Modeling mosquito population dynamics has become an important part of understanding the transmission of mosquito-borne arboviruses. Of these models, those including meteorological variables have mainly focused on conditions during or immediately preceding the mosquito breeding season. While these conditions are clearly critical biologically and statistically, it is also biologically plausible that conditions during the off-season may contribute to interannual variation in mosquito population size. To examine the effect of off-season factors, we develop a pair of Poisson regression models for July captures of Aedes sollicitans and Culex salinarius, two East Coast vector species of arboviruses including Eastern equine encephalitis virus and West Nile virus. Model results indicate that average maximum temperature, total heating degree-days, and the total number of days with a minimum temperature below freezing during the winter months was predictive of mosquito populations. In 123 280 Environ Ecol Stat (2008) 15:279-291 addition, the average maximum relative humidity from the preceding fall and total rainfall and total heating degree-days during the preceding spring were also associated with vector population dynamics. The descriptive and predictive power of these models is discussed.
Numerous studies have investigated the role of weather on insect species. For mosquitoes, these studies have yielded mixed results. Although it is clear that weather impacts mosquito population dynamics, these investigations have failed to accurately characterize their fluctuations. We use a novel graphical method to examine large numbers of meteorological aggregations of varying lengths and lags simultaneously to establish relationships between these summary variables and mosquito counts, to gain a better understanding of the weather effects. Poisson regression models were developed to characterize the population dynamics of Aedes sollicitans (Walker) by using meteorological data and a 34-yr set of daily mosquito count data. The models accurately characterize mosquito dynamics over time and space. The aggregated meteorological variables included in the model were lowest minimum tides between days 27 and 14 before trapping, total precipitation between days 22 and 9, total precipitation on day 1 and the day of trapping, cooling degree-days on day 0, average minimum relative humidity between days 28 and 9, lowest stream flow from day 11 to day 0, and lowest minimum temperature between days 28 and 13.
Numerous studies have investigated the role of weather on insect species. For mosquitoes, these studies have yielded mixed results. Although it is clear that weather impacts mosquito population dynamics, these investigations have failed to accurately characterize their fluctuations. We use a novel graphical method to examine large numbers of meteorological aggregations of varying lengths and lags simultaneously to establish relationships between these summary variables and mosquito counts, to gain a better understanding of the weather effects. Poisson regression models were developed to characterize the population dynamics of Aedes sollicitans (Walker) by using meteorological data and a 34-yr set of daily mosquito count data. The models accurately characterize mosquito dynamics over time and space. The aggregated meteorological variables included in the model were lowest minimum tides between days 27 and 14 before trapping, total precipitation between days 22 and 9, total precipitation on day 1 and the day of trapping, cooling degree-days on day 0, average minimum relative humidity between days 28 and 9, lowest stream flow from day 11 to day 0, and lowest minimum temperature between days 28 and 13.
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