Title
Lessons from COP15 on effective scientific engagement in biodiversity policy processes
Author(s)
Carroll, Carlos; Hoban, Sean; Ray, Justina C.
Published
2024
Publisher
Conservation Biology
Abstract
The Kunming-Montreal Global Biodiversity Framework was recently adopted by parties to the Convention on Biological Diversity. The aftermath of these negotiations provides an opportunity to draw lessons as to how ecological and evolutionary science can more effectively inform policy. We examine key challenges that limit effective engagement by scientists in the biodiversity policy process, drawing parallels with analogous challenges within global climate negotiations. Biodiversity is multi-faceted, yet represents only one framing for nature's contributions to people, complicating the nexus between evidence and values in development of the framework's targets. Processes generating biodiversity and driving its loss are multi-scalar, challenging development of an evidence base for globally-standardized targets. We illustrate these challenges by contrasting development of two key elements of the framework. The genetic diversity element of the framework's Target 4 is directly related to topline goals, but its complexity required development of novel engagement skills. The target for protected areas was easily communicated but more indirectly related to biodiversity outcomes; evidence from ecological and social science was essential in communicating the context and limitations of this relationship. Scientists can strengthen the effectiveness of global agreements and address challenges arising from complexity, scaling, capacity limitations, and the interplay of science and values, if they can prioritize communications, consensus-building and networking skills and engage throughout the process from development of an evidence base to implementation. This article is protected by copyright. All rights reserved
Keywords
biodiversity monitoring;Convention on Biological Diversity;ecological scale;genetic diversity;Global Biodiversity Framework
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PUB36146