Future geographical distribution of Aedes albopictus in China under climate change scenarios

PLOS One | https://doi.org/10.1371/journal.pone.0327818 August 6, 2025 4 / 14 2.2 Environmental variables By reviewing relevant literature, 19 bioclimatic factors related to temperature and precipitation were selected data from the global climate dataset WorldClim (https://www.worldclim.org) (Table 1). These include current climate data (1970–2000) and future climate data (2050s, 2070s), with a spatial resolution of 2.5 arc-minutes [30]. The future climate data is based on the medium-resolution climate system model (BC-CSM2-MR) from the China (Beijing) Climate Center for the sixth Coupled Model Intercomparison Project (CMIP6), which is most suitable for China. The climate projections are based on four Shared Socioeconomic Pathways (SSP). SSP1–2.6 envisions a sustainable world with green energy, low population growth, and reduced fossil fuel use. SSP2–4.5 assumes moderate population and economic growth with current energy trends. SSP3–7.0 depicts a fragmented world with high population growth, slow economic progress, and limited coopera- tion. SSP5–8.5 projects high population growth, heavy fossil fuel reliance, and severe climate impacts [31,32]. 2.3 Model construction The distribution modeling of Ae. albopictus was conducted using the Biomod2 software package [22], which involved Ran- dom Forest (RF), Artificial Neural Networks (ANN), Generalized Linear Models (GLM), Boosted Regression Tree Models (BRT), Classification and Regression Tree Models (CART), Surface Range Envelope Models (SRE), Flexible Discriminant

Analysis (FDA) and Maximum Entropy Models (MaxEnt). The MaxEnt model was calibrated using optimized parameters from the ENMeval package, with a regularization multiplier (RM) set to 0.5 and the feature combination designated as LQ, while the default model parameters of Biomod2 were adopted for the remaining models [33]. 75% of the distribution points were randomly selected as the training dataset. To avoid errors associated with a single modeling event, each model underwent the aforementioned process 10 times, resulting in a total of 80 modeling outcomes [23,25]. Table 1. List of 19 bioclimatic factors. Variable Description bio1 Annual Mean Temperature bio2 Mean Diurnal Range (Mean of monthly (max temp – min temp)) bio3 Isothermality (BIO2/BIO7) (×100) bio4 Temperature Seasonality (standard deviation ×100) bio5 Max Temperature of Warmest Month bio6 Min Temperature of Coldest Month bio7 Temperature Annual Range (BIO5-BIO6) bio8 Mean Temperature of Wettest Quarter bio9 Mean Temperature of Driest Quarter bio10 Mean Temperature of Warmest Quarter bio11 Mean Temperature of Coldest Quarter bio12 Annual Precipitation bio13 Precipitation of Wettest Month bio14 Precipitation of Driest Month bio15 Precipitation Seasonality (Coefficient of Variation) bio16 Precipitation of Wettest Quarter bio17 Precipitation of Driest Quarter bio18 Precipitation of Warmest Quarter bio19 Precipitation of Coldest Quarter https://doi.org/10.1371/journal.pone.0327818.t001