Modeling the non-stationary climate dependent temporal dynamics of Aedes aegypti. - Taynãna C Simões; Cláudia T Codeço; Aline A Nobre; Alvaro E Eiras (PLoS ONE, 2013)
BACKGROUND: Temperature and humidity strongly affect the physiology, longevity, fecundity and dispersal behavior of Aedes aegypti, vector of dengue fever. Contrastingly, the statistical associations measured between time series of mosquito abundance and meteorological variables are often weak and contradictory. Here, we investigated the significance of these relationships at different time scales. METHODS AND FINDINGS: A time series of the adult mosquito abundance from a medium-sized city in Brazil, lasting 109 weeks was analyzed. Meteorological variables included temperature, precipitation, wind velocity and humidity. As analytical tools, generalized linear models (GLM) with time lags and interaction terms were used to identify average effects while the wavelet analysis was complementarily used to identify transient associations. The fitted GLM showed that mosquito abundance is significantly affected by the interaction between lagged temperature and humidity, and also by the mosquito abundance a week earlier. Extreme meteorological variables were the best predictors, and the mosquito population tended to increase at values above [Formula: see text] and 54% humidity. The wavelet analysis identified non-stationary local effects of these meteorological variables on abundance throughout the study period, with peaks in the spring-summer period. The wavelet detected weak but significant effects for precipitation and wind velocity. CONCLUSION: Our results support the presence of transient relationships between meteorological variables and mosquito abundance. Such transient association may be explained by the ability of Ae. aegypti to buffer part of its response to climate, for example, by choosing sites with proper microclimate. We also observed enough coupling between the abundance and meteorological variables to develop a model with good predictive power. Extreme values of meteorological variables with time lags, interaction terms and previous mosquito abundance are strong predictors and should be considered when understanding the climate effect on mosquito abundance and population growth.