Probability and uncertainty maps showing the potential and realized distribution for the Norway spruce (Picea abies, L. H. Karst.) for Europe from the dataset prepared by Bonannella et al. (2022) and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods: 2000 - 2002, 2002 - 2006, 2006 - 2010, 2010 - 2014, 2014 - 2018, 2018 - 2020. Files are named according to the following naming convention, e.g: veg_picea.abies_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.2 with the following fields: theme: e.g. veg, species code: e.g. picea.abies, species distribution type: e.g. anv (= actual natural vegetation), species estimation method: e.g. eml, species estimation type: e.g. md ( = model deviation), resolution in meters e.g. 30m, reference depths (vertical dimension): e.g. 0..0cm, reference period begin end: e.g. 2000..2002, reference area: e.g. eumap, coordinate system: e.g. epsg3035, data set version: e.g. v0.2. For each species is then easy to identify probability and uncertainty distribution maps: veg_picea.abies_anv.eml_md: model uncertainty for realized distribution veg_picea.abies_anv.eml_p: probability for realized distribution veg_picea.abies_pnv.eml_md: model uncertainty for potential distribution veg_picea.abies_pnv.eml_p: probability for potential distribution Files are provided as Cloud Optimized GeoTIFFs and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both SLD and QML format. If you would like to know more about the creation of the maps and the modeling: watch the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) access the repository with our R/Python scripts and follow the instructions (GitLab) access the repository with the training dataset (Zenodo) A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is under preparation. You can access the preprint on ResearchSquare. To suggest any improvement/fix use https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues.

This work is co-financed under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 by the European Union (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).