Predicting the Distribution of Phlebotomus papatasi (Diptera: Psychodidae), the Primary Vector of Zoonotic Cutaneous Leishmaniasis, in Golestan Province of Iran Using Ecological Niche Modeling: Comparison of MaxEnt and GARP Models

Zoonotic cutaneous leishmaniasis (ZCL) is a prevalent vector- borne disease in the Golestan province of Iran, with Phlebotomus papatasi (Scopoli, 1786) serving as the main vector. The aim of this study was to model the probability of presence of this species in the study area, and to determine the underlying factors affecting its distribution. Three villages were selected from each county of the province and visited monthly for investigating ZCL. Sticky paper traps were used for collecting the sand flies to determine the species present. The presence of Ph. papatasi was modeled using genetic algorithm for rule- set production (GARP) and maximum entropy (MaxEnt) techniques. Both models showed the central and northern parts of the province with lowland areas were more vulnerable to Ph. papatasi propagation, in comparison with the southern parts withmountainous and forest areas. The area under curve (AUC) of MaxEnt model for the training points was calculated as 0.90, indicating excellent performance of the model in predicting Ph. papatasi distribution. Jackknife test showed that the factors with the greatest influence in vector distribution were slope, vegetation cover, annual mean temperature, and altitude. By using ecological niche models, it is possible to identify areas with higher probability of presence of Ph. papatasi, which guides public health policymakers for planning better vector control interventions.