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Perspectives in machine learning for wildlife conservation

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...

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Autores principales: Tuia, Devis, Kellenberger, Benjamin, Beery, Sara, Costelloe, Blair R., Zuffi, Silvia, Risse, Benjamin, Mathis, Alexander, Mathis, Mackenzie W., van Langevelde, Frank, Burghardt, Tilo, Kays, Roland, Klinck, Holger, Wikelski, Martin, Couzin, Iain D., van Horn, Grant, Crofoot, Margaret C., Stewart, Charles V., Berger-Wolf, Tanya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8828720/
https://www.ncbi.nlm.nih.gov/pubmed/35140206
http://dx.doi.org/10.1038/s41467-022-27980-y
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author Tuia, Devis
Kellenberger, Benjamin
Beery, Sara
Costelloe, Blair R.
Zuffi, Silvia
Risse, Benjamin
Mathis, Alexander
Mathis, Mackenzie W.
van Langevelde, Frank
Burghardt, Tilo
Kays, Roland
Klinck, Holger
Wikelski, Martin
Couzin, Iain D.
van Horn, Grant
Crofoot, Margaret C.
Stewart, Charles V.
Berger-Wolf, Tanya
author_facet Tuia, Devis
Kellenberger, Benjamin
Beery, Sara
Costelloe, Blair R.
Zuffi, Silvia
Risse, Benjamin
Mathis, Alexander
Mathis, Mackenzie W.
van Langevelde, Frank
Burghardt, Tilo
Kays, Roland
Klinck, Holger
Wikelski, Martin
Couzin, Iain D.
van Horn, Grant
Crofoot, Margaret C.
Stewart, Charles V.
Berger-Wolf, Tanya
author_sort Tuia, Devis
collection PubMed
description 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.
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spelling pubmed-88287202022-03-04 Perspectives in machine learning for wildlife conservation Tuia, Devis Kellenberger, Benjamin Beery, Sara Costelloe, Blair R. Zuffi, Silvia Risse, Benjamin Mathis, Alexander Mathis, Mackenzie W. van Langevelde, Frank Burghardt, Tilo Kays, Roland Klinck, Holger Wikelski, Martin Couzin, Iain D. van Horn, Grant Crofoot, Margaret C. Stewart, Charles V. Berger-Wolf, Tanya Nat Commun Perspective 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. Nature Publishing Group UK 2022-02-09 /pmc/articles/PMC8828720/ /pubmed/35140206 http://dx.doi.org/10.1038/s41467-022-27980-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Perspective
Tuia, Devis
Kellenberger, Benjamin
Beery, Sara
Costelloe, Blair R.
Zuffi, Silvia
Risse, Benjamin
Mathis, Alexander
Mathis, Mackenzie W.
van Langevelde, Frank
Burghardt, Tilo
Kays, Roland
Klinck, Holger
Wikelski, Martin
Couzin, Iain D.
van Horn, Grant
Crofoot, Margaret C.
Stewart, Charles V.
Berger-Wolf, Tanya
Perspectives in machine learning for wildlife conservation
title Perspectives in machine learning for wildlife conservation
title_full Perspectives in machine learning for wildlife conservation
title_fullStr Perspectives in machine learning for wildlife conservation
title_full_unstemmed Perspectives in machine learning for wildlife conservation
title_short Perspectives in machine learning for wildlife conservation
title_sort perspectives in machine learning for wildlife conservation
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8828720/
https://www.ncbi.nlm.nih.gov/pubmed/35140206
http://dx.doi.org/10.1038/s41467-022-27980-y
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