Spatial dependence and the role of landscape factors as predictors of insecticide resistance in Aedes aegypti mosquitoes across Florida

Authors: Stephanie J. Mundis*, University of Florida, Department of Geography, Quantitative Disease Ecology and Conservation Lab; University of Florida Emerging Pathogens Institute, Sadie J. Ryan, University of Florida, Department of Geography, Quantitative Disease Ecology and Conservation Lab; University of Florida Emerging Pathogens Institute
Topics: Medical and Health Geography, Biogeography, Applied Geography
Keywords: insecticide resistance, Aedes aegypti, Ripley's K, landscape genetics
Session Type: Paper
Day: 4/4/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Tyler, Marriott, Mezzanine Level
Presentation File: No File Uploaded


The evolution of insecticide resistance in the mosquitoes that transmit disease can result in vector control failure and disease resurgence. In Florida, insecticides are widely used, but the resistance status of vector species is often unknown. In this study, I examined the resistance status of Aedes aegypti, a mosquito species that is closely associated with humans and can transmit dengue, chikungunya, and Zika viruses. This was explored using previously published genetic data from Aedes aegypti collected at 59 sites across 18 counties in Florida. The objectives of this study were to determine if genotypes, or genetic variants, predicting high levels of resistance are spatially clustered, evaluate the spatial scale of that clustering, and identify landscape factors that influence this phenomenon. Ripley’s K-function, a multinomial SaTScan analysis, and a linear regression modeling approach were used to meet these objectives. The results indicate that the resistant genotype exhibits significant clustering across multiple scales. Additionally, urban areas along the western coast of the state had higher than expected frequencies of the resistant genotype, though areas with higher than expected frequencies of susceptible genotypes were also identified along both coasts. The linear regression models indicated that landscape characteristics including distance from agricultural land cover and normalized difference vegetation index had statistically significant effects on the outcome of insecticide resistance. Demographic factors, namely population density and median income at the census tract level, were also influential. These outcomes indicate that spatial factors could be used to predict and potentially manage insecticide resistance in the field.

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