The Italian National Forest Inventory (IFNI2025): a new opportunity for plant diversity monitoring

Monitoring forest resources through National Forest Inventories (NFIs) is a fundamental tool for policymaking particularly regarding environmental planning and sustainable land management. In Italy, the first NFI dates back to the 1980s followed by a second in 2005 and a third in 2015, each introducing improvements in sampling design and survey protocols to address emerging information needs. The new Italian National Forest Inventory (IFNI2025) represents an innovative program marked by a transition from decennial periodicity to a continuous survey approach with the production of annual estimates. The sampling design involves a firstphase tessellation stratified sampling on a 4 km × 4 km grid followed by a second phase of field surveys on points classified by remote sensing imagery as forest. The second phase sampling will take place annually over a five-year cycle. For the first time, IFNI2025 will include a systematic survey of variables related to plant and lichen diversity, complementing the assessment of traditional forest attributes. Specifically, data on six plant morpho-functional groups (i.e., terricolous lichens, bryophytes, pteridophytes, forbs, grasses, and woody species) will be collected along transects at second-phase points (approximately 1,300 per year) by personnel from the Carabinieri Command for Forest, Environmental, and Agri-food Protection (CUFAA). These data will be used to estimate the presence/absence and average abundance of the morpho-functional groups at national, regional, and biogeographic scales. The protocol also includes a spatially balanced sampling design covering 10% of the second-phase points with vegetation surveys performed by expert botanists. Diversity indicators will be calculated, including the average number of species (with a focus on alien species and conservation-relevant categories such as specialists and endemics) and their relative abundance. The integration of field data with expert-derived floristic checklists will also be explored to enhance species richness estimates. Custom estimators will be developed to quantify the target parameters and their associated uncertainties. Epiphytic macrolichens will be detected using a simplified method in which an artificial intelligence algorithm is trained to interpret photos of lichens present within a sampling grid. The data will be interpreted based on the main functional traits. This new protocol represents a significant advancement in national forest monitoring—a major step forward in integrating biodiversity assessments into NFIs—providing consistent and scalable data to support forest conservation and management in Italy.