Partly cloudy with a chance of mosquitoes: Developing a flexible approach to forecasting mosquito populations

Ecosphere
By: , and 

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Abstract

Climate-induced shifts in mosquito phenology and population structure have important implications for the health of humans and wildlife. The timing and intensity of mosquito interactions with infected and susceptible hosts are a primary determinant of vector-borne disease dynamics. Like most ectotherms, rates of mosquito development and corresponding phenological patterns are expected to change under shifting climates. However, developing accurate forecast of mosquito phenology under climate change that can be used to inform management programs remains challenging despite an abundance of available data. As climate change will have variable effects on mosquito demography and phenology across species it is vital that we identify associated traits which may explain the observed variation. Here, we review a suite of modeling approaches that could be applied to generate forecasts of mosquito activity under climate change and evaluate the strengths and weaknesses of the different approaches. We describe four primary life-history and physiological traits that can be used to constrain models and demonstrate how this prior information can be harnessed to develop a more general understanding of how mosquito activity will shift under changing climates. Combining a trait-based approach with appropriate modeling techniques can allow for the development of actionable, flexible, and multi-scale forecasts of mosquito population dynamics and phenology for diverse stakeholders.

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Publication type Article
Publication Subtype Journal Article
Title Partly cloudy with a chance of mosquitoes: Developing a flexible approach to forecasting mosquito populations
Series title Ecosphere
DOI 10.1002/ecs2.70074
Volume 15
Issue 12
Year Published 2024
Language English
Publisher Wiley
Contributing office(s) National Wildlife Health Center
Description e70074, 17 p.
Country United States
State Michigan, Wisconsin
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