Enhancing Natura 2000 habitat monitoring: A framework for biodiversity conservation assessment

• Practical, interpretable method supports EU Habitats Directive Article 17 reporting. • Structured framework standardizes monitoring and strengthens biodiversity data. • AI, ML, satellites, and field data enable large-scale Annex I habitat mapping. • HDTM identifies suitable areas, supporting habitat detection and prioritization. • Validated field data underpin standardized national Habitats Directive databases. Habitat monitoring and conservation assessment have gained importance in the EU, especially since the adoption of the Habitats Directive and the creation of the Natura 2000 network. Accurate reporting on habitat conservation status depends on detailed knowledge of habitat distribution. Advances in open-access biodiversity databases, remote sensing, and machine learning algorithms have improved ecosystem mapping, while field surveys remain essential for understanding habitat dynamics. This study proposes a standardized framework for habitat mapping and monitoring, applied to the Annex I habitat types of the Habitats Directive in the Latium region - Central Italy. A large dataset of georeferenced vegetation plots was collected through systematic fieldwork and classified using expert systems and clustering analysis. These labeled plots were then used to train Habitat Distribution Models based on Random Forest algorithms, generating high-resolution habitat maps. The maps were refined through probability thresholding and validated using the Article 17 reporting dataset, primarily based on expert judgment. The methodology successfully identified 41 habitat types, including coastal, dune, grassland, rocky, and forest ecosystems. Beyond mapping known habitats, the proposed modeling framework also serves as a tool for identifying knowledge gaps and uncovering conservation opportunities, providing ecologically meaningful signals that can reveal overlooked biodiversity hotspots and uncover previously undocumented vegetation history events. This reproducible and scalable workflow delivers robust spatial data on habitat distribution, extent, and condition, supporting both regional and national conservation reporting. It offers a valuable tool for practitioners and researchers, enhancing long-term monitoring, informing restoration actions, and improving evidence-based conservation planning.