Ecological Niche Modeling of main reservoir hosts of zoonotic cutaneous leishmaniasis in Iran

• Zoonotic cutaneous leishmaniasis is a common disease in Iran.
• Developing the prediction models and risk maps are necessary for disease management.
• MaxEnt model was used to find the most appropriate ecological niches for reservoirs.
• Wide areas from northeast to southwest provide the favorite niches for main reservoirs.

Zoonotic cutaneous leishmaniasis (ZCL), caused by Leishmania major, is a common zoonotic vector-borne disease in Iran. Close contact with infected reservoir hosts increases the probability of transmission of Leishmania parasite infections to susceptible humans. Four gerbil species (Rodentia: Gerbillidae) serve as the main reservoir hosts for ZCL in different endemic foci of Iran. These species include Rhombomys opimus, Meriones libycus, Meriones hurrianae and Tatera indica; while notable infection has been reported in Nesokia indica as well. The purpose of this study is to model the distribution of these reservoirs to identify the risk areas of ZCL. A data bank was developed including all published data during the period of 1970–2015. Maximum entropy model was used to find the most appropriate ecological niches for each species. The areas under curve obtained were 0.961, 0.927, 0.922, 0.997 and 0.899, instead of 1, for training test in R. opimus, M. libycus, T. indica, M. hurrianae and N. indica, respectively. The environmental variable with the highest gain when used in isolation was slope for R. opimus and N. indica, annual mean temperature for M. libycus, and seasonal precipitation for T. indica and M. hurrianae. Summation of presence probabilities for three main species, i.e., R. opimus, M. libycus and T. indica revealed favorable ecological niches in wide areas of 16 provinces. This is the first study to predict the distribution of ZCL reservoir hosts in Iran. Climatology and topography variables had high contributions toward the prediction of potential distribution of the main reservoir species; therefore, as climate changes, the models should be updated periodically with novel data, and the results should be used in disease-monitoring programs.

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