Biodiversity monitoring as data practices: Moving beyond actor-centric science-policy interfaces

The uptake of biodiversity monitoring data for policy and decision-making has remained limited. While several studies and initiatives address the gap between science and policy-making, biodiversity monitoring frameworks such as the CBD’s Global Biodiversity Framework increasingly expect biodiversity monitoring data to directly inform reporting and subsequently decision-making frameworks. Most studies and initiatives approach this interface assuming predefined actor groups (e.g. scientists and policymakers). At the same time, a lack of knowledge persists on the actual data practices of the individuals involved in biodiversity-related monitoring, reporting and policy-making from local observation sites to research institutions, repositories and national or regional decision-making bodies. This study examines these practices by employing a latent class analysis using data from an online survey within the biodiversity monitoring community. The analysis examines shared and divergent data practices and tests whether respondents’ affiliations explain their data practices. It highlights the added value of a person- and practice-centric approach in comparison to an actor- or affiliation-centric analysis by demonstrating that certain practices transcend actor groups while also revealing differences. These findings advance the discourse on science-policy interfaces in biodiversity monitoring and environmental governance more broadly, arguing for understanding the biodiversity monitoring science-policy interface as a data-to-policy interface in which a community of individuals from different actor groups converge around and build common data practices rather than a bilateral bridging between two actor groups. • A latent class analysis using survey data reveals three distinct biodiversity data practice profiles. • Data practices are not determined by institutional affiliation but by individual engagement patterns. • The "actor-centric" science-policy gap can be reframed as a "practice-centric" data-policy interface. • All classes report similar challenges but differ in the use of essential variables and international repositories. • Understanding shared data practices can enhance GBF reporting.