Potential and realized distribution at 30m for pedunculate oak (Quercus robur) in Europe for 2000 - 2020

Probability and uncertainty maps showing the potential and realized distribution for the pedunculate oak (<em>Quercus robur, L.</em>) 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_quercus.robur_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.2 with the following fields: theme: e.g. <strong>veg</strong>, species code: e.g. <strong>quercus.robur</strong>, species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation), species estimation method: e.g. <strong>eml</strong>, species estimation type: e.g. <strong>md</strong> ( = model deviation), resolution in meters e.g. <strong>30m</strong>, reference depths (vertical dimension): e.g. <strong>0..0cm</strong>, reference period begin end: e.g. <strong>2000..2002</strong>, reference area: e.g. <strong>eumap</strong>, coordinate system: e.g. <strong>epsg3035</strong>, data set version: e.g. <strong>v0.2</strong>. For each species is then easy to identify probability and uncertainty distribution maps: veg_quercus.robur_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_quercus.robur_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_quercus.robur_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_quercus.robur_<strong>pnv</strong>.eml_<strong>p</strong>: 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 <em>SLD</em> and <em>QML</em> format. If you would like to know more about the creation of the maps and the modeling: <strong>watch</strong> the talk at Open Data Science Workshop 2021 (TIB AV-PORTAL) <strong>access </strong>the repository with our R/Python scripts and follow the instructions (GitLab) <strong>access </strong>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.