Applications of GIS in biodiversity and faunal studies (Zoological Records and Reviews, 2025)
Geographic Information Systems (GIS) have become indispensable analytical infrastructure for biodiversity and faunalresearch, enabling spatial integration of species occurrence data with environmental predictor layers, landscapeconnectivity analysis, protected area network design, and biodiversity monitoring at scales ranging from individual habitatpatches to global biogeographic analyses. This study presents a comprehensive application of GIS-based analyticalmethods to three biodiversity and faunal assessment objectives across Finland, Germany, and Denmark: (i) speciesdistribution modelling (SDM) for 42 vertebrate species using MaxEnt with Copernicus land cover and CHELSA climatelayers; (ii) landscape connectivity analysis for six focal mammal species using least-cost path and circuit theory(Circuitscape) approaches; and (iii) systematic conservation planning using Marxan to identify optimal protected areanetwork extensions for 28 Habitats Directive Annex II species. SDM models achieved mean test AUC of 0.84 +- 0.06across 42 species; GIS-derived landscape heterogeneity index was the strongest SDM predictor for 31 of 42 species(74%). Circuitscape analysis identified 284 functional connectivity bottlenecks for focal mammals across the threecountries; 68.4% of these coincide with road crossing sites previously identified as wildlife mortality hotspots (paper 69this series). Marxan planning solutions identified 48 priority planning units for Natura 2000 network extension, covering all 28 Annex II species at their respective representation targets at 38.4% lower total cost than current network expansion plans. These results demonstrate GIS as an integrative platform enabling evidence-based spatial biodiversity planning under EU Nature Restoration Law targets.
Keywords: biodiversity spatial analysis; EU Nature Restoration Law; MaxEnt; Natura 2000; systematic conservation planning; Circuitscape; Marxan; landscape connectivity; species distribution modelling; GIS