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The potential for AI to revolutionize conservation: a horizon scan

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The potential for AI to revolutionize conservation: a horizon scan. / Reynolds, Sam A; Beery, Sara; Burgess, Neil et al.
In: Trends in Ecology and Evolution, Vol. 40, No. 2, 28.02.2025, p. 191-207.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

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 2025, 'The potential for AI to revolutionize conservation: a horizon scan', Trends in Ecology and Evolution, vol. 40, no. 2, pp. 191-207. 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. (2025). The potential for AI to revolutionize conservation: a horizon scan. Trends in Ecology and Evolution, 40(2), 191-207. https://doi.org/10.1016/j.tree.2024.11.013

Vancouver

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. 2025 Feb 28;40(2):191-207. Epub 2025 Feb 4. 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. In: Trends in Ecology and Evolution. 2025 ; Vol. 40, No. 2. pp. 191-207.

Bibtex

@article{c188b55913b842fb909cb0df4e7e9641,
title = "The potential for AI to revolutionize conservation: a horizon scan",
abstract = "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. ",
keywords = "Delphi, artificial intelligence, biodiversity, machine learning, conservation",
author = "Reynolds, {Sam A} and Sara Beery and Neil Burgess and Mark Burgman and Butchart, {Stuart H M} and Cooke, {Steven J} and David Coomes and Finn Danielsen and {Di Minin}, Enrico and Dur{\'a}n, {Am{\'e}rica Paz} and Francis Gassert and Amy Hinsley and Sadiq Jaffer and Jones, {Julia P G} and Li, {Binbin V} and {Mac Aodha}, Oisin and Anil Madhavapeddy and O'Donnell, {Stephanie A L} and Oxbury, {William M} and Lloyd Peck and Nathalie Pettorelli and Rodr{\'i}guez, {Jon Paul} and Emily Shuckburgh and Bernardo Strassburg and Hiromi Yamashita and Zhongqi Miao and Sutherland, {William J}",
year = "2025",
month = feb,
day = "28",
doi = "10.1016/j.tree.2024.11.013",
language = "English",
volume = "40",
pages = "191--207",
journal = "Trends in Ecology and Evolution",
issn = "0169-5347",
publisher = "ELSEVIER SCIENCE LONDON",
number = "2",

}

RIS

TY - JOUR

T1 - The potential for AI to revolutionize conservation

T2 - 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 - 2025/2/28

Y1 - 2025/2/28

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 - Delphi

KW - artificial intelligence

KW - biodiversity

KW - machine learning

KW - conservation

U2 - 10.1016/j.tree.2024.11.013

DO - 10.1016/j.tree.2024.11.013

M3 - Journal article

C2 - 39694720

VL - 40

SP - 191

EP - 207

JO - Trends in Ecology and Evolution

JF - Trends in Ecology and Evolution

SN - 0169-5347

IS - 2

ER -