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.

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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