Perspectives in machine learning for wildlife conservation

Tuia, Devis; Kellenberger, Benjamin; Beery, Sara; Costelloe, Blair R.; Zuffi, Silvia; Risse, Benjamin; Mathis, Alexander; Wikelski, Martin; Couzin, Iain D.; Crofoot, Margaret C.

Perspectives in machine learning for wildlife conservation

Dateien

Tuia_56536.pdfGröße: 10.49 MBDownloads: 197

Datum

2022

Autor:innen

Tuia, Devis

Kellenberger, Benjamin

Beery, Sara

Costelloe, Blair R.

Zuffi, Silvia

Risse, Benjamin

Mathis, Alexander

Wikelski, Martin

Couzin, Iain D.

Crofoot, Margaret C.

et al.

Open Access-Veröffentlichung

Open Access Gold

Sammlungen

Exzellenzcluster "Centre for the Advanced Study of Collective Behaviour"
Biologie

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Nature Communications. Nature Publishing Group. 2022, 13, 792. eISSN 2041-1723. Available under: doi: 10.1038/s41467-022-27980-y

Zusammenfassung

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.

Fachgebiet (DDC)

570 Biowissenschaften, Biologie

Zitieren

ISO 690

TUIA, Devis, Benjamin KELLENBERGER, Sara BEERY, Blair R. COSTELLOE, Silvia ZUFFI, Benjamin RISSE, Alexander MATHIS, Martin WIKELSKI, Iain D. COUZIN, Margaret C. CROFOOT, 2022. Perspectives in machine learning for wildlife conservation. In: Nature Communications. Nature Publishing Group. 2022, 13, 792. eISSN 2041-1723. Available under: doi: 10.1038/s41467-022-27980-y

BibTex

@article{Tuia2022Persp-56536,
  year={2022},
  doi={10.1038/s41467-022-27980-y},
  title={Perspectives in machine learning for wildlife conservation},
  volume={13},
  journal={Nature Communications},
  author={Tuia, Devis and Kellenberger, Benjamin and Beery, Sara and Costelloe, Blair R. and Zuffi, Silvia and Risse, Benjamin and Mathis, Alexander and Wikelski, Martin and Couzin, Iain D. and Crofoot, Margaret C.},
  note={Article Number: 792}
}

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Universitätsbibliographie

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Ja