A machine-learning approach to map landscape connectivity in <i>Aedes aegypti</i> with genetic and environmental data.

Abstract

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, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the <i>Aedes aegypti</i> mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza-Edwards distance (CSE) performs better than linearized F_ST The correlation (R) between the model's predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for <i>Ae. aegypti</i>'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.