Predicting Age in Aedes aegypti (Diptera: Culicidae) Females to Monitor Changes in Transmission Potential

Abstract Background: The Aedes aegypti mosquito is a vector of several viruses including dengue, chikungunya, zika, and yellow fever. Vector surveillance and control are the primary methods used for the control and prevention of disease transmission, however, there is an overreliance on measures of population abundance in surveillance programs as a trigger for initiating control activities. At the northern edge of Ae. aegypti’s geographic range, survival seems to be the factor limiting disease transmission. In this study, we sought to test the utility of using body size as an entomological index to surveil changes in the age structure of field collected, female Aedes aegypti. Methods: We collected female Ae. aegypti mosquitoes using BG sentinel traps in three cities at the northern edge of their geographic range. Collections took place during their active season over the course of three years. Female wing size was measured as an estimate of body size and reproductive status was characterized by examining ovary tracheation. Chronological age was determined by measuring transcript abundance of an age-dependent gene. These data were then combined with weather data from the estimated larval development period and adulthood (one week prior to capture). Two sources of weather data were tested to see which was more appropriate for evaluating impacts on mosquito physiology. All variables were then used to test models for predicting age via structural equation modeling. Results: We found that there was a bias in the body size of mosquitoes collected alive from the BG sentinel traps that favored large females. In comparing city-specific NOAA weather data and site-specific data from HOBO remote temperature and humidity loggers, we found that HOBO data was more tightly associated with body size. We found that body size itself was not associated with age. Of all the variables measured, we found that temperature during development, body size, and relative humidity in the one week prior to capture produced the strongest model for predicting age. The strength of models improved drastically when testing one city at a time, with Hermosillo having the strongest model for predicting age.Conclusions: Body size increased the strength of weather-based models for predicting variation in age. Importantly, we found that variability of the factors measured was greater within cities than between cities, meaning that age predictions must be made on a city by city basis. These results contribute to efforts to use weather forecasts to predict changes in the probability of disease transmission by mosquito vectors.