Conserv Biol. 2026 Aug 10:e70367. doi: 10.1111/cobi.70367. Online ahead of print.
ABSTRACT
Identifying ecologically matching areas is both challenging and critical for understanding spatial patterns of biodiversity and advancing conservation planning. However, existing approaches often lack spatial flexibility, overlook shape and size constraints, or rely on limited environmental parameters, reducing their effectiveness in real-world applications. To address these limitations, we developed and tested a four-step framework for identifying ecologically and spatially matching areas. Our framework method involved defining the parameters for the comparative analyses; assessing similar environmental conditions between target protected areas and potential matching areas, through a modified version of the multivariate environmental similarity surface (mMESS); simulating randomly distributed polygons that mirror both the shape and surface area of the target protected area; and integrating the simulated polygons with the resulting mMESS to identify areas where environmental similarity, shape, and surface match. Finally, we applied decision criteria to identify real-world matching areas. We tested our approach in both the Northern and the Central Apennines (Italy), comparing a national park in each of these two regions to similar but unprotected areas-specifically excluding national parks, regional parks, and Natura 2000 Network sites. Our framework offers a robust tool for identifying matching and mismatching areas and enhancing our understanding of these areas’ spatial distributions and environmental conditions to support conservation planning and management. The framework may be applied to a broad range of ecological studies at various scales and has the potential to guide future conservation efforts and policies.
PMID:42572926 | DOI:10.1111/cobi.70367
