Predicting the environmental suitability for malaria vectors in Iran using ENM and GCM models and climate change scenarios
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عنوان دوره: سومین کنگره بین المللی حشره شناسی ایران
نویسندگان
Department of Medical Entomology & Vector Control, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran,
چکیده
Forecasting the geographical distribution of malaria vectors is very important for effective malaria control planning. Changes in environmental conditions, population dynamic and land use are important barriers to effective malaria control programs. This study aimed to predict the most suitable ecological niches for the malaria vectors in Iran under climate change scenarios in 2030s and 2050s. Literature search was performed to find documents on the spatial distribution of Anopheles stephensi, An. culicifacies s.l., An. fluviatilis s.l., An. superpictus s.l., An. dthali, An. maculipennis s.l., and An. sacharovi published between 1970 and 2017. The bioclimatic data under three climate change scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) were downloaded from the worldclim database at a spatial resolution of 1 km2. MaxEnt model was used to predict the ecological niches for each species under the climate change scenarios in the 2030s and 2050s. Comparison between the two study periods under the three scenarios for each species revealed that RCP 8.5 would reduce the area at risk for An. culicifacies s.l., An. dthali and An. superpictus s.l. in the 2050s compared to the 2030s, but the reverse will be induced by RCP 2.6 and RCP 4.5 scenarios. For An. fluviatilis s.l., RCP 2.6 will reduce the risk areas in the 2050s, whereas an increase is expected under the two other scenarios. Moreover, all scenarios would decrease the high-risk areas of An. maculipennis s.l. in the 2050s. For An. sacharovi, RCP 2.6 would increase its high risk areas, whereas RCP 4.5 and RCP 8.5 would decrease its exposure. The high risk area of An. stephensi is expected to increase under RCP8.5 in the 2030s, and RCP 4.5 in 2050s, but it will be almost unchanged or reduced under other scenarios
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