Modeling of spatial distribution of Anopheles maculipennis complex and Culex theileri (Diptera: Culicidae) in Isfahan province, Central Iran
عنوان دوره: دومین کنگره بین المللی حشره شناسی ایران
نویسندگان
چکیده
Introduction: Mosquitoes are one of the most important groups of medical arthropods transmit malaria, filariasis, arboviruses and cause harassment due to their bites. Despite activities regarding mosquito control in Iran there is still the problem of painful bites of these insects, especially in central areas like Isfahan Province. This study was conducted to find spatial distribution and the best ecological niches for two main mosquito species: Anopheles maculipennis complex and Culex theileri.
Materials and Methods: Dipping standard method was used for larval collection in this study. Also earlier studies were reviewed and data on spatial distribution of the target species were extracted from their results. All data were transferred to a database in ArcGIS and then the distribution maps were created. MaxEnt model and climatology variables were used to predict and map the geographical distribution of medically important species with enough occurrence data points. Jackknife analysis in MaxEnt model was used to find the effective variables for each species.
Results and Discussion: In this study 1143 larvae were collected including 6 species: Anopheles maculipennis, An. superpictus s.l., An. merteri, Culex hortensis, Cx. theileri and Culiseta longoareolata. MaxEnt modeling showed the area under curve was calculated as 0.951 and 0.873 rather 1 for An. maculipennis and Cx. theileri, respectively. These numbers show an acceptable validity for the exported maps. Based on the maps, Cx. theileri has wider and more appropriate niches across the province, except for the eastern area. More appropriate niches for An. maculipennis were restricted to the western and southwestern areas. Based on Jackknife analysis, temperature seasonality was the environmental variable with highest gain when used in isolation. Regarding Cx. theileri the environmental variable with highest gain in modeling was temperature of the wettest quarter. Considering the results of niche modeling for vector species, it is recommended to do additional studies on vectorial capacity of these species, their physiological age and parasitic infection, in order to predict the risk of establishing filariasis, West Nile fever, and malaria in Isfahan province, Iran.
Materials and Methods: Dipping standard method was used for larval collection in this study. Also earlier studies were reviewed and data on spatial distribution of the target species were extracted from their results. All data were transferred to a database in ArcGIS and then the distribution maps were created. MaxEnt model and climatology variables were used to predict and map the geographical distribution of medically important species with enough occurrence data points. Jackknife analysis in MaxEnt model was used to find the effective variables for each species.
Results and Discussion: In this study 1143 larvae were collected including 6 species: Anopheles maculipennis, An. superpictus s.l., An. merteri, Culex hortensis, Cx. theileri and Culiseta longoareolata. MaxEnt modeling showed the area under curve was calculated as 0.951 and 0.873 rather 1 for An. maculipennis and Cx. theileri, respectively. These numbers show an acceptable validity for the exported maps. Based on the maps, Cx. theileri has wider and more appropriate niches across the province, except for the eastern area. More appropriate niches for An. maculipennis were restricted to the western and southwestern areas. Based on Jackknife analysis, temperature seasonality was the environmental variable with highest gain when used in isolation. Regarding Cx. theileri the environmental variable with highest gain in modeling was temperature of the wettest quarter. Considering the results of niche modeling for vector species, it is recommended to do additional studies on vectorial capacity of these species, their physiological age and parasitic infection, in order to predict the risk of establishing filariasis, West Nile fever, and malaria in Isfahan province, Iran.
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