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The  Culex pipiens  complex of mosquitoes are significant vectors of several pathogens resulting in infectious human diseases in North America, including but not limited to West Nile encephalitis, Rift Valley Fever, and Lymphatic filariasis. Among this complex are  C .  pipiens  form pipiens and  Culex quinquefasciatus . While morphologically similar, the mosquitoes exhibit unique life histories that suit them uniquely to divergent niches, wherein  C .  pipiens  can thrive despite the cold winters of the northern United States and  C .  quinquefasciatus  is able to survive periods of drought typical in the southern states. Here, Random Forests machine-learning algorithms were employed to model and explore which environmental parameters best explain mosquito occurrence in historical trapping data across the continental United States of America, and test correlation with abundance data. The models explained between 71 and 97% of the presence or absence of the two mosquitoes based on historical climatic data. The results of this study will improve vector management programs by explaining which environmental variables will provide the most accurate predictions of mosquito presence at a given site.

Random Forest Analysis of Impact of Abiotic Factors on Culex pipiens and Culex quinquefasciatus Occurrence