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High-resolution analysis of power plant land requirements for GODEEEP
<p>This dataset contains data associated with Mongird et al. (under review). Files include output from the following three analyses found in the paper: (1) Projected power plant siting intersections with US Disadvantaged Communities (DACs), important farmland, and natural areas; (2) onshore wind and solar photovoltaic capacity factor availability under 27 different siting restriction cases, and (3) output from an analysis that determines how many DACs are projected to see both fossil fuel generation retirement and new renewable power plant development. Each of the files associated with these components are described below. </p> <p>For more detailed information please refer to Mongird et al. (under review), "High-resolution analysis of power plant land requirements for the evolving Western United States power grid indicates coordinated land use policies will be essential"</p> <p>Outputs included in this dataset are associated with two different scenarios. Summaries of each of the two scenarios included are provided below. For additional information, see <a href="https://doi.org/10.1016/j.egycc.2023.100117">Ou et al. 2023.</a></p> <h2>Scenario Descriptions</h2> <ul> <li><strong>business-as-usual</strong>: <ul> <li>This scenario does not include any long-term federal policies requiring decarbonization.</li> <li>It does include the US Inflation Reduction Act (IRA) incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> <li><strong>high renewables</strong>: <ul> <li>This scenario includes a clean electricity grid in the U.S. by 2035 and a net-zero economy by 2050.</li> <li>It does include US IRA incentives.</li> <li>It assumes that CCS technologies are available.</li> </ul> </li> </ul> <h2>Data Descriptions</h2> <h3>1. Projected power plant siting intersections</h3> <p><strong>Description</strong></p> <p>These files identify the intersection of projected power plant locations with three types of land: federall identified disadvantaged communities (DACs), important farmland, and land in close proximity to natural areas.</p> <p><strong>Scenario Files:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>bau_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>bau_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>bau_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the busines-as-usual scenario intersect with important farmland by technology type and Western US state</td> </tr> <tr> <td>hr_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state</td> </tr> <tr> <td>hr_env_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state</td> </tr> <tr> <td>hr_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>state</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type inclusive of turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>technology_simple</td> <td>Power plant technology type excluding turbine type, presence of CCS, and cooling type (as applicable)</td> <td>N/A</td> </tr> <tr> <td>layer_name</td> <td>Descriptive name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>layer</td> <td>Name of geospatial raster layer used for intersection analysis</td> <td>N/A</td> </tr> <tr> <td>total_plants</td> <td>Number of projected power plants of specified technology in specified state under given scenario</td> <td>#</td> </tr> <tr> <td>intersection</td> <td>Number of projected power plant intersections of specified technology in specified state with given layer under given scenario </td> <td>#</td> </tr> <tr> <td>fraction</td> <td>ratio of intersection and total_plants</td> <td>fraction</td> </tr> </tbody> </table> <p> </p> <p><strong>Scenario Difference Analysis Files:</strong></p> <table> <tbody> <tr> <td>difference_dac_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with federally identified US DACs by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_env_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with areas within 1 km, 5 km, and 10km of environmental areas by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> <tr> <td>difference_farm_analysis_2050.csv</td> <td>Results from analysis identifying how many more projected power plant sitings through 2050 under the high renewables scenario intersect with important farmland by technology type and Western US state compared to projected power plant sitings through 2050 under the business-as-usual scenario. Negative results indicate that the business-as-usual scenario had a greater number of intersections.</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Dictionary:</strong></p> <table style="width: 85.255198%; height: 152px;"> <tbody> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;"><strong>Column</strong></td> <td style="width: 82.845413%; height: 19px;"><strong>Description</strong></td> <td style="width: 4.029241%; height: 19px;"><strong>Units</strong></td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">state</td> <td style="width: 82.845413%; height: 19px;">Name of US state</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">technology</td> <td style="width: 82.845413%; height: 19px;">Power plant technology type</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">layer</td> <td style="width: 82.845413%; height: 19px;">Name of geospatial raster layer used for intersection analysis</td> <td style="width: 4.029241%; height: 19px;">N/A</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">hr</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the high renewables scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 19px;"> <td style="width: 8.458634%; height: 19px;">bau</td> <td style="width: 82.845413%; height: 19px;">Number of projected power plant intersections with given layer under the business-as-usual scenario</td> <td style="width: 4.029241%; height: 19px;">#</td> </tr> <tr style="height: 38px;"> <td style="width: 8.458634%; height: 38px;">intersection</td> <td style="width: 82.845413%; height: 38px;">Difference in projected power plant intersections between the high renewables scenario and the business-as-usual scenario</td> <td style="width: 4.029241%; height: 38px;">#</td> </tr> </tbody> </table> <h3> </h3> <h3>2. Projected onshore wind and solar photovoltaic capacity factor availability under 27 siting restriction cases</h3> <p>Description:</p> <p>This file contains results from an analysis on the capability of reaching high renewables scenario solar and wind generation in 2050 under 27 different siting restriction cases.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>capacity_factor_analysis_2050.csv</td> <td>Amount of solar PV or onshore wind generation projected to be available in a given state under a specified siting restriction case </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>region_name</td> <td>Name of US state</td> <td>N/A</td> </tr> <tr> <td>technology</td> <td>Power plant technology type (either solar PV or Wind)</td> <td>N/A</td> </tr> <tr> <td>capacity_density_mw</td> <td>Assumed MW per square-km</td> <td>MW</td> </tr> <tr> <td>case</td> <td>Name of siting exclusion case</td> <td>N/A</td> </tr> <tr> <td>total_generation_mwh</td> <td>Projected total generation available given remaining available land after exclusions</td> <td>MWh</td> </tr> <tr> <td>target_generation_mwh</td> <td>Projected target annual generation in 2050 for technology type under high renewables scenario</td> <td>MWh</td> </tr> <tr> <td>gcam_trading_region</td> <td>Name of zonal representation of electricity trading regions as defined in the capacity expansion model</td> <td>N/A</td> </tr> </tbody> </table> <h3> </h3> <h3>3. US DACs that see both fossil fuel generation retirement and new renewable power plant development by 2050</h3> <p><strong>Description:</strong></p> <p>This data contains US census tract GEOIDs that see both new renewable sitings and the retirement of fossil generating resources.</p> <p><strong>Relevant File:</strong></p> <table> <tbody> <tr> <td>File Name</td> <td>File Description</td> </tr> <tr> <td>dac_fossil_retire_analysis_2050.csv</td> <td>List of US census tracts that see both fossil fuel generation retirement and new renewable generation siting by 2050 </td> </tr> </tbody> </table> <p><strong>Data Dictionary:</strong></p> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> census_tract</td> <td> US census tract GEOID</td> </tr> <tr> <td>state_name</td> <td>Name of US state</td> </tr> <tr> <td>county_name</td> <td>Name of US county</td> </tr> <tr> <td>scenario</td> <td>scenario name</td> </tr> </tbody> </table> <p> </p> <h2>Funding statement</h2> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p> <h2>Changelog</h2> <p>v1.1</p> <p> The following updates were made following manuscript revision:</p> <ul> <li>"power_density_mw" variable name in `capacity_factor_analysis_2050.csv` file changed to "capacity_density_mw"</li> <li>More estimates are provided in `capacity_factor_analysis_2050.csv` reflecting additional capacity density and turbine hub height assumptions.</li> <li>Scenario naming adjusted to align with manuscript naming</li> </ul>
Global scale leaf broadband optical properties derived from CliMA Land and associated CESM simulations
<p>Leaf level broadband reflectance and transmittance computed from leaf traits.</p> <ul> <li>clm_refl_tran_1m_weighted.nc: monthly data (144*96 pixels)</li> <li>surfdata_CMIP6_fluspect_v3.nc: surface data to run CESM (144*96 pixels)</li> </ul> <p>Global scale simulation results</p> <ul> <li>research_data_coupled_future_v2.nc: CESM coupled future simulations</li> <li>research_data_coupled_history_v2.nc: CESM coupled historical simulations</li> <li>research_data_uncoupled_history_v2.nc: CESM uncoupled future simulations</li> <li>research_data_uncoupled_ssp_v2.nc: CESM uncoupled SSP245 and SSP585 simulations</li> </ul> <p>Code changes</p> <ul> <li>SurfaceAlbedoMod.F90: modified CLM module</li> <li>Julia-and-Python-Code.tar.gz: code used for processing the data and plot the figures</li> </ul>
Kaduna state Land Use Land Cover 2022
<p>The map shows the land use land Cover of Kaduna state as at 2022 showing Build up area, water body,green areas, and open space which was achieved by the use of ESRI LULC data 2019-2022 open Atlas, the data was process using ArcGIS 10.8 using arctoolbox techniques which includes Extract by mask, Conversion from raster to polygon, working with add field from the attribute table and geoprocessing tool i.e Dissolve where used to analyze the data, and finally the data was switched to layout view to add the map element and the work was exported to a Soft copy for usages in form of an A4 ISO standard respectively</p>
HR land use maps final version
<p>High resolution land use maps raster layers (.tif) with a spatial resolution of 10 m created by RethinkAcion project. The .tif files of the 12 maps are uploaded with visualization files. These datasets are in WGS84 in line with those that will be uploaded to the platform of the project and also this last version (v2) includes metadata</p>
Land use-based Adaptation and Mitigation Solution (LAMS) suitability maps final version
<div>These maps provide the suitable area (maximum available area) for the potential implementation of the specific proposed LAMS. The suitability map includes 4 classes: not suitable (0), least suitable (1), moderately suitable (2) and most suitable (3). Maps for 13 different LAMS (from the LAMS catalogue V1-1) have been developed for the six RethinkAction case studies, when possible. This v2 includes the metadata.</div> <div>The codes and the names of the LAMS are the following:</div> <div>LAMS03-EstGra: Establishment (conversion to) of permanent grassland</div> <div>LAMS14-SpaPla: Spatial planning for the sustainable deployment of energy on land</div> <div>LAMS15-PhoPla: Photovoltaic plants</div> <div>LAMS21-AgrPla: Agrovoltaic farms</div> <div>LAMS22-IncFor: Increased portion of forests included under protected areas</div> <div>LAMS23-RefAff: Reforestation/afforestation</div> <div>LAMS31-UrbSpr: Limiting urban sprawl</div> <div>LAMS32-GreUrb: Establishment and maintenance of green urban ecosystems</div> <div>LAMS44-IncCul: Increase in cultivated area</div> <div>LAMS49-FloSol: Floating solar photovoltaic panels in water bodies</div> <div>LAMS50-SolPan: Solar panels in rooftops/buildings</div> <div>LAMS55-WatHar: Water harvesting: collect and store rain water in reservoirs</div> <div>LAMS59-LanMan: Land management of solar photovoltaic systems land</div>
Land cover map of the Greater Paramaribo region 2019
<p>This map shows the land cover of the Greater Paramaribo region for 2019 based on Sentinel 2 images. See details in the metadata document. </p><p>The map was made by Razi Taus for the Tropenbos Suriname and the University of Twente-Faculty Geo-information Science and Earth Observation (ITC) project "Naar een groen en leefbaarder Paramaribo" and must be accredited as follows.</p><p><i>Razia Taus, Lisa Best, Rudi van Kanten, Nina Schwarz, Louise Willemen, 2020, Land cover map of the Greater Paramaribo Region 2019, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC. DOI: 10.5281/zenodo.7696951</i></p>
Global Land Water Mask
<p>This is a global land water mask dataset stored in a GeoTIFF file. Water surface types are stored as value 0, whereas land surface types are stored as value 100.</p><p>The land water mask has been generated using the Global Self-consistent Hierarchical High-resolution Geography (GSHHG, Version 2.3.7) dataset. To construct the land water mask the <i>intermediate </i>(<i>i</i>) resolution has been used and the following shoreline categories have been considered and used for the land/water classification :</p><ul><li>L1: boundary between land and ocean, except Antarctica (continents) -> land</li><li>L2: boundary between lake and land (lakes) -> water</li><li>L3: boundary between island-in-lake and lake (islands in lakes) -> land</li><li>L6: boundary between Antarctica grounding-line and ocean (Antarctica) -> land</li></ul><p>Hence ponds on islands (L4) are ignored and consequently classified as land and Antarctic sea ice (L5) is classified as water. Furthermore, the shapefiles for rivers have not been used, meaning that rivers are not resolved in this dataset and thus classified as land.</p><p>The land water mask dataset has been stored in a GeoTIFF file with global coverage (-90 to 90 degrees North and -180 to 180 degrees East) and a geographic coordinate system referenced to the WGS84 datum (proj4 string: '+proj=longlat +datum=WGS84 +no_defs +type=crs'). The GeoTIFF image has the shape 6750 x 13500 pixels meaning that the dataset has a resolution of 0.0267 degrees or approx. 3 km (at the equator).</p>
Land Use/cover dataset of the Guangdong-Hong Kong Macao Greater Bay Area at 30 m Resolution from 1976 to 2020
<p>High spatiotemporal resolution, large-scale, multi-category land use data are crucial for studying regional ecological and environmental changes, urbanization impacts, and sustainable development planning.</p><p>In this study, the Google Earth Engine platform, Landsat satellite imagery, and a substantial number of manually interpreted samples were used to develop a dataset of annual land use in the Guangdong-Hong Kong Macao Greater Bay Area (GBA) at a 30 m resolution from 1976 to 2020. This dataset, termed Annual Land Use/Cover of the Greater Bay Area (LUC-GBA), was used to analyze the annual land use variation in 11 cities within the GBA. The high level accuracy achieved with the LUC-GBA dataset was evidenced by an overall accuracy (OA) of 93.98% and a kappa coefficient of 0.92 in 2020. The OA of interannual classification models ranges from 83.9% to 93.9%, and the kappa coefficients from 0.805 to 0.923, thus demonstrating the good performance of the model in multi-category land use classification within the GBA.</p><p>Resolution: 30m</p><p>Time span: 1976-2020 (excluding 1981-1985)</p>
FIGURE 2 in Fossil Land Tortoises (Testudines: Testudinidae) from the Dominican Republic, West Indies, with a Description of a New Species
FIGURE 2. Photos of Oleg's Bat Cave. A. Main cavern. B. Cave diver examining the MHD 1000 tortoise shell; C. tortoise shell in place before it was collected.
FIGURE 5 in Fossil Land Tortoises (Testudines: Testudinidae) from the Dominican Republic, West Indies, with a Description of a New Species
FIGURE 5. Internal views of the plastron of Chelonoidis dominicensis (upper panels) showing: A. complete plastron; B. close-up of anterior plastral lobe; C. posterior plastral lobe. Comparative views of C. alburyorum (lower panels; specimen T4) showing D. complete plastron; E. close-up of anterior plastral lobe; F. posterior plastral lobe.
FIGURE 7 in Fossil Land Tortoises (Testudines: Testudinidae) from the Dominican Republic, West Indies, with a Description of a New Species
FIGURE 7. Comparative views of the skulls of Chelonoidis dominicensis (left column) in A. dorsal view; C. lateral view; E. posterior view; and C. alburyorum (right column) B. dorsal view; D. lateral view; F. ventral posterior view. Separate scale bars represent 10 mm for each specimen.
FIGURE 1 in Fossil Land Tortoises (Testudines: Testudinidae) from the Dominican Republic, West Indies, with a Description of a New Species
FIGURE 1. Map of the West Indies region and Hispaniola (modified from Velazco et al., 2013). Localities of Hispaniolan caves with tortoise fossils (inset) are: 1. Oleg's Bat Cave; 2. Bayaguana (Franz and Woods, 1983); 3. Barahona area (Turvey et al., 2017). Dotted line delineates the border between Haiti and the Dominican Republic; the heavy dashed line indicates the Enriquillo-Plantain Garden Fault. Other tortoise records, summarized in Franz and Franz (2009) and Steadman et al. (2017), are shown on the larger map. The Bahamas: 4. Abaco; 5. Moore's Island; 6. Andros; 7. New Providence; 8. Eleuthera; 9. San Salvador; 10. Crooked Island; 11. Acklins Island; 12. Mayaguana. Turks and Caicos Islands, British West Indies: 13. Middle Caicos; 14. Grand Turk. Greater Antilles: 15. Cuba; 16. Navassa Island; 17. Mona Island. Lesser Antilles: 18. Sombrero Island; 19. Anguilla; 20. Barbados.
FIGURE 4 in Fossil Land Tortoises (Testudines: Testudinidae) from the Dominican Republic, West Indies, with a Description of a New Species
FIGURE 4. Outline showing scutes as a solid line; sulci as a double line on Chelonoidis dominicensis: A. carapace, dorsal; B. plastron, ventral; C. carapace, right lateral. Abbreviations followed by roman numerals indicate bone order in a series. Bones = black; scutes = red. Carapace bones: cos, costal; neu, neural; nuc, nuchal plate; per, peripheral; pyg, pygal; spy I, suprapygal I; spy II, suprapygal II. Plastron bones: epi, epiplastron; ent, entoplastron; hyo, hyoplastron; hyp, hypoplastron; xip, xiphiplastron. Carapace scutes: mar, marginal; ple, pleural; scd, supracaudal; ver, vertebral. Plastron scutes: abd, abdominal; ana, anal; axi, axillary; fem, femoral; gul, gular; hum, humeral; ing, inguinal; pec, pectoral.
FIGURE 3 in Fossil Land Tortoises (Testudines: Testudinidae) from the Dominican Republic, West Indies, with a Description of a New Species
FIGURE 3. Comparative views of the shells of Chelonoidis dominicensis (left column) in A. dorsal view; C. lateral view; E. ventral (plastral) view; and C. alburyorum (right column) B. dorsal view; D. lateral view; F. ventral (plastral) view. Separate scale bars represent 10 cm for each specimen. Scale bars = 10 mm.
FIGURE 6 in Fossil Land Tortoises (Testudines: Testudinidae) from the Dominican Republic, West Indies, with a Description of a New Species
FIGURE 6. Internal views of carapace of Chelonoidis dominicensis (upper panels) showing the A. vertebral column; B. first dorsal vertebra; C. sacral rib complex. Comparative views of C. alburyorum (lower panels) showing the D. vertebral column; E. first dorsal vertebra; F. sacral rib complex.
Biodiversity impact assessment considering land use intensities and fragmentation
<p>The data provide supplementary information for the paper entitled "Biodiversity impact assessment considering land use intensities and fragmentation".</p><p> </p><p><strong>Coverage of the characterization factors</strong></p><ul><li>5 species groups: plants, amphibians, birds, mammals, and reptiles</li><li>5 broad land use types: cropland, pasture, plantations, managed forests, and urban areas</li><li>3 land use intensities: minimal, light, intense (sometimes, intensity levels had to be merged because the data did not allow to differentiate between them)</li><li>825 terrestrial ecoregions of the world (according to WWF / Olson et al. 2001)</li></ul><p> </p><p><strong>Files</strong></p><p>Main data</p><ul><li>CF.csv: characterization factors (CFs) for ecoregions and 5 species groups</li></ul><p>Taxonomically aggregated data</p><ul><li>CF_kingdom.csv: CFs aggregated from 5 species groups to plant and animal kingdoms</li><li>CF_domain.csv: CFs aggregated from plant and animal kingdoms to the domain of Eukaryota</li></ul><p>Spatially aggregated data</p><ul><li>CF_country.csv: CFs aggregated from ecoregions to countries</li><li>CF_global.csv: CFs aggregated from ecoregions to the globe</li></ul><p>Taxonomically and spatially aggregated data</p><ul><li>CF_kingdom_country.csv: CFs aggregated to countries and plant and animal kingdoms</li><li>CF_domain_country.csv: CFs aggregated to countries and the domain of Eukaryota</li><li>CF_kingdom_global.csv: CFs aggregated to the globe and plant and animal kingdoms</li><li>CF_domain_global.csv: CFs aggregated to the globe and the domain of Eukaryota</li></ul><p> </p><p><strong>Units</strong></p><p>CFs for land occupation: PDF/m2</p><p>CFs for land transformation: PDF⋅yr/m2</p><p> </p><p><strong>Columns</strong></p><ul><li>realm: 2-letter code to identify one of 8 biogeographical realms</li><li>biome: ID to identify one of 14 biomes</li><li>eco_id: ID to identify the ecoregion, combining numbers for the realm, biome, and ecoregion within each biome nested within each realm</li><li>eco_name: ecoregion name</li><li>species_group: species group</li><li>kingdom: kingdom as a taxonomic rank</li><li>habitat_id: ID to link to the land use type and intensity as used in land_use.tif. An ID with .5 represents a merged land use class considering the two habitats with the IDs when rounding the value both up and down.</li><li>habitat: land use type and intensity</li><li>CF_*: characterization factor</li><li>*_occ*: land occupation</li><li>*_tra*: land transformation</li><li>*_avg*: average approach</li><li>*_mar*: marginal approach</li><li>*_reg: regional relative species loss</li><li>*_glo: global relative species loss</li><li>*_rsd: relative standard deviation as a measure of spatial uncertainty due to aggregation (only concerns country and globally aggregated CFs)</li><li>quality_*: data quality, distinguishing between original estimates and the use of proxies</li><li>objectid: object id of the country</li><li>iso3cd: iso3 code of the country</li><li>romnam: romanized name of the country</li><li>m49code: M49 code of the country, a standard code used by the United Nations</li><li>weighting: aspect based on which the CFs were weighted (only concerns globally aggregated CFs)</li></ul><p> </p><p><strong>Data quality</strong></p><ul><li>original: original estimate (for globally aggregated CFs: mostly original estimates, proxies only considered in areas with current land use)</li><li>proxy_intensity: intensity level was missing; CF was derived from another CF of the same ecoregion and land use type but different intensity level and scaled to the right intensity level</li><li>proxy_type: land use type was missing; CF was derived from the average regional CFs for light use in the same biome and the ecoregion-specific GEP and scaled to the right intensity level if needed</li><li>proxy_gep: global extinction probability (GEP) was missing (only concerns CFs for global relative species loss); GEP estimated based on average GEP per area unit in the same biome and the ecoregion area</li><li>proxy_partial: some species groups were missing but not all (only concerns taxonomically aggregated CFs); aggregation done based on partly original estimates and partly proxies</li><li>proxy_neighbours: country was missing (only concerns country-aggregated CFs); values were estimated based on the average of the three nearest neighbouring countries</li><li>proxy: proxies were considered even in areas without current land use (only concerns globally aggregated CFs)</li></ul><p>Note: proxies in country-aggregated CFs apply to at least one of the ecoregions overlapping with the country and not necessarily all ecoregions</p><p> </p><p><strong>Land use type and intensity data</strong></p><p>Raster file: land_use.tif</p><p>Spatial resolution: 0.08333333, 0.08333333 (x, y)</p><p>Spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p><p>Coordinate reference system: WGS 84 (EPSG:4326)</p><p> </p><p>Codes</p><ol><li>Primary_vegetation_Minimal (incl. sparse/no vegetation)</li><li>Cropland_Intense</li><li>Cropland_Light</li><li>Cropland_Minimal</li><li>Managed_forest_Intense</li><li>Managed_forest_Light</li><li>Managed_forest_Minimal</li><li>Pasture_Intense</li><li>Pasture_Light</li><li>Pasture_Minimal</li><li>Plantation_Intense</li><li>Plantation_Light</li><li>Plantation_Minimal</li><li>Urban_Intense</li><li>Urban_Light</li><li>Urban_Minimal</li></ol>
Land use maps Bono East, Ghana (2020 and 2050)
<p>Land use GIS files of the Bono East region in Ghana at a resolution of 30x30 meters. This publication contains two geo-tif files and an accompanying QGIS style. The '2020' GIS file represents the current land use. The '2050' file represents a possible future as created through the application of the iCLUE land use projection model.</p><p>For more information, see Duku et al (2023) - <a href="https://doi.org/10.18174/584095">https://doi.org/10.18174/584095</a> or <a href="https://edepot.wur.nl/584095">https://edepot.wur.nl/584095</a></p>
FIG. 6. — A in Shifting shell morphology in a Late Miocene-Pliocene land snail species lineage (Gastropoda: Stylommatophora: Spiraxidae), with the description of a new species
FIG. 6. — A, Palaeoglandina aquensis (Matheron, 1843) from Aix (Burdigalian, Aix Basin), MHNM 6034.9314.1; B, P. galloprovincialis (Matheron, 1843) from Peyrolles (Tortonian, Durance Basin), MHNM 6034.9317.1; C1, C2, P. montenati Truc, 1971 from Quibas (Calabrian, Quibas site), MVHN-180222GM05; D1, D2, P. aquensis var. obtusa (Depéret, 1890), UCBL-FSL 148027; E1, E2, P. gracilis (Zieten, 1832), NHMW 2013/0572/0040; F1, F2, P. taurinensis (Sacco, 1886), BS.093.02.001; G, P. turolensis n. sp., MAP-8420, detail of protoconch; H, P. gracilis (Zieten, 1832), NHMW 2013/0572/0040, detail of protoconch; I, P. taurinensis (Sacco, 1886), BS.093.02.001, detail of protoconch;J, P. montenati Truc,1971 from Quibas,MVHN-180222GM05,detail of protoconch. Scale bars: A-F,10 mm; G,1 mm; H-J, 2 mm.
FIG. 2. — A in Shifting shell morphology in a Late Miocene-Pliocene land snail species lineage (Gastropoda: Stylommatophora: Spiraxidae), with the description of a new species
FIG. 2. — A, Shell measurements, following Solem & Climo (1985), and arrangement of landmarks (large circles) and semi-landmarks (small dots). The numbers in parentheses indicate the number of points for each of the two semi-landmark curves. See text for abbreviations. B, Protoconch measurements, following Verduin (1977). Abbreviations: N, nucleus; P/T boundary, protoconch/teleoconch boundary. Scale bar: A, 5 mm; B, 6 mm.
FIG. 4. — A, Palaeoglandina turolensis n in Shifting shell morphology in a Late Miocene-Pliocene land snail species lineage (Gastropoda: Stylommatophora: Spiraxidae), with the description of a new species
FIG. 4. — A, Palaeoglandina turolensis n. sp. from La Gloria 4 (Ruscinian, Teruel Basin): A1-A3, MAP-8405, holotype; B, MAP-8406, paratype; C, MAP-8407, paratype; D, MAP-8408, paratype; E, MAP-8409, paratype; F, MAP-8410, paratype; G, MAP-8411, paratype; H, MAP-8412, paratype; I, MAP-8413, paratype; J, MAP-8414, paratype; K, MAP-8415; L, MAP-8416; M, MAP-8417; N, MAP-8418; O, MAP-8419. Scale bar: 10 mm.
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