wikelski, Iain D. Couzin, Grant Horn, Margaret C. Crofoot, Charles V. Stewart and Tanya Berger-Wolf
Additional contact information
Devis Tuia: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Benjamin Kellenberger: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Sara Beery: California Institute of Technology (Caltech)
Blair R. Costelloe: Max Planck Institute of Animal Behavior
Silvia Zuffi: Institute for Applied Mathematics and Information Technologies, IMATI-CNR
Benjamin Risse: University of Münster
Alexander Mathis: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Mackenzie W. Mathis: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Frank Langevelde: Wageningen University
Tilo Burghardt: University of Bristol
Roland Kays: North Carolina State University
Holger Klinck: Cornell University
Martin Wikelski: Max Planck Institute of Animal Behavior
Iain D. Couzin: Max Planck Institute of Animal Behavior
Grant Horn: Cornell University
Margaret C. Crofoot: Max Planck Institute of Animal Behavior
Charles V. Stewart: Rensselaer Polytechnic Institute
Tanya Berger-Wolf: The Ohio State University

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.

Date: 2022
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Citations: View citations in EconPapers (5)

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DOI: 10.1038/s41467-022-27980-y

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