Detecting seasonal transient correlations between populations of the West Nile Virus vector Culex sp. and temperatures with wavelet coherence analysis

Abstract

Cullex sp. is one of the most important mosquito disease vector and climate is considered to be a key factor affecting its population dynamics. In this study we use straightforward techniques based on correlations and wavelet analysis to determine the non-trivial associations between Culex sp. mosquito abundance and weather variables (lagged population abundance, mean temperatures, rain and wind speed) in Northern Greece during two successive years. In particular, mosquito abundances were examined for collinearity using Pearson's correlation matrices and redundant or low correlated variables were excluded from further analyses. Multiple linear regression analysis was performed to fit normalized mosquito abundance weekly counts as the dependent variable to the independent variables including: mean temperatures, rain as well as lagged mosquito populations. There was a high and positive correlation between temperature and mosquito abundance during both observation years (r = 0.6). However, a very poor correlation was observed between rain and weekly mosquito abundances (r = 0.29), as well as between wind speed (r = 0.29), respectively. Additionally, according to the multiple linear regression model the effect of temperature, was significant. The continuous power spectrum of the mosquito abundance counts and mean temperatures depict in most cases similar power for periods which are close to 1 week, indicating the point of the lowest variance of the time series, although appearing on slight different moments of time. The cross wavelet coherent analysis showed that inter weekly cycles with a period between 2 and 3 weeks between mosquito abundance and temperature were coherent mostly during the first and the last weeks of the season. Hence, the wavelet analysis show a progressive oscillation in mosquito occurrences with time, which is higher at the start and the end of the season. Moreover, in contrast with standard methods of analysis, wavelets can provide useful insights into the time-resolved oscillation structure of mosquito data and accompanying revealing a non-stationary association with temperature.