CaltechAUTHORS
Published February 9, 2022 | Version Accepted Version + Supplemental Material + Published
Journal Article Open

Perspectives in machine learning for wildlife conservation

Creators

  • 1. ROR icon École Polytechnique Fédérale de Lausanne
  • 2. ROR icon California Institute of Technology
  • 3. ROR icon Max Planck Institute of Animal Behavior
  • 4. ROR icon University of Konstanz
  • 5. ROR icon Istituto di Matematica Applicata e Tecnologie Informatiche
  • 6. ROR icon University of Münster
  • 7. ROR icon Wageningen University & Research
  • 8. ROR icon University of Bristol
  • 9. ROR icon North Carolina State University
  • 10. ROR icon North Carolina Museum of Natural Sciences
  • 11. ROR icon Cornell University
  • 12. ROR icon Rensselaer Polytechnic Institute
  • 13. ROR icon The Ohio State University

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.

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 http://creativecommons.org/licenses/by/4.0/. 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.

Attached Files

Published - s41467-022-27980-y.pdf

Accepted Version - 2110.12951.pdf

Supplemental Material - 41467_2022_27980_MOESM1_ESM.pdf

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Additional details

Additional titles

Alternative title
Seeing biodiversity: perspectives in machine learning for wildlife conservation

Identifiers

Eprint ID
113896
Resolver ID
CaltechAUTHORS:20220311-169189900

Related works

Describes
https://arxiv.org/abs/2110.12951 (URL)

Funding

NSF
IIS-1514174
NSF
IOS-1250895
David and Lucile Packard Foundation
2016-65130
Alexander von Humboldt Foundation
Bundesministerium für Bildung und Forschung (BMBF)
NSF
CNS-1453555
NSF
EF-1550853
NSF Graduate Research Fellowship
DGE-1745301
Resnick Sustainability Institute
Office of Naval Research (ONR)
N00014-19-1-2556
Deutsche Forschungsgemeinschaft (DFG)
EXC 2117-422037984
Bertarelli Foundation

Dates

Created
2022-03-11
Created from EPrint's datestamp field
Updated
2022-03-11
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Resnick Sustainability Institute