Bridging the research-implementation gap in IUCN Red List assessments. (April 2022)

• Record Type: Journal Article
• Authors: Cazalis, Victor; Di Marco, Moreno; Butchart, Stuart H.M.; Akçakaya, H. Reşit; González-Suárez, Manuela; Meyer, Carsten; Clausnitzer, Viola; Böhm, Monika; Zizka, Alexander; Cardoso, Pedro; Schipper, Aafke M.; Bachman, Steven P.; Young, Bruce E.; Hoffmann, Michael; Benítez-López, Ana; Lucas, Pablo M.; Pettorelli, Nathalie; Patoine, Guillaume; Pacifici, Michela; Jörger-Hickfang, Theresa; Brooks, Thomas M.; Rondinini, Carlo; Hill, Samantha L.L.; Visconti, Piero; Santini, Luca
• Is Part Of: Trends in ecology & evolution. Volume 37:Number 4(2022)
• Page Start: 359
• Page End: 370
• Publication Date: 2022-04
• Subjects: extinction risk -- species conservation -- biodiversity -- remote-sensing -- automated assessment -- user-friendly platforms; Ecology -- Periodicals; Evolution (Biology) -- Periodicals; 576.8
• DOI: 10.1016/j.tree.2021.12.002
• Languages: English
• ISSNs: 0169-5347

Abstract

Abstract : The International Union for Conservation of Nature (IUCN) Red List of Threatened Species is central in biodiversity conservation, but insufficient resources hamper its long-term growth, updating, and consistency. Models or automated calculations can alleviate those challenges by providing standardised estimates required for assessments, or prioritising species for (re-)assessments. However, while numerous scientific papers have proposed such methods, few have been integrated into assessment practice, highlighting a critical research–implementation gap. We believe this gap can be bridged by fostering communication and collaboration between academic researchers and Red List practitioners, and by developing and maintaining user-friendly platforms to automate application of the methods. We propose that developing methods better encompassing Red List criteria, systems, and drivers is the next priority to support the Red List. Highlights: The IUCN Red List of Threatened Species plays a central role in monitoring biodiversity and informing conservation actions. To best inform conservation, the Red List must be frequently updated, become more taxonomically and geographically representative, and be consistent within and among taxonomic groups, but this is hampered by limited resources. A variety of models and automated calculations has been proposed in the literature to support Red List assessments, for instance using citizen science or remote-sensing data to predict extinction risk. We highlight a major research–implementation gap in the application of these methods, which could be bridged by providing assessors with easy access to the most relevant tools, hands-on training, and strengthening communication. Further efforts are needed to develop relevant methods to prioritise assessments or better predict extinction risk.