Trait-based prediction of disease-vector mosquito invasion potential

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Abstract
Mosquito-borne diseases pose a growing global health threat, largely driven by the human-mediated introduction of vector species beyond their native regions. Although only a few mosquito species have historically established populations outside their native ranges, the number of such invasions has increased rapidly in recent decades. Once established, these non-native invasive mosquitoes are difficult to control, emphasizing the need for proactive identification before introduction occurs. Here, we present a framework to predict invasion potential for 184 mosquito species of medical importance based on their ecological, life-history, and macroecological traits. We compiled a comprehensive dataset of 26 traits characterizing each species and used random forest models to relate these traits to the probabilities of introduction into non-native regions (before and after 1950, marking the onset of widespread trade globalization) and establishment following introduction. Models achieved moderate to good predictive performance (AUC = 0.78–0.85) and revealed that species native to Asia and Australia, adapted to human-made breeding sites, and associated with high-precipitation and thermally extreme environments were consistently more likely to be introduced or establish in non-native regions. Among species with no known invasion history, we identified 24 with high predicted introduction probabilities, of which 17 also showed high establishment probabilities. These results demonstrate that invasion potential can be inferred from species traits and provide a quantitative basis for proactive surveillance and prioritization of species most likely to be introduced in the future.
Funding Information
  • R&D unit Global Health and Tropical Medicine (UIDB/04413/2025)
  • R&D unit Global Health and Tropical Medicine (UIDB/04413/2025)
  • Associated Laboratory in Translation and Innovation Towards Global Health REAL (LA/P/0117/2020)
  • Associated Laboratory in Translation and Innovation Towards Global Health REAL (LA/P/0117/2020)
  • FCT (PRT/BD/153694/2021)
  • FCT (https://doi.org/10.54499/2023.12533.PEX)
  • FCT (https://doi.org/10.54499/UID/00295/2025)

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