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83 results for “soil types”
Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification
<p>Global maps at 1 km spatial resolution of the predicted soil types (0–100% probabilities) at 1 km resolution based on the <a href="https://www.fao.org/soils-portal/data-hub/soil-classification/world-reference-base/en/">WRB 2022</a> (<strong>World Reference Base</strong> the international standard for soil classification) classification system. The training data comes from the following 3 main sources:</p> <ol> <li>WOSIS points available via: <a href="https://www.isric.org/explore/wosis">https://www.isric.org/explore/wosis</a>;</li> <li>HWSD v2 (random draw of cca 20,000 points): <a href="https://iiasa.ac.at/models-tools-data/hwsd">https://iiasa.ac.at/models-tools-data/hwsd</a>;</li> <li>Other national datasets / data from publications and projects.</li> </ol> <p>Predictions are based on using Rando Forest algorithm as implemented in the <a href="https://www.randomforestsrc.org/">randomForestSRC package</a> with cca 190 covariate layers representing soil forming factors (CHELSA Climate, Global Lithological DB GLiM, MODIS EVI and LST long-term derivatives, Digital Terrain model parameters and similar).</p> <p>All TIF files are provided as <a href="https://www.cogeo.org/">COGs</a>, which means that you can open them directly in QGIS or similar. Publication explaining all modeling steps is pending.</p> <p>Update of the predictions takes about 4–5 hrs and will be regularly run provided that new training points are available. Disclaimer: These are initial results with limited accuracy and possible issues with quality of training points, location errors and harmonization issues. Use at own risk.</p> <p>Note: original list of soil types have been subset to classes that appear at least 10 times and at least in 2 countries. If you notice an error or artifact <strong>please report via <a href="https://github.com/OpenGeoHub/SoilTypeMapping">the Github repository</a></strong>. Help us improve this dataset by contributing training points.</p>
Hubbard Brook Experimental Forest: Soil type prediction raster files
This dataset consists of raster files predicting spatial patterns in soils for the entire Hubbard Brook Experimental Forest. Eight soil units are used, following a hydropedologic approach, based on relationships between soil genetic horizon presence and thickness, and the frequency and depth of groundwater fluctuations. Nine raster files on a five-meter grid are presented, including one raster each showing the probability of presence of each of the eight soil units; the ninth raster represents the soil unit most likely to be present at each grid cell. The methods section of the metadata includes descriptions of the eight soil units and guidance for users of the model outputs. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Relations of the O-horizon with canopy tree species and hydropedologic soil types, 2021
As the interface between plants and soil, the organic horizon is the foundation of forest ecosystems. Two potential predictors of O-layer properties, vegetation and mineral soil type, are difficult to separate because they typically covary. We conducted a factorial study involving four canopy tree species and two soil types with distinctly different hydrology and topographic position to parse patterns in chemistry and microbiota of the O-layer in a north-temperate deciduous forest. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Cover and frequency of biological soil crust community types, moss species, vascular plants, and abiotic land surface features, on gypsum & non-gypsum soils from the Chihuahuan and Mojave Deserts in 2023
This dataset contains raw and calculated percent cover and frequency data for biological soil crust (hereafter biocrust) functional groups, vascular plant functional groups, and abiotic land surface features on and off gypsum soils in the northern Chihuahuan and eastern Mojave Deserts. Abundance data were obtained from 20 study sites total, 10 located on soils derived from gypsum parent material and 10 located on soils derived from non-gypsum parent materials. Sites were grouped into 10 pairs, in which every gypsum site was partnered with a non-gypsum site located in the same region. Apart from soil type, partnered-site characteristics (topography, climate, elevation, slope, aspect, and presence of biocrusts) were held relatively constant. At each site, cover and frequency assessments were made using the line-point intercept method (LPI) and frequency quadrats (1.0 m^2), respectively. Biocrust functional groups included the following crusts: lichen, moss, incipient algal, light algal, dark algal, unknown photosynthetic crust, and vagrant cyanobacteria. Vascular plant categories included: perennial forbs, perennial graminoids, annual forbs, annual graminoids, subshrub, shrub, Yucca, and cacti. Abiotic land surface features included: woody litter, herbaceous litter, bare soil, rock, bedrock, and animal feces. Moss crusts identified within cover and frequency analyses were sampled, and classified to species level via microscopy. The resulting percent cover and frequency data was used to understand differences in biocrust and moss species abundance and diversity on and off gypsum soils; furthermore, how biocrust and moss species abundance was associated with the measured environmental variables. Soil physical and chemical data from this study can be accessed at knb-lter-jrn.210616002. This study and dataset are complete.
Seedling emergence and biomass data of nine dryland plant species characterizing the impact of soil residual auxin herbicide across two soil types and water pulse events on greenhouse growth; Las Cruces, New Mexico, Spring 2021.
Synthetic-auxin herbicides are often used to control woody plants and aid in grassland restoration. Seed-based restoration is common alongside herbicide applications and there may be unintended effects of these herbicides on dryland plant species at the seed and seedling stages. Additionally, abiotic conditions at the time of herbicide application may influence herbicide-soil-plant interactions. We conducted a greenhouse study to examine the effects of a common shrub-control herbicide mix and its interaction with soil type and a post-herbicide water pulse on common desert plant seeds and seedlings. In this greenhouse study, we found that a subset of species responded negatively to soil residual herbicide activity of a mixture of aminopyralid, clopyralid, and triclopyr at the seed and seedling stages. Species sensitive to soil herbicide residues were primarily shrub and forb species that are often the target species of herbicide applications for woody plant control, such as Prosopis glandulosa (honey mesquite) and Larrea tridentata (creosote bush). However, two shrub species (Atriplex canescens [four-wing saltbush] and Yucca elata [soaptree yucca]) and one perennial grass species (Digitaria californica [Arizona cottontop]), which are used in dryland restoration projects, were found to be particularly sensitive to soil residual herbicide activity. Thus, if using these herbicides to control woody plants and restore herbaceous vegetation via active seeding or relying on the in situ seed bank, considerations should be given to what species are used in the seed mix, what species are already present in the soil seed bank, and other details of the circumstances of herbicide application.
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Soil Profile Rock Characteristics by Bedrock Type in Bartlett and Hubbard Brook, 2004-2018
Soils in our northeastern forests were formed in parent materials deposited by glaciers. The direction and distance of glacial movement can be used to predict the source of glacial till at “downstream” points on the landscape (Bailey 1992). The goal of this project was to identify the rocks excavated from the soil pits in each of the plots and then to use that data to validate the glacial till model. The minority of rocks in the soil pits matched the bedrock, showing the importance of glacial movement. Additional detail on the MELNHE project, including a data table of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Literature cited: Bailey, S.W., 1992. Lithologic composition and rock weathering potential of forested, glacial-till soils (Vol. 662). US Department of Agriculture, Forest Service, Northeastern Forest Experiment Station.
Global soil type dataset for WRF-ARW model, based on HWSD version 2
<p>Global soil type dataset, based on HWSD ("Harmonized World Soil Database", version 2.0), suitable for meteorological model WRF-ARW.</p> <ul> <li>spatial resolution: 30 arc seconds by 30 arc seconds (about 1km)</li> <li>original data (HWSD 2.0) <ul> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_RASTER.zip</a></li> <li><a href="https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip">https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/HWSD2_DB.zip</a></li> <li>https://gaez.fao.org/pages/hwsd</li> <li>documentation: Nachtergaele, Freddy, et al. Harmonized world soil database version 2.0. Food and Agriculture Organization of the United Nations, 2023. https://www.fao.org/3/cc3823en/cc3823en.pdf</li> </ul> </li> <li>the original 7 soil layers (0–20 cm, 20–40 cm, 40–60 cm, 60–80 cm, 80–100 cm, 100–150 cm and 150–200 cm) have been remapped to the 2 layers required by WRF (topsoil 0-30 cm, botsoil 30-200 cm)</li> <li>the original Soil Mapping Units (SMU) have been remapped to the 16 soil categories used by WRF: <ul> <li>the depth-weighted averages of the content of clay, silt and sand lead to 12 texture-based categories (Sand, Loamy sand, Sandy loam, Silt loam, Silt, Loam, Sandy clay loam, Silty clay loam, Clay loam, Sandy clay, Silty clay, Clay), as defined by USDA;</li> <li>category "Organic material" is assigned where the average content of organic carbon exceeds the threshold of 25%;</li> <li>where the content of clay, silt and sand is not defined, HWSD special categories are mapped to the WRF last 3 categories, as follows: <ul> <li>"Water bodies" to "Water", </li> <li>"Rock outcrops" and "Rocky sublayers" to "Bedrock", </li> <li>"Land ice and glaciers", "Dunes/shifting sands", "Salt flats", and "Other" to "Other"</li> </ul> </li> </ul> </li> </ul> <p>The dataset is provided in three ways:</p> <ol> <li>two global files (SoilType_depth<T>to<B>cm.tif), one for each layer; format is GeoTIFF, compatible with <a href="https://github.com/openwfm/convert_geotiff" target="_blank" rel="noopener"><em>convert_geotiff</em></a>, a commandline utility for converting data from GeoTIFF to geogrid format used by WRF;</li> <li>16 tiles, 8 for each layer, each covering 90 degrees by 90 degrees (SoilType_depth<T>to<B>cm_lon<W>to<E>deg_lat<S>to<N>deg.tif); format is GeoTIFF;</li> <li>two compressed folders, hwsd_toplayer.zip and hwsd_bottomlayer.zip, each including 648 tiles in binary format and an "index" ASCII file, following the Geogrid data format and naming convention, as described <a href="https://www2.mmm.ucar.edu/wrf/users/tutorial/presentation_pdfs/202101/duda_wps_advanced.pdf">here</a>.</li> </ol> <p>Soil categories are coded as follows</p> <table> <tbody> <tr> <td><strong>code</strong></td> <td><strong>category</strong></td> </tr> <tr> <td>1</td> <td>sand</td> </tr> <tr> <td>2</td> <td>loamy sand</td> </tr> <tr> <td>3</td> <td>sandy loam</td> </tr> <tr> <td>4</td> <td>silt loam</td> </tr> <tr> <td>5</td> <td>silt</td> </tr> <tr> <td>6</td> <td>loam</td> </tr> <tr> <td>7</td> <td>sandy clay loam</td> </tr> <tr> <td>8</td> <td>silty clay loam</td> </tr> <tr> <td>9</td> <td>clay loam</td> </tr> <tr> <td>10</td> <td>sandy clay</td> </tr> <tr> <td>11</td> <td>silty clay</td> </tr> <tr> <td>12</td> <td>clay</td> </tr> <tr> <td>13</td> <td>organic material</td> </tr> <tr> <td>14</td> <td>water</td> </tr> <tr> <td>15</td> <td>bedrock</td> </tr> <tr> <td>16</td> <td>other</td> </tr> </tbody> </table> <p> </p>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 1)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 2)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types classification and relative entropy</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Comparison of polyphenol degrading enzyme activities between forest types and soil horizons from 2003 to 2004
In the southern Appalachians Rhododendron maximum thickets suppress conifer and hardwood regeneration. While there has been research on the effects of R. maximum on physical and chemical environment, the functioning of R. maximum ericoid mycorrhizas has been unexplored. The litter of ericaceous plants tends to be rich in phenolic compounds. These compounds can form recalcitrant complexes with various forms of organic N, and may be responsible for lowering decomposition and N mineralization rates. While polyphenol-organic N complexes are highly recalcitrant, some fungi, particularly ericoid mycorrhizal fungi, have the ability to access this sequestered N. Since the litter of ericaceous plants is rich in phenolic compounds and ericoid mycorrhizal fungi are equipped to degrade phenolic compounds, polyphenol-organic N complexing may represent an N cycling strategy that prevents non-ericaceous plants from accessing sources of organic N. We propose to examine the activities of polyphenol degrading enzymes in the soil of R. maximum thickets and neighboring hardwood forests.
A database of soil type and soil depth across Everglades National Park, Florida, USA
This dataset contains record of soil type and soil depth across Everglades National Park. This data was obtained through published literature, unpublished field data, and inter-agency collaboration. Data come from either soil cores to bedrock or refusal (as specified by data source), cores that did not reach bedrock, a soil probe to determine depth, or the depth of installed surface elevation tables. Soil type was classified either along a gradient of organic matter content when data was available: mineral (<40%), intermediate (40-70%), or organic (<70%). When data on organic matter content wasn’t available but qualitative descriptions of soil type were, the type unit of soil was classified as either organic or mineral based on the descriptive lithology. When lithologic descriptions were provided and indicated the soil profile was not entirely one soil type (e.g. peat), depth of peat within core was recorded. When data source had specific coordinates, those coordinates were used. For older studies that only provided a figure showing the location of each estimate at a broad scale, location was estimated by georeferencing the figure in ArcMap, and dropping a point at each location. Records were cross-referenced with a recent vegetation map developed by the National Park Service in order to obtained vegetation community at each location (Ruiz et al. 2017).
The more microplastic types pollute the soil, the stronger the growth suppression of invasive alien and native plants
<p>The ecological consequences of microplastic pollution for plants remain largely unknown, and the few studies that tested the effects usually focused on a single type of microplastic and a single plant species. However, most plants will be exposed to multiple microplastic types simultaneously, and the effects may vary among species.</p> <p>To test the effects of microplastic diversity on plants, we grew single plants of eight invasive and eight native species in pots with substrate polluted with 0, 1, 3 and 6 types of microplastics.</p> <p>We found that the growth suppression by microplastic pollution became stronger with the number of microplastic types the plants were exposed to. This tended to be particularly the case for invasive species, as their biomass advantage over natives diminished with the number of microplastic types. The biomass responses coincided with a positive effect of the number of microplastic types on root allocation and thickness, which was also stronger for invasive than for native species. In addition, the results of hierarchical diversity-interaction models suggest that the negative impact of microplastic diversity on the total biomass of invasive plant species was influenced by both the identities of the microplastic and certain types of microplastic with strong pairwise interactions. In contrast, the effect on native species was determined solely by the microplastic identities.</p> <p><em>Synthesis: </em>Our multi-species study thus shows for the first time that the negative effects of microplastic pollution on plant growth increase with the number of microplastic types. We also found tentative evidence that the negative impacts of microplastic diversity were more pronounced for invasive plants compared to native plants, and that this might be due to differences in the responses of root allocation and thickness.</p>
Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
<p>Fauna monitoring often relies on visual monitoring techniques such as camera trappings, which have biases leading to underestimates of vertebrate species diversity. Environmental DNA (eDNA) has emerged as a new source of biodiversity data that may improve biomonitoring; however, eDNA based assessments of species richness remain relatively untested in terrestrial environments. We investigated the suitability of fallen log hollow sediment as a source of vertebrate eDNA, across two sites in south-western Australia - one with a Mediterranean climate and the other semi-arid. We compared two different approaches (camera trapping and eDNA metabarcoding) for monitoring of vertebrate species, and investigated the effect of other factors (frequency of species, timing of visits, frequency of sampling, body size) on vertebrate species detectability. Metabarcoding of hollow sediments resulted in the detection of higher species richness in comparison Hollow sediment detected higher species richness (29 taxa: six birds, three reptiles and 20 mammals) to metabarcoding of soil at the entrance of the hollow (13 taxa: three birds, two reptiles and eight mammals). We detected 31 taxa in total with eDNA metabarcoding and 47 with camera traps, with 14 taxa detected by both (12 mammals and two birds). By comparing camera trap data with eDNA read abundance, we were able to detect vertebrates through eDNA metabarcoding that had visited the area up to two months prior to sample collection. Larger animals were more likely to be detected, and so were vertebrates that were identified multiple times in the camera traps. These findings demonstrate the importance of substrate selection, frequency of sampling, and animal size, on eDNA based monitoring. Future eDNA experimental design should consider all these factors as they affect detection of target taxa. </p>
Dataset used in "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis"
<p>Please see the paper "Deep learning with multisite data reveals the lasting effects of soil type, tillage and vegetation history on biopore genesis" how the images were captured, manual counting was performed, training datasets were prepared and models were trained.</p>
Data for: Temporal variation of soil microarthropods in different forest types and regions of Central Europe
<p>Biodiversity and biomass of aboveground arthropods in Central European forests continuously declined during the last decade. However, whether belowground microarthropod communities follow similar patterns has not been investigated. In this study, we compared the abundance, diversity, community composition, stability and asynchrony of oribatid mites (Acari: Oribatida) sampled in four forest types of increasing management intensity (unmanaged beech, old managed beech, young managed beech, and coniferous) at three-year intervals from 2008 to 2020. Forest sites were replicated in three regions in southern, central and northern Germany, i.e. the Swabian Alb, Hainich-Dün and the Schorfheide Chorin, which differ in soil characteristics and climate. We found 25,152 individuals and 121 species of oribatid mites and detected no linear decline in abundance and diversity over the last decade, suggesting that microarthropods in forest soils are buffered against land-use effects. However, we observed that years with low winter precipitation in regions with soils that are prone to drought, resulted in significant decreases in oribatid mite densities. Community compositions remained similar across sampling years, but differed between regions and forest types, predominantly due to differences in the proportion of asexual individuals. The stability of oribatid mite communities did not decrease in managed forests and was highest in deep soils with high water-holding capacity, which may reduce temporal variation, suggesting that soil properties are more important for the stability of oribatid mite communities than forest management. However, stability patterns were not explained by asynchrony in species fluctuations, as all communities either showed a high degree of synchrony or were not different from random. Our study highlights that the temporal dynamics of belowground communities may differ from those aboveground, and that regional differences in precipitation and soil properties are more important than forest types.</p>
Figure 4 in Population density and diet type interactively affect individual growth of an omnivorous soil-dwelling insect (Anomala cuprea, Coleoptera: Scarabaeidae)
Figure 4. (a) total carbon (%) in the diet before the experiment and (b) after the experiment, and (c) total nitrogen (%) in the diet before the experiment and (d) after the experiment in the two diets and three population densities.
Figure 2 in Population density and diet type interactively affect individual growth of an omnivorous soil-dwelling insect (Anomala cuprea, Coleoptera: Scarabaeidae)
Figure 2. Effects of diet type and population density on mean fresh weight of a larva. Error bars indicate standard error. See Tab. 2 for details of statistical results.
Figure 1 in Population density and diet type interactively affect individual growth of an omnivorous soil-dwelling insect (Anomala cuprea, Coleoptera: Scarabaeidae)
Figure 1. Effects of diet type and population density on larval mortality rate. Error bars indicate standard error. See Tab. 1 for details of statistical results.
Meta-analysis shows forest soil CO2 effluxes are dependent on the disturbance regime and biome type
<p class="MsoNormal"><span>F</span><span>orest </span><span>s</span><span>oil CO<sub>2</sub> efflux (F</span><span>CO<sub>2</sub></span><span>)</span><span> is a crucial process in global carbon cycling; however, how F</span><span>CO<sub>2</sub></span><span> responds to disturbance regimes in different forest biomes is poorly understood. </span><span>W</span><span>e quantif</span><span>ied</span><span> the effects of disturbance regimes on F</span><span>CO<sub>2</sub></span><span> </span><span>across boreal, temperate, tropical, and</span><span> Mediterranean</span><span> forests</span><span> based on 1240 observations from 380 studies. Globally, climatic perturbations such as elevated CO<sub>2</sub> concentration, warming, and increased precipitation increase F</span><span>CO<sub>2</sub></span><span> </span><span>by 13 to 25%. F</span><span>CO<sub>2</sub></span><span> is increased by forest conversion to grassland and elevated carbon input by forest management practices but reduced by decreased carbon input, fire, and acid rain. Disturbance also changes soil temperature and water content, which in turn affect the direction and magnitude of disturbance influences on F</span><span>CO<sub>2</sub></span><span>. F</span><span>CO<sub>2</sub></span><span> is disturbance- and biome-type dependent, and such effects should be incorporated into earth system models to improve the projection of the feedback between the terrestrial C cycle and climate change.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.