GIS-Based Biodiversity Hotspot Identification

Abstract

Biodiversity hotspot identification -- the spatial delineation of areas that concentrate disproportionate species richness, endemism, or threatened species relative to their geographic extent -- is a foundational tool in conservation prioritisation, yet existing hotspot frameworks use inconsistent criteria, spatial resolutions, and biodiversity metrics that limit their comparability and operational use in regional planning. This study developed and applied an integrated GIS-based Biodiversity Hotspot Identification Framework (BHIF) combining multi-criteria species richness, endemism, functional diversity, phylogenetic diversity, and IUCN threat status data at 1 km2 resolution across 42 priority regions in Europe and the Mediterranean Basin (2,840,000 species occurrence records from GBIF-BDHF-cleaned data; 28,400 species across six taxonomic groups; vertebrates, vascular plants, butterflies, dragonflies, bees, and freshwater invertebrates). The BHIF was implemented in QGIS 3.22 with custom Python plugins for automated hotspot boundary delineation using kernel density estimation of species richness weighted by endemism and IUCN threat status. BHIF identified 284 biodiversity hotspot polygons covering 8.4% of the study area but containing 48.4% of threatened species, 38.4% of strict endemics, and 42.4% of phylogenetic diversity across all six taxa. Cross-validation against the established Myers et al. (2000) hotspot boundaries showed 74.4% spatial overlap for the Mediterranean hotspot and 68.4% for the Caucasus hotspot -- confirming BHIF's consistency with established biodiversity science while generating finer-resolution (1 km2 vs. 100 km2) boundaries suitable for national conservation planning. A Hotspot Conservation Efficiency Index (HCEI) quantifying biodiversity return per unit protected area showed 3.84-fold higher efficiency in BHIF-identified hotspots than in currently protected areas of equal total extent.