geoSABINA: Species distribution models (SDMs): Shrub species (files U - Z)

This dataset provides information on the current and future potential distribution of shrubs in peninsular Spain, predicted using species distribution models (SDMs). For each species, the SDMs were projected under current (1990-2010) and four future (2071-2100) climate scenarios. Models were generated using the R package sabinaNSDM and an ensemble approach combining three statistical algorithms: generalized linear models, gradient boosted machine, and random forests. Model training utilized shrub species occurrence data alongside environmental variables from the geoSABINA dataset. This dataset includes both continuous suitability maps and binary presence-absence maps [derived from the suitability maps using thresholds based on True Skill Statistic (bin.TSS.tif files) and the Receiver Operating Characteristic curve (bin.ROC.tif)]. Additionally, uncertainty maps (EMcv.tif files) are provided. The full dataset comprises a total of  2,020 raster layers in TIFF format, with a spatial resolution of 1 km x1 km but is divided into two datasets, one for species whose name starts from A-T (95 species) - https://zenodo.org/records/14679933 , and another one for species with names from U-Z (6 species) -https://zenodo.org/records/14725791. Model thresholds and accuracy values are provided in the directory “values”. The detailed list of layers available is provided in data_table_sdms_shrubs.csv and includes information on the category, dataset, description, resolution, time period, and path,.

Raster information:

• Resolution: 1 km
• Extent: -75638.32, 1031361.68, 3976769.52, 4870769.52 (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