Increasing Mosquito Abundance Under Global Warming
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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Earth's Future |
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| Online Access: | https://doi.org/10.1029/2024EF005629 |
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| author | Gokul Nair Hong‐Yi Li Jon Schwenk Kaitlyn Martinez Carrie Manore Chonggang Xu |
| author_facet | Gokul Nair Hong‐Yi Li Jon Schwenk Kaitlyn Martinez Carrie Manore Chonggang Xu |
| author_sort | Gokul Nair |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b50e8aa87ecf451f93519399eaa1673e |
| institution | OA Journals |
| issn | 2328-4277 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Earth's Future |
| spelling | doaj-art-b50e8aa87ecf451f93519399eaa1673e2025-08-20T02:22:09ZengWileyEarth's Future2328-42772025-06-01136n/an/a10.1029/2024EF005629Increasing Mosquito Abundance Under Global WarmingGokul Nair0Hong‐Yi Li1Jon Schwenk2Kaitlyn Martinez3Carrie Manore4Chonggang Xu5Department of Environmental and Civil Engineering University of Houston Houston TX USADepartment of Environmental and Civil Engineering University of Houston Houston TX USAEarth and Environmental Sciences Division, Los Alamos National Laboratory Los Alamos NM USAEarth and Environmental Sciences Division, Los Alamos National Laboratory Los Alamos NM USAEarth and Environmental Sciences Division, Los Alamos National Laboratory Los Alamos NM USAEarth and Environmental Sciences Division, Los Alamos National Laboratory Los Alamos NM USAAbstract 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.https://doi.org/10.1029/2024EF005629mosquitoregional scalehydrology |
| spellingShingle | Gokul Nair Hong‐Yi Li Jon Schwenk Kaitlyn Martinez Carrie Manore Chonggang Xu Increasing Mosquito Abundance Under Global Warming Earth's Future mosquito regional scale hydrology |
| title | Increasing Mosquito Abundance Under Global Warming |
| title_full | Increasing Mosquito Abundance Under Global Warming |
| title_fullStr | Increasing Mosquito Abundance Under Global Warming |
| title_full_unstemmed | Increasing Mosquito Abundance Under Global Warming |
| title_short | Increasing Mosquito Abundance Under Global Warming |
| title_sort | increasing mosquito abundance under global warming |
| topic | mosquito regional scale hydrology |
| url | https://doi.org/10.1029/2024EF005629 |
| work_keys_str_mv | AT gokulnair increasingmosquitoabundanceunderglobalwarming AT hongyili increasingmosquitoabundanceunderglobalwarming AT jonschwenk increasingmosquitoabundanceunderglobalwarming AT kaitlynmartinez increasingmosquitoabundanceunderglobalwarming AT carriemanore increasingmosquitoabundanceunderglobalwarming AT chonggangxu increasingmosquitoabundanceunderglobalwarming |