Advancing the Species Protection Index for GBF Reporting Through Integration of High-Resolution Species Distribution Models and New Earth Observation Data

The Kunming-Montreal Global Biodiversity Framework (GBF) emphasizes the need for standardized, transparent, and scalable indicators to evaluate national progress toward global biodiversity goals. As both an Essential Biodiversity Variable (EBV) and a component indicator for GBF Target 3, the Species Protection Index (SPI) measures how well species’ geographic distributions are represented within various forms of protected and conserved areas. Recent updates to the SPI build on advances in species distribution modeling, improved global habitat mapping, and expanded data coverage across taxa, creating new opportunities to strengthen national-level reporting.

In this contribution, we present ongoing efforts to enhance the SPI through integration of high-resolution species distribution models, refined habitat suitability layers, and emerging Earth-observation–derived land-cover datasets. These developments directly address long-standing challenges for GBF reporting: capturing ecological patterns at spatial grains relevant to management decisions and reflecting real-time changes in habitat integrity and species ranges resulting from land-use dynamics, climate pressures, and conservation interventions. Increasingly, new Earth Observation (EO) products driven by commercial data providers, combined with nationally produced EO datasets, require the SPI workflow to flexibly incorporate heterogeneous inputs while remaining globally consistent and reproducible.

We evaluate how these new data sources influence SPI calculations across regions and taxonomic groups, identifying where EO inputs most substantially improve national indicator values. We highlight trade-offs among spatial resolution, temporal frequency, and global completeness, and assess the local data and tools required for countries seeking consistent, repeatable metrics for their GBF monitoring cycles.

Finally, we outline current examples of SPI operationalization within national biodiversity monitoring systems and describe how updated workflows, supported by cloud-based computation, private data integration, and repeatable modeling pipelines, enable Parties to update their data and more easily integrate the SPI into routine reporting. These enhancements position the SPI as a more robust, scalable, and flexible indicator for assessing species-level outcomes under the GBF.