Creating the vision of rapid, repeatable, reactive data workflows for policy on biodiversity

作者：Quentin Groom, Laura Abraham, Tim Adriaens, Lissa Breugelmans, David A. Clarke, Michele Di Musciano, Shawn Dove, Lina M. Estupiñán-Suárez, Katelyn T. Faulkner, Miguel Fernández, Louise Antonia Hendrickx, Cang Hui, Alexis Joly, Sabrina Kumschick, Ward Langeraert, Matilde Martini, Joseph T. Miller, Damiano Oldoni, Henrique M. Pereira, Cristina Preda, Tim Robertson, Duccio Rocchini, Andrew Rodrigues, Maxime Ryckewaert, Hanno Seebens, Yanina V. Sica, Heliana Teixeira, Maarten Trekels, Toon Vandaele, John R. Wilson, Tsungai A. Zengeya, Peter Desmet · 发表于：Ecological Solutions and Evidence · 年份：2025 · DOI：10.1002/2688-8319.70113 · 被引用次数：3 · 研究领域：Species Distribution and Climate Change、Wildlife Ecology and Conservation、Data Analysis with R

Abstract Effective biodiversity management and policymaking requires timely access to accurate and reliable scientific data on biodiversity status, trends and threats. However, current biodiversity monitoring processes are often time‐consuming, complex and irreproducible. Moreover, the quality and types of biodiversity data are diverse, which challenges their integration and impedes effective monitoring. A major step to overcome such challenges would be the availability of standardized species occurrence data. However, challenges arise in aggregating and integrating these heterogeneous data with environmental and landscape data. By creating standardized biodiversity data cubes and automated workflows for post‐processing, we envision that (1) information from complex datasets will be available in a known format to efficiently communicate biodiversity variables to policymakers; (2) the adoption of repeatable Open Data workflows will make biodiversity data more accessible, efficient and cost‐effective; and (3) cloud computing will make it easier to analyse large datasets, benefit from a broader range of models, share resources and work together on biodiversity projects. This revolution in biodiversity monitoring will rely on community collaboration. By bridging the gap between policymakers' needs, bioinformation specialists' skills and data collectors' motivations, biodiversity monitoring can become a more inclusive and community‐driven effort. As such, we advocate for the devel...