Towards a modern and efficient European biodiversity observation network fit for multiple policies

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. To address the biodiversity crisis, global and regional policy frameworks like the Kunming-Montreal Global Biodiversity Framework and the European Green Deal demand to monitor biodiversity. Despite these efforts, existing approaches for monitoring biodiversity remain fragmented and lack data integration. Here, we review and synthesize crucial information for developing an integrated European-wide biodiversity monitoring framework using Essential Biodiversity Variables (EBVs), with the aim to improve data coverage, enhance transnational coordination, adopt advanced technologies, and better inform environmental policies. Using a participatory approach involving over 1500 stakeholders, we prioritized EBVs for assessing biodiversity status and trends and supporting European policies, identified relevant monitoring technologies, developed recommendations for a spatial sampling design, and estimated the costs of implementing a continent-wide biodiversity observation network that covers terrestrial, freshwater, and marine ecosystems. A total of 84 EBVs addressing genetic, species, community and ecosystem-level biodiversity attributes were prioritized. A broad range of monitoring methods is required, especially structured in-situ monitoring schemes and satellite and airborne remote sensing, complemented with citizen science observations, DNA-based methods, digital sensors, and biological observations derived from weather radar. Our suggestions for a more effective spatial sampling design ensure a broad representation of European biodiversity, especially through stratified random sampling, incorporation of existing monitoring sites, filling of spatial gaps, and co-location of monitoring activities. Developing the prioritized EBVs will require to integrate multiple biodiversity data streams, apply advanced modelling techniques for gap-filling, and account for different sources of uncertainty. A digital infrastructure is required with supporting services, and with data being shared using interoperable standards and published on open platforms. The costs of such a European biodiversity observation network were estimated to be at least 5.7 billion Euro over 10 years, including initial investments and annual maintenance. A European Biodiversity Observation Coordination Centre (EBOCC) is needed to coordinate monitoring activities and data management. The network’s benefits for addressing multiple policies, including improved ecosystem services, will by far outweigh the expenses involved in establishing and maintaining the entire network. The illustrated co-design offers a scalable model for developing biodiversity monitoring networks in other continents, with potential adaptations to local policies and conditions. https://doi.org/10.32942/X2K34F Life Sciences biodiversity policy, community composition, cost effectiveness, Data Cubes, Ecosystem functioning, ecosystem structure, genetic composition, monitoring, multi-taxa biodiversity assessments, species populations, stakeholder co-creation, stratified random sampling Published: 2024-11-01 18:06 CC BY Attribution 4.0 International Conflict of interest statement: None Data and Code Availability Statement: The following data are already publicly available: The list of selected Essential Biodiversity Variables (EBVs) as identified by the EuropaBON project is available from GitHub (https://github.com/EuropaBON/EBV-Descriptions/wiki). Descriptions of EBV workflows as collected during the EuropaBON online workshop on EBV workflows (February 2023) are available from the Zenodo repository (https://zenodo.org/doi/10.5281/zenodo.10680435). The following data are not yet provided, but will be permanently archived on the open Zenodo repository if the paper is accepted for publication: Raw data and metadata used to generate Figure 3, 4, 6, 8, 9, and 11–15. Raw data and metadata used for the cost estimation (Appendix S2 and S3, Table 5 & 6). No code has been used for this study. Language: English