Increasing Mosquito Abundance Under Global Warming

IF 7.3 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Earths Future Pub Date : 2025-06-03 DOI:10.1029/2024EF005629
Gokul Nair, Hong-Yi Li, Jon Schwenk, Kaitlyn Martinez, Carrie Manore, Chonggang Xu
{"title":"Increasing Mosquito Abundance Under Global Warming","authors":"Gokul Nair,&nbsp;Hong-Yi Li,&nbsp;Jon Schwenk,&nbsp;Kaitlyn Martinez,&nbsp;Carrie Manore,&nbsp;Chonggang Xu","doi":"10.1029/2024EF005629","DOIUrl":null,"url":null,"abstract":"<p>Mosquitoes are a key virus vector that poses significant health threats globally, affecting 700 million individuals and causing 1 million deaths annually. Accurately predicting mosquito abundance and dispersion remains a challenge. Complex interactions between mosquito dynamics and various environmental factors, notably hydrology, contribute to this challenge. Existing models typically focus on precipitation and temperature and often overlook further impacts of hydrological variables within mosquito modeling. In this study, we developed an artificial intelligence-based model for mosquito dynamics, explicitly accounting for different hydrological variables, such as precipitation, soil moisture and streamflow. Using Toronto, Canada, as a case study, we identified causal relationships between changes in mosquito populations, hydrological factors, vegetation (e.g., leaf area index), and climate variables (e.g., daylight length, precipitation, and temperature). We embedded these relationships into a Long Short-Term Memory (LSTM) Neural Network Model capable of accurately detecting mosquito dynamics across annual, seasonal, and monthly time scales. The LSTM is able to explain, on average, approximately 40% of the variance in the observed mosquito abundance data. Using the calibrated model, we predicted that the summer season mosquito abundance would increase by ∼16% and ∼19% under an intermediate greenhouse emission scenario, Shared Socioeconomic Pathway (SSP) 2–4.5, and a high greenhouse emission scenario, SSP5-8.5, respectively. We expect that this model can serve as a valuable tool and inform science-based decisions affecting mosquito dynamics and public health. It can also build a foundation for future risk analysis at the regional and larger scales.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 6","pages":""},"PeriodicalIF":7.3000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005629","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earths Future","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024EF005629","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Mosquitoes are a key virus vector that poses significant health threats globally, affecting 700 million individuals and causing 1 million deaths annually. Accurately predicting mosquito abundance and dispersion remains a challenge. Complex interactions between mosquito dynamics and various environmental factors, notably hydrology, contribute to this challenge. Existing models typically focus on precipitation and temperature and often overlook further impacts of hydrological variables within mosquito modeling. In this study, we developed an artificial intelligence-based model for mosquito dynamics, explicitly accounting for different hydrological variables, such as precipitation, soil moisture and streamflow. Using Toronto, Canada, as a case study, we identified causal relationships between changes in mosquito populations, hydrological factors, vegetation (e.g., leaf area index), and climate variables (e.g., daylight length, precipitation, and temperature). We embedded these relationships into a Long Short-Term Memory (LSTM) Neural Network Model capable of accurately detecting mosquito dynamics across annual, seasonal, and monthly time scales. The LSTM is able to explain, on average, approximately 40% of the variance in the observed mosquito abundance data. Using the calibrated model, we predicted that the summer season mosquito abundance would increase by ∼16% and ∼19% under an intermediate greenhouse emission scenario, Shared Socioeconomic Pathway (SSP) 2–4.5, and a high greenhouse emission scenario, SSP5-8.5, respectively. We expect that this model can serve as a valuable tool and inform science-based decisions affecting mosquito dynamics and public health. It can also build a foundation for future risk analysis at the regional and larger scales.

Abstract Image

全球变暖下蚊子数量增加
蚊子是一种主要的病毒载体,对全球健康构成重大威胁,每年影响7亿人,造成100万人死亡。准确预测蚊子的数量和分布仍然是一个挑战。蚊子动态与各种环境因素(特别是水文)之间复杂的相互作用促成了这一挑战。现有模型通常侧重于降水和温度,往往忽略了蚊虫模型中水文变量的进一步影响。在这项研究中,我们开发了一个基于人工智能的蚊子动态模型,明确地考虑了不同的水文变量,如降水、土壤湿度和河流流量。以加拿大多伦多为例,我们确定了蚊子种群、水文因素、植被(如叶面积指数)和气候变量(如日照长度、降水和温度)变化之间的因果关系。我们将这些关系嵌入到一个长短期记忆(LSTM)神经网络模型中,该模型能够准确地检测蚊子在年、季节和月时间尺度上的动态。平均而言,LSTM能够解释观察到的蚊子丰度数据中大约40%的差异。利用校准后的模型,我们预测在共享社会经济路径(SSP) 2-4.5的中间温室排放情景和SSP5-8.5的高温室排放情景下,夏季蚊子丰度将分别增加~ 16%和~ 19%。我们期望这个模型可以作为一个有价值的工具,为影响蚊子动态和公共卫生的科学决策提供信息。它还可以为将来在区域和更大范围内进行风险分析奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Earths Future
Earths Future ENVIRONMENTAL SCIENCESGEOSCIENCES, MULTIDI-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
11.00
自引率
7.30%
发文量
260
审稿时长
16 weeks
期刊介绍: Earth’s Future: A transdisciplinary open access journal, Earth’s Future focuses on the state of the Earth and the prediction of the planet’s future. By publishing peer-reviewed articles as well as editorials, essays, reviews, and commentaries, this journal will be the preeminent scholarly resource on the Anthropocene. It will also help assess the risks and opportunities associated with environmental changes and challenges.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信