Research Article: Predicting the Timing and Magnitude of Tropical Mosquito Population Peaks for Maximizing Control Efficiency

Date Published: February 24, 2009

Publisher: Public Library of Science

Author(s): Guo-Jing Yang, Barry W. Brook, Corey J. A. Bradshaw, Xiao-Nong Zhou

Abstract: The transmission of mosquito-borne diseases is strongly linked to the abundance of the host vector. Identifying the environmental and biological precursors which herald the onset of peaks in mosquito abundance would give health and land-use managers the capacity to predict the timing and distribution of the most efficient and cost-effective mosquito control. We analysed a 15-year time series of monthly abundance of Aedes vigilax, a tropical mosquito species from northern Australia, to determine periodicity and drivers of population peaks (high-density outbreaks). Two sets of density-dependent models were used to examine the correlation between mosquito abundance peaks and the environmental drivers of peaks or troughs (low-density periods). The seasonal peaks of reproduction (r) and abundance () occur at the beginning of September and early November, respectively. The combination of low mosquito abundance and a low frequency of a high tide exceeding 7 m in the previous low-abundance (trough) period were the most parsimonious predictors of a peak’s magnitude, with this model explaining over 50% of the deviance in . Model weights, estimated using AICc, were also relatively high for those including monthly maximum tide height, monthly accumulated tide height or total rainfall per month in the trough, with high values in the trough correlating negatively with the onset of a high-abundance peak. These findings illustrate that basic environmental monitoring data can be coupled with relatively simple density feedback models to predict the timing and magnitude of mosquito abundance peaks. Decision-makers can use these methods to determine optimal levels of control (i.e., least-cost measures yielding the largest decline in mosquito abundance) and so reduce the risk of disease outbreaks in human populations.

Partial Text: Only a few of the approximately 3000 mosquito species known worldwide feed on human blood [1]. Unfortunately, these human pest species are responsible for the deaths of millions of people each year by transmitting some of the deadliest-known diseases, such as malaria, yellow fever, dengue, and Rift Valley fever [2]. Different mosquito species transmit different diseases; for example, Aedes aegypti is the primary vector of the arboviruses dengue and yellow fevers [3],[4]; several Anopheles species carry different forms of the malaria protozoan parasite [5], and Aedes vigilax transmits Ross River and Barmah Forest virus [6],[7].

Ae. vigilax is a particularly notorious and heavily controlled mosquito species in Australia because of its role in the spread and maintenance of several serious infectious human diseases such as Ross River and Barmah Forest fevers [30],[31]. Indeed, these two diseases were the most common mosquito-borne diseases reported in Australia in 2005–2006 [32], with over 5000 cases of Ross River fever reported annually. Previous studies suggested that human infection rates are related to the appearance of large numbers of adult Ae. vigilax[8], with the recommendation that targeted control of adults in disease-prone areas could reduce the number of cases substantially. As such, identifying the environmental and biological precursors that herald the onset of peaks in mosquito abundance provide health and land-use managers the capacity to predict the timing and distribution of the most efficient and cost-effective mosquito control. Our results presented here on the amplitude and timing of outbreaks, when combined with the more general previous work on intrinsic and environmental determinants of mosquito population dynamics [9],[10], clearly demonstrate that a relatively simple set of conditions – low abundance of adult mosquitoes in the trough season, coupled with a low frequency of high tide and low rainfall – can predict peaks in mosquito abundance and potentially outbreaks of human disease with sufficient reliability to be a useful decision-making tool for managers.



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