Predicting the timing and magnitude of tropical mosquito population peaks for maximizing control efficiency.

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 distributio...

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Main Authors: Guo-Jing Yang, Barry W Brook, Corey J A Bradshaw
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2009-01-01
Series:PLoS Neglected Tropical Diseases
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19238191/pdf/?tool=EBI
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author Guo-Jing Yang
Barry W Brook
Corey J A Bradshaw
author_facet Guo-Jing Yang
Barry W Brook
Corey J A Bradshaw
author_sort Guo-Jing Yang
collection DOAJ
description 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 (N(peak)) 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 N(peak). Model weights, estimated using AIC(c), 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.
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spelling doaj.art-7f5dce9637aa4fe5b8c353f28c6b8f5c2022-12-21T22:58:04ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352009-01-0132e38510.1371/journal.pntd.0000385Predicting the timing and magnitude of tropical mosquito population peaks for maximizing control efficiency.Guo-Jing YangBarry W BrookCorey J A BradshawThe 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 (N(peak)) 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 N(peak). Model weights, estimated using AIC(c), 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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19238191/pdf/?tool=EBI
spellingShingle Guo-Jing Yang
Barry W Brook
Corey J A Bradshaw
Predicting the timing and magnitude of tropical mosquito population peaks for maximizing control efficiency.
PLoS Neglected Tropical Diseases
title Predicting the timing and magnitude of tropical mosquito population peaks for maximizing control efficiency.
title_full Predicting the timing and magnitude of tropical mosquito population peaks for maximizing control efficiency.
title_fullStr Predicting the timing and magnitude of tropical mosquito population peaks for maximizing control efficiency.
title_full_unstemmed Predicting the timing and magnitude of tropical mosquito population peaks for maximizing control efficiency.
title_short Predicting the timing and magnitude of tropical mosquito population peaks for maximizing control efficiency.
title_sort predicting the timing and magnitude of tropical mosquito population peaks for maximizing control efficiency
url https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19238191/pdf/?tool=EBI
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AT barrywbrook predictingthetimingandmagnitudeoftropicalmosquitopopulationpeaksformaximizingcontrolefficiency
AT coreyjabradshaw predictingthetimingandmagnitudeoftropicalmosquitopopulationpeaksformaximizingcontrolefficiency