Ecological niche modeling of mosquito vectors of West Nile virus in St. John’s County, Florida, USA

BACKGROUND: The lack of available vaccines and consistent sporadic transmission of WNV justify the need for mosquito vector control and prediction of their geographic distribution. However, the distribution of WNV transmission is dependent on the mosquito vector and the ecological requirements, which vary from one place to another. METHODS: Presence/density data of two WNV mosquito vectors, Culex nigripalpus and Cx. quinquefasciatus, was extracted within 5 km buffer zones around seropositive records of sentinel chickens in order to delineate their predicting variables and model the habitat suitability of probable infective mosquito using MaxEnt software. Different correlations between density data of the extracted mosquito vectors and 27 climate, land use-land cover, and land surface terrain variables were analyzed using linear regression analysis. Accordingly, the correlated predicting variables were used in building up habitat suitability model for the occurrence records of both mosquito vectors using MaxEnt. RESULTS: The density of both WNV mosquito vectors showed variation in their ecological requirements. Eight predicting variables, out of 27, had significant influence on density of Cx. nigripalpus. Precipitation of driest months was shown to be the best predicting variable for the density of this vector (R(2) = 41.70). Whereas, two variables were proven to predict the distribution of Cx. quinquefasciatus density. Vegetation showed the maximum predicting gain to the density of this mosquito vector (R(2) = 15.74), where nestling birds, in particular exotics, are found. Moreover, Jackknife analysis in MaxEnt demonstrated that urbanization and vegetation data layers significantly contribute in predicting habitat suitability of Cx. nigripalpus and Cx. quinquefasciatus occurrence, respectively, which justifies the contribution of the former in urban and the latter in epizootic transmission cycles of WNV. In addition, habitat suitability risk maps were produced for both vectors in response to their predicting variables. CONCLUSIONS: For the first time in the study area, a quantitative relationship between 27 predicting variables and two WNV mosquito vectors within their foraging habitats was highlighted at the local scale. Accordingly, the predicting variables were used to produce a practical distribution map of probable infective mosquito vectors. This substantially helps in determining where suitable habitats are found. This will potentially help in designing target surveillance and control programmes, saving money, time and man-power. However, the suitability risk maps should be updated when serological and entomological data updates are available.