Development and Application of a Climate-Driven Environmental Sustainability Indicator for Assessing Future Expansion Risk of Phlebotomus Vectors of Zoonotic Cutaneous Leishmaniasis in Semi-Arid Iran under CMIP6 Climate Change Scenarios

Introduction: Climate change is altering ecosystem dynamics and the distribution of climate-sensitive disease vectors, creating emerging environmental and public health challenges. This study developed a Climate-Driven Environmental Sustainability Indicator (ESI) to assess future expansion risk of Phlebotomus vectors of zoonotic cutaneous leishmaniasis (ZCL) in semi-arid Iran.Materials and Methods: A total of 312 spatially filtered occurrence records were analyzed using the Maximum Entropy (MaxEnt) model with bioclimatic and topographic predictors. Future habitat suitability was projected for 2041–2060 using an ensemble of five CMIP6 climate models under SSP2-4.5 and SSP5-8.5 scenarios. Habitat suitability outputs were integrated with climate exposure, habitat transition dynamics, and environmental vulnerability metrics to construct the ESI. Hotspot, uncertainty, and sensitivity analyses were performed to evaluate future environmental risks and indicator robustness.Results: The MaxEnt model showed excellent predictive performance (mean test AUC = 0.942 ± 0.018). Temperature-related variables were the primary drivers of vector distribution. Suitable habitats increased by 8.1% under SSP2-4.5 and 16.4% under SSP5-8.5, with pronounced northward and altitudinal shifts. High- and Very High-Risk areas identified by the ESI expanded from 20.7% under current conditions to 28.0% and 38.9% under SSP2-4.5 and SSP5-8.5, respectively. Emerging hotspots were concentrated in ecological transition zones, and ensemble projections demonstrated high inter-model agreement.Discussion: Climate change is likely to substantially redistribute vector-related environmental risks across semi-arid Iran. The proposed ESI provides a practical and transferable framework for environmental assessment, climate adaptation planning, hotspot prioritization, and sustainability-oriented public health decision-making.