0e50beb8-555d-43df-aac6-e23a7f7ffdb0 Vaz Sandrine IFREMER Station de Sète - Avenue Jean Monnet - CS 30171 - 34203 Sète Cedex Sete France Sandrine.Vaz@ifremer.fr pointOfContact 2026-06-25T15:57:01.424051Z ISO 19115-3 - SEXTANT 1.0 2 355200 false WGS 84 (EPSG:4326) EPSG 8.6 Environmental predictors associated with Mediterranean bioregions of megabenthic communities 2025-01-17 creation 2025-05-17 publication https://sextant.ifremer.fr/record/0e50beb8-555d-43df-aac6-e23a7f7ffdb0 These rasters correspond to the environmental predictors used in the production of Mediterranean bioregions of megabenthic communities Ifremer Jade Millot Ifremer Jade.Millot@ifremer.fr author Sandrine Vaz Ifremer Sandrine.Vaz@ifremer.fr author sextant Ifremer sextant@ifremer.fr publisher Valentina Lauria IRBIM CNR valentina.lauria@cnr.it author https://sextant.ifremer.fr/geonetwork/srv/api/records/0e50beb8-555d-43df-aac6-e23a7f7ffdb0/attachments/Envir_pred.JPG /Biological Environment/Habitats Thèmes Sextant 2025-12-09 publication geonetwork.thesaurus.local.theme.sextant-theme Habitats and biotopes GEMET - INSPIRE themes, version 1.0 2018-07-27T14:39:48 publication 2018-07-27 publication geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme D1: Coastal benthic habitats DCSMM : Descripteurs 2025-12-09 publication geonetwork.thesaurus.local.theme.dcsmm-descripteur Sous-regions marines 2025-12-09 publication geonetwork.thesaurus.local.place.dcsmm.area /Environmental Status/Habitats /Environmental Status/Species Thématiques - SIMM 2025-12-09 publication geonetwork.thesaurus.local.theme.simm.thematiques CC-BY (Creative Commons - Attribution) We do our best to ensure that the data is complete, accurate and useful. However, because the processing required to generate the data is complex, we cannot be liable for omissions or inaccuracies. The data and any related materials contained therein are provided “as is”, without warranty of any kind, either expressed or implied, including but not limited to, fitness for a particular purpose, non-interference, system integration, or non-infringement. The entire risk of use of the data shall be with the user. 79050557-6f8a-48cc-9e60-4112cae9fd18 50bc62bb-a226-445d-a2b9-b45c59eaaec1 b0a85bec-a8a7-40f8-ae83-b4302328c5f4 e4521877-e3a0-4dbf-867e-6a339e1c957f grid 0.04 utf8 oceans environment -6.00 37.00 30.00 46.00 2016-01-01 2023-12-31 https://www.journals.uchicago.edu/doi/10.1086/626171 WWW:LINK The Distinction between Grain Size and Mineral Composition in Sedimentary-Rock Nomenclature https://emodnet.ec.europa.eu/geonetwork/static/api/records/0a1cb988-22de-48b2-8cda-d90947ef77d1 WWW:LINK EUSeaMap 2023, A European broad-scale seabed habitat map https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.13813 WWW:LINK Bio-ORACLE v3.0. Pushing marine data layers to the CMIP6 Earth System Models of climate change research https://ntrl.ntis.gov/NTRL/dashboard/searchResults/titleDetail/PB97153704.xhtml WWW:LINK Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). FILE current_speed_mean Average bottom current speed above 1000m (tif) https://sextant.ifremer.fr/services/wms/redress OGC:WMS env_predict_current_speed_mean Average bottom current speed above 1000m FILE depth Depth above 1000m (tif) https://sextant.ifremer.fr/services/wms/redress OGC:WMS env_predict_depth Depth above 1000m FILE ph_mean Average bottom pH above 1000m (tif) https://sextant.ifremer.fr/services/wms/redress OGC:WMS env_predict_ph_mean Average bottom pH above 1000m FILE salinity_mean Average bottom salinity above 1000m (tif) https://sextant.ifremer.fr/services/wms/redress OGC:WMS env_predict_salinity_mean Average bottom salinity above 1000m FILE si_mean Average bottom silicate concentration above 1000m (tif) https://sextant.ifremer.fr/services/wms/redress OGC:WMS env_predict_si_mean Average bottom silicate concentration above 1000m FILE substrate Substrate average grain size above 1000m (tif) https://sextant.ifremer.fr/services/wms/redress OGC:WMS env_predict_substrate Substrate average grain size above 1000m FILE temp_mean Average bottom sea water temperature above 1000m (tif) https://sextant.ifremer.fr/services/wms/redress OGC:WMS env_predict_temp_mean Average bottom sea water temperature above 1000m https://doi.org/10.12770/0e50beb8-555d-43df-aac6-e23a7f7ffdb0 DOI Digital Object Identifier (DOI) RÈGLEMENT (UE) N o 1089/2010 DE LA COMMISSION du 23 novembre 2010 portant modalités d'application de la directive 2007/2/CE du Parlement européen et du Conseil en ce qui concerne l'interopérabilité des séries et des services de données géographiques 2010-12-08 publication See the referenced specification false This layer is linked to the following study: "Characterizing Soft-bottom Megabenthic Communities of the Mediterranean Continental Shelf: A Biogeographic Approach", 2025, Jade Millot, Sandrine Vaz, Tarek Hattab, Christopher J. Smith, Carlos Dominguez-Carrió, Beatriz Guijarro, Grégoire Certain, Vasilis Gerovasileiou, Vincent Georges, Caterina Stamouli, Michele Casini, Chiara Manfredi, Emanuela Fanelli, Germana Garofalo, Marie-Claire Fabri, Daniela Massi, Angélique Jadaud, Adriana Profeta, Pierluigi Carbonara, Evgenia Lefkaditou, Slavica Petović, Athanasios Evangelopoulos, Nikolaos Kamidis, Ioannis Thasitis, Jurgen Mifsud, Kelly Camilleri, Valentina Lauria. This study is submitted but not yet published. A bathymetric raster layer (depth, m) was extracted from the EMODnet portal (https://emodnet.ec.europa.eu/en) at a resolution of 0.001° (i.e., approximately 0.1 km). The layer was re-scaled at a resolution of 0.04° to better match the resolution of biological data from MEDITS survey. Pixels corresponding to depths greater than 1000m were masked. Extracted a bathymetric layer to generate Random Forest predictions of megabenthic bioregions. Environmental raster layers were extracted from the platform Bio-ORACLE (Assis et al., 2024), which uses monthly data of sea bottom variables from 2010 to 2020 to generate climatologies at a resolution of 0.05° (i.e., approximately 5 km). After a correlation analysis and selection, average layers from the seafloor were selected for : seawater temperature (°C), salinity (PSU), current speed (m.s-1), pH, silicate (mmol.m-3). Layers were rescaled to a resolution of 0.04° to better match the resolution of biological data from MEDITS survey. Pixels corresponding to depths greater than 1000m were masked. Extracted environmental variables to generate Random Forest predictions of megabenthic bioregions. The European broad-scale seabed habitat map (EUSeaMap) (Vasquez et al., 2023) downloaded from EMODnet was used to compute the average grain size of the sediment (mm) using Folk’s sediments fractions (Folk, 1954) and applying equations from (Renard et al., 1997). The layer was rescaled to a resolution of 0.04° to better match the resolution of biological data from MEDITS survey. Pixels corresponding to depths greater than 1000m were masked. The raster layer and more detailed information on its construction were available on the publication : "Modelled seabed sediment average grain size in the European North-East Atlantic and Mediterranean waters", Millot and Vaz, 2024. Build a substrate layer to generate Random Forest predictions of megabenthic bioregions.