Spatial Zoning Design for Multi-Species Protected areas using Marxan and Hotspot analysis

Abstract

Systematic conservation planning has become crucial for protecting biodiversity and sustaining ecosystem resilience in Alula, a Saudia Arabia County where distinctive habitats are threatened by urbanization and climate change. Despite the pressing need to safeguard biodiversity and ecosystem services, traditional conservation efforts frequently fail to optimize cost-effectiveness while addressing complex ecological and socioeconomic issues. Spatial zoning of terrestrial areas using Marxan approach provides a systematic, data-driven alternative that may overcome these constraints and improve conservation outcomes. This research aimed to use Marxan to identify biodiversity hotspots in Alula and create an effective network of protected areas for species conservation and ecological connection. This study employed ArcGIS for topographical analysis, followed by the Getis ord approach for hotspot analysis. Marxan is used to create a protected area network with conservation objectives of 30%, 50%, and 70% for species representation, respectively, together with a Boundary Length Modifier (BLM) to reduce fragmentation and Penalty Factors (SPF) to prioritize rare species. The findings showed that the 30% target allocated 55% of the area for human use, 39% for strict protection, and 5% for mixed-use; the 50% target resulted in 49% human use, 39% strict protection, and 12% mixed-use; and the 70% target assigned 23% for human use, 39% for strict protection, and 38% for mixed-use. The study found that, although a 50% conservation target efficiently met biodiversity objectives, larger targets, such as 70%, encountered difficulties in accomplishing habitat conservation in response to the complexity of objective defining and resource allocation. Future conservation initiatives should incorporate adaptive management approaches and the involvement of stakeholders to improve the efficacy and sustainability of spatial zoning and conservation assignments.