Nine changes needed to deliver a radical transformation in biodiversity measurement

Journal article

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.

Authors: Sutherland, WJ; Burgess, ND; Goetz, SJ; Jones, JPG; Soltis, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: National Academy of Sciences
• Journal: Proceedings of the National Academy of Sciences
• Volume: 123
• Issue: 10
• Article number: e2519345123
• Publication date: 2026-03-04
• Acceptance date: 2026-01-06
• DOI: 10.1073/pnas.2519345123
• EISSN: 1091-6490
• ISSN: 0027-8424
• Language: English
• Keywords: artificial intelligence; remote sensing; image recognition; Indigenous Knowledge; auditory data; eDNA; genomics; biodiversity; biological collections