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

Probability and uncertainty maps showing the potential and realized distribution for the Turkey oak (<em>Quercus cerris, 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.cerris_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.cerris</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.cerris_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution veg_quercus.cerris_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution veg_quercus.cerris_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution veg_quercus.cerris_<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.本数据集包含由Bonannella等人（2022）编制、基于集成机器学习（Ensemble Machine Learning, EML）预测的欧洲土耳其栎（*Quercus cerris, L.*）潜在分布与实际分布的概率及不确定性图谱。其中，潜在分布图谱对应2018—2020年时段；实际分布图谱对应2000—2020年，且拆分为以下子时段：2000—2002、2002—2006、2006—2010、2010—2014、2014—2018、2018—2020。 文件遵循如下命名规范，示例为：`veg_quercus.cerris_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.2`，各字段含义如下：主题（如`veg`）、物种代码（如`quercus.cerris`）、物种分布类型（如`anv`，即实际天然植被）、物种估算方法（如`eml`）、物种估算类型（如`md`，即模型偏差）、空间分辨率（单位为米，如`30m`）、参考深度（垂直维度，如`0..0cm`）、参考时段起止（如`2000..2002`）、研究区域（如`eumap`）、坐标系（如`epsg3035`）、数据集版本（如`v0.2`）。 可通过文件名快速识别对应物种的概率与不确定性图谱： - `veg_quercus.cerris_anv.eml_md`：实际分布的模型不确定性图谱 - `veg_quercus.cerris_anv.eml_p`：实际分布的概率图谱 - `veg_quercus.cerris_pnv.eml_md`：潜在分布的模型不确定性图谱 - `veg_quercus.cerris_pnv.eml_p`：潜在分布的概率图谱 文件以云优化GeoTIFF（Cloud Optimized GeoTIFF）格式存储，投影坐标系采用ETRS89 / LAEA Europe（即EPSG编码3035）。配套样式文件同时提供SLD和QML格式。 若需了解图谱制作与建模细节，可观看2021年开放数据科学研讨会（Open Data Science Workshop 2021，TIB AV-PORTAL）的报告演讲，获取我们的R/Python脚本代码仓库（GitLab）并按照指引操作，亦可访问训练数据集仓库（Zenodo）。一篇详细阐述物种分布图谱全流程处理、精度评估与综合分析的学术论文正在筹备中，您可在ResearchSquare获取其预印本。若需提出改进建议或修复问题，请访问https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues。