geoSABINA: Environmental variables

This dataset provides environmental variables from Spain, selected for their relevance in modeling the distribution of plant species. However, they can also be applied to other species and purposes. It includes a total of 103 raster layers in tif format at 250-meter resolution. The detailed list of layers available is provided in data_table_env_variables.csv, which includes information on the category, dataset, description, resolution, time period, and path.

This dataset includes:  

1.  Climatic variables: 19 bioclimatic variables under current (1990-2010) and 4 future (2071-2100) climate scenarios. Climatic variables were sourced from the climatologies for the Earth’s Land Surface Areas (CHELSA) and downscaled to 250 m in https://doi.org/10.1111/ecog.07328. The future scenarios were generated according two global climate models [the latest Institute Pierre Simon Laplace climate model (IPSL_CM6A_LR) and the Meteorological Research Institute Earth System Model version 2.0 (MRI_ESM2)] and two socio-economic pathways: an optimistic low greenhouse gas emissions scenario (SSP126) and a pessimistic high emissions scenario (SSP585). The climatic variables of each scenario are compressed within a zip file.
2. Edaphic variables: Four soil characteristics (pH, nitrogen, sand content, organic carbon) at 0 to 5 cm depth sourced from SoilGrids. The four edaphic variables are compressed within a zip file.
3. Hydrologic variables: Three hydrologic variables (distance to rivers, flow accumulation, and topographic index) derived from the digital elevation model DAT-193-en (Copernicus Land Cover Service) in https://doi.org/10.1111/ecog.07328.  
4. Solar radiation:  Annual solar exposure values calculated from the digital elevation model DAT-193-en  in https://doi.org/10.1111/ecog.07328. 

Raster information:

• Resolution: 250 m
• Extent: -75888.32, 1031611.68, 3977269.52, 4870519.52 (xmin, xmax, ymin, ymax)
• CRS: WGS 84 / UTM Zone 30N (EPSG:32630)

References: The references in this list should be added to any publication using these data:

• Goicolea, T., Morales-Barbero, J., García-Viñas, J.I, Gastón, A., Aroca-Fernández, M.J., Calleja, J.A., Moren, J.C. , Ramos-Gutiérrez, I., Rodríguez, M.A., Lima, H., Broennimann, O., Guisan, A, Adde, A., Pérez-Latorre, A.V., G. Mateo, R. (2025) Scientific Data.
• Goicolea, T., Adde, A., Broennimann, O., García-Viñas, J.I., Gastón, A., Aroca-Fernández, M.J. et al. (2024). Spatially-Nested Hierarchical Species Distribution Models to Overcome Niche Truncation in National-Scale Studies. Ecography. https://doi.org/10.1111/ecog.07328