A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, the advantages of a machine learning framework combined with an iterative optimization process to develop a method for integrating genetic and environmental data. Here, this method is validated and demonstrated for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. A map of genetic connectivity is produced for Ae. aegypti's range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control. (MapVEu VBP0000715)