Ecological determinants and indicator-based analysis of Aedes albopictus expansion in a Central European metropolis:...

The invasive bridge vector mosquito Aedes albopictus has been increasingly detected across Europe, posing potential risks for arboviral disease transmission. Urban-scale assessments of its expansion remain scarce in Central Europe. The establishment and spread of Ae. albopictus in Budapest were analysed between 2018 and 2025 using spatio-temporal mapping, seasonal observations, and indicator-based ecological modelling, complemented by ensemble machine learning approaches. Occurrence patterns followed a logistic growth trajectory (R² = 0.995), with colonization rising from sparse foci in 2020 to over 85% of districts by 2025. Seasonal activity extended from late April to mid-October, peaking in early September. Ensemble machine learning models consistently achieved high predictive performance, with key predictors included urban fabric, temperature, topography, and precipitation-related indices (bio18; Köppen Aridity Index) while other factors contributed variably. Ecological associations were strongest with urbanized land cover (discontinuous and continuous urban fabric, industrial areas), specific soil types such as fluvent entisols, and humid temperate climates (Köppen Cfa). K-means clustering and decision tree analyses distinguished seven ecological clusters across Budapest, ranging from warm, densely built urban cores to cooler, shaded peri‑urban and forested zones. Conceptually linking ecological clusters to the Sustainable Development Goals highlighted spatially heterogeneous intersections with health (SDG 3), urban sustainability (SDG 11), water management (SDG 6), climate action (SDG 13), and biodiversity conservation (SDG 15). These findings provide a baseline for predicting urban vector expansion, informing early warning systems, and guiding public health interventions and vector control strategies in European metropolitan regions.