TY - JOUR
T1 - Nine changes needed to deliver a radical transformation in biodiversity measurement
AU - Sutherland, William J.
AU - Burgess, Neil D.
AU - Edwards, Scott V.
AU - Jones, Julia P. G.
AU - Soltis, Pamela S.
AU - Tilman, David
AU - Allen, Julie M.
AU - Andrianandrasana, Herizo T.
AU - Armour, Cathrine J.
AU - August, Tom
AU - Bawa, Kamaljit S.
AU - Bailey, Sallie
AU - Birch, Tanya
AU - Boersch-Supan, Philipp H.
AU - Cavender-Bares, Jeannine
AU - Blaxter, Mark
AU - Chaplin-Kramer, Rebecca
AU - Daru, Barnabas H.
AU - De Palma, Adriana
AU - Eisenberg, Cristina
AU - Elphick, Chris S.
AU - Freckleton, Robert P.
AU - Frick, Winifred F.
AU - Gonzalez, Andrew
AU - Goetz, Scott J.
AU - Greenspoon, Lior
AU - Grozingeree, Christina M.
AU - Hankins, Don L.
AU - Hazell, Jonny
AU - Isaac, Nick J. B.
AU - Lambertini, Marco
AU - Lewin, Harris A.
AU - Mac Aodha, Oisin
AU - Madhavapeddy, Anil
AU - Milner-Gulland, EJ
AU - Milo, Ron
AU - O’Dwyer, James
AU - Purvis, Andy
AU - Salafsky, Nick
AU - Tallis, Heather
AU - Tanshi, Iroro
AU - Vijay, Varsha
AU - Wikelski, Martin
AU - Williams, David R.
AU - Woodard, S. Hollis
AU - Robinson, Gene E.
PY - 2026/3/10
Y1 - 2026/3/10
N2 - Biodiversity is declining in many parts of the world. Biological diversity measurement and monitoring are fundamental to the assessment of the causes and consequences of environmental changes, identification of key areas for the protection of biodiversity or ecosystem services, determining the effectiveness of actions, and the creation of decision-support tools critical to maintaining a sustainable planet. Biodiversity measurement is rapidly changing due to advances in citizen science, image recognition, acoustic monitoring, environmental DNA, genomics, remote sensing, and AI. In this perspective, we outline the exciting opportunities these developments offer but also consider the challenges. Our key recommendations are to 1) Capitalize on the ability of novel technology to integrate data sources 2) agree to standard methods for data collection 3) ensure new technologies are calibrated with existing data; 4) fill data gaps by using emerging technologies and increasing capacity, especially in the tropics; 5) create living safeguarded databases of trusted information to reduce the risk of poisoning by AI hallucinated, or false, information; 6) ensure data generation is valued; 7) ensure respectful incorporation of Indigenous Knowledge; 8) ensure measurements enable the quantification of effectiveness of actions, and 9) increase the resilience of global datasets to technical and societal change. Radical new collaborations are needed between computer scientists, engineers, molecular biologists, data scientists, field ecologists, citizen scientists, Indigenous peoples, policymakers, and local communities to create the rigorous, resilient, accessible biodiversity information systems required to underpin policies and practices that ensure the maintenance and restoration of ecological systems.
AB - Biodiversity is declining in many parts of the world. Biological diversity measurement and monitoring are fundamental to the assessment of the causes and consequences of environmental changes, identification of key areas for the protection of biodiversity or ecosystem services, determining the effectiveness of actions, and the creation of decision-support tools critical to maintaining a sustainable planet. Biodiversity measurement is rapidly changing due to advances in citizen science, image recognition, acoustic monitoring, environmental DNA, genomics, remote sensing, and AI. In this perspective, we outline the exciting opportunities these developments offer but also consider the challenges. Our key recommendations are to 1) Capitalize on the ability of novel technology to integrate data sources 2) agree to standard methods for data collection 3) ensure new technologies are calibrated with existing data; 4) fill data gaps by using emerging technologies and increasing capacity, especially in the tropics; 5) create living safeguarded databases of trusted information to reduce the risk of poisoning by AI hallucinated, or false, information; 6) ensure data generation is valued; 7) ensure respectful incorporation of Indigenous Knowledge; 8) ensure measurements enable the quantification of effectiveness of actions, and 9) increase the resilience of global datasets to technical and societal change. Radical new collaborations are needed between computer scientists, engineers, molecular biologists, data scientists, field ecologists, citizen scientists, Indigenous peoples, policymakers, and local communities to create the rigorous, resilient, accessible biodiversity information systems required to underpin policies and practices that ensure the maintenance and restoration of ecological systems.
KW - auditory data
KW - Indigenous Knowledge
KW - image recognition
KW - AI
KW - eDNA
U2 - 10.1073/pnas.2519345123
DO - 10.1073/pnas.2519345123
M3 - Article
SN - 0027-8424
VL - 123
JO - Proceedings of the National Academy of Sciences of the USA
JF - Proceedings of the National Academy of Sciences of the USA
IS - 10
M1 - e2519345123
ER -