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Climate-change-driven shifts in the population dynamics of the invasive tiger mosquito (Aedes albopictus) in the Alps
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Abstract
The recent global expansion of the Asian tiger mosquito (Aedes albopictus) across tropical and temperate regions provides a clear example of the mobility and adaptability of invasive species. Among multiple drivers influencing the spread of this species, climate change is emerging as a major driver, creating conditions that favour its persistence and expansion into higher latitudes and elevations. Hence, as global warming continues to make the climate of mountain areas milder, understanding Ae. albopictus' range and seasonality expansion in the Alpine area is fundamental, as it could introduce and propagate mosquito-borne diseases in previously free areas.
Our objective is to evaluate the likely impact of climate change on the distribution and seasonality of Ae. albopictus for the periods 2036-2055 and 2066-2085 in the Alps. We use entomological data collected for public health surveillance, along with temperature and precipitation datasets from surface observations and regional climate model simulations, to train a machine-learning mosquito population model and predict its spatial distribution under both current and future climate conditions.
Our results demonstrate how increasing temperatures and altered precipitation regimes increase the abundance and seasonal expansion of Ae. albopictus. In particular, rising temperatures are projected to push the species' range to higher altitudes and to lengthen the duration of climatically suitable conditions. Projected warming increased mean season length by 1.8–3.6 weeks, depending on location, and expanded suitable elevation by several hundred metres across future climates, demonstrating consistent climate-driven increases in both seasonal activity and spatial reach of Ae. albopictus in the Alps.
DOI
https://doi.org/10.32942/X2T952
Subjects
Computer Sciences, Ecology and Evolutionary Biology, Entomology
Keywords
climate warming, global change, invasive species, machine learning, mosquito, range shift, species distribution modelling
Dates
Published: 2026-03-25 10:47
Last Updated: 2026-03-25 10:47
License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
None.
Data and Code Availability Statement:
The data and scripts that support the findings of this study are openly available in a Zenodo repository at https://doi.org/10.5281/zenodo.18620181.
Language:
English
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