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99 results for “Mauritania”
CLMS Water Bodies monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Water Bodies from Copernicus Land Monitoring Service (CLMS) as monthly time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)</p> <p>Source data:<br>- CLMS: Water Bodies 2014-2020 (raster 300 m), global, 10-daily – version 1: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v1-0-300m</a> <br>- CLMS: Water Bodies 2020-present (raster 300 m), global, monthly – version 2: <a href="https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m">https://land.copernicus.eu/en/products/water-bodies/water-bodies-global-v2-0-300m</a></p> <p>Water is fundamental to life on Earth. Water quality, including aspects like turbidity and trophic state, is vital for assessing a water body's ecological well-being and its suitability for drinking. Understanding the water's surface temperature is key for monitoring climate change and can influence weather patterns. Tracking water levels in lakes and rivers helps in flood prediction, irrigation planning, and hydroelectric power generation. The presence and extent of ice on lakes and rivers can have significant implications for regional climates, ecosystems, and human activities. Moreover, the surface extent of water bodies, whether permanent or ephemeral, informs land management across various sectors. In an era marked by environmental change, these metrics offer insights into sustainable water resource management.<br>The Water Bodies product group aims to address these critical issues by providing tailored datasets to users which are applicable across a wide array of sectors. It includes Lake Surface Water Temperature, providing real-time and historical data; Lake Water Quality in various resolutions; Water Bodies datasets for surface extent; Lake and River Water Level information; the River and Lake Ice Extent product for ice presence; and the Aggregated River and Lake Ice Extent product, showing percent ice coverage. These products support applications like food security, public health safeguarding, climate studies, and responsible water management practices.</p> <p>Processing steps:<br>To cover the complete time period from 2019 to 2023 two data products of the Water Bodies product group are processed. Up to December of 2020 the Water Bodies at 10-daily resolution have been used, from January 2021 the Water Bodies at monthly resolution have been used. Both original datasets have been downloaded for the area of Mauritania (NUTS MR) within Latitude-Longitude/WGS84 spatial reference system. Then both datasets have been downsampled to 30 arc seconds (ca. 1000 meter) using the most frequent occuring value. The 10-daily data have been aggregated to monthly resolution using the most frequent occurring value.</p> <p>File naming:<br>Until December 2020: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_PROBAV_V1.0.1_MR_WB_res_2020_12_01T00_00_00.tif</code> <br>From January 2021 on: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_YYYY_MM_01T00_00_00.tif</code> <br>e.g.: <code>c_gls_WB300_GLOBE_S2_V2.0.1_MR_WB_res_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.<br>NOTE: data for 2023-04 are missing, since they are not available from CLMS</p> <p>Pixel values:<br>0: Sea<br>70: Water<br>255: No water</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 27:17:30N<br>south: 14:43:30N<br>west: 17:04:30W<br>east: 04:48:00W</p> <p>Temporal extent:<br>January 2019 - December 2023 (except: April 2023)</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>Generated using European Union's Copernicus Land Monitoring Service information</p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
MODIS NDVI, monthly aggregated time series for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023)
<p>Normalized Difference Vegetation Index (NDVI) from MODIS data for Mauritania at 30 arc seconds (ca. 1000 meter) resolution (2019 - 2023).</p> <p>Source data:<br>- MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid (MOD13A2 v061): <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p><br>The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MOD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value.</p> <p>For the time period January 2019 - December 2023, the NDVI layer of the original data has been processed. Bad quality pixels or pixels with snow/ice and/or cloud cover have been masked using the provided quality assurance (QA) layers and appear as "no data". These 16-Day data are then aggregated to monthly temporal resolution using the maximum and reprojected to Latitude-Longitude/WGS84.</p> <p>File naming:<br><code>ndvi_filt_YYYY_MM_01T00_00_00.tif</code><br>e.g.: <code>ndvi_filt_2023_12_01T00_00_00.tif</code></p> <p>The date within the filename is year and month of aggregated timestamp.</p> <p>Pixel values:<br>NDVI * 10000 Scaled to Integer, example: value 6473 = 0.6473</p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>north: 28N<br>south: 14N<br>west: 18W<br>east: 4W</p> <p>Temporal extent:<br>January 2019 - December 2023</p> <p>Spatial resolution:<br>30 arc seconds (approx. 1000 m)</p> <p>Temporal resolution:<br>monthly</p> <p>Software used:<br>GRASS GIS 8.3.2</p> <p>Format: GeoTIFF</p> <p>Original dataset license:<br>All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod13a2v061/">https://lpdaac.usgs.gov/products/mod13a2v061/</a></p> <p>Processed by:<br>mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact: <br>mundialis GmbH & Co. KG, info@mundialis.de</p> <p> </p>
National Checklists 2017: Mauritania Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Mauritania collected using effechecka and geonames polygons
National Checklists 2019: Mauritania Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Mauritania collected using effechecka and geonames polygons
qdgc Mauritania
<p>QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 21th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Mauritania
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Transport Starter Data Kit: Historical socio-transport data for Mauritania
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
Fig. 9 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 9. Granulina sigridae sp. nov., Mauritania.A –I. Timiris coral mound chain, MSM16–3/GeoB14876. A –B. Paratype (SMF373040). A. Ventral view, height 2.9 mm, width 1.8 mm. B. Columellar folds. C–D. Holotype (SMF359034). C. Ventral view, height 3.1 mm, width 1.9 mm. D. Columellar folds. E– G. Paratype (SMF373040). E –F. Ventral and side views, height 3.2 mm, width 1.8 mm, tumidity 1.6 mm. G. Micro-sculpture above second columellar tooth, see arrow in E–F. H. Paratype (SMF373040), ventral view, height 3.0 mm, width 1.8 mm. I. Paratype (SMF373040), side view, height 3.1 mm, tumidity 1.6 mm. J–K. Tamxat Mounds, MSM16–3/GeoB14904, ventral view, height 2.8 mm, width 1.7 mm.
Fig. 7 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 7. Granulina ronaldi sp. nov., Mauritania, off Banc d'Arguin, CANCAP/3.154. A–D. Holotype (SMF359026). A–B. Ventral and side views, height 2.9 mm, width 1.7 mm, tumidity 1.4 mm. C. Columellar folds. D. Micro-sculpture above second columellar fold, see arrow in C. E–F. Paratype (SMF359020), ventral and side views, height 2.5 mm, width 1.5 mm, tumidity 1.3 mm. G–H. Paratype (SMF359027), central and side views, height 2.5 mm, width 1.4 mm, tumidity 1.3 mm, callus line indicated by white dots. I–J. Paratype (SMF359029), ventral and side views, height 2.5 mm, width 1.5 mm, tumidity 1.2 mm.
Fig. 6 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 6. Granulina reginae sp. nov., Mauritania. A –D. Timiris Mound Complex. A –C. Holotype, POS346/ GeoB11587 (SMF359019). A–B. Ventral view, height 2.2 mm, width 1.4 mm, C. Micro-sculpture above second fold, see arrow in B. D. Paratype, POS346/GeoB11588 (SMF359021), ventral view, height 2.0 mm, tumidity 1.2 mm. E–J. Paratypes (SMF359025), off Banc d'Arguin, MSM16–3/GeoB14799. E–F. Ventral and side view, height 2.1 mm, width 1.3 mm, tumidity 1.1 mm. G. Micro-sculpture above second fold, see arrows in E, H. H. Columellar folds. I–J. Ventral and side view, height 2.2 mm, width 1.4 mm, tumidity 1.1 mm.
Fig. 5 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 5. Location map of the new species in Granulina Jousseaume, 1888 off Mauritania and Western Sahara. White circles show all investigated stations; colour symbols show locations of shells from new species. Granulina reginae sp. nov. is presented by yellow squares, G. sigridae sp. nov. as red diamonds, G. sandrae sp. nov. as blue triangles, and G. ronaldi sp. nov. as green circles. Bathymetric data from GEBCO; contours 500 m
Fig. 4 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 4. Ganulina aff. crassa, off Nouakchott, Mauritania. A–D. M44/133–KG615. E–F. M60/77–KG960. A–B. Ventral and side view, height 1.8 mm, width 1.2 mm, tumidity 1.0 mm. C. Micro-sculpture above second columellar fold, see arrow in D. D. Columellar folds and labial denticles. E. Ventral view, height 2.0 mm, width 1.3 mm. F. Ventral view, height 2.0 mm, width 1.3 mm.
Fig. 3 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 3. Granulina crassa Smriglio, Gubbioli & Mariottini, 2000, Western Sahara, off Cap Blanc, M44/235–KG649. A–D. Ventral and side views, height 2.1 mm, width 1.3 mm, tumidity 1.0 mm. C. Columellar folds. D. Micro–sculpture above second columellar fold, see arrow in C. E–F. Ventral and side views, height 2.1 mm, width 1.3 mm, tumidity 1.0 mm. G–H. Views and dimensions as E–F. I – J. Views and dimensions as E–F.
Fig. 8 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 8. Granulina sandrae sp. nov., Mauritania. – A –F. Off Cap Timiris, CANCAP/3.154. A –B. Holotype (SMF359030), ventral view, height 3.5 mm, width 2.2 mm, micro-sculpture above second columellar tooth, see arrow in A. C. Paratype (SMF359031), side view with thickened lip, height 3.2 mm, tumidity 1.8 mm. D–F. Paratype (SMF359031). D, F. Ventral and side views, height 3.2 mm, width 2.1 mm, tumidity 1.8 mm. E. Columellar view with teeth. – G–J. Southern Banc d'Arguin, MSM16–3/ GeoB14847. G–I. Paratype (SMF359033). G. Ventral view height 3.6 mm, width 2.5 mm. H. Microsculpture above second columellar fold, see arrow in I. I. Columellar folds. J. Paratype (SMF359033), side view with thickened lip, height 3.4 mm, tumidity 2.0 mm.
Fig. 1 in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 1. Location map of the known species of Granulina Jousseaume, 1888 found off Mauritania and Western Sahara. All investigated stations are shown by white circles; specimens of Granulina were only found at locations indicated by colour symbols. Granulina cerea Smriglio, Gubbioli & Mariottini, 2000 is presented as yellow squares, G. crassa Smriglio, Gubbioli & Mariottini, 2000 as red diamonds, G. crystallina Smriglio, Gubbioli & Mariottini, 2000 as blue triangles and G. nofronii Smriglio, Gubbioli & Mariottini, 2000 as green circles. Bathymetric data from GEBCO, contours at 500 m intervals.
Fig. 2. Granulina Jousseaume, 1888 from Mauritania. A–D in Revision of deep-water species in Granulina (Gastropoda: Granulinidae) from Mauritania and Western Sahara
Fig. 2. Granulina Jousseaume, 1888 from Mauritania. A–D. Granulina cerea Smriglio, Gubbioli & Mariottini, 2000, off Banc d'Arguin, CANCAP/3.120. A–B. Shell. A. Ventral view, height 2.8 mm, width 1.7 mm. B. Micro-sculpture above second columellar fold, see arrow in A. C–D. Shell, ventral view and side view, height 2.6 mm, width 1.6 mm, tumidity 1.3 mm. E. Granulina crystallina Smriglio, Gubbioli & Mariottini, 2000, off Nouakchott, M44/193–KG626, ventral view, height 3.1 mm, width 2.0 mm. F–G. Granulina nofronii Smriglio, Gubbioli & Mariottini, 2000, MSM16–3/GeoB14714. F. Ventral view, height 2.3 mm, width 1.4 mm. G. Ventral view, height 2.5 mm, width 1.6 mm
National Checklists: Mauritania Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Fig. 1 in First report of kdr mutations in the voltage-gated sodium channel gene in the arbovirus vector, Aedes aegypti, from Nouakchott, Mauritania
Fig. 1 The combinations of kdr point mutations S989P, V1016G, and F1534C in adult female Aedes aegypti mosquitoes in Nouakchott, Mauritania
FIG. 4 in A new species of bathymodioline mussel (Mollusca, Bivalvia, Mytilidae) from Mauritania (West Africa), with comments on the genus Bathymodiolus Kenk & Wilson, 1985
FIG. 4. — Half-schematic drawings of the insides of the right valves of Bathymodiolus mauritanicus n. sp.; A, holotype (MNHN); B, paratype (USNM); C, paratype (NSMT). Scale bars: 10 mm.
FIG. 5 in A new species of bathymodioline mussel (Mollusca, Bivalvia, Mytilidae) from Mauritania (West Africa), with comments on the genus Bathymodiolus Kenk & Wilson, 1985
FIG. 5. — Half-schematic drawings of the insides of the right valves of Bathymodiolus species; A, Bathymodiolus childressi Gustafson, Turner, Lutz & Vrijenhoek, 1998, paratype 3137-21 (MNHN), Green Canyon-272, Louisiana continental slope, 27°41.1'N, 91°32.2'W, 723 m, Johnson Sea-Link-1 cruise, dive 3137; B, Bathymodiolus childressi Gustafson, Turner, Lutz & Vrijenhoek, 1998, paratype 3129-52 (MNHN); C, D, Bathymodiolus sp. I, Barbados Accretionary Prism, Orénoque site, 1688 m, Diapisub cruise, stn DS 05; E, Bathymodiolus platifrons Hashimoto & Okutani, 1994, paratype II (MNHN); F, Bathymodiolus platifrons Hashimoto & Okutani, 1994, paratype I (MNHN); G, Bathymodiolus puteoserpentis Cosel, Métivier & Hashimoto, 1994, "Les Ruches" site, Snake Pit hydrothermal field, MAR, 23°22'N, 47°57'W, 3478 m, HYDROSNAKE cruise, stn HS 10 (MNHN) (for comparison). Scale bars: 10 mm.
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