Abstract
Invasive Aedes mosquitoes are major vectors of arboviral diseases such as dengue, Zika, and chikungunya, posing an increasing threat to global public health. Their recent geographic expansion calls for predictive models to simulate population dynamics and transmission risk. Temperature is a key driver in these models, influencing traits that affect vector competence. Numerous datasets on temperature-dependent traits exist for Aedes aegypti and Aedes albopictus, though they are scattered, inconsistent, and difficult to synthesise. For emerging species like Aedes japonicus and Aedes koreicus, such datasets are scarce. To address these gaps, we developed AedesTraits, an open-access, machine-readable dataset aligned with VecTraits standards. It compiles and systematises experimental data on temperature-dependent traits across these four Aedes species, covering life-history, morphological, physiological, and behavioural traits. Our synthesis highlights existing knowledge gaps and identifies under-studied species and traits. By promoting data systematisation and accessibility, AedesTraits supports Aedes–borne disease modelling and fosters international collaboration in the development of forecasting tools for arbovirus outbreaks.
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Data availability
AedesTraits is permanently archived in a Zenodo repository (https://doi.org/10.5281/zenodo.15149903). In addition, AedesTraits is also deposited in and available for download from the VecTraits database30.
Code availability
No custom code was used to create this dataset.
References
WHO, Global Arbovirus Initiative. Preparing for the Next Pandemic by Tackling Mosquito-Borne Viruses with Epidemic and Pandemic Potential. WHO: Geneva, Switzerland, (2024).
Fauci, A. S. & Morens, D. M. Zika virus in the Americas—yet another arbovirus threat. New England Journal of Medicine 374(7), 601–604 (2016).
Estallo, E. L. et al. Increased risks of mosquito-borne disease emergence in temperate regions of South America. The Lancet Regional Health – Americas 40, 100946 (2024).
Cattaneo, P. et al. Transmission of autochthonous Aedes-borne arboviruses and related public health challenges in Europe 2007-2023: a systematic review and secondary analysis. The Lancet Regional Health-Europe. (2025).
Otero, M., Solari, H. G. & Schweigmann, N. A stochastic population dynamics model for Aedes aegypti: formulation and application to a city with temperate climate. Bulletin of Mathematical Biology 68(8), 1945–1974 (2006).
Erguler, K. et al. A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology. PLOS ONE 12(3), e0174293 (2017).
Aguirre, E. et al. Implementation of a proactive system to monitor Aedes aegypti populations using open access historical and forecasted meteorological data. Ecological Informatics 64, 101351 (2021).
Da Re, D. et al. dynamAedes: a unified modelling framework for invasive Aedes mosquitoes. Parasites & Vectors 15(1), 414 (2022).
Brass, D. P. et al. Role of vector phenotypic plasticity in disease transmission as illustrated by the spread of dengue virus by Aedes albopictus. Nature Communications 15(1), 7823 (2024).
San Miguel, T. V., Da Re, D., & Andreo, V. “A systematic review of Aedes aegypti population dynamics models based on differential equations.” Acta Tropica 107459 (2024).
Johnson, L. R. et al. Understanding uncertainty in temperature effects on vector-borne disease: a Bayesian approach. Ecology 96(1), 203–213 (2015).
Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLoS Neglected Tropical Diseases 11(4), e0005568 (2017).
Molnár, P. K., Sckrabulis, J. P., Altman, K. A. & Raffel, T. R. Thermal performance curves and the metabolic theory of ecology—a practical guide to models and experiments for parasitologists. Journal of Parasitology 103(5), 423–439 (2017).
Johnson, L. R. et al. Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: A dengue case study. The Annals of Applied Statistics 12(1), 27 (2018).
Cator, L. J. et al. The role of vector trait variation in vector-borne disease dynamics. Frontiers in Ecology and Evolution 8, 189 (2020).
Amarasekare, P. & Savage, V. A framework for elucidating the temperature dependence of fitness. The American Naturalist 179(2), 178–191 (2012).
Eisen, L. et al. The impact of temperature on the bionomics of Aedes (Stegomyia) aegypti, with special reference to the cool geographic range margins. Journal of Medical Entomology 51(3), 496–516 (2014).
Gloria-Soria, A., Armstrong, P. M., Powell, J. R. & Turner, P. E. Infection rate of Aedes aegypti mosquitoes with dengue virus depends on the interaction between temperature and mosquito genotype. Proceedings of the Royal Society B 284(1864), 20171506 (2017).
Reinhold, J. M., Lazzari, C. R. & Lahondère, C. Effects of the environmental temperature on Aedes aegypti and Aedes albopictus mosquitoes: a review. Insects 9(4), 158 (2018).
Lahondère, C. & Bonizzoni, M. Thermal biology of invasive Aedes mosquitoes in the context of climate change. Current Opinion in Insect Science 51, 100920 (2022).
Scott, J. J. (2003). The Ecology of the Exotic Mosquito Ochlerotatus japonicus japonicus and an Examination of its Role in the West Nile Virus Cycle in New Jersey. Ph.D. thesis, Rutgers University.
Ciocchetta, S. et al. Laboratory colonization of the European invasive mosquito Aedes (Finlaya) koreicus. Parasites & Vectors 10(1), 1–6 (2017).
Marini, G. et al. First report of the influence of temperature on the bionomics and population dynamics of Aedes koreicus. Parasites & Vectors 12(1), 1–12 (2019).
Reuss, F. et al. Thermal experiments with the Asian bush mosquito (Aedes japonicus japonicus) (Diptera: Culicidae) and implications for its distribution in Germany. Parasites & Vectors 11(1), 1–10 (2018).
Wieser, A. et al. Modelling seasonal dynamics, population stability, and pest control in Aedes japonicus japonicus (Diptera: Culicidae). Parasites & Vectors 12(1), 1–12 (2019).
Moretti, M. et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits. Functional Ecology 31(3), 558–567 (2017).
Ryan, S.J. et al. MIReVTD, a Minimum Information Standard for Reporting Vector Trait Data. bioRxiv 2025–01 (2025).
Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. Annals of Internal Medicine 151(4), W-65–W-94 (2009).
Rohatgi, A.WebPlotDigitizer: Version 4.4. https://automeris.io/WebPlotDigitizer (2020).
Johnson, L.R. et al. VecTraits Explorer. University of Notre Dame Available at: https://doi.org/10.7274/28020782 (2023).
Rund, S.S.C. et al. VecDyn Explorer. University of Notre Dame Available at: https://doi.org/10.7274/10083626 (2023).
Sorek, S., Smith Jr, J.W., Huxley, P.J., Johnson, L.R. bayesTPC: Bayesian inference for thermal performance curves in R. Methods in Ecology and Evolution https://doi.org/10.1111/2041-210X.70004 (2025).
Hoffmann, A. A. & Ross, P. A. Rates and patterns of laboratory adaptation in (mostly) insects. Journal of Economic Entomology 111(2), 501–509 (2018).
Huxley, P. J. et al. AedesTraits: A global dataset of temperature-dependent trait responses in Aedes mosquitoes (v.3). Zenodo, Available at: https://zenodo.org/records/17752818. https://doi.org/10.5281/zenodo.15149903 (2025).
Huxley, P. J., Murray, K. A., Pawar, S. & Cator, L. J. The effect of resource limitation on the temperature dependence of mosquito population fitness. Proceedings of the Royal Society B 288(1949), 20203217 (2021).
Huxley, P. J., Murray, K. A., Pawar, S. & Cator, L. J. Competition and resource depletion shape the thermal response of population fitness in Aedes aegypti. Communications Biology 5(1), 66 (2022).
Kramer, I. M. et al. The ecophysiological plasticity of Aedes aegypti and Aedes albopictus concerning overwintering in cooler ecoregions is driven by local climate and acclimation capacity. Science of The Total Environment 778, 146128 (2021).
Dennington, N. L. et al. Phenotypic adaptation to temperature in the mosquito vector, Aedes aegypti. Global Change Biology 30(1), e17041 (2023).
Couper, L. I. et al. Evolutionary adaptation under climate change: Aedes sp. demonstrates potential to adapt to warming. Proceedings of the National Academy of Sciences 122(2), e2418199122 (2025).
R Core Team R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Disponible en: https://www.R-project.org/ (2024).
Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology 49(4), 764–766 (2013).
Nakase, T., Giovanetti, M., Obolski, U. & Lourenço, J. Global transmission suitability maps for dengue virus transmitted by Aedes aegypti from 1981 to 2019. Scientific Data 10(1), 275 (2023).
Acknowledgements
Daniele Da Re was supported by the Marie Skłodowska-Curie Actions – Postdoctoral fellowship Nr. 101106664. Veronica Andreo and Tomas San Miguel were supported by Agencia Nacional de Promoción Científica y Tecnológica, Argentina (PICT Nr. 00372-2021). Paul Huxley, Joe Harrison, Sean Sorek and Leah Johnson were funded by NSF DBI #2016264 and NSF DMS/DEB #1750113. Marharyta Blaha and Roberto Rosà were funded by the Italian research grant PRIN “MosqIT” funding. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. The authors thank Dr Eisen for kindly providing access to the raw data of his publication and also thank Lauren Chapman, Thomas Byrne, and Wills McGraw for the datasets that they worked on.
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Daniele Da Re, Veronica Andreo and Paul Huxley conceived the study; Paul Huxley led the literature review and digitisation efforts, with relevant contributions from Daniele Da Re, Veronica Andreo, Tomas San Miguel, Marharyta Blaha, Joe Harrison and Sean Sorek; Paul Huxley reviewed all the digitised information, ensuring that it adhered to the VecTraits standards. Daniele Da Re and Veronica Andreo performed the summary analyses of the dataset; Daniele Da Re led the writing of the manuscript, with relevant contributions from Veronica Andreo and Paul Huxley. All authors contributed critically to the drafts and gave their final approval for publication.
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Da Re, D., Andreo, V., San Miguel, T.V. et al. AedesTraits: A global dataset of temperature–dependent trait responses in Aedes mosquitoes.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06461-z
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DOI: https://doi.org/10.1038/s41597-025-06461-z
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