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The potential for AI to revolutionize conservation: a horizon scan

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

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The potential for AI to revolutionize conservation: a horizon scan. / Reynolds, Sam A.; Beery, Sara; Burgess, Neil et al.
Yn: Trends in Ecology and Evolution, 17.12.2024.

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

HarvardHarvard

Reynolds, SA, Beery, S, Burgess, N, Burgman, M, Butchart, SHM, Cooke, SJ, Coomes, D, Danielsen, F, Di Minin, E, Durán, AP, Gassert, F, Hinsley, A, Jaffer, S, Jones, JPG, Li, BV, Mac Aodha, O, Madhavapeddy, A, O'Donnell, SAL, Oxbury, WM, Peck, L, Pettorelli, N, Rodríguez, JP, Shuckburgh, E, Strassburg, B, Yamashita, H, Miao, Z & Sutherland, WJ 2024, 'The potential for AI to revolutionize conservation: a horizon scan', Trends in Ecology and Evolution. https://doi.org/10.1016/j.tree.2024.11.013

APA

Reynolds, S. A., Beery, S., Burgess, N., Burgman, M., Butchart, S. H. M., Cooke, S. J., Coomes, D., Danielsen, F., Di Minin, E., Durán, A. P., Gassert, F., Hinsley, A., Jaffer, S., Jones, J. P. G., Li, B. V., Mac Aodha, O., Madhavapeddy, A., O'Donnell, S. A. L., Oxbury, W. M., ... Sutherland, W. J. (2024). The potential for AI to revolutionize conservation: a horizon scan. Trends in Ecology and Evolution. Cyhoeddiad ar-lein ymlaen llaw. https://doi.org/10.1016/j.tree.2024.11.013

CBE

Reynolds SA, Beery S, Burgess N, Burgman M, Butchart SHM, Cooke SJ, Coomes D, Danielsen F, Di Minin E, Durán AP, et al. 2024. The potential for AI to revolutionize conservation: a horizon scan. Trends in Ecology and Evolution. https://doi.org/10.1016/j.tree.2024.11.013

MLA

Reynolds, Sam A. et al. "The potential for AI to revolutionize conservation: a horizon scan". Trends in Ecology and Evolution. 2024. https://doi.org/10.1016/j.tree.2024.11.013

VancouverVancouver

Reynolds SA, Beery S, Burgess N, Burgman M, Butchart SHM, Cooke SJ et al. The potential for AI to revolutionize conservation: a horizon scan. Trends in Ecology and Evolution. 2024 Rhag 17. Epub 2024 Rhag 17. doi: 10.1016/j.tree.2024.11.013

Author

Reynolds, Sam A. ; Beery, Sara ; Burgess, Neil et al. / The potential for AI to revolutionize conservation: a horizon scan. Yn: Trends in Ecology and Evolution. 2024.

RIS

TY - JOUR

T1 - The potential for AI to revolutionize conservation: a horizon scan

AU - Reynolds, Sam A.

AU - Beery, Sara

AU - Burgess, Neil

AU - Burgman, Mark

AU - Butchart, Stuart H.M.

AU - Cooke, Steven J.

AU - Coomes, David

AU - Danielsen, Finn

AU - Di Minin, Enrico

AU - Durán, América Paz

AU - Gassert, Francis

AU - Hinsley, Amy

AU - Jaffer, Sadiq

AU - Jones, Julia P.G.

AU - Li, Binbin V.

AU - Mac Aodha, Oisin

AU - Madhavapeddy, Anil

AU - O'Donnell, Stephanie A.L.

AU - Oxbury, William M.

AU - Peck, Lloyd

AU - Pettorelli, Nathalie

AU - Rodríguez, Jon Paul

AU - Shuckburgh, Emily

AU - Strassburg, Bernardo

AU - Yamashita, Hiromi

AU - Miao, Zhongqi

AU - Sutherland, William J.

PY - 2024/12/17

Y1 - 2024/12/17

N2 - Artificial Intelligence (AI) is an emerging tool that could be leveraged to identify the effective conservation solutions demanded by the urgent biodiversity crisis. We present the results of our horizon scan of AI applications likely to significantly benefit biological conservation. An international panel of conservation scientists and AI experts identified 21 key ideas. These included species recognition to uncover 'dark diversity', multimodal models to improve biodiversity loss predictions, monitoring wildlife trade, and addressing human–wildlife conflict. We consider the potential negative impacts of AI adoption, such as AI colonialism and loss of essential conservation skills, and suggest how the conservation field might adapt to harness the benefits of AI while mitigating its risks.

AB - Artificial Intelligence (AI) is an emerging tool that could be leveraged to identify the effective conservation solutions demanded by the urgent biodiversity crisis. We present the results of our horizon scan of AI applications likely to significantly benefit biological conservation. An international panel of conservation scientists and AI experts identified 21 key ideas. These included species recognition to uncover 'dark diversity', multimodal models to improve biodiversity loss predictions, monitoring wildlife trade, and addressing human–wildlife conflict. We consider the potential negative impacts of AI adoption, such as AI colonialism and loss of essential conservation skills, and suggest how the conservation field might adapt to harness the benefits of AI while mitigating its risks.

KW - artificial intelligence

KW - machine learning

KW - conservation

KW - biodiversity

KW - Delphi

U2 - 10.1016/j.tree.2024.11.013

DO - 10.1016/j.tree.2024.11.013

M3 - Article

JO - Trends in Ecology and Evolution

JF - Trends in Ecology and Evolution

SN - 0169-5347

ER -