Perspectives in machine learning for wildlife conservation

Tuia, Devis; Kellenberger, Benjamin; Beery, Sara; Costelloe, Blair R.; Zuffi, Silvia; Risse, Benjamin; Mathis, Alexander; Mathis, Mackenzie Weygandt; Langevelde, Frank van; Burghardt, Tilo; Kays, Roland; Klinck, Holger; Wikelski, Martin; Couzin, Iain D.; Horn, Grant Van; Crofoot, Margaret C.; Stewart, Charles V.; Berger‐Wolf, Tanya

doi:10.1038/s41467-022-27980-y

reviewNature CommunicationsFeb 9, 2022GOLD OA

Perspectives in machine learning for wildlife conservation

DTDevis Tuia BKBenjamin Kellenberger SBSara Beery BRBlair R. Costelloe SZSilvia Zuffi

École Polytechnique Fédérale de Lausanne · California Institute of Technology · +11 more institutions

PubMed

Indexed incrossrefdoajpubmed

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…

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637

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Authors

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Topics & keywords

Topics

Keywords

Wildlife
Wildlife conservation
Conservation science
Conservation biology
Computer science
Environmental planning
Data science
Geography

UN Sustainable Development Goals

Life in Land

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