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A horizon scan of biological conservation issues for 2026.

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Title: A horizon scan of biological conservation issues for 2026.
Authors: Sutherland, William J.1 (AUTHOR) w.sutherland@zoo.cam.ac.uk, Butchart, Stuart H.M.1,2 (AUTHOR), Clarke, Stewart J.3 (AUTHOR), Doar, Nigel R.4 (AUTHOR), Doran, Helen5 (AUTHOR), Douglas, Imogen C.6 (AUTHOR), Field, Daniel J.7,8,9 (AUTHOR), Fleishman, Erica10 (AUTHOR), Gaston, Kevin J.11 (AUTHOR), Herbert-Read, James E.12 (AUTHOR), Hughes, Alice C.13 (AUTHOR), Kaartokallio, Hermanni14 (AUTHOR), Maggs, Luke15 (AUTHOR), Palardy, James E.16 (AUTHOR), Pearce-Higgins, James W.1,17 (AUTHOR), Peck, Lloyd S.18 (AUTHOR), Pettorelli, Nathalie19 (AUTHOR), Schloss, Irene R.20,21,22 (AUTHOR), Spalding, Mark D.1,23 (AUTHOR), Timoshyna, Anastasiya24 (AUTHOR)
Source: Trends in Ecology & Evolution. Jan2026, Vol. 41 Issue 1, p91-101. 11p.
Subjects: Biodiversity, Technological innovations, Artificial intelligence, Seawater salinity, Land use, Conservation biology, Seagrass restoration, Machine learning
Abstract: Our 17th annual horizon scan identified 15 emerging issues of concern for global biodiversity conservation. The issues cover technological advances such as tiny machine learning (TinyML) and low power optical artificial intelligence (AI) chips that could revolutionize conservation monitoring. One issue highlights an unanticipated change in the salinity of the Southern Ocean. A 10-year retrospective shows that one issue we presented in 2016 (the rise of artificial superintelligence) has undergone a rapid development. The 15 issues presented here are essential reading for anyone interested in global biodiversity conservation and potential future trajectories. We present outcomes from our 17th horizon scan of issues potentially impacting global biodiversity conservation in the next decade. Issues are novel, or represent a significant step-change in impact, and are currently not well-known or understood within the conservation community. Our panel of 26 scientists, practitioners, and policymakers scored an initial list of 96 issues, discussed the highest ranked 35 issues at a workshop, and identified the 15 top-ranked issues. This year, technology innovations, including low-power optic artificial intelligence (AI) chips and tiny machine learning (TinyML) models, could revolutionize biodiversity monitoring. We highlight impacts from changes in land-use driven by appetite-suppressing pharmaceuticals and the unknown effects of mirror biomolecules. Highlighting these issues may increase awareness of any impacts on global biodiversity conservation. [ABSTRACT FROM AUTHOR]
Copyright of Trends in Ecology & Evolution is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: A horizon scan of biological conservation issues for 2026.
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  Data: <searchLink fieldCode="JN" term="%22Trends+in+Ecology+%26+Evolution%22">Trends in Ecology & Evolution</searchLink>. Jan2026, Vol. 41 Issue 1, p91-101. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Biodiversity%22">Biodiversity</searchLink><br /><searchLink fieldCode="DE" term="%22Technological+innovations%22">Technological innovations</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Seawater+salinity%22">Seawater salinity</searchLink><br /><searchLink fieldCode="DE" term="%22Land+use%22">Land use</searchLink><br /><searchLink fieldCode="DE" term="%22Conservation+biology%22">Conservation biology</searchLink><br /><searchLink fieldCode="DE" term="%22Seagrass+restoration%22">Seagrass restoration</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: Our 17th annual horizon scan identified 15 emerging issues of concern for global biodiversity conservation. The issues cover technological advances such as tiny machine learning (TinyML) and low power optical artificial intelligence (AI) chips that could revolutionize conservation monitoring. One issue highlights an unanticipated change in the salinity of the Southern Ocean. A 10-year retrospective shows that one issue we presented in 2016 (the rise of artificial superintelligence) has undergone a rapid development. The 15 issues presented here are essential reading for anyone interested in global biodiversity conservation and potential future trajectories. We present outcomes from our 17th horizon scan of issues potentially impacting global biodiversity conservation in the next decade. Issues are novel, or represent a significant step-change in impact, and are currently not well-known or understood within the conservation community. Our panel of 26 scientists, practitioners, and policymakers scored an initial list of 96 issues, discussed the highest ranked 35 issues at a workshop, and identified the 15 top-ranked issues. This year, technology innovations, including low-power optic artificial intelligence (AI) chips and tiny machine learning (TinyML) models, could revolutionize biodiversity monitoring. We highlight impacts from changes in land-use driven by appetite-suppressing pharmaceuticals and the unknown effects of mirror biomolecules. Highlighting these issues may increase awareness of any impacts on global biodiversity conservation. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Trends in Ecology & Evolution is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.tree.2025.10.016
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        Text: English
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