README GeoSABINA  SPECIES DISTRIBUTION MODELS  Shrub species

Dataset description:
--------------------
This dataset provides information on the current and future potential distribution of shrubs in peninsular Spain predicted using species distribution models (SDMs). The SDMs were projected under current (1990-2010) and four future (2071-2100) climate scenarios. SDMs were generated using the R package sabinaNSDM and an ensemble approach that combines three statistical algorithms: generalized linear models, gradient boosted machine, and random forests. The model training utilized shrubs 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 are provided (EMcv.tif files). The full dataset comprises a total of 2,020 raster layers in TIFF format, with a spatial resolution of 1x1 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 those 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)


DOI: 
----
10.5281/zenodo.14679933 for species with names from A-T and 10.5281/zenodo.14725791 for species with names from U-Z.


Dataset download: 
-----------------
https://zenodo.org/ records/14679933 and https://zenodo.org/ records/14725791 


Associated publications: 
------------------------
The references in this list should be added to any publication using these data:

- Goicolea, T., Morales-Barbero, J., Garca-Vias, J.I, Gastn, A., Aroca-Fernndez, M.J., Calleja, J.A., Moren, J.C. , Ramos-Gutirrez, I., Rodrguez, M.A., Lima, H., Broennimann, O., Guisan, A, Adde, A., Prez-Latorre, A.V., G. Mateo, R. (2025) Scientific Data.
- Goicolea, T., Adde, A., Broennimann, O., Garca-Vias, J.I., Gastn, A., Aroca-Fernndez, 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


Authors:  
--------
Teresa Goicolea, Jennifer Morales-Barbero, Juan Ignacio Garca-Vias, Aitor Gastn, Mara Jos Aroca-Fernndez, Juan Antonio Calleja, Juan Carlos Moreno, Ignacio Ramos-Gutirrez, Miguel . Rodrguez, Herlander Lima, Olivier Broennimann, Antoine Guisan, Antoine Adde, Andrs V. Prez-Latorre, Rubn G. Mateo

Available data:
-------------------
1. Current 
2. Future optimistic A: IPSL_CM6A_LR_2070_SSP126
3. Future pessimistic A: IPSL_CM6A_LR_2070_SSP585  
4. Future optimistic B: MRI_ESM2_0_2070_SSP126      
5. Future pessimistic B: MRI_ESM2_0_2070_SSP585     

    
