This dataset provides bioclimatic and environmental predictor variables used in Species Distribution Models (SDMs) developed within the OneSTOP Project (Task 5.1). The original climatic data are based on the CHELSA v2.1 BIOCLIM+ dataset, which provides high-resolution (~1 km) bioclimatic variables derived from downscaled and bias-corrected climate data. For future projections, CHELSA v2.1 climate layers were obtained for all five available CMIP6 Global Circulation Models (GCMs): GFDL-ESM4, UKESM1-0-LL, MPI-ESM1-2-HR, IPSL-CM6A-LR, and MRI-ESM2-0) and corresponding to the relevant SSP scenario. To produce a consistent climatic baseline that matches land-cover projections in the Chen et al. (2022) dataset, all available GCMs were averaged to generate a single ensemble mean for each time period and scenario. This ensemble approach reduces individual model biases and aims to provide a robust representation of mid- and late-century climatic conditions for SDMs. Raster data has been internally scaled and reprojected (bilinear method) in the terra R package. All raster layers are provided as GeoTIFF (float) files in the coordinate reference system EPSG:6933, WGS 1984/NSIDC EASE-Grid 2.0 Global (Cylindrical Equal Area projection). The datasets correspond to one of the following temporal windows and SSP scenarios: - Historical Baseline: 1981–2010 (used for model training) - Future Mid-Century (2041–2070): SSP1–2.6, SSP3–7.0, SSP5–8.5 (used for model projection) - Future Late-Century (2071–2100): SSP1–2.6, SSP3–7.0, SSP5–8.5 (used for model projection) These predictor/projection datasets are intended for use in SDM workflows to assess climatic suitability and potential species distributions under changing environmental conditions. They were prepared to support the OneSTOP project's modeling of Invasive Alien Species (IAS) and integration with land-cover projections (Chen et al., 2022). Climatic variables were obtained from CHELSA v2.1 (https://www.chelsa-climate.org/datasets), including the full set of bioclimatic variables describing temperature and precipitation regimes. In addition to the standard BIOCLIM variables, the predictor set includes BIOCLIM+ metrics such as growing-season length, growing-season precipitation, growing-season mean temperature, growing-degree days above 0 °C, 5 °C, and 10 °C, and net primary productivity, providing an expanded representation of climate conditions relevant to species' ecological requirements. --- Chen, G., Li, X., & Liu, X. (2022). Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios. Scientific Data, 9, 125. https://doi.org/10.1038/s41597-022-01208-6

List of variables in the dataset Variable Name Description BIO1 Annual Mean Temperature Mean annual air temperature. BIO2 Mean Diurnal Range Mean of monthly (Tmax − Tmin). BIO3 Isothermality BIO2 / BIO7 × 100. BIO4 Temperature Seasonality Standard deviation of temperature × 100. BIO5 Max Temperature of Warmest Month Highest monthly mean temperature. BIO6 Min Temperature of Coldest Month Lowest monthly mean temperature. BIO7 Annual Temperature Range BIO5 − BIO6. BIO8 Mean Temperature of Wettest Quarter Average temperature during the wettest quarter. BIO9 Mean Temperature of Driest Quarter Average temperature during the driest quarter. BIO10 Mean Temperature of Warmest Quarter Average temperature during the warmest quarter. BIO11 Mean Temperature of Coldest Quarter Average temperature during the coldest quarter. BIO12 Annual Precipitation Total annual precipitation. BIO13 Precipitation of Wettest Month Highest monthly precipitation. BIO14 Precipitation of Driest Month Lowest monthly precipitation. BIO15 Precipitation Seasonality Coefficient of variation of monthly precipitation. BIO16 Precipitation of Wettest Quarter Total precipitation in the wettest quarter. BIO17 Precipitation of Driest Quarter Total precipitation in the driest quarter. BIO18 Precipitation of Warmest Quarter Total precipitation in the warmest quarter. BIO19 Precipitation of Coldest Quarter Total precipitation in the coldest quarter. GDD0 Growing Degree Days >0 °C Annual sum of degree-days above 0 °C. GDD5 Growing Degree Days >5 °C Annual sum of degree-days above 5 °C. GDD10 Growing Degree Days >10 °C Annual sum of degree-days above 10 °C. GSL Growing Season Length Number of days with mean temperature above the growing-season threshold. GSP Growing Season Precipitation Total precipitation during the growing season. GST Growing Season Temperature Mean temperature during the growing season. NPP Net Primary Productivity Modelled net primary productivity (vegetation biomass production).