The potential for AI to revolutionize conservation: a horizon scan

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; William J. Sutherland

doi:10.1016/j.tree.2024.11.013

The potential for AI to revolutionize conservation: a horizon scan

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, William J. Sutherland

Research output: Contribution to journal › Review article › peer-review

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
Pages (from-to)	191-207
Number of pages	17
Journal	Trends in Ecology & Evolution
Volume	40
Issue number	2
DOIs	https://doi.org/10.1016/j.tree.2024.11.013
Publication status	Published - 17 Dec 2024

Keywords / Materials (for Non-textual outputs)

artificial intelligence
machine learning
conservation
biodiversity
Delphi

Access to Document

10.1016/j.tree.2024.11.013Licence: Creative Commons: Attribution (CC-BY)

ReynoldsEtalTEE2024ThePotentialForAIToRevolutionizeConservationFinal published version, 656 KBLicence: Creative Commons: Attribution (CC-BY)

Cite this

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., Peck, L., & Sutherland, W. J. (2024). The potential for AI to revolutionize conservation: a horizon scan. Trends in Ecology & Evolution, 40(2), 191-207. https://doi.org/10.1016/j.tree.2024.11.013

@article{cee6e7c3d43a46f2ba7a762632e8f800,

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

author = "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 Sutherland, {William J.}",

year = "2024",

month = dec,

day = "17",

doi = "10.1016/j.tree.2024.11.013",

language = "English",

volume = "40",

pages = "191--207",

journal = "Trends in Ecology & Evolution",

issn = "0169-5347",

publisher = "Elsevier",

number = "2",

}

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 & Sutherland, WJ 2024, 'The potential for AI to revolutionize conservation: a horizon scan', Trends in Ecology & Evolution, vol. 40, no. 2, pp. 191-207. https://doi.org/10.1016/j.tree.2024.11.013

TY - JOUR

T1 - The potential for AI to revolutionize conservation

T2 - a horizon scan

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 - 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 - Review article

SN - 0169-5347

VL - 40

SP - 191

EP - 207

JO - Trends in Ecology & Evolution

JF - Trends in Ecology & Evolution

IS - 2

ER -

The potential for AI to revolutionize conservation: a horizon scan

Abstract

Keywords / Materials (for Non-textual outputs)

Access to Document

Fingerprint

Cite this