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Perspectives in machine learning for wildlife conservation

Tuia, Devis and Kellenberger, Benjamin and Beery, Sara and Costelloe, Blair R. and Zuffi, Silvia and Risse, Benjamin and Mathis, Alexander and Mathis, Mackenzie W. and van Langevelde, Frank and Burghardt, Tilo and Kays, Roland and Klinck, Holger and Wikelski, Martin and Couzin, Iain D. and van Horn, Grant and Crofoot, Margaret C. and Stewart, Charles V. and Berger-Wolf, Tanya (2022) Perspectives in machine learning for wildlife conservation. Nature Communications, 13 . Art. No. 792. ISSN 2041-1723. doi:10.1038/s41467-022-27980-y.

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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.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Tuia, Devis0000-0003-0374-2459
Beery, Sara0000-0002-2544-1844
Costelloe, Blair R.0000-0001-5291-788X
Zuffi, Silvia0000-0003-1358-0828
Risse, Benjamin0000-0001-5691-4029
Mathis, Alexander0000-0002-3777-2202
Mathis, Mackenzie W.0000-0001-7368-4456
van Langevelde, Frank0000-0001-8870-0797
Kays, Roland0000-0002-2947-6665
Wikelski, Martin0000-0002-9790-7025
Couzin, Iain D.0000-0001-8556-4558
Berger-Wolf, Tanya0000-0001-7610-1412
Alternate Title:Seeing biodiversity: perspectives in machine learning for wildlife conservation
Additional Information:© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit Received 25 May 2021; Accepted 08 December 2021; Published 09 February 2022. We thank Mike Costelloe for assistance with figure design and execution. S.B. would like to thank the Microsoft AI for Earth initiative, the Idaho Department of Fish and Game, and Wildlife Protection Solutions for insightful discussions and providing data for figures. M.C.C. and T.B.W. were supported by the National Science Foundation (IIS 1514174 & IOS 1250895). M.C.C. received additional support from a Packard Foundation Fellowship (2016-65130), and the Alexander von Humboldt Foundation in the framework of the Alexander von Humboldt Professorship endowed by the Federal Ministry of Education and Research. C.V.S. and T.B.W. were supported by the US National Science Foundation (Awards 1453555 and 1550853). S.B. was supported by the National Science Foundation Grant No. 1745301 and the Caltech Resnick Sustainability Institute. I.D.C. acknowledges support from the ONR (N00014-19-1-2556), and I.D.C., B.R.C., M.W., and M.C.C. from, the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy-EXC 2117-422037984. M.W.M. is the Bertarelli Foundation Chair of Integrative Neuroscience. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies. Data availability: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. These authors contributed equally: Devis Tuia, Benjamin Kellenberger, Sara Beery, Blair R. Costelloe. Contributions: D.T. coordinated the writing team; D.T., B.K., S.B., and B.C. structured and organized the paper with equal contributions; all authors wrote the text; B.C. created the figures. The authors declare no competing interests. Peer review information: Nature Communications thanks Aileen Nielsen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Group:Resnick Sustainability Institute
Funding AgencyGrant Number
David and Lucile Packard Foundation2016-65130
Alexander von Humboldt FoundationUNSPECIFIED
Bundesministerium für Bildung und Forschung (BMBF)UNSPECIFIED
NSF Graduate Research FellowshipDGE-1745301
Resnick Sustainability InstituteUNSPECIFIED
Office of Naval Research (ONR)N00014-19-1-2556
Deutsche Forschungsgemeinschaft (DFG)EXC 2117-422037984
Bertarelli FoundationUNSPECIFIED
Subject Keywords:Computer science; Conservation biology
Record Number:CaltechAUTHORS:20220311-169189900
Persistent URL:
Official Citation:Tuia, D., Kellenberger, B., Beery, S. et al. Perspectives in machine learning for wildlife conservation. Nat Commun 13, 792 (2022).
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113896
Deposited By: Tony Diaz
Deposited On:11 Mar 2022 23:33
Last Modified:11 Mar 2022 23:33

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