The potential for AI to revolutionize conservation: a horizon scan

Sam A. Reynolds; Sara Beery; Neil Burgess; Mark Burgman; Stuart H.M. Butchart; Steven J. Cooke; David Coomes; Finn Danielsen; Enrico Di Minin; América Paz Durán; Francis Gassert; Amy Hinsley; Sadiq Jaffer; Julia P.G. Jones; Binbin V. Li; Oisin Mac Aodha; Anil Madhavapeddy; Stephanie A.L. O'Donnell; William M. Oxbury; Lloyd Peck; Nathalie Pettorelli; Jon Paul Rodríguez; Emily Shuckburgh; Bernardo Strassburg; Hiromi Yamashita; Zhongqi Miao; William J. Sutherland

doi:10.1016/j.tree.2024.11.013

The potential for AI to revolutionize conservation: a horizon scan

Sam A. Reynolds, Sara Beery, Neil Burgess, Mark Burgman, Stuart H.M. Butchart, Steven J. Cooke, David Coomes, Finn Danielsen, Enrico Di Minin, América Paz Durán, Francis Gassert, Amy Hinsley, Sadiq Jaffer, Julia P.G. Jones, Binbin V. Li, Oisin Mac Aodha, Anil Madhavapeddy, Stephanie A.L. O'Donnell, William M. Oxbury, Lloyd PeckNathalie Pettorelli, Jon Paul Rodríguez, Emily Shuckburgh, Bernardo Strassburg, Hiromi Yamashita, Zhongqi Miao, William J. Sutherland

School of Environmental & Natural Sciences

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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.

Original language	English
Journal	Trends in Ecology and Evolution
Early online date	17 Dec 2024
DOIs	https://doi.org/10.1016/j.tree.2024.11.013
Publication status	E-pub ahead of print - 17 Dec 2024

Keywords

artificial intelligence
machine learning
conservation
biodiversity
Delphi

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10.1016/j.tree.2024.11.013Licence: CC BY

1-s2.0-S0169534724002866-mainFinal published version, 1.11 MBLicence: CC BY

https://www.sciencedirect.com/science/article/pii/S0169534724002866

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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. Advance online publication. https://doi.org/10.1016/j.tree.2024.11.013

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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 = "artificial intelligence, machine learning, conservation, biodiversity, Delphi",

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.}",

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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

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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

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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

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DO - 10.1016/j.tree.2024.11.013

M3 - Article

SN - 0169-5347

JO - Trends in Ecology and Evolution

JF - Trends in Ecology and Evolution

ER -

The potential for AI to revolutionize conservation: a horizon scan

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Keywords

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