Estimations of Fine-Scale Species Distributions of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in Eastern Florida

Many species distribution maps indicate the ranges of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) overlap in Florida despite the well-documented range reduction of Ae. aegypti. Within the last 30 yr, competitive displacement of Ae. aegypti by Ae. albopictus has resulted in partial spatial segregation of the two species, with Ae. aegypti persisting primarily in urban refugia. We modeled fine-scale distributions of both species, with the goal of capturing the outcome of interspecific competition across space by building habitat suitability maps. We empirically parameterized models by sampling 59 sites in south and central Florida over time and incorporated climatic, landscape, and human population data to identify predictors of habitat suitability for both species. Our results show human density, precipitation, and urban land cover drive Ae. aegypti habitat suitability, compared with exclusively climatic variables driving Ae. albopictus habitat suitability. Remotely sensed variables (macrohabitat) were more predictive than locally collected metrics (microhabitat), although recorded minimum daily temperature showed significant, inverse relationships with both species. We detected minor Aedes habitat segregation; some periurban areas that were highly suitable for Ae. albopictus were unsuitable for Ae. aegypti. Fine-scale empirical models like those presented here have the potential for precise risk assessment and the improvement of operational applications to control container-breeding Aedes mosquitoes.

Automated summary

The distribution ranges of Aedes aegypti and Aedes albopictus overlap in Florida, but due to competitive displacement, the two species show partial spatial segregation. Factors such as human density, precipitation, and urban land cover drive the habitat suitability of Ae. aegypti, while only climatic variables drive the suitability of Ae. albopictus. Remote sensing data are more predictive in modeling than locally collected metrics.