iAtlantic WFSThis WFS service offers vector data collected within the within the framework of the iAtlantic project.WFSWMSGEOSERVERiAtlanticWFS2.0.0NONENONESeascape BelgiumTim CollartOostende8400Belgiumtim.collart@seascapebelgium.be1.0.01.1.02.0.0text/xmlServiceIdentificationServiceProviderOperationsMetadataFeatureTypeListFilter_Capabilitiesapplication/gml+xml; version=3.2resultshitsapplication/gml+xml; version=3.2GML2KMLSHAPE-ZIPapplication/jsonapplication/vnd.google-earth.kml xmlapplication/vnd.google-earth.kml+xmlcsvexcelexcel2007gml3gml32jsontext/xml; subtype=gml/2.1.2text/xml; subtype=gml/3.1.1text/xml; subtype=gml/3.2nonelocalFALSE1000000nonelocalapplication/gml+xml; version=3.2urn:ogc:def:queryLanguage:OGC-WFS::WFSQueryExpressionALLSOMEresultshitsapplication/gml+xml; version=3.2GML2KMLSHAPE-ZIPapplication/jsonapplication/vnd.google-earth.kml xmlapplication/vnd.google-earth.kml+xmlcsvexcelexcel2007gml3gml32jsontext/xml; subtype=gml/2.1.2text/xml; subtype=gml/3.1.1text/xml; subtype=gml/3.2nonelocalapplication/gml+xml; version=3.2ALLSOMETRUETRUETRUETRUETRUETRUEFALSEFALSETRUETRUETRUETRUEFALSETRUEFALSEwfs:Querywfs:StoredQuerygeonode:a__01_2020_12_313This part of the Atlantic comprises a wide range of benthic (seafloor) and pelagic (water column) ecosystems that live on the continental shelf and slope, the mid-ocean ridge and surrounding abyssal plains. Vulnerable Marine Ecosystems (those that are particularly sensitive to disturbance by activities such as fishing) are primarily found on the shelf, slopes and ridges in Icelandic waters. The area is heavily influenced by major oceanographic features – the warm Atlantic water masses and the cold waters of the East Greenland Current and the East Iceland Current – which have a profound influence on the primary productivity and functioning of marine pelagic wildlife, such as whales, zooplankton and fish.iAtlanticurn:ogc:def:crs:EPSG::4326-41.1460723876953 52.1727104187012-8.00257301330566 74.0096206665039geonode:a__10_2020_12_311The continental margin off SE Brazil comprises the Santos and Campos sedimentary basins, which sustain some of the world’s most intensive oil and gas exploitation activities. These basins were formed nearly 100 million years ago, back when the South Atlantic was a narrow marine environment between South America and Africa. The region is characterised by extensive deep sedimentary environments and pockmark fields, where it is speculated that natural gas may be emitted from the seabed and potentially sustain chemosynthetic ecosystems. There are also steep rocky slopes that are home to important cold-water coral reef systems, irregularly threatened by fisheries activity. Many knowledge gaps exist in this region, and filling them is critical for responsible management of current and future human activities.iAtlanticurn:ogc:def:crs:EPSG::4326-48.4635009765625 -30.320426940918-40.619499206543 -23.2034797668457geonode:a__11_2020_12_311The Vitória-Trindade Chain of seamounts extends some 1200 km from the eastern continental shelf of Brazil out to the oceanic islands of Trindade and Martin Vaz. These 17 seamounts rise up around 2,500 m from the seafloor, topping out at between 50-110 m below the sea surface, and are home to reef ecosystems and associated fish communities. Seamounts are generally known to be hotspots of biological diversity, but relatively little is known about the marine life around these undersea mountains. iAltantic’s iCorsage expedition, planned for 2021, will undertake extensive geological and biological surveys of these seamounts to better understand their ecology and dynamics.iAtlanticurn:ogc:def:crs:EPSG::4326-41.1806030273438 -23.7219848632812-28.3195190429688 -18.640625geonode:a__12_2020_12_311Along the seaward rim of the wide Argentinian shelf in the SW Atlantic, huge amounts of nutrients are brought up from the ocean depths via oceanic upwelling. These nutrients support high marine phytoplankton productivity, which in turn fuels diverse ecosystems at the seabed. Recent discoveries reveal that one of the most diverse group of deep-sea ecosystems – cold-water coral reefs – benefits from this abundant food supply. These Argentinian cold-water coral reefs are unique as they are built by an ecosystem engineer, the cold-water coral Bathelia candida, which is found nowhere else in the Atlantic Ocean.iAtlanticurn:ogc:def:crs:EPSG::4326-56.0 -40.0-52.631031036377 -36.0943222045898geonode:a__02_2020_12_311This study area reaches from the northern Rockall Trough, along the Atlantic margins of the UK and Ireland, down to the centre of the Porcupine Abyssal Plain. The area is home to cold-water coral reefs, submarine canyons, seamounts and banks, each with their own biological communities. A long-term ocean observatory on the Porcupine Abyssal Plain gives insights into the environmental changes that this northern sector of the Atlantic has experienced over time, while oceanographic moorings and annual ocean measurements along the Extended Ellett Line across the Rockall Trough provide invaluable input data for the oceanographic models that estimate the flux of heat and carbon through the NE Atlantic.iAtlanticurn:ogc:def:crs:EPSG::4326-20.6913166046143 46.8563346862793-5.71059894561768 60.3774566650391geonode:a__03_2020_12_311The spine of the Atlantic, the Mid-Atlantic Ridge is where new ocean crust is formed as plate tectonics pull the two sides of the Atlantic Ocean apart. In a narrow zone along the axis of the ridge the heat from volcanic activity drives hydrothermal venting at the seafloor, which supports a unique and diverse ecosystem that includes specialist chemosynthetic organisms and species that can tolerate the harsh conditions in and around the active hydrothermal vents. On the rocky flanks of the ridge and nearby seamounts, deep-sea corals attach themselves and feed on the organic-rich detritus brought in on the ocean currents. Mid-ocean ridge ecosystems are potentially at risk from the emerging deep-sea mining industry, which is set to target seafloor massive sulphide deposits that occur along the mid-ocean ridge.iAtlanticurn:ogc:def:crs:EPSG::4326-43.8938293457031 28.5999984741211-20.5552101135254 43.0987739562988geonode:a__04_2020_12_311The continental slopes form the oceanic rim, surrounding the deep ocean basins and supporting ecosystems distinct from, and yet connected to, those of both the basins and the continental shelves. The slopes are furrowed by submarine canyons, which provide important pathways connecting the shelves to deep ocean and often support rich biodiversity, from cold-water corals to deep-diving whales. The Gully, off Nova Scotia, is the largest canyon incised into the western margin of the North Atlantic and is Canada’s “flagship” Marine Protected Area. With its subsidiary canyons, the surrounding continental slope and the adjacent volume of the deep Atlantic, the area centred on The Gully provides a prime example of the inter-connected ecosystems around the oceanic rim.iAtlanticurn:ogc:def:crs:EPSG::4326-62.6603965759277 40.6639137268066-55.5359001159668 44.7071342468262geonode:a__05_2021_04_141This subtropical open-ocean ecosystem is bounded by four different ocean currents, which form an ocean gyre. The Sargasso Sea plays a crucial role in the wider North Atlantic ecosystem as habitat, foraging area, spawning ground and important migratory corridor, and is named after the endemic Sargassum seaweed that is found here. This sea is a haven for biodiversity, and is recognised as an Ecologically or Biologically Significant Area by the UN Convention on Biological Diversity. It is the planet’s only sea without a land boundary.iAtlanticurn:ogc:def:crs:EPSG::4326-78.1688919067383 22.1159038543701-43.6129264831543 38.5420303344727geonode:a__06_2020_12_311The large marine ecosystem in the Eastern Tropical North Atlantic (ETNA) off West Africa is among the most productive, diverse, and economically important marine regions worldwide due to a strong ocean-atmosphere coupling. Massive amounts of Saharan dust are deposited every year onto the sea, fuelling biological productivity in the ocean. But other major ocean processes are at work here too. The coastal upwelling system off West Africa – where cold, nutrient-rich waters well up from ocean depths – also feeds the marine ecosystem, and the seamounts around Cabo Verde are hotspots of marine biodiversity. However, a harmful oxygen minimum zone exists in the ocean’s interior, which in the Atlantic is not yet as pronounced as its counterparts in the Pacific and Indian oceans, but in recent decades has shown trends of expansion and could become a challenge for marine life in the region.iAtlanticurn:ogc:def:crs:EPSG::4326-40.0 11.4887018203735-19.4327335357666 20.5169277191162geonode:a__07_2020_12_311The Romanche Fracture Zone is a prominent geological feature known as a transform fault, which offsets the line of the Mid-Atlantic Ridge axis by about 900 km – the largest such offset in the Atlantic. The fracture zone manifests at the seafloor as long and deep valley reaching 7,000 m water depth, which acts as the main conduit for the flow of deep water masses between the North and South Atlantic basins. Seafloor habitats are shaped by the crests and ridges that flank the valley, but also include flatter sediment-covered areas and gentle slopes. These habitats are influenced by a high flux of organic matter from phytoplankton blooms caused by seasonal equatorial upwelling. Such high productivity in the middle of the vast oligotrophic (nutrient-poor) zones of the subtropical north and south Atlantic is thought to have an ‘oasis’ effect, enhancing abundance and diversity of marine life in and around the Romanche Fracture Zone. As such, it has been identified as an Ecologically and Biologically Significant Area by the UN Convention on Biological Diversity, and is of high conservation interest in light of the developing deep-sea mining industry.iAtlanticurn:ogc:def:crs:EPSG::4326-26.0687274932861 -3.02487421035767-11.4999990463257 3.34296846389771geonode:a__08_2020_12_314The complex oceanic currents in this part of the Atlantic result in the widespread occurrence of strongly oxygen-depleted subsurface water masses. Despite such seemingly unfavourable conditions, diverse benthic ecosystems thrive along the Angolan continental slope, including cold-water corals and cold seep ecosystems. While the corals are fuelled by organic matter filtering down from surface plankton productivity, the cold seep ecosystems obtain their energy from natural methane emissions at the seafloor. As oxygen levels in the world ocean are predicted to decrease as a result of global change, the subtropical SE Atlantic can serve as a natural laboratory to understand how deep-sea marine ecosystems adapt to low oxygen conditions and, thus, to cope with one of the most serious threats to marine ecosystems.iAtlanticurn:ogc:def:crs:EPSG::43265.0 -10.512.5 -4.5geonode:a__09_2020_12_311The Benguela Current is an important ocean current that flows northwards along the coast of South Africa, Namibia, and Angola. The current mixes water from the Atlantic and Indian Oceans as they meet off the capes of South Africa, and is driven northwards by the prevailing south-easterly winds. These winds are also responsible for generating oceanic upwelling, resulting in high biological productivity that supports rich fish stocks. Running from the African coast to the southern mid-Atlantic Ridge is a prominent chain of volcanic seamounts known as the Walvis Ridge. This ridge not only forms a significant physical barrier, creating a range of oceanographic phenomena, but also creates a variety of habitats that host a wide diversity of species and ecosystems.iAtlanticurn:ogc:def:crs:EPSG::4326-0.29857274889946 -38.07170486450220.7953395843506 -18.9999980926514geonode:AbyssThe abyss base layer represents the spatial extent of the abyssal areas of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). The abyss is the area of seafloor located at depths below the foot of the continental slope and above the depth of the hadal zone (defined as deeper than 6,000 m). The abyss feature layer was created by clipping a layer representing the ocean with the shelf, slope and hadal layers.AbyssfeaturesD1: Coastal benthic habitatsDownloadable Dataseafloor depthseafloor, geomorphic features, habitatsurn:ogc:def:crs:EPSG::4326-180.0 -76.4211654663086180.000015258789 90.0geonode:Abyssal_ClassificationThe abyssal classification layer represents the spatial extent of the abyssal areas of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). The abyss is the area of seafloor located at depths below the foot of the continental slope and above the depth of the hadal zone (defined as deeper than 6,000 m). The abyssal classification feature layer was created by clipping a layer representing the ocean with the shelf, slope and hadal layers. The resulting layer was then classification into areas of plains, hills and mountains based on variation in topography.D1: Coastal benthic habitatsAbyssal_ClassificationfeaturesDownloadable Dataseafloor depthseafloor, geomorphic features, habitatsurn:ogc:def:crs:EPSG::4326-180.000015258789 -76.4211654663086180.000015258789 90.0geonode:AnnelidaBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityfeaturesAnnelidaSouth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timewater massesiAtlanticDeep-seaBenthosurn:ogc:def:crs:EPSG::4326-48.1590003967285 -29.9860000610352-29.269998550415 -15.7949991226196geonode:AquaMaps_Species_RichnessAquaMaps is a tool for generating model-based, large-scale predictions of natural occurrences of species. For marine species, the model uses estimates of environmental preferences with respect to depth, water temperature, salinity, primary productivity, and association with sea ice or coastal areas. These estimates of species preferences, called environmental envelopes, are derived from large sets of occurrence data available from online collection databases such as GBIF (www.gbif.org) and OBIS (www.iobis.org), and from independent knowledge from the literature about the distribution of a given species and its habitat usage that are available in FishBase (and in SeaLifeBase and AlgaeBase for non-fish). The environmental envelopes are matched against local environmental conditions to determine the suitability of a given area in the ocean for a particular species. Predictions of relative probabilities of species occurrence are shown as color- coded species range maps in a global grid of half-degree latitude and longitude cell dimensions. The maps are displayed on the web through the use of C-squares Mapper developed at CSIRO Marine and Atmospheric Research in Australia (Rees 2002, 2003). The AquaMaps approach of incorporating species occurrences and expert knowledge into an environmental envelope is modified from an ecological niche model originally developed by Kaschner et al. (2006) for predicting global distributions of marine mammals. It is specifically applied to correct for biases in occurrence data such as non-representative coverage of a species’ distribution, biases in sampling effort and data provision, and species misidentifications. Further, this approach is applicable to a wide range of marine organisms thus allowing AquaMaps modeling of both fish and non-fish species.featuresAquaMaps_Species_Richnessurn:ogc:def:crs:EPSG::4326-84.0 -61.521.0 81.0geonode:AquaMaps_Species_Richness_CetaceansAquaMaps is a tool for generating model-based, large-scale predictions of natural occurrences of species. For marine species, the model uses estimates of environmental preferences with respect to depth, water temperature, salinity, primary productivity, and association with sea ice or coastal areas. These estimates of species preferences, called environmental envelopes, are derived from large sets of occurrence data available from online collection databases such as GBIF (www.gbif.org) and OBIS (www.iobis.org), and from independent knowledge from the literature about the distribution of a given species and its habitat usage that are available in FishBase (and in SeaLifeBase and AlgaeBase for non-fish). The environmental envelopes are matched against local environmental conditions to determine the suitability of a given area in the ocean for a particular species. Predictions of relative probabilities of species occurrence are shown as color- coded species range maps in a global grid of half-degree latitude and longitude cell dimensions. The maps are displayed on the web through the use of C-squares Mapper developed at CSIRO Marine and Atmospheric Research in Australia (Rees 2002, 2003). The AquaMaps approach of incorporating species occurrences and expert knowledge into an environmental envelope is modified from an ecological niche model originally developed by Kaschner et al. (2006) for predicting global distributions of marine mammals. It is specifically applied to correct for biases in occurrence data such as non-representative coverage of a species’ distribution, biases in sampling effort and data provision, and species misidentifications. Further, this approach is applicable to a wide range of marine organisms thus allowing AquaMaps modeling of both fish and non-fish species.featuresAquaMaps_Species_Richness_Cetaceansurn:ogc:def:crs:EPSG::4326-84.0 -61.521.0 81.0geonode:AquaMaps_Species_Richness_Elasmobranchs0AquaMaps is a tool for generating model-based, large-scale predictions of natural occurrences of species. For marine species, the model uses estimates of environmental preferences with respect to depth, water temperature, salinity, primary productivity, and association with sea ice or coastal areas. These estimates of species preferences, called environmental envelopes, are derived from large sets of occurrence data available from online collection databases such as GBIF (www.gbif.org) and OBIS (www.iobis.org), and from independent knowledge from the literature about the distribution of a given species and its habitat usage that are available in FishBase (and in SeaLifeBase and AlgaeBase for non-fish). The environmental envelopes are matched against local environmental conditions to determine the suitability of a given area in the ocean for a particular species. Predictions of relative probabilities of species occurrence are shown as color- coded species range maps in a global grid of half-degree latitude and longitude cell dimensions. The maps are displayed on the web through the use of C-squares Mapper developed at CSIRO Marine and Atmospheric Research in Australia (Rees 2002, 2003). The AquaMaps approach of incorporating species occurrences and expert knowledge into an environmental envelope is modified from an ecological niche model originally developed by Kaschner et al. (2006) for predicting global distributions of marine mammals. It is specifically applied to correct for biases in occurrence data such as non-representative coverage of a species’ distribution, biases in sampling effort and data provision, and species misidentifications. Further, this approach is applicable to a wide range of marine organisms thus allowing AquaMaps modeling of both fish and non-fish species.AquaMaps_Species_Richness_Elasmobranchsfeaturesurn:ogc:def:crs:EPSG::4326-84.0 -61.521.0 80.5geonode:AquaMaps_Species_Richness_Cetaceans0featuresAquaMaps_Species_Richness_Cetaceansurn:ogc:def:crs:EPSG::4326-84.0 -61.521.0 81.0geonode:BasinsThe basin geomorphic feature layer represents the spatial extent of the basins of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Basins are “a depression, in the sea floor, more or less equidimensional in plan and of variable extent”(IHO, 2008). In this study basins are restricted to seafloor depressions that are defined by closed bathymetric contours. Basins were mapped based on the identification of the most shoal, closed, bathymetric contours, examined regionally for the major ocean basins and shelf seas.BasinsDownloadable Dataseafloor, geomorphic features, habitatsfeaturesurn:ogc:def:crs:EPSG::4326-180.0 -78.6016616821289180.000015258789 90.0geonode:DCDB_200320_BathyCoverage_iAtlanticA dataset containing the outlines of Bathymetric surveys, relevant for the iAtlantic Consortium. This was exported from the IHO Data Centre for Digital Bathymetry (DCDB), https://www.ngdc.noaa.gov/iho/MultibeamSurveyAWI_BathymetryCoveragefeaturesurn:ogc:def:crs:EPSG::4326-88.310417175293 -63.689586639404327.7729167938232 76.8604202270508geonode:iAtlantic_StudyArea01_Shapes_MFRI_expShapefiles of existing Bathymetry collected by MFRI for iAtlantic study area 01.iAtlanticBathymetryurn:ogc:def:crs:EPSG::4326-27.8071022033691 62.4766654968262-7.52831411361694 69.0951995849609geonode:iAtlantic_StudyArea01_Shapes_MerianShapefiles of existing Bathymetry collected by R/V Maria S. Merian for iAtlantic study area 01.iAtlanticBathymetryurn:ogc:def:crs:EPSG::4326-52.7667465209961 -35.554489135742213.3032264709473 79.056755065918geonode:iAtlantic_StudyArea01_Shapes_MeteorShapefiles of existing Bathymetry collected by R/V Meteor for iAtlantic study area 01.iAtlanticBathymetryurn:ogc:def:crs:EPSG::4326-38.9328346252441 -0.1009569093585010.079184450209141 69.5429229736328geonode:iAtlantic_StudyArea02_ShapesShapefiles of existing Bathymetry for iAtlantic study area 02.iAtlanticAWI_BathymetryCoverageurn:ogc:def:crs:EPSG::32629-57.74938917449067 12.6833798158208941.532680411539301 55.91531563616501geonode:iAtlantic_StudyArea03_ShapesShapefiles of existing Bathymetry for iAtlantic study area 03.iAtlanticAWI_BathymetryCoverageurn:ogc:def:crs:EPSG::4326-2861360.25 15.589617729187-11.6201848983765 3546399.75geonode:iAtlantic_StudyArea03_Shapes_ExternalNo abstract providediAtlanticBathymetryurn:ogc:def:crs:EPSG::4326-2861360.25 32.9944190979004-11.6201848983765 3546399.75geonode:iAtlantic_StudyArea03_Shapes_MeteorShapefiles of existing Bathymetry collected by R/V Meteor for iAtlantic study area 03.iAtlanticBathymetryurn:ogc:def:crs:EPSG::4326-57.1201324462891 15.589617729187-13.3106880187988 39.6823425292969geonode:iAtlantic_StudyArea04_ShapesShapefiles of existing Bathymetry for iAtlantic study area 04.iAtlanticAWI_BathymetryCoverageurn:ogc:def:crs:EPSG::32629-54.01279353758988 33.5412909662770343.747689167376436 69.48968628275118geonode:iAtlantic_StudyArea05_ShapesShapefiles of existing Bathymetry for iAtlantic study area 05.iAtlanticAWI_BathymetryCoverageurn:ogc:def:crs:EPSG::4326-61.9146842956543 9.09334850311279-21.751293182373 50.4919815063477geonode:iAtlantic_StudyArea06_Shapes_MerianShapefiles of existing Bathymetry collected by R/V Maria S. Merian for iAtlantic study area 06.iAtlanticAWI_BathymetryCoverageurn:ogc:def:crs:EPSG::4326-79.8745193481445 -50.42931365966813.3029260635376 53.6734085083008geonode:iAtlantic_StudyArea06_Shapes_MeteorShapefiles of existing Bathymetry collected by R/V Meteor for iAtlantic study area 06.iAtlanticBathymetryurn:ogc:def:crs:EPSG::32629-58.64112752941866 -26.616841637989422.161461934434417 50.63843538987976geonode:iAtlantic_StudyArea07_ShapesShapefiles of existing Bathymetry for iAtlantic study area 07.iAtlanticBathymetryurn:ogc:def:crs:EPSG::32628-52.533206673838194 -38.7474867662905320.8102287921688 42.28841779376188geonode:iAtlantic_StudyArea08_ShapesShapefiles of existing Bathymetry for iAtlantic study area 08.iAtlanticBathymetryurn:ogc:def:crs:EPSG::32728-51.43225527863059 -25.9493442168968215.564181663237036 4.714528850349557geonode:iAtlantic_StudyArea09_ShapesShapefiles of existing Bathymetry for iAtlantic study area 09.iAtlanticBathymetryurn:ogc:def:crs:EPSG::4326-81.2372512817383 -85.52600860595735.380973815918 13.2918634414673geonode:iAtlantic_StudyArea10_ShapesShapefiles of existing Bathymetry for iAtlantic study area 10.iAtlanticBathymetryurn:ogc:def:crs:EPSG::32721-110.31357130670371 -4640.73470967312156.2179855216581 -6.975179511564536geonode:iAtlantic_StudyArea12_ShapesShapefiles of existing Bathymetry for iAtlantic study area 12.iAtlanticBathymetryurn:ogc:def:crs:EPSG::32722-55.591371402985786 -39.391996924301814-52.6649295593327 -34.358032392442496geonode:Bottom_EMU_Cluster0Ecological Marine Units (EMUs) for the entire Ocean are discoverable through this ArcMap Map Package. Users are encouraged to download and explore this dataset using ArcMap. Alternately, ArcGIS Pro Project Packages (available within this Group) are available for download for use in ArcGIS Pro. A group on GeoNet is available for user collaboration and feedback.Bottom_EMU_Clusterfeaturesurn:ogc:def:crs:EPSG::4326-97.625 -59.87519.8750019073486 67.6250076293945geonode:BrachiopodaBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityiAtlanticfeaturesSouth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timewater massesBrachiopodaDeep-seaBenthosurn:ogc:def:crs:EPSG::4326-46.3330001831055 -26.181001663208-44.1649971008301 -24.3469982147217geonode:BridgesThe bridge geomorphic feature layer represents the spatial extent of the bridges of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Bridge geomorphic features were first described by Gardner and Armstrong (2011) as blocks of material that partially infill the Mariana Trench in four locations, forming a “bridge”across the trench. In this study we have extended Gardner and Armstrong’s (2011) interpretation and applied it to all troughs and trenches and have identified a number of bridge features that appear to partially infill trenches and troughs in the global ocean.Bridgesseafloor, geomorphic features, habitatsfeaturesDownloadable Dataurn:ogc:def:crs:EPSG::4326-178.462295532227 -69.1857223510742178.037109375 83.1117935180664geonode:BryozoaBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityBryozoaSouth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timewater massesiAtlanticDeep-seaBenthosfeaturesurn:ogc:def:crs:EPSG::4326-46.4630012512207 -26.6900005340576-44.1149978637695 -24.3469982147217geonode:CCAMLRConvention Area for the Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR)CCAMLRfeaturesurn:ogc:def:crs:EPSG::4326-180.0 -88.0180.0 -45.0geonode:CnidariaBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityfeaturesSouth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timewater massesiAtlanticCnidariaDeep-seaBenthosurn:ogc:def:crs:EPSG::4326-51.9336700439453 -34.6675033569336-28.8549995422363 2.70000004768372geonode:Region_10_Eunis_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion 10Region_10_Eunis_Classification_ConfidenceBrazil margin and Santos and Campos BasinEUNIS habitat typesiAtlanticClassification Confidenceurn:ogc:def:crs:EPSG::4326-48.4635047912598 -30.320426940918-40.619499206543 -23.2034797668457geonode:Region_11_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingVitoria-Trindade Seamount ChainRegion 11EUNIS habitat typesiAtlanticClassification ConfidenceRegion_11_EUNIS_Classification_Confidenceurn:ogc:def:crs:EPSG::4326-41.1806030273438 -23.5971088409424-36.6666641235352 -18.6151466369629geonode:Region_12_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Region 12Region_12_EUNIS_Classification_ConfidencefeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingEUNIS habitat typesiAtlanticClassification ConfidenceMalvinas Currenturn:ogc:def:crs:EPSG::4326-55.9933319091797 -39.9951934814453-52.6374969482422 -36.0998687744141geonode:Region_2_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Rockall Trough to PAPfeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion_2_EUNIS_Classification_ConfidenceRegion 2EUNIS habitat typesiAtlanticClassification Confidenceurn:ogc:def:crs:EPSG::4326-20.6913166046143 46.8563346862793-5.71059894561768 60.3770866394043geonode:Region_3_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeRegion 3Habitat MappingCentral mid-Atlantic RidgeRegion_3_EUNIS_Classification_ConfidenceEUNIS habitat typesiAtlanticClassification Confidenceurn:ogc:def:crs:EPSG::4326-35.5770492553711 33.59521484375-20.849630355835 43.0788841247559geonode:Region_4_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion_4_EUNIS_Classification_ConfidenceRegion 4NW Atlantic Gully CanyonEUNIS habitat typesiAtlanticClassification Confidenceurn:ogc:def:crs:EPSG::4326-62.6577262878418 40.6639976501465-55.535961151123 44.7064170837402geonode:Region_5_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeRegion_5_EUNIS_Classification_ConfidenceHabitat MappingRegion 5EUNIS habitat typesiAtlanticClassification ConfidenceSargasso Seaurn:ogc:def:crs:EPSG::4326-78.1500015258789 22.0999984741211-43.6124992370605 38.5500030517578geonode:Region_6_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Region_6_EUNIS_Classification_ConfidencefeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingEastern Tropical North Atlantic Cape VerdeRegion 6EUNIS habitat typesiAtlanticClassification Confidenceurn:ogc:def:crs:EPSG::4326-26.4764671325684 14.0269556045532-21.0268650054932 17.9759464263916geonode:Region_7_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Equatorial Atlantic Romanche Fracture ZoneRegion_7_EUNIS_Classification_ConfidencefeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion 7EUNIS habitat typesiAtlanticClassification Confidenceurn:ogc:def:crs:EPSG::4326-26.0666675567627 -3.01875019073486-11.502082824707 3.34166669845581geonode:Region_8_ColA_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Lobe A of the CongolobefeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion_8_ColA_EUNIS_Classification_ConfidenceSlope and Margin off Angola and Congo LobeEUNIS habitat typesiAtlanticClassification ConfidenceRegion 8urn:ogc:def:crs:EPSG::43266.02783250808716 -6.478072166442876.03927898406982 -6.44434547424316geonode:Region_8_ColC_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Region_8_ColC_EUNIS_Classification_ConfidencefeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingLobe C of the CongolobeSlope and Margin off Angola and Congo LobeEUNIS habitat typesiAtlanticClassification ConfidenceRegion 8urn:ogc:def:crs:EPSG::43265.47151136398315 -6.716833591461185.49421501159668 -6.65913772583008geonode:Region_8_wacs2011_02AB_EUNIS_Classification_CThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion_8_wacs2011_02AB_EUNIS_Classification_ConfidenceSlope and Margin off Angola and Congo LobeEUNIS habitat typesiAtlanticClassification ConfidenceRegion 8The Regab pockmarkurn:ogc:def:crs:EPSG::43269.71004104614258 -5.798469543457039.71200752258301 -5.79700708389282geonode:Region_9_EUNIS_Classification_ConfidenceThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeRegion_9_EUNIS_Classification_ConfidenceHabitat MappingBenguela Current Walvis Ridge to South AfricaEUNIS habitat typesiAtlanticClassification ConfidenceRegion 9urn:ogc:def:crs:EPSG::432613.349178314209 -38.115604400634820.8118762969971 -28.6523094177246geonode:ATL_EBSAS_v2019_0104_WGS84The EBSAs are special areas in the ocean that serve important purposes, in one way or another, to support the healthy functioning of oceans and the many services that it provides. For more details on the EBSA criteria, please see: cbd.int/doc/meetings/mar/ebsaws-2014-01/other/ebsaws-2014-01-azores-brochure-en.pdf This layer is tailored from 3 Atlantic Regional Workshops (i.e. Wider Caribbean & Western Mid-Atlantic, South-Eastern Atlantic, North-West Atlantic) held to Facilitate Description of Areas Meeting EBSA Criteria. For more information please see: https://www.cbd.int/ebsa/ebsasATL_EBSAS_v2019_0104_WGS84featuresEcologically or Biologically Significant Areas (EBSAs)urn:ogc:def:crs:EPSG::4326-88.9268112182617 -45.521633148193421.0189933776855 60.9999351501465geonode:MS13_Published_raw_Bathy_Coverage_WGS84This shape file shows the coverage of raw multibeam bathymetry data published during the EU Horizon 2020 iAtlantic project for Milestone 13. The underlying raw data are unprocessed and may contain outliers. The coverage may change slightly once the data are being processed. The raw MBES data were published within work package 2 of the EU Horizon 2020 project iAtlantic - Integrated Assessment of Atlantic Marine Ecosystem in Space and Time (https://www.iatlantic.eu/). This publication is the outline of a compilation of all the above mentioned datasets and aimed to give an overview of where multibeam data exist and about the area covered. Note that this publication does not contain any depth information, it only informs about the covered area. All of the underlying raw datasets are linked below with further information on how and where they were acquired. The following cruises are included: --- RV Meteor: M156 EM710 (2019) M156 EM122 (2019) M156 Total Area: 25267.149949 km² M61-1 HS (2004) Area: 9134.686848 km² M61-2 HS (2004) Area: 9714.855025 km² M62-3 HS (2004) Area: 25028.338653 km² M62-4 HS (2004) Area: 70137.035777 km² M68-3 EM122 (2006) M68-3 EM710 (2006) M68-3 Total Area: 9080.020747 km² M78-3A EM122 (2009) Area: 19410.644047 km² M78-3A EM710 (2009) Area: 274.146303 km² --- RV Maria S. Merian: MSM17-1 EM122 (2010) Area: 173871.40310 km²; MSM19-3 EM122 (2011) Area: 55813.358391 km² --- RV Sonne datasets (Brix et al. 2020, 2021, 2022): SO280 EM122 Transit (2020) Area: 32996.51 km² SO276 Transit (2020) Area: 24553.707472 km² SO286 EM122 (2021) Area: 49013.305085 m²featuresMultibeamIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeMS13_Published_raw_Bathy_Coverage_WGS84iAtlanticFluxes_Bathy_IMa3_shapefileAWI_BathymetryCoverageurn:ogc:def:crs:EPSG::4326-55.3484230041504 -45.555603027343815.640025138855 66.0995407104492geonode:CrustaceaBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityfeaturesSouth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timewater massesCrustaceaiAtlanticDeep-seaBenthosurn:ogc:def:crs:EPSG::4326-51.5833015441895 -34.4167022705078-29.269998550415 2.07000017166138geonode:larvfoci_west_africa_margin_20431201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5Bathymodiolus2043-12-01Atlanticseep musselsClimate change predictionsiAtlantic+25Y-1260.0 m a.s.l.urn:ogc:def:crs:EPSG::4326-40.3968925476074 -1.267432808876041.36706912517548 5.5467324256897geonode:larvfoci_west_africa_margin_20431201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975Bathymodiolus2043-12-01Atlanticseep musselsClimate change predictionsiAtlantic+25Y-1260.0 m a.s.l.urn:ogc:def:crs:EPSG::4326-40.3968925476074 -1.267432808876041.36706912517548 5.5467324256897geonode:larvfoci_west_africa_margin_20440301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5Bathymodiolus2044-03-01Atlanticseep musselsClimate change predictionsiAtlantic+25Y-1260.0 m a.s.l.urn:ogc:def:crs:EPSG::4326-35.5877532958984 -3.96423912048345.12340402603149 6.14637136459351geonode:larvfoci_west_africa_margin_20440301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975Bathymodiolus2044-03-01Atlanticseep musselsClimate change predictionsiAtlantic+25Y-1260.0 m a.s.l.urn:ogc:def:crs:EPSG::4326-35.5877532958984 -3.96423912048345.12340402603149 6.14637136459351geonode:larvfoci_amazonie_20431201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingAmazon fan (AM)Gigantidas-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeVIKING20X0.5BathymodiolusNorth Brazil margin2043-12-01Atlanticseep musselsClimate change predictionsiAtlantic+25Yurn:ogc:def:crs:EPSG::4326-78.8987503051758 -8.8033647537231412.0938053131104 17.3857860565186geonode:larvfoci_amazonie_20431201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingAmazon fan (AM)Gigantidas-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975VIKING20XBathymodiolusNorth Brazil margin2043-12-01Atlanticseep musselsClimate change predictionsiAtlantic+25Yurn:ogc:def:crs:EPSG::4326-78.8987503051758 -8.8033647537231412.0938053131104 17.3857860565186geonode:larvfoci_amazonie_20440301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingAmazon fan (AM)Gigantidas-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeVIKING20X0.5BathymodiolusNorth Brazil margin2044-03-01Atlanticseep musselsClimate change predictionsiAtlantic+25Yurn:ogc:def:crs:EPSG::4326-68.153434753418 -9.6213216781616212.3692684173584 13.8800582885742geonode:larvfoci_amazonie_20440301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingAmazon fan (AM)Gigantidas-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975VIKING20XBathymodiolusNorth Brazil margin2044-03-01Atlanticseep musselsClimate change predictionsiAtlantic+25Yurn:ogc:def:crs:EPSG::4326-68.153434753418 -9.6213216781616212.3692684173584 13.8800582885742geonode:larvfoci_nolfork_canyon_20431201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-1450.0 m a.s.l.GigantidasIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5BathymodiolusNorfolk Canyon (NC)2043-12-01US Atlantic MarginAtlanticseep musselsClimate change predictionsiAtlantic+25Yurn:ogc:def:crs:EPSG::4326-75.4339141845703 29.3618335723877-14.7182397842407 54.5750007629395geonode:larvfoci_nolfork_canyon_20431201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-1450.0 m a.s.l.GigantidasIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975BathymodiolusNorfolk Canyon (NC)2043-12-01US Atlantic MarginAtlanticseep musselsClimate change predictionsiAtlantic+25Yurn:ogc:def:crs:EPSG::4326-75.4339141845703 29.3618335723877-14.7182397842407 54.5750007629395geonode:larvfoci_nolfork_canyon_20440301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-1450.0 m a.s.l.GigantidasIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5BathymodiolusNorfolk Canyon (NC)US Atlantic Margin2044-03-01Atlanticseep musselsClimate change predictionsiAtlantic+25Yurn:ogc:def:crs:EPSG::4326-74.8709869384766 27.2205543518066-17.2702865600586 54.1601066589355geonode:larvfoci_nolfork_canyon_20440301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-1450.0 m a.s.l.GigantidasIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975BathymodiolusNorfolk Canyon (NC)US Atlantic Margin2044-03-01Atlanticseep musselsClimate change predictionsiAtlantic+25Yurn:ogc:def:crs:EPSG::4326-74.8709869384766 27.2205543518066-17.2702865600586 54.1601066589355geonode:larvfoci_alaminos_canyon_20431201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-2200.0 m a.s.l.Alaminos Canyon (AC)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5Bathymodiolus2043-12-01iAtlanticGigantidasAtlanticseep musselsClimate change predictionsGulf of Mexico+25Yurn:ogc:def:crs:EPSG::4326-97.8702621459961 18.6012897491455-44.0213279724121 42.8699607849121geonode:larvfoci_alaminos_canyon_20431201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-2200.0 m a.s.l.Alaminos Canyon (AC)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975Bathymodiolus2043-12-01iAtlanticGigantidasAtlanticseep musselsClimate change predictionsGulf of Mexico+25Yurn:ogc:def:crs:EPSG::4326-97.8702621459961 18.6012897491455-44.0213279724121 42.8699607849121geonode:larvfoci_alaminos_canyon_20440301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-2200.0 m a.s.l.Alaminos Canyon (AC)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5BathymodiolusiAtlantic2044-03-01GigantidasAtlanticseep musselsClimate change predictionsGulf of Mexico+25Yurn:ogc:def:crs:EPSG::4326-97.4235000610352 18.4579486846924-33.5697135925293 49.1072235107422geonode:larvfoci_alaminos_canyon_20440301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-2200.0 m a.s.l.Alaminos Canyon (AC)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975BathymodiolusiAtlantic2044-03-01GigantidasAtlanticseep musselsClimate change predictionsGulf of Mexico+25Yurn:ogc:def:crs:EPSG::4326-97.4235000610352 18.4579486846924-33.5697135925293 49.1072235107422geonode:larvfoci_kick_em_jenny_crater_20431201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasClimate change predictionsIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados Prism0.5Bathymodiolus2043-12-01Kick°em Jenny crater (KJC)Atlanticseep mussels-260.0 m a.s.l.iAtlantic+25Yurn:ogc:def:crs:EPSG::4326-78.8601608276367 3.81669688224792-41.0412788391113 17.608190536499geonode:larvfoci_kick_em_jenny_crater_20431201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasClimate change predictionsIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975Barbados PrismBathymodiolus2043-12-01Kick°em Jenny crater (KJC)Atlanticseep mussels-260.0 m a.s.l.iAtlantic+25Yurn:ogc:def:crs:EPSG::4326-78.8601608276367 3.81669688224792-41.0412788391113 17.608190536499geonode:larvfoci_kick_em_jenny_crater_20440301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasClimate change predictionsIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados Prism0.5Bathymodiolus2044-03-01Kick°em Jenny crater (KJC)Atlanticseep mussels-260.0 m a.s.l.iAtlantic+25Yurn:ogc:def:crs:EPSG::4326-86.9419555664062 1.05118572711945-36.7925033569336 20.0825805664062geonode:larvfoci_kick_em_jenny_crater_20440301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasClimate change predictionsIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975Barbados PrismBathymodiolus2044-03-01Kick°em Jenny crater (KJC)Atlanticseep mussels-260.0 m a.s.l.iAtlantic+25Yurn:ogc:def:crs:EPSG::4326-86.9419555664062 1.05118572711945-36.7925033569336 20.0825805664062geonode:larvfoci_west_africa_margin_20681201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5Bathymodiolus2068-12-01Atlanticseep musselsClimate change predictionsiAtlantic+50Y-1260.0 m a.s.l.urn:ogc:def:crs:EPSG::4326-40.3968925476074 -1.267432808876041.36706912517548 5.5467324256897geonode:larvfoci_west_africa_margin_20681201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975Bathymodiolus2068-12-01Atlanticseep musselsClimate change predictionsiAtlantic+50Y-1260.0 m a.s.l.urn:ogc:def:crs:EPSG::4326-40.3968925476074 -1.267432808876041.36706912517548 5.5467324256897geonode:larvfoci_west_africa_margin_20690301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5BathymodiolusAtlantic2069-03-01Climate change predictionsiAtlantic+50Y-1260.0 m a.s.l.seep musselsurn:ogc:def:crs:EPSG::4326-35.5877532958984 -3.96423912048345.12340402603149 6.14637136459351geonode:larvfoci_west_africa_margin_20690301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975BathymodiolusAtlantic2069-03-01Climate change predictionsiAtlantic+50Y-1260.0 m a.s.l.seep musselsurn:ogc:def:crs:EPSG::4326-35.5877532958984 -3.96423912048345.12340402603149 6.14637136459351geonode:larvfoci_amazonie_20681201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingAmazon fan (AM)Gigantidas-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeVIKING20X0.5BathymodiolusNorth Brazil margin2068-12-01Atlanticseep musselsClimate change predictionsiAtlantic+50Yurn:ogc:def:crs:EPSG::4326-78.8987503051758 -8.8033647537231412.0938053131104 17.3857860565186geonode:larvfoci_amazonie_20681201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingAmazon fan (AM)Gigantidas-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975VIKING20XBathymodiolusNorth Brazil margin2068-12-01Atlanticseep musselsClimate change predictionsiAtlantic+50Yurn:ogc:def:crs:EPSG::4326-78.8987503051758 -8.8033647537231412.0938053131104 17.3857860565186geonode:larvfoci_amazonie_20690301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingAmazon fan (AM)Gigantidas-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeVIKING20X0.5BathymodiolusNorth Brazil marginAtlantic2069-03-01Climate change predictionsiAtlantic+50Yseep musselsurn:ogc:def:crs:EPSG::4326-68.153434753418 -9.6213216781616212.3692684173584 13.8800582885742geonode:larvfoci_amazonie_20690301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingAmazon fan (AM)Gigantidas-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975VIKING20XBathymodiolusNorth Brazil marginAtlantic2069-03-01Climate change predictionsiAtlantic+50Yseep musselsurn:ogc:def:crs:EPSG::4326-68.153434753418 -9.6213216781616212.3692684173584 13.8800582885742geonode:larvfoci_nolfork_canyon_20681201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-1450.0 m a.s.l.GigantidasIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5BathymodiolusNorfolk Canyon (NC)US Atlantic Margin2068-12-01Atlanticseep musselsClimate change predictionsiAtlantic+50Yurn:ogc:def:crs:EPSG::4326-75.4339141845703 29.3618335723877-14.7182397842407 54.5750007629395geonode:larvfoci_nolfork_canyon_20681201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-1450.0 m a.s.l.GigantidasIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975BathymodiolusNorfolk Canyon (NC)US Atlantic Margin2068-12-01Atlanticseep musselsClimate change predictionsiAtlantic+50Yurn:ogc:def:crs:EPSG::4326-75.4339141845703 29.3618335723877-14.7182397842407 54.5750007629395geonode:larvfoci_nolfork_canyon_20690301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-1450.0 m a.s.l.GigantidasIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5BathymodiolusNorfolk Canyon (NC)US Atlantic MarginAtlantic2069-03-01Climate change predictionsiAtlantic+50Yseep musselsurn:ogc:def:crs:EPSG::4326-74.8709869384766 27.2205543518066-17.2702865600586 54.1601066589355geonode:larvfoci_nolfork_canyon_20690301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-1450.0 m a.s.l.GigantidasIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975BathymodiolusNorfolk Canyon (NC)US Atlantic MarginAtlantic2069-03-01Climate change predictionsiAtlantic+50Yseep musselsurn:ogc:def:crs:EPSG::4326-74.8709869384766 27.2205543518066-17.2702865600586 54.1601066589355geonode:larvfoci_alaminos_canyon_20681201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-2200.0 m a.s.l.Alaminos Canyon (AC)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5BathymodiolusiAtlantic2068-12-01GigantidasAtlanticseep musselsClimate change predictionsGulf of Mexico+50Yurn:ogc:def:crs:EPSG::4326-97.8702621459961 18.6012897491455-44.0213279724121 42.8699607849121geonode:larvfoci_alaminos_canyon_20681201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-2200.0 m a.s.l.Alaminos Canyon (AC)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975BathymodiolusiAtlantic2068-12-01GigantidasAtlanticseep musselsClimate change predictionsGulf of Mexico+50Yurn:ogc:def:crs:EPSG::4326-97.8702621459961 18.6012897491455-44.0213279724121 42.8699607849121geonode:larvfoci_alaminos_canyon_20690301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-2200.0 m a.s.l.Alaminos Canyon (AC)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.5BathymodiolusiAtlanticGigantidasAtlantic2069-03-01Climate change predictionsGulf of Mexico+50Yseep musselsurn:ogc:def:crs:EPSG::4326-97.4235000610352 18.4579486846924-33.5697135925293 49.1072235107422geonode:larvfoci_alaminos_canyon_20690301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20X-2200.0 m a.s.l.Alaminos Canyon (AC)Integrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975BathymodiolusiAtlanticGigantidasAtlantic2069-03-01Climate change predictionsGulf of Mexico+50Yseep musselsurn:ogc:def:crs:EPSG::4326-97.4235000610352 18.4579486846924-33.5697135925293 49.1072235107422geonode:larvfoci_kick_em_jenny_crater_20681201_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasClimate change predictionsIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados Prism0.5Bathymodiolus2068-12-01Kick°em Jenny crater (KJC)Atlanticseep mussels-260.0 m a.s.l.iAtlantic+50Yurn:ogc:def:crs:EPSG::4326-78.8601608276367 3.81669688224792-41.0412788391113 17.608190536499geonode:larvfoci_kick_em_jenny_crater_20681201_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasClimate change predictionsIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975Barbados PrismBathymodiolus2068-12-01Kick°em Jenny crater (KJC)Atlanticseep mussels-260.0 m a.s.l.iAtlantic+50Yurn:ogc:def:crs:EPSG::4326-78.8601608276367 3.81669688224792-41.0412788391113 17.608190536499geonode:larvfoci_kick_em_jenny_crater_20690301_q0_5A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasClimate change predictionsIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados Prism0.5BathymodiolusKick°em Jenny crater (KJC)Atlantic2069-03-01-260.0 m a.s.l.iAtlantic+50Yseep musselsurn:ogc:def:crs:EPSG::4326-86.9419555664062 1.05118572711945-36.7925033569336 20.0825805664062geonode:larvfoci_kick_em_jenny_crater_20690301_q0_975A projection of larval dispersal patterns of Atlantic cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang was carried out for the next 50 years under the constraint of global warming predicted by the IPCC for the most pessismistic scenario. Simulations were run at +00 years, +25 years and +50 years from initial years of 2014 to 2019 (+00Y) at 21 locations on the US, European and African coasts using the VIKING20X model, in which the Atlantic water temperatures predicted by the FOCI model were forced to the future dates. The dataset consists of a number of 5775 simulations carried out over 5 years X 5 spawning dates per prediction period (+00Y, +25Y, +50Y) with, for predictions at +25Y and +50Y, a repetition of simulations per quantile (0.025, 0.16, 0.5, 0.67 and 0.975) to take into account for the most extreme variations in water mass temperatures predicted by the FOCI model for a given date. The original data can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.966720.larval dispersal modellingVIKING20XGigantidasClimate change predictionsIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time0.975Barbados PrismBathymodiolusKick°em Jenny crater (KJC)Atlantic2069-03-01-260.0 m a.s.l.iAtlantic+50Yseep musselsurn:ogc:def:crs:EPSG::4326-86.9419555664062 1.05118572711945-36.7925033569336 20.0825805664062geonode:larvae_atlantic_fz_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.North Mid-Atlantic RidgeCold seepsGigantidasNorth AtlanticAtlantis Fracture Zone (LOST)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltMusseliAtlantic2017-01-01-1000.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-58.9622917175293 22.3038597106934-34.3627548217773 38.9403228759766geonode:larvae_atlantic_fz_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.North Mid-Atlantic RidgeCold seepsGigantidasNorth AtlanticAtlantis Fracture Zone (LOST)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-02-01Equatorial Atlantic beltiAtlanticMussel-1000.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-64.7769241333008 21.3724575042725-35.6522445678711 39.384090423584geonode:larvae_atlantic_fz_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.North Mid-Atlantic RidgeCold seepsGigantidasNorth AtlanticAtlantis Fracture Zone (LOST)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltiAtlanticMussel2017-03-01-1000.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-60.4917373657227 24.7664031982422-35.3230094909668 41.3463821411133geonode:larvae_atlantic_fz_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.North Mid-Atlantic RidgeCold seepsGigantidasNorth AtlanticAtlantis Fracture Zone (LOST)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-11-01Equatorial Atlantic beltiAtlanticMussel-1000.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-56.6904373168945 23.8922119140625-29.440788269043 39.0918121337891geonode:larvae_atlantic_fz_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.North Mid-Atlantic RidgeCold seepsGigantidasNorth AtlanticAtlantis Fracture Zone (LOST)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltMusseliAtlantic2017-12-01-1000.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-57.9049911499023 23.2674579620361-29.6120777130127 37.8859710693359geonode:larvae_cadiz_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time-1115.0 m a.s.l.Bathymodioluslarval dispersalNE Atlantic marginEquatorial Atlantic beltMusseliAtlantic2017-01-01Gulf of Cadiz (GC)urn:ogc:def:crs:EPSG::4326-20.1694107055664 26.057741165161113.4963493347168 43.3885116577148geonode:larvae_cadiz_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time-1115.0 m a.s.l.Bathymodioluslarval dispersal2017-02-01NE Atlantic marginEquatorial Atlantic beltiAtlanticMusselGulf of Cadiz (GC)urn:ogc:def:crs:EPSG::4326-19.0398387908936 26.536674499511713.1207094192505 43.340015411377geonode:larvae_cadiz_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time-1115.0 m a.s.l.Bathymodioluslarval dispersalNE Atlantic marginEquatorial Atlantic beltiAtlanticMussel2017-03-01Gulf of Cadiz (GC)urn:ogc:def:crs:EPSG::4326-19.8911781311035 23.571838378906214.3218727111816 43.2720336914062geonode:larvae_cadiz_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time-1115.0 m a.s.l.Bathymodioluslarval dispersal2017-11-01NE Atlantic marginEquatorial Atlantic beltiAtlanticMusselGulf of Cadiz (GC)urn:ogc:def:crs:EPSG::4326-17.2207183837891 29.986135482788121.6790084838867 44.0143623352051geonode:larvae_cadiz_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Time-1115.0 m a.s.l.Bathymodioluslarval dispersalNE Atlantic marginEquatorial Atlantic beltMusseliAtlantic2017-12-01Gulf of Cadiz (GC)urn:ogc:def:crs:EPSG::4326-24.0582294464111 24.321166992187519.6742744445801 44.5739974975586geonode:larvae_arguin_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltArguin bank (ARG)MusseliAtlantic2017-01-01West Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-32.0147476196289 14.6174468994141-10.2815551757812 30.0327606201172geonode:larvae_arguin_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-02-01Equatorial Atlantic beltArguin bank (ARG)iAtlanticMusselWest Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-31.7376670837402 16.0354518890381-10.2474069595337 30.1687412261963geonode:larvae_arguin_201703010These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltArguin bank (ARG)iAtlanticMussel2017-03-01West Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-30.3161716461182 16.0570755004883-15.0544681549072 27.5217266082764geonode:larvae_arguin_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-11-01Equatorial Atlantic beltArguin bank (ARG)iAtlanticMusselWest Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-31.981273651123 9.59504318237305-15.8095321655273 26.8922634124756geonode:larvae_arguin_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltArguin bank (ARG)MusseliAtlantic2017-12-01West Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-33.3985633850098 12.5927686691284-15.4960641860962 26.9309616088867geonode:larvae_cadamosto_seamount_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltCadamostro Seamount (CS)MusseliAtlantic2017-01-01West Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-29.7129974365234 11.8978576660156-16.7084636688232 18.6419696807861geonode:larvae_cadamosto_seamount_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-02-01Equatorial Atlantic beltCadamostro Seamount (CS)iAtlanticMusselWest Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-30.2824573516846 11.9050874710083-16.5995864868164 19.5451908111572geonode:larvae_cadamosto_seamount_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltCadamostro Seamount (CS)iAtlanticMussel2017-03-01West Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-30.5421543121338 12.383960723877-16.6060047149658 20.6016120910645geonode:larvae_cadamosto_seamount_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-11-01Equatorial Atlantic beltCadamostro Seamount (CS)iAtlanticMusselWest Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-34.062198638916 14.0235967636108-16.5392761230469 23.7182197570801geonode:larvae_cadamosto_seamount_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltCadamostro Seamount (CS)MusseliAtlantic2017-12-01West Africa Marginlarval dispersalurn:ogc:def:crs:EPSG::4326-34.3971824645996 12.2432994842529-16.5745162963867 24.3454875946045geonode:larvae_swim_fault_volcanoes_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeSWIM fault (SWIM)BathymodiolusNE Atlantic marginEquatorial Atlantic beltMusseliAtlantic2017-01-01larval dispersalurn:ogc:def:crs:EPSG::4326-14.4645977020264 35.063125610351610.6531915664673 43.9074554443359geonode:larvae_swim_fault_volcanoes_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeSWIM fault (SWIM)Bathymodiolus2017-02-01NE Atlantic marginEquatorial Atlantic beltiAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-15.3083229064941 34.480216979980513.596137046814 44.2033424377441geonode:larvae_swim_fault_volcanoes_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeSWIM fault (SWIM)BathymodiolusNE Atlantic marginEquatorial Atlantic beltiAtlanticMussel2017-03-01larval dispersalurn:ogc:def:crs:EPSG::4326-15.6733474731445 34.253627777099613.4992170333862 44.1743698120117geonode:larvae_swim_fault_volcanoes_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeSWIM fault (SWIM)Bathymodiolus2017-11-01NE Atlantic marginEquatorial Atlantic beltiAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-17.1391716003418 31.897262573242214.7550945281982 44.4011306762695geonode:larvae_swim_fault_volcanoes_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-1200.0 m a.s.l.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeSWIM fault (SWIM)BathymodiolusNE Atlantic marginEquatorial Atlantic beltMusseliAtlantic2017-12-01larval dispersalurn:ogc:def:crs:EPSG::4326-17.9867477416992 31.950702667236315.4464740753174 45.1607818603516geonode:larvae_west_africa_margin_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltMusseliAtlantic2017-01-01-1260.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-40.2961120605469 -1.534678816795358.83228874206543 6.91062784194946geonode:larvae_west_africa_margin_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-02-01Equatorial Atlantic beltiAtlanticMussel-1260.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-36.6118240356445 0.2459670156240464.19511651992798 6.14539909362793geonode:larvae_west_africa_margin_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltiAtlanticMussel2017-03-01-1260.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-21.6356678009033 -0.0208143386989834.18488693237305 5.10590410232544geonode:larvae_west_africa_margin_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-11-01Equatorial Atlantic beltiAtlanticMussel-1260.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-41.4444732666016 -3.84240150451667.34853076934814 6.87996912002563geonode:larvae_west_africa_margin_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of GuineaWest Africa margin (WAM)Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusEquatorial Atlantic beltMusseliAtlantic2017-12-01-1260.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-42.8645973205566 -0.5625613331794747.05309104919434 7.66438102722168geonode:larvae_sao_paulo_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus-1300.0 m a.s.l.South Brazil marginSao Paulo 1 (SP)Equatorial Atlantic beltMusseliAtlantic2017-01-01larval dispersalurn:ogc:def:crs:EPSG::4326-65.7228622436523 -33.986450195312510.1686325073242 15.6604146957397geonode:larvae_sao_paulo_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus-1300.0 m a.s.l.2017-02-01Sao Paulo 1 (SP)Equatorial Atlantic beltiAtlanticMusselSouth Brazil marginlarval dispersalurn:ogc:def:crs:EPSG::4326-60.1410827636719 -33.986450195312511.5025396347046 12.7405595779419geonode:larvae_sao_paulo_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus-1300.0 m a.s.l.South Brazil marginSao Paulo 1 (SP)Equatorial Atlantic beltiAtlanticMussel2017-03-01larval dispersalurn:ogc:def:crs:EPSG::4326-58.920352935791 -33.986450195312510.6320810317993 11.3176908493042geonode:larvae_sao_paulo_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus-1300.0 m a.s.l.2017-11-01Sao Paulo 1 (SP)Equatorial Atlantic beltiAtlanticMusselSouth Brazil marginlarval dispersalurn:ogc:def:crs:EPSG::4326-58.1890068054199 -33.98587417602549.6162109375 9.75020408630371geonode:larvae_sao_paulo_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus-1300.0 m a.s.l.South Brazil marginSao Paulo 1 (SP)Equatorial Atlantic beltMusseliAtlantic2017-12-01larval dispersalurn:ogc:def:crs:EPSG::4326-60.4504623413086 -33.98294067382819.50736427307129 12.2009868621826geonode:larvae_amazonie_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Amazon fan (AM)Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorth Brazil marginEquatorial Atlantic beltMusseliAtlantic2017-01-01larval dispersalurn:ogc:def:crs:EPSG::4326-70.2894668579102 -8.3496923446655311.1660461425781 14.4334564208984geonode:larvae_amazonie_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Amazon fan (AM)Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorth Brazil margin2017-02-01Equatorial Atlantic beltiAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-57.8925895690918 -11.371285438537613.0063095092773 10.5924882888794geonode:larvae_amazonie_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Amazon fan (AM)Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorth Brazil marginEquatorial Atlantic beltiAtlanticMussel2017-03-01larval dispersalurn:ogc:def:crs:EPSG::4326-73.1763153076172 -7.327105045318610.6351346969604 16.3244113922119geonode:larvae_amazonie_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Amazon fan (AM)Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorth Brazil margin2017-11-01Equatorial Atlantic beltiAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-85.0615005493164 -10.472818374633811.3648958206177 19.0406398773193geonode:larvae_amazonie_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Amazon fan (AM)Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorth Brazil marginEquatorial Atlantic beltMusseliAtlantic2017-12-01larval dispersalurn:ogc:def:crs:EPSG::4326-83.3675231933594 -12.063075065612813.5271100997925 21.9072322845459geonode:larvae_new_england_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusUS Atlantic MarginEquatorial Atlantic beltMusseliAtlantic2017-01-01larval dispersalNew England (NE)urn:ogc:def:crs:EPSG::4326-74.8836135864258 29.0658721923828-20.6915435791016 53.2922821044922geonode:larvae_new_england_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-02-01US Atlantic MarginEquatorial Atlantic beltiAtlanticMussellarval dispersalNew England (NE)urn:ogc:def:crs:EPSG::4326-75.2523727416992 28.6451091766357-16.8811893463135 53.9572219848633geonode:larvae_new_england_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusUS Atlantic MarginEquatorial Atlantic beltiAtlanticMussel2017-03-01larval dispersalNew England (NE)urn:ogc:def:crs:EPSG::4326-74.8940505981445 27.2626495361328-15.3820371627808 53.7914276123047geonode:larvae_new_england_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-11-01US Atlantic MarginEquatorial Atlantic beltiAtlanticMussellarval dispersalNew England (NE)urn:ogc:def:crs:EPSG::4326-74.9584121704102 30.841459274292-16.5469951629639 53.9639625549316geonode:larvae_new_england_201712010These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusUS Atlantic MarginEquatorial Atlantic beltMusseliAtlantic2017-12-01larval dispersalNew England (NE)urn:ogc:def:crs:EPSG::4326-74.9609146118164 32.3104057312012-16.7718467712402 53.3562774658203geonode:larvae_trinidad_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismTrinidad prism (TRI)BathymodiolusEquatorial Atlantic beltMusseliAtlantic2017-01-01larval dispersalurn:ogc:def:crs:EPSG::4326-84.3582763671875 -2.73313450813293-20.1478252410889 19.8767585754395geonode:larvae_trinidad_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismTrinidad prism (TRI)Bathymodiolus2017-02-01Equatorial Atlantic beltiAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-87.207275390625 -2.90575742721558-9.72414684295654 39.5283813476562geonode:larvae_trinidad_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismTrinidad prism (TRI)BathymodiolusEquatorial Atlantic beltiAtlanticMussel2017-03-01larval dispersalurn:ogc:def:crs:EPSG::4326-87.7931900024414 0.33025661110878-23.710262298584 39.8213844299316geonode:larvae_trinidad_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismTrinidad prism (TRI)Bathymodiolus2017-11-01Equatorial Atlantic beltiAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-87.8577423095703 0.898551881313324-28.8782787322998 19.7545490264893geonode:larvae_trinidad_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic-1400.0 m a.s.l.Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismTrinidad prism (TRI)BathymodiolusEquatorial Atlantic beltMusseliAtlantic2017-12-01larval dispersalurn:ogc:def:crs:EPSG::4326-85.3209609985352 3.01011037826538-37.7804336547852 21.4964466094971geonode:larvae_nolfork_canyon_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorfolk Canyon (NC)US Atlantic MarginEquatorial Atlantic beltMussel-1450.0 m a.s.l.iAtlantic2017-01-01larval dispersalurn:ogc:def:crs:EPSG::4326-75.0840225219727 29.5907821655273-14.6470632553101 58.5569763183594geonode:larvae_nolfork_canyon_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorfolk Canyon (NC)2017-02-01US Atlantic MarginEquatorial Atlantic belt-1450.0 m a.s.l.iAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-74.8776626586914 29.4915790557861-17.0097732543945 54.0282096862793geonode:larvae_nolfork_canyon_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorfolk Canyon (NC)US Atlantic MarginEquatorial Atlantic belt-1450.0 m a.s.l.iAtlanticMussel2017-03-01larval dispersalurn:ogc:def:crs:EPSG::4326-74.8818817138672 27.8040809631348-15.8397808074951 55.1261177062988geonode:larvae_nolfork_canyon_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorfolk Canyon (NC)2017-11-01US Atlantic MarginEquatorial Atlantic belt-1450.0 m a.s.l.iAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-74.9051132202148 28.7378253936768-14.8958101272583 55.7397613525391geonode:larvae_nolfork_canyon_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusNorfolk Canyon (NC)US Atlantic MarginEquatorial Atlantic beltMussel-1450.0 m a.s.l.iAtlantic2017-12-01larval dispersalurn:ogc:def:crs:EPSG::4326-74.8789443969727 31.8806591033936-12.9933414459229 58.7070465087891geonode:larvae_brine_pool_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusBrine Pool (BP)iAtlanticEquatorial Atlantic beltMusselGulf of Mexico2017-01-01-1700.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-97.3699340820312 18.7343788146973-21.0326232910156 53.1918601989746geonode:larvae_brine_pool_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasiAtlanticNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusBrine Pool (BP)2017-02-01Equatorial Atlantic beltGulf of MexicoMussel-1700.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-97.3631744384766 18.8094043731689-17.1224365234375 53.2112922668457geonode:larvae_brine_pool_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusBrine Pool (BP)iAtlanticEquatorial Atlantic beltGulf of MexicoMussel-1700.0 m a.s.l.2017-03-01larval dispersalurn:ogc:def:crs:EPSG::4326-97.4146041870117 18.8306159973145-20.1754417419434 52.8666763305664geonode:larvae_brine_pool_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth Atlantic2017-11-01Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusBrine Pool (BP)iAtlanticEquatorial Atlantic beltGulf of MexicoMussel-1700.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-97.6250152587891 18.796350479126-19.8807754516602 53.873950958252geonode:larvae_brine_pool_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusBrine Pool (BP)iAtlanticEquatorial Atlantic beltMusselGulf of Mexico2017-12-01-1700.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-97.2565765380859 19.9897747039795-15.3930215835571 53.1898231506348geonode:larvae_alaminos_canyon_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-2200.0 m a.s.l.GigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusiAtlanticAlaminos Canyon (AC)Equatorial Atlantic beltMusselGulf of Mexico2017-01-01larval dispersalCold seepsurn:ogc:def:crs:EPSG::4326-97.4210968017578 18.608606338501-24.916223526001 50.547721862793geonode:larvae_alaminos_canyon_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-2200.0 m a.s.l.Alaminos Canyon (AC)iAtlanticNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeGigantidasBathymodiolus2017-02-01Cold seepsEquatorial Atlantic beltGulf of MexicoMussellarval dispersalurn:ogc:def:crs:EPSG::4326-97.3741226196289 18.7747783660889-31.2682323455811 48.251823425293geonode:larvae_alaminos_canyon_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-2200.0 m a.s.l.GigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusiAtlanticAlaminos Canyon (AC)Equatorial Atlantic beltGulf of MexicoMussel2017-03-01larval dispersalCold seepsurn:ogc:def:crs:EPSG::4326-97.3703842163086 19.1183242797852-29.4049835205078 48.827320098877geonode:larvae_alaminos_canyon_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-2200.0 m a.s.l.GigantidasNorth Atlantic2017-11-01Integrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusiAtlanticAlaminos Canyon (AC)Equatorial Atlantic beltGulf of MexicoMussellarval dispersalCold seepsurn:ogc:def:crs:EPSG::4326-97.6249618530273 18.6661491394043-25.5632858276367 48.0705261230469geonode:larvae_alaminos_canyon_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.-2200.0 m a.s.l.Alaminos Canyon (AC)North AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeGigantidasBathymodiolusiAtlanticCold seepsEquatorial Atlantic beltMusselGulf of Mexico2017-12-01larval dispersalurn:ogc:def:crs:EPSG::4326-97.6250152587891 18.6684093475342-34.2513084411621 45.6573677062988geonode:larvae_kick_em_jenny_crater_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismBathymodiolus2017-01-01Equatorial Atlantic beltMussel-260.0 m a.s.l.iAtlanticKick°em Jenny crater (KJC)larval dispersalurn:ogc:def:crs:EPSG::4326-86.8236770629883 7.45339727401733-53.8018226623535 24.3968906402588geonode:larvae_kick_em_jenny_crater_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismBathymodiolus2017-02-01Equatorial Atlantic beltMussel-260.0 m a.s.l.iAtlanticKick°em Jenny crater (KJC)larval dispersalurn:ogc:def:crs:EPSG::4326-87.4706954956055 8.97363567352295-59.023811340332 37.5842132568359geonode:larvae_kick_em_jenny_crater_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismBathymodiolusEquatorial Atlantic beltMussel-260.0 m a.s.l.iAtlanticKick°em Jenny crater (KJC)2017-03-01larval dispersalurn:ogc:def:crs:EPSG::4326-87.943962097168 8.24456596374512-47.528938293457 39.8284378051758geonode:larvae_kick_em_jenny_crater_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismBathymodiolus2017-11-01Equatorial Atlantic beltMussel-260.0 m a.s.l.iAtlanticKick°em Jenny crater (KJC)larval dispersalurn:ogc:def:crs:EPSG::4326-87.6170959472656 7.23636054992676-53.8726959228516 25.9460868835449geonode:larvae_kick_em_jenny_crater_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBarbados PrismBathymodiolusKick°em Jenny crater (KJC)Equatorial Atlantic beltMussel-260.0 m a.s.l.iAtlantic2017-12-01larval dispersalurn:ogc:def:crs:EPSG::4326-87.1459732055664 7.28652048110962-52.7957992553711 23.5724906921387geonode:larvae_logatchev_seep_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusMid-Atlantic RidgeEquatorial Atlantic beltMusselLogatchev seeps (LOG)iAtlantic2017-01-01-3038.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-58.1927223205566 9.67132663726807-43.2421646118164 17.1118202209473geonode:larvae_logatchev_seep_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticMid-Atlantic RidgeIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-02-01Equatorial Atlantic beltMusseliAtlanticLogatchev seeps (LOG)-3038.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-59.5411224365234 10.0057783126831-44.1452293395996 18.9240074157715geonode:larvae_logatchev_seep_201703010These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusMid-Atlantic RidgeEquatorial Atlantic beltMusseliAtlanticLogatchev seeps (LOG)-3038.0 m a.s.l.2017-03-01larval dispersalurn:ogc:def:crs:EPSG::4326-62.9643325805664 9.85377883911133-44.4260177612305 21.6515731811523geonode:larvae_logatchev_seep_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticMid-Atlantic RidgeIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolus2017-11-01Equatorial Atlantic beltMusseliAtlanticLogatchev seeps (LOG)-3038.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-57.0437965393066 7.94861268997192-43.739875793457 18.5802211761475geonode:larvae_logatchev_seep_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodiolusMid-Atlantic RidgeEquatorial Atlantic beltMusselLogatchev seeps (LOG)iAtlantic2017-12-01-3038.0 m a.s.l.larval dispersalurn:ogc:def:crs:EPSG::4326-58.1289405822754 7.64712047576904-44.5424995422363 15.0143022537231geonode:larvae_bodie_island_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodioluslarval dispersalUS Atlantic MarginEquatorial Atlantic beltMussel-400.0 m a.s.l.iAtlantic2017-01-01Bodie Island (BI)urn:ogc:def:crs:EPSG::4326-75.0576553344727 27.5715389251709-14.7859535217285 54.5942153930664geonode:larvae_bodie_island_201702010These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodioluslarval dispersal2017-02-01US Atlantic MarginEquatorial Atlantic belt-400.0 m a.s.l.iAtlanticMusselBodie Island (BI)urn:ogc:def:crs:EPSG::4326-75.4016036987305 27.205545425415-15.7292222976685 53.8596687316895geonode:larvae_bodie_island_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodioluslarval dispersalUS Atlantic MarginEquatorial Atlantic belt-400.0 m a.s.l.iAtlanticMussel2017-03-01Bodie Island (BI)urn:ogc:def:crs:EPSG::4326-75.0576553344727 25.6136951446533-14.9268045425415 54.7929420471191geonode:larvae_bodie_island_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodioluslarval dispersal2017-11-01US Atlantic MarginEquatorial Atlantic belt-400.0 m a.s.l.iAtlanticMusselBodie Island (BI)urn:ogc:def:crs:EPSG::4326-75.0576553344727 29.748140335083-13.015266418457 58.3147506713867geonode:larvae_bodie_island_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeBathymodioluslarval dispersalUS Atlantic MarginEquatorial Atlantic beltMussel-400.0 m a.s.l.iAtlantic2017-12-01Bodie Island (BI)urn:ogc:def:crs:EPSG::4326-75.0576553344727 23.3581695556641-15.21204662323 54.7471504211426geonode:larvae_guiness_20170101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of Guinea-680.0 m a.s.l.Guiness (GUIN)BathymodiolusEquatorial Atlantic beltMusselIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeiAtlantic2017-01-01larval dispersalurn:ogc:def:crs:EPSG::4326-16.2119274139404 -9.9205665588378913.0355157852173 6.04285001754761geonode:larvae_guiness_20170201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of Guinea-680.0 m a.s.l.Guiness (GUIN)Bathymodiolus2017-02-01Equatorial Atlantic beltIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeiAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-18.9243698120117 -9.6187553405761713.0091724395752 6.12291097640991geonode:larvae_guiness_20170301These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of Guinea-680.0 m a.s.l.Guiness (GUIN)BathymodiolusEquatorial Atlantic beltIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeiAtlanticMussel2017-03-01larval dispersalurn:ogc:def:crs:EPSG::4326-21.4459075927734 -8.8630361557006813.0297832489014 5.2838134765625geonode:larvae_guiness_20171101These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of Guinea-680.0 m a.s.l.Guiness (GUIN)Bathymodiolus2017-11-01Equatorial Atlantic beltIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeiAtlanticMussellarval dispersalurn:ogc:def:crs:EPSG::4326-22.6689701080322 -10.690355300903313.1883039474487 6.02555894851685geonode:larvae_guiness_20171201These data aim at evaluating the hypothesis of long-distance dispersal across the North Atlantic and the Equatorial Atlantic belt for the cold seep mussels Gigantidas childressi, G. mauritanicus, Bathymodiolus heckerae and B. boomerang. We combined mitochondrial Cox1 barcoding of some mussel specimens from both sides of the Atlantic (American vs European/African margins) with larval dispersal trajectories simulated from the VIKING20X model of the Atlantic circulation at a spatial scale not yet investigated. Larval dispersal modelling data correspond to transports of larvae over one year in surface waters from 21 geographic localities over 5 consecutive years (2015, 2016, 2017, 2018 and 2019) and 5 spawning dates (November, December, January, February and March) per year. Genetic data correspond to the geo-referenced sequences obtained for the 4 mussel species from some of the localities where larvae have been released during the modelling approach.Cold seepsGigantidasNorth AtlanticGulf of Guinea-680.0 m a.s.l.Guiness (GUIN)BathymodiolusEquatorial Atlantic beltMusselIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeiAtlantic2017-12-01larval dispersalurn:ogc:def:crs:EPSG::4326-21.5806941986084 -11.14849185943612.9903678894043 3.90030789375305geonode:EurOBIS_Desmophyllum_pertusumDesmophyllum pertusum occurence records from Eurobiseurobis_viewfeaturesurn:ogc:def:crs:EPSG::4326-180.0 -81.9953180.05 90.0geonode:Region_10_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresRegion_10_EUNIS_ClassificationIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion 10Brazil margin and Santos and Campos BasiniAtlanticEUNIS habitat typesurn:ogc:def:crs:EPSG::4326-48.4635047912598 -30.320426940918-40.619499206543 -23.2034797668457geonode:Region_11_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Region_11_EUNIS_ClassificationfeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingVitoria-Trindade Seamount ChainRegion 11iAtlanticEUNIS habitat typesurn:ogc:def:crs:EPSG::4326-41.1806030273438 -23.5971088409424-36.6666641235352 -18.6151466369629geonode:Region_12_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Region 12Region_12_EUNIS_ClassificationfeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingiAtlanticEUNIS habitat typesMalvinas Currenturn:ogc:def:crs:EPSG::4326-55.9933319091797 -39.9951934814453-52.6374969482422 -36.0998687744141geonode:Region_2_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Rockall Trough to PAPfeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion_2_EUNIS_ClassificationRegion 2iAtlanticEUNIS habitat typesurn:ogc:def:crs:EPSG::4326-20.6913166046143 46.8563346862793-5.71059894561768 60.3770866394043geonode:Region_3_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeRegion_3_EUNIS_ClassificationHabitat MappingCentral mid-Atlantic RidgeRegion 3iAtlanticEUNIS habitat typesurn:ogc:def:crs:EPSG::4326-35.5770492553711 33.59521484375-20.849630355835 43.0788841247559geonode:Region_4_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresRegion_4_EUNIS_ClassificationIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion 4NW Atlantic Gully CanyoniAtlanticEUNIS habitat typesurn:ogc:def:crs:EPSG::4326-62.6577262878418 40.6639976501465-55.535961151123 44.7064170837402geonode:Region_5_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion 5Region_5_EUNIS_ClassificationiAtlanticEUNIS habitat typesSargasso Seaurn:ogc:def:crs:EPSG::4326-78.1500015258789 22.0999984741211-43.6124992370605 38.5500030517578geonode:Region_6_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingEastern Tropical North Atlantic Cape VerdeRegion_6_EUNIS_ClassificationRegion 6iAtlanticEUNIS habitat typesurn:ogc:def:crs:EPSG::4326-26.4764671325684 14.0269556045532-21.0268650054932 17.9759464263916geonode:Region_7_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Equatorial Atlantic Romanche Fracture ZonefeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeRegion_7_EUNIS_ClassificationHabitat MappingRegion 7iAtlanticEUNIS habitat typesurn:ogc:def:crs:EPSG::4326-26.0666675567627 -3.01875019073486-11.502082824707 3.34166669845581geonode:Region_8_ColC_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Region_8_ColC_EUNIS_ClassificationfeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingLobe C of the CongolobeSlope and Margin off Angola and Congo LobeiAtlanticEUNIS habitat typesRegion 8urn:ogc:def:crs:EPSG::43265.47151136398315 -6.716833591461185.49421501159668 -6.65913772583008geonode:Region_8_ColA_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.Lobe A of the CongolobefeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion_8_ColA_EUNIS_ClassificationSlope and Margin off Angola and Congo LobeiAtlanticEUNIS habitat typesRegion 8urn:ogc:def:crs:EPSG::43266.02783250808716 -6.478072166442876.03927898406982 -6.44434547424316geonode:Region_8_wacs2011_02AB_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingSlope and Margin off Angola and Congo LobeRegion_8_wacs2011_02AB_EUNIS_ClassificationiAtlanticEUNIS habitat typesRegion 8The Regab pockmarkurn:ogc:def:crs:EPSG::43269.71004104614258 -5.798469543457039.71200752258301 -5.79700708389282geonode:Region_9_EUNIS_ClassificationThis dataset includes 11 regional EUNIS-classified habitat maps (100-1000 km) and associated confidence maps that were created as a project milestone (Nr. 12) of the EU H2020 project 'iAtlantic'. The 12 iAtlantic regions encompass 1. Subpolar Mid-Atlantic Ridge, off Iceland MFRI, 2. Rockall Trough to PAP, 3. Central mid-Atlantic Ridge, 4. NW Atlantic, Gully Canyon, 5. Sargasso Sea, 6. Eastern Tropical North Atlantic, Cape Verde, 7. Equatorial Atlantic, Romanche Fracture Zone, 8. Slope & margin off Angola & Congo Lobe, 9. Benguela Current, Walvis Ridge to South Africa, 10. Brazil margin & Santos and Campos Basin, 11. Vitória-Trindade Seamount Chain and 12. Malvinas Current. For each of the regions 2-12, a shapefile of polygons classified according to the 2022 EUNIS classification level 3 and a second shapefile of the same polygons attributed with their confidence level according to the MESH Accuracy & Confidence Working approach was created. EUNIS classifications combined biozone and substrate data. Biozones were assigned from bathymetry. Where MBES was not available, GEBCO bathymetry was used. Substrate data were extracted from pre-existing geological/substrate mapping efforts and converted to EUNIS classifications via cross walks or, where substrate data were limited, substrate layers were modelled using Random Forest. The EUNIS habitat map for Region 4 was based on the pre-existing surficial geology compilation of the Scotian Shelf bioregion compiled by the Geological Survey of Canada. The EUNIS habitat map for Region 9 was based on the pre-existing South African habitat map that uses a modified IUCN hierarchical classification system. No additional information to that used in the EUSeaMap was available for Region 1. Therefore, shapefiles were not created for Region 1. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.962621.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeHabitat MappingRegion_9_EUNIS_ClassificationBenguela Current Walvis Ridge to South AfricaiAtlanticEUNIS habitat typesRegion 9urn:ogc:def:crs:EPSG::432613.349178314209 -38.115604400634820.8118762969971 -28.6523094177246geonode:EchinodermataBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityfeaturesSouth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timewater massesiAtlanticDeep-seaBenthosEchinodermataurn:ogc:def:crs:EPSG::4326-47.8500022888184 -29.617000579834-27.7999992370605 1.85000002384186geonode:EscarpmentsThe escarpment geomorphic feature layer represents the spatial extent of the escarpments of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Escarpments are “an elongated, characteristically linear, steep slope separating horizontal or gently sloping sectors of the sea floor in non-shelf areas. Also abbreviated to scarp” (IHO, 2008). Escarpments, like basins, overlay other features (i.e. other individual features may be partly or wholly covered by escarpments). Thus features like the continental slope, seamounts, guyots, ridges and submarine canyons (for example) may be sub-classified in terms of their area of overlain escarpment.Escarpments were calculated based on the gradient of the SRTM30_PLUS model.Downloadable Dataseafloor, geomorphic features, habitatsfeaturesEscarpmentsurn:ogc:def:crs:EPSG::4326-180.0 -76.9891815185547180.000015258789 89.6022033691406geonode:FansThe fan geomorphic feature layer represents the spatial extent of the fans of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Fans are “a relatively smooth, fan-like, depositional feature normally sloping away from the outer termination of a canyon or canyon system” (IHO, 2008). Since submarine fans are sediment deposits, the NGDC map of global ocean sediment thickness (Divins, 2003) was used to assist with identifying them. Fans overlay and comprise part of the continental rise and are located offshore from the base of the continental slope (Curray et al., 2002; Dowdeswell et al., 2008; Covault et al., 2011). Fans are inter-related with submarine canyons and sediment drift deposits; in cases where canyon axes extend across the rise, the canyon-channels may be flanked by sediment drift deposits, which have been grouped with fans in this study. Fans are defined in the present study by 100 m isobaths that form a concentric series exhibiting an expanding spacing in a seaward direction away from the base of the slope, sometimes clearly associated with a canyon mouth, but also comprising low-relief ridges between canyon-channels on the abyssal plain.Downloadable DataFansseafloor, geomorphic features, habitatsfeaturesurn:ogc:def:crs:EPSG::4326-176.524276733398 -76.4211654663086179.961639404297 84.5691833496094geonode:Fishing_Effort_All_Gear_2016Global Fishing Watch’s flagship dataset is apparent fishing effort based on transmissions broadcast using the automatic identification system (AIS, a vessel tracking system originally designed for collision avoidance). In 2018, we published the first global assessment of commercial fishing activity in Science. Our research found that fishing is both widespread—occurring throughout approximately 50 percent of the ocean—and highly concentrated, with more than half of fishing activity occurring in just 0.5 percent of the ocean. Fishing is also minimally affected by seasons but strongly affected by culture; the biggest drops occur during weekends, holidays, and the annual Chinese summer moratorium. Today we receive over 60 million AIS messages each day and continually update our technology and algorithms to improve our ability to monitor global commercial fishing. This entire dataset can be explored on our map, dating all the way back to 2012, or downloaded from our data download portal.Fishing_Effort_All_Gear_2016featuresurn:ogc:def:crs:EPSG::4326-80.8500061035156 -60.850002288818420.9500007629395 80.9500045776367geonode:Fishing_Effort_Fixed_Gear_2016Global Fishing Watch’s flagship dataset is apparent fishing effort based on transmissions broadcast using the automatic identification system (AIS, a vessel tracking system originally designed for collision avoidance). In 2018, we published the first global assessment of commercial fishing activity in Science. Our research found that fishing is both widespread—occurring throughout approximately 50 percent of the ocean—and highly concentrated, with more than half of fishing activity occurring in just 0.5 percent of the ocean. Fishing is also minimally affected by seasons but strongly affected by culture; the biggest drops occur during weekends, holidays, and the annual Chinese summer moratorium. Today we receive over 60 million AIS messages each day and continually update our technology and algorithms to improve our ability to monitor global commercial fishing. This entire dataset can be explored on our map, dating all the way back to 2012, or downloaded from our data download portal.Fishing_Effort_Fixed_Gear_2016featuresurn:ogc:def:crs:EPSG::4326-80.3500061035156 -58.950000762939520.9500007629395 80.5500030517578geonode:Fishing_Effort_Longlines_2016Global Fishing Watch’s flagship dataset is apparent fishing effort based on transmissions broadcast using the automatic identification system (AIS, a vessel tracking system originally designed for collision avoidance). In 2018, we published the first global assessment of commercial fishing activity in Science. Our research found that fishing is both widespread—occurring throughout approximately 50 percent of the ocean—and highly concentrated, with more than half of fishing activity occurring in just 0.5 percent of the ocean. Fishing is also minimally affected by seasons but strongly affected by culture; the biggest drops occur during weekends, holidays, and the annual Chinese summer moratorium. Today we receive over 60 million AIS messages each day and continually update our technology and algorithms to improve our ability to monitor global commercial fishing. This entire dataset can be explored on our map, dating all the way back to 2012, or downloaded from our data download portal.featuresFishing_Effort_Longlines_2016urn:ogc:def:crs:EPSG::4326-80.8500061035156 -60.850002288818420.9500007629395 69.75geonode:Fishing_Effort_Purse_Seine_2016Global Fishing Watch’s flagship dataset is apparent fishing effort based on transmissions broadcast using the automatic identification system (AIS, a vessel tracking system originally designed for collision avoidance). In 2018, we published the first global assessment of commercial fishing activity in Science. Our research found that fishing is both widespread—occurring throughout approximately 50 percent of the ocean—and highly concentrated, with more than half of fishing activity occurring in just 0.5 percent of the ocean. Fishing is also minimally affected by seasons but strongly affected by culture; the biggest drops occur during weekends, holidays, and the annual Chinese summer moratorium. Today we receive over 60 million AIS messages each day and continually update our technology and algorithms to improve our ability to monitor global commercial fishing. This entire dataset can be explored on our map, dating all the way back to 2012, or downloaded from our data download portal.featuresFishing_Effort_Purse_Seine_2016urn:ogc:def:crs:EPSG::4326-80.8500061035156 -47.7520.9500007629395 80.5500030517578geonode:Fishing_Effort_Trawlers_2016Global Fishing Watch’s flagship dataset is apparent fishing effort based on transmissions broadcast using the automatic identification system (AIS, a vessel tracking system originally designed for collision avoidance). In 2018, we published the first global assessment of commercial fishing activity in Science. Our research found that fishing is both widespread—occurring throughout approximately 50 percent of the ocean—and highly concentrated, with more than half of fishing activity occurring in just 0.5 percent of the ocean. Fishing is also minimally affected by seasons but strongly affected by culture; the biggest drops occur during weekends, holidays, and the annual Chinese summer moratorium. Today we receive over 60 million AIS messages each day and continually update our technology and algorithms to improve our ability to monitor global commercial fishing. This entire dataset can be explored on our map, dating all the way back to 2012, or downloaded from our data download portal.Fishing_Effort_Trawlers_2016featuresurn:ogc:def:crs:EPSG::4326-80.8500061035156 -60.850002288818420.9500007629395 80.9500045776367geonode:Fishing_Effort_All_Gear_20160featuresFishing_Effort_All_Gear_2016urn:ogc:def:crs:EPSG::4326-80.8500061035156 -60.850002288818420.9500007629395 80.9500045776367geonode:Fracture_ZoneThe GEBCO Sub-Committee on Undersea Feature Names (SCUFN) maintains and makes available a digital gazetteer of the names, generic feature type and geographic position of features on the seafloor. The gazetteer is available to view and download via a web map application, hosted by the International Hydrographic Organization Data Centre for Digital Bathymetry (IHO DCDB) co-located with the US National Centers for Environmental Information (NCEI). The data are available in a number of formats including spreadsheet, shapefile, KML, WMS and ArcGIS layer and can be accessed as a REST-style API. Name proposals can be submitted to SCUFN for consideration for inclusion in the gazetteer.Fracture_Zonefeaturesurn:ogc:def:crs:EPSG::4326-179.999008178711 -69.6432037353516179.0 80.25geonode:GFW_FishingHours_longline_2020These data are derived from 2020 daily fishing effort data from Global Fishing Watch (GFW). Fishing hours are aggregated into an annual sum for selected gear types at each point location.longlinefeaturesGFW_FishingHours_SelectGear_2020urn:ogc:def:crs:EPSG::4326-97.4000015258789 -59.900001525878920.9000015258789 70.0geonode:GFW_FishingHours_SelectGear_2020These data are derived from 2020 daily fishing effort data from Global Fishing Watch (GFW). Fishing hours are aggregated into an annual sum for selected gear types at each point location.featuresurn:ogc:def:crs:EPSG::4326-97.4000015258789 -59.900001525878920.9000015258789 70.0geonode:GFW_FishingHours_fixed_gear_2020These data are derived from 2020 daily fishing effort data from Global Fishing Watch (GFW). Fishing hours are aggregated into an annual sum for selected gear types at each point location.featuresGFW_FishingHours_SelectGear_2020urn:ogc:def:crs:EPSG::4326-97.4000015258789 -59.900001525878920.9000015258789 70.0geonode:GFW_FishingHours_purse_sein_2020These data are derived from 2020 daily fishing effort data from Global Fishing Watch (GFW). Fishing hours are aggregated into an annual sum for selected gear types at each point location.purse seinfeaturesGFW_FishingHours_SelectGear_2020urn:ogc:def:crs:EPSG::4326-97.4000015258789 -59.900001525878920.9000015258789 70.0geonode:GFW_FishingHours_trawlers_2020These data are derived from 2020 daily fishing effort data from Global Fishing Watch (GFW). Fishing hours are aggregated into an annual sum for selected gear types at each point location.featuresGFW_FishingHours_SelectGear_2020urn:ogc:def:crs:EPSG::4326-97.4000015258789 -59.900001525878920.9000015258789 70.0geonode:GOODS_Abyssal_ProvincesNo abstract providedfeaturesGOODS_Abyssal_Provincesurn:ogc:def:crs:EPSG::4326-180.0 -76.2541732788086180.000015258789 88.1708374023438geonode:GOODS_Bathyal_ProvincesNo abstract providedGOODS_Bathyal_Provincesfeaturesurn:ogc:def:crs:EPSG::4326-180.0 -78.2875061035156180.0 89.5791702270508geonode:Fluxes_Bathy_IMa3_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.number of migrants (Nm)featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsdeep coralsBathymodiolus azoricus/puteoserpentisvent faunaconnectivity mapsseep musselsgenomic dataiAtlanticFluxes_Bathy_IMa3_shapefileIMa3North Atlanticurn:ogc:def:crs:EPSG::4326-44.9785003662109 -9.55000019073486-12.1156425476074 37.2933006286621geonode:Fluxes_Bboom_DivM_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.number of migrants (Nm)featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsdeep coralsNorth Atlanticvent faunaBathymodiolus boomerang/heckeraeseep musselsgenomic dataconnectivity mapsiAtlanticDivMigrateFluxes_Bboom_DivM_shapefileurn:ogc:def:crs:EPSG::4326-84.9120025634766 -7.825894832611089.71150016784668 32.6213035583496geonode:Fluxes_Bboom_dadi_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.dadifeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsdeep coralsnumber of migrants (Nm)vent faunaBathymodiolus boomerang/heckeraeseep musselsgenomic dataconnectivity mapsiAtlanticFluxes_Bboom_dadi_shapefileNorth Atlanticurn:ogc:def:crs:EPSG::4326-84.9120025634766 -7.825894832611089.71150016784668 32.6213035583496geonode:Fluxes_Bchild_DivM_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.Fluxes_Bchild_DivM_shapefilefeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsGigantidas childressi/mauritanicusdeep coralsnumber of migrants (Nm)vent faunaconnectivity mapsseep musselsgenomic dataiAtlanticDivMigrateNorth Atlanticurn:ogc:def:crs:EPSG::4326-94.4976043701172 12.0410470962524-7.17999982833862 45.6091651916504geonode:Fluxes_Bchild_dadi_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.number of migrants (Nm)dadifeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsGigantidas childressi/mauritanicusdeep coralsNorth Atlanticvent faunaconnectivity mapsseep musselsgenomic dataiAtlanticFluxes_Bchild_dadi_shapefileurn:ogc:def:crs:EPSG::4326-94.4976043701172 12.0410470962524-7.17999982833862 45.5449066162109geonode:Fluxes_Lepeto_DivM_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.featuresFluxes_Lepeto_DivM_shapefileIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsdeep coralsnumber of migrants (Nm)vent faunaconnectivity mapsLepetodrilus atlanticusgenomic dataiAtlanticDivMigrateseep musselsNorth Atlanticurn:ogc:def:crs:EPSG::4326-43.171703338623 -4.80580043792725-12.3188419342041 37.8417015075684geonode:Fluxes_Lepeto_dadi_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.dadifeaturesseep musselsIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeFluxes_Lepeto_dadi_shapefilepopulation geneticsdeep coralsnumber of migrants (Nm)vent faunaconnectivity mapsLepetodrilus atlanticusgenomic dataiAtlanticNorth Atlanticurn:ogc:def:crs:EPSG::4326-43.171703338623 -4.80580043792725-12.3188419342041 37.8417015075684geonode:Fluxes_Lophelia_shapefile_DivM_DIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.Fluxes_Lophelia_shapefile_DivM_DfeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsLophelia pertusadeep coralsvent faunaconnectivity mapsseep musselsgenomic dataJost distance (D) metrixiAtlanticDivMigrateNorth Atlanticurn:ogc:def:crs:EPSG::4326-11.0661764144897 39.30555725097667.0 61.0geonode:Fluxes_Lophelia_shapefile_DivM_NmIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsLophelia pertusadeep coralsnumber of migrants (Nm)vent faunaconnectivity mapsFluxes_Lophelia_shapefile_DivM_Nmseep musselsgenomic dataiAtlanticDivMigrateNorth Atlanticurn:ogc:def:crs:EPSG::4326-11.0661764144897 39.30555725097667.0 61.0geonode:Fluxes_Lophelia_shapefile_moments_mIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.featuresmomentsIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsLophelia pertusadeep coralsvent faunaconnectivity mapsseep musselsgenomic dataFluxes_Lophelia_shapefile_moments_miAtlanticmigration rate (m)North Atlanticurn:ogc:def:crs:EPSG::4326-11.0661764144897 39.30555725097667.0 61.0geonode:Fluxes_Lophelia_shapefile_moments_NmIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.population geneticsfeaturesmomentsIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeFluxes_Lophelia_shapefile_moments_NmLophelia pertusadeep coralsnumber of migrants (Nm)vent faunaconnectivity mapsseep musselsgenomic dataiAtlanticNorth Atlanticurn:ogc:def:crs:EPSG::4326-11.0661764144897 39.30555725097667.0 61.0geonode:Fluxes_Pelto_DivM_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.featuresIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsdeep coralsnumber of migrants (Nm)vent faunaFluxes_Pelto_DivM_shapefileconnectivity mapsseep musselsgenomic dataiAtlanticPeltospira smaragdinaDivMigrateNorth Atlanticurn:ogc:def:crs:EPSG::4326-45.2371482849121 23.3682994842529-27.8499984741211 45.4833030700684geonode:Fluxes_Pelto_dadi_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.population geneticsdadifeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeFluxes_Pelto_dadi_shapefiledeep coralsnumber of migrants (Nm)vent faunaconnectivity mapsseep musselsgenomic dataiAtlanticPeltospira smaragdinaNorth Atlanticurn:ogc:def:crs:EPSG::4326-43.1738014221191 29.1671485900879-27.8499984741211 45.4833030700684geonode:Fluxes_chacei_DivM_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.Fluxes_chacei_DivM_shapefilefeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsdeep coralsnumber of migrants (Nm)vent faunaconnectivity mapsseep musselsgenomic dataiAtlanticDivMigrateRimicaris chaceiNorth Atlanticurn:ogc:def:crs:EPSG::4326-81.7182006835938 16.0452747344971-32.2732963562012 37.6544036865234geonode:Fluxes_chacei_dadi_shapefileIn order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.961199.dadifeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timepopulation geneticsdeep coralsnumber of migrants (Nm)vent faunaFluxes_chacei_dadi_shapefileconnectivity mapsseep musselsgenomic dataiAtlanticRimicaris chaceiNorth Atlanticurn:ogc:def:crs:EPSG::4326-81.7182006835938 16.9494132995605-32.2732963562012 37.6544036865234geonode:Geographic_Area_of_Competence_of_InternationaNo abstract providedfeaturesGeographic_Area_of_Competence_of_International_Commission_for_the_Conservation_of_Atlantic_Tunas_ICCAT_urn:ogc:def:crs:EPSG::4326-180.000015258789 -89.9500045776367180.000015258789 90.0027084350586geonode:Geographic_Area_of_Competence_of_Internationa0No abstract providedfeaturesGeographic_Area_of_Competence_of_International_Commission_for_the_Conservation_of_Atlantic_Tunas_ICCAT_urn:ogc:def:crs:EPSG::4326-180.000015258789 -89.9500045776367180.000015258789 90.0027084350586geonode:Geographic_Area_of_Competence_of_Internationa1No abstract providedfeaturesGeographic_Area_of_Competence_of_International_Commission_for_the_Conservation_of_Atlantic_Tunas_ICCAT_urn:ogc:def:crs:EPSG::4326-180.000015258789 -89.9500045776367180.000015258789 90.0027084350586geonode:Glacial_troughsThe glacial trough geomorphic feature layer represents the spatial extent of the glacial troughs of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Shelf valleys at high latitudes incised by glacial erosion during the Pleistocene ice ages form elongate troughs, typically trending across the continental shelf and extending inland as fjord complexes (Hambrey, 1994). The largest of these features are glacial troughs, characterised by depths of over 100 m (often exceeding 1,000 m depth) and are distinguished from shelf valleys by an over-deepened longitudinal profile that reaches a maximum depth inboard of the shelf break, thus creating a perched basin on the shelf with an associated sill (Hambrey, 1994). Glacial troughs were digitized by hand based on 50 m contoured data for the Antarctic and 10 m contoured data for other shelf areas.Downloadable Dataseafloor, geomorphic features, habitatsfeaturesGlacial_troughsurn:ogc:def:crs:EPSG::4326-180.0 -78.6016616821289180.0 83.7960891723633geonode:Global_EBSA_workshop_boundaryfeaturesGlobal_EBSA_workshop_boundaryurn:ogc:def:crs:EPSG::4326-180.0 -60.0180.0 90.0geonode:Global_EBSA_workshop_boundary0featuresGlobal_EBSA_workshop_boundaryurn:ogc:def:crs:EPSG::4326-180.0 -60.0180.0 90.0geonode:Global_EBSA_workshop_boundary1featuresGlobal_EBSA_workshop_boundaryurn:ogc:def:crs:EPSG::4326-180.0 -60.0180.0 90.0geonode:Global_EBSA_workshop_boundary2featuresGlobal_EBSA_workshop_boundaryurn:ogc:def:crs:EPSG::4326-180.0 -60.0180.0 90.0geonode:GuyotsThe guyot geomorphic feature layer represents the spatial extent of the guyots of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Guyots are “an isolated (or group of) seamount (s) having a comparatively smooth flat top. Also called tablemount(s)” (IHO, 2008). In this study the seamount base layer was used to mask the SRTM30_PLUS model. The gradient of the resulting grid was calculated (ArcGIS 10->DEM Surface Tools (Jenness 2012)->Slope, Slope computation method = 4-cell method). The gradient was classified into areas >2 degrees and areas <2 degrees. The areas less than two degrees were converted into vector layers. Where these occurred at the top of seamounts and were greater than a minimum size threshold (10 km2) they were flagged as possible guyots. These possible guyots were then visually checked and either classified as a guyot or a seamount. Additionally the remaining seamounts were visually checked to see whether any with flat tops had been missed in the classification process. The geomorphic features map of Agapova et al (1979) was used in addition to the GEBCO Gazetteer of geographic names of undersea features (IHO-IOC, 2012), to ensure all previously mapped features were assessed for inclusion in our map.Downloadable Dataseafloor, geomorphic features, habitatsfeaturesGuyotsurn:ogc:def:crs:EPSG::4326-179.947219848633 -69.0158538818359178.557327270508 57.641471862793geonode:HadalThe hadal base layer represents the spatial extent of the hadal areas of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). The hadal zone is defined in this study as seafloor occurring at depths >6000m (based on the SRTM bathymetry grid).D1: Coastal benthic habitatsfeaturesHadalDownloadable Dataseafloor depthseafloor, geomorphic features, habitatsurn:ogc:def:crs:EPSG::4326-180.0 -60.9492835998535180.0 55.1083335876465geonode:Large_Marine_EcosystemsLarge Marine Ecosystems are regions of ocean space encompassing coastal areas from river basins and estuaries to the seaward boundaries of continental shelves and the outer margins of the major current systems. They are relatively large regions on the order of 200,000 km2 or greater, characterized by distinct: (1) bathymetry, (2) hydrography, (3) productivity, and (4) trophically dependent populations. On a global scale, 64 LMEs produce 95 percent of the world's annual marine fishery biomass yields. Within their waters, most of the global ocean pollution, overexploitation, and coastal habitat alteration occur. For 33 of the 64 LMES, studies have been conducted of the principal driving forces affecting changes in biomass yields. They have been peer- reviewed and published in ten volumes (http://www.lme.noaa.gov). Based on lessons learned from the LME case studies, a five module strategy has been developed to provide science-based information for the monitoring, assessment, and management of LMES. The modules are focused on LME: (1) productivity, (2) fish and fisheries, (3) pollution and health, (4) socioeconomics, and (5) governance.sustainabilityfisheryenvironmental resourcesvulnerable marine ecosystemsurn:ogc:def:crs:EPSG::4326-180.000015258789 -85.4702835083008180.0 82.8930358886719geonode:SeamountIn supplement to: Yesson, C et al. (2011): The global distribution of seamounts based on 30-second bathymetry data. Deep Sea Research Part I: Oceanographic Research Papers, 58(4), 442-453, https://doi.org/10.1016/j.dsr.2011.02.004CoralFISHSeamountEcosystem based management of corals, fish and fisheries in the deep waters of Europe and beyondfeaturesurn:ogc:def:crs:EPSG::4326-179.97917175293 -76.9291687011719179.97917175293 84.9875030517578geonode:Longhurst_ProvincesThe dataset represents the division of the world oceans into provinces as defined by Longhurst (1995; 1998; 2006). The division has been based on the prevailing role of physical forcing as a regulator of phytoplankton distribution. The dataset contains the initial static boundaries developed at the Bedford Institute of Oceanography, Canada. Note that the boundaries of these provinces are not fixed in time and space, but are dynamic and move under seasonal and interannual changes in physical forcing. At the first level of reduction, Longhurst recognised four principal biomes: the Polar biome, the Westerlies biome, the Trade winds biome, and the Coastal biome. These four biomes are recognised in every major ocean basin. At the next level of reduction, the ocean basins are divided into provinces, roughly ten for each basin. These regions provide a template for data analysis or for making parameter assignments on a global scale. Please refer to Longhurst's publications when using these shapefiles.featuresBiogeochemicalOceansBoundariesDownloadable DataEcologicalWorldLonghurst_ProvincesLonghursturn:ogc:def:crs:EPSG::4326-180.000015258789 -78.5001602172852180.0 90.0000076293945geonode:EurOBIS_Madrepora_oculataMadrepora oculata occurences from EurOBISeurobis_viewfeaturesurn:ogc:def:crs:EPSG::4326-180.0 -81.9953180.05 90.0geonode:Marine_ProvincesMarine Ecoregions of the World (MEOW) is a biogeographic classification of the world's coasts and shelves. It is the first ever comprehensive marine classification system with clearly defined boundaries and definitions and was developed to closely link to existing regional systems. The ecoregions nest within the broader biogeographic tiers of Realms and Provinces. MEOW represents broad-scale patterns of species and communities in the ocean, and was designed as a tool for planning conservation across a range of scales and assessing conservation efforts and gaps worldwide. The current system focuses on coast and shelf areas and does not consider realms in pelagic or deep benthic environment. It is hoped that parallel but distinct systems for pelagic and deep benthic biotas will be devised in the near future. The project was led by WWF and The Nature Conservancy, with broad input from a working group representing key NGO, academic and intergovernmental conservation partners.BiogeographyMarine_ProvincesfeaturesMarine LayerShelfOceansDownloadable DataWorldRealmEcoregionurn:ogc:def:crs:EPSG::4326-101.149711608887 -86.405723571777371.1657180786133 86.9194030761719geonode:Marine_Protected_AreasThe World Database on Protected Areas (WDPA) is the only global database of protected areas, and it is one of the component databases of the Protected Planet Initiative. Protected Planet® is a joint product of UNEP and IUCN, managed by UNEP-WCMC and the IUCN working with governments, communities and collaborating partners. The WDPA can be viewed and downloaded at www.protectedplanet.net, where it is integrated with other relevant information.Marine_Protected_Areasfeaturesurn:ogc:def:crs:EPSG::4326-180.0 -68.6525650024414180.0 79.3828048706055geonode:Marine_Protected_Areas0featuresMarine_Protected_Areasurn:ogc:def:crs:EPSG::4326-180.0 -68.6525650024414180.0 79.3828048706055geonode:MesoPelagic_ProvincesA global biogeographic classification of the mesopelagic zone to reflect the regional scales over which the ocean interior varies in terms of biodiversity and function. Developed by Tracey T.Sutton et al. More information: https://www.sciencedirect.com/science/article/pii/S0967063717301437mesopelagiczonesMesoPelagic_Provincesfeaturesurn:ogc:def:crs:EPSG::4326-180.0 -90.0180.0 90.0geonode:MolluscaBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityfeaturesSouth AtlanticIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timewater massesMolluscaiAtlanticDeep-seaBenthosurn:ogc:def:crs:EPSG::4326-51.7861022949219 -34.5678024291992-29.269998550415 1.85000002384186geonode:NAFO_Bottom_Fishing_AreaThe vulnerable marine ecosystem (VME) concept emerged from discussions at the United Nations General Assembly (UNGA) and gained momentum after UNGA Resolution 61/105. The FAO International Guidelines for the Management of Deep-sea Fisheries in the High Seas (FAO DSF Guidelines) build on the resolution and provide details on the VME concept for fisheries management. VMEs are now firmly embedded in regimes for the management of deep-sea fisheries in the areas beyond national jurisdiction (ABNJ). VMEs are groups of species, communities or habitats that may be vulnerable to impacts from fishing activities. The vulnerability of an ecosystem is related to the vulnerability of its constituent population, communities or habitats. Specific criteria are included in the FAO DSF Guidelines to assist States in defining VMEs and how to identify them. Deep-sea fishing activities sometimes employ types of fishing gears that can, in the normal course of operation, come into contact with the sea floor. This can have a negative effect on both living marine resources and ecosystems and damage can occur, thereby increasing the physical vulnerability of the ecosystem. Another concern is overfishing and the resulting vulnerability of target stocks, associated species and habitats. Selective removal of a species may change the manner in which the ecosystem functions, making the ecosystem functionally vulnerable. Significant adverse impacts (SAIs) to an ecosystem can occur as a result of fishing activities. Once a VME has been designated and potential SAIs assessed, the FAO DSF Guidelines recommend specific conservation and management measures.featuresNAFO_Bottom_Fishing_Areaurn:ogc:def:crs:EPSG::4326-52.176456451416 42.631664276123-43.4374961853027 49.038890838623geonode:NEAFC_Bottom_Fishing_AreaThe vulnerable marine ecosystem (VME) concept emerged from discussions at the United Nations General Assembly (UNGA) and gained momentum after UNGA Resolution 61/105. The FAO International Guidelines for the Management of Deep-sea Fisheries in the High Seas (FAO DSF Guidelines) build on the resolution and provide details on the VME concept for fisheries management. VMEs are now firmly embedded in regimes for the management of deep-sea fisheries in the areas beyond national jurisdiction (ABNJ). VMEs are groups of species, communities or habitats that may be vulnerable to impacts from fishing activities. The vulnerability of an ecosystem is related to the vulnerability of its constituent population, communities or habitats. Specific criteria are included in the FAO DSF Guidelines to assist States in defining VMEs and how to identify them. Deep-sea fishing activities sometimes employ types of fishing gears that can, in the normal course of operation, come into contact with the sea floor. This can have a negative effect on both living marine resources and ecosystems and damage can occur, thereby increasing the physical vulnerability of the ecosystem. Another concern is overfishing and the resulting vulnerability of target stocks, associated species and habitats. Selective removal of a species may change the manner in which the ecosystem functions, making the ecosystem functionally vulnerable. Significant adverse impacts (SAIs) to an ecosystem can occur as a result of fishing activities. Once a VME has been designated and potential SAIs assessed, the FAO DSF Guidelines recommend specific conservation and management measures.featuresNEAFC_Bottom_Fishing_Areaurn:ogc:def:crs:EPSG::4326-34.2442016601562 36.552120208740244.7578735351562 76.8997192382812geonode:NematodaBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityfeaturesNematodaIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeSouth Atlanticwater massesiAtlanticDeep-seaBenthosurn:ogc:def:crs:EPSG::4326-40.0330009460449 -22.3820018768311-37.5879974365234 -13.2379999160767geonode:VME_indexWe applied the International Council for the Exploration of the Sea (ICES) multi criteria assessment (MCA) method for identifying VMEs in the North-East Atlantic (ICES, 2016a,b; Morato et al., 2018) from ATLAS VME database to provide the first North Atlantic Ocean basin-scale VME assessment. This MCA is a taxa-dependent spatial method that incorporates the fact that not all VME indicators have the same vulnerability to human impacts, and thus should not be weighted equally. By including a measure of the confidence associated with each VME record, this methodology also considers some of the uncertainties associated with the sampling methodologies, the reported taxonomy, and data quality issues. Equally important, this dataset highlights areas in the North Atlantic that have been poorly sampled and that require further attention. Finally, this methodology also allows for the evaluation and comparison of the VME likelihood with spatial fisheries data that may directly generate significant adverse impacts on VMEs. In the data report, we made the “North Atlantic basin-scale VME index dataset” publicly, thus allowing its consultation and use by scientists, managers, or other relevant stakeholders.VME_indexNorth AtlanticVME_Dataset_PublicRecordsArea based management toolsMulti-criteria assessmentVulnerable Marine EcosystemsATL_EBSAS_v2019_0104_WGS84Deep-seafeaturesurn:ogc:def:crs:EPSG::4326-99.7448501586914 -0.09214664995670333.0828018188477 85.4778213500977geonode:OBIS_Observation_CountNo abstract providedOBIS_Observation_Countfeaturesurn:ogc:def:crs:EPSG::4326-84.3250350952148 -61.52741622924822.7042293548584 81.4339065551758geonode:OBIS_Species_RichnessNo abstract providedOBIS_Species_Richnessfeaturesurn:ogc:def:crs:EPSG::4326-84.3250350952148 -61.52741622924822.7042293548584 81.4339065551758geonode:Oceanic_RidgeThe GEBCO Sub-Committee on Undersea Feature Names (SCUFN) maintains and makes available a digital gazetteer of the names, generic feature type and geographic position of features on the seafloor. The gazetteer is available to view and download via a web map application, hosted by the International Hydrographic Organization Data Centre for Digital Bathymetry (IHO DCDB) co-located with the US National Centers for Environmental Information (NCEI). The data are available in a number of formats including spreadsheet, shapefile, KML, WMS and ArcGIS layer and can be accessed as a REST-style API. Name proposals can be submitted to SCUFN for consideration for inclusion in the gazetteer.featuresOceanic_Ridgeurn:ogc:def:crs:EPSG::4326-179.94010925293 -65.6182022094727179.935012817383 87.0169067382812geonode:PlateausThe plateau geomorphic feature layer represents the spatial extent of the plateaus of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Plateaus are “flat or nearly flat elevations of considerable areal extent, dropping off abruptly on one or more sides” (IHO, 2008). Plateaus were digitised by hand based on 100 m contours. In areas where plateaus abut the margin, the foot of slope was allowed to flow offshore to encompass the plateau feature, where a clear seaward dipping gradient was apparent. In other locations marginal plateaus are distinctly separate from the continental slope and form isolated, raised platforms. The geomorphic features map of Agapova et al (1979) and the GEBCO Gazetteer of geographic names of undersea features were used to ensure all named features were included.Downloadable Dataseafloor, geomorphic features, habitatsfeaturesPlateausurn:ogc:def:crs:EPSG::4326-180.0 -73.3285827636719180.000015258789 89.6167526245117geonode:PoriferaBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityfeaturesPoriferaIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeSouth Atlanticwater massesiAtlanticDeep-seaBenthosurn:ogc:def:crs:EPSG::4326-51.8160018920898 -34.4500007629395-29.269998550415 -12.11669921875geonode:Acanella_CaboVerdeSeamountsPresence-absence records for four cold-water coral (CWC) taxa (Enallopsammia rostrata, Acanella arbuscula, Metallogorgia spp. and Paramuricea spp.) were gathered to conduct distribution models on seamounts (Cadamosto, Nola, Senghor and Cabo Verde) of the Cabo Verde archipelago (NW Africa), covering a bathymetric range from 2100 to 750 m water depth. Data were extracted from video footage collected with Remotely Operated Vehicles during the M80/3 Meteor (2010) and the iMirabilis2 (2021) research expeditions. Video data from the iMirabilis2 expedition was analysed, quantitively, using the open-source software BIIGLE (Langenkämper et al. 2017). Observations from five continuous 1 to 2 km-long video transects between 2000 and 1400 m depth at Cadamosto Seamount were converted into presence-absence data points. Similar data were not available for the seamounts explored during M80/3 Meteor. However, all the available images and short video clips from that expedition were analysed to identify presence and absence points for each of the four target CWC taxa. All the available presence/absence data from the two expeditions was transformed into one point per grid cell of a 100 m resolution bathymetry grid, with the prevalence of the presence records over the absence records, in grid cells where both categories overlapped.Atlantic OceaniAtlanticfeaturesAcanella_CaboVerdeSeamountscold-water coralCabo Verdedistribution modellingDeep-seaIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timeurn:ogc:def:crs:EPSG::4326-25.5403575897217 14.6344890594482-21.8753318786621 17.240291595459geonode:Enallopsammia_CaboVerdeSeamountsPresence-absence records for four cold-water coral (CWC) taxa (Enallopsammia rostrata, Acanella arbuscula, Metallogorgia spp. and Paramuricea spp.) were gathered to conduct distribution models on seamounts (Cadamosto, Nola, Senghor and Cabo Verde) of the Cabo Verde archipelago (NW Africa), covering a bathymetric range from 2100 to 750 m water depth. Data were extracted from video footage collected with Remotely Operated Vehicles during the M80/3 Meteor (2010) and the iMirabilis2 (2021) research expeditions. Video data from the iMirabilis2 expedition was analysed, quantitively, using the open-source software BIIGLE (Langenkämper et al. 2017). Observations from five continuous 1 to 2 km-long video transects between 2000 and 1400 m depth at Cadamosto Seamount were converted into presence-absence data points. Similar data were not available for the seamounts explored during M80/3 Meteor. However, all the available images and short video clips from that expedition were analysed to identify presence and absence points for each of the four target CWC taxa. All the available presence/absence data from the two expeditions was transformed into one point per grid cell of a 100 m resolution bathymetry grid, with the prevalence of the presence records over the absence records, in grid cells where both categories overlapped.Atlantic OceaniAtlanticfeaturesEnallopsammia_CaboVerdeSeamountsIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timecold-water coralCabo Verdedistribution modellingDeep-seaurn:ogc:def:crs:EPSG::4326-25.5401744842529 14.6344890594482-21.8753318786621 17.2402362823486geonode:Metallogorgia_CaboVerdeSeamountsPresence-absence records for four cold-water coral (CWC) taxa (Enallopsammia rostrata, Acanella arbuscula, Metallogorgia spp. and Paramuricea spp.) were gathered to conduct distribution models on seamounts (Cadamosto, Nola, Senghor and Cabo Verde) of the Cabo Verde archipelago (NW Africa), covering a bathymetric range from 2100 to 750 m water depth. Data were extracted from video footage collected with Remotely Operated Vehicles during the M80/3 Meteor (2010) and the iMirabilis2 (2021) research expeditions. Video data from the iMirabilis2 expedition was analysed, quantitively, using the open-source software BIIGLE (Langenkämper et al. 2017). Observations from five continuous 1 to 2 km-long video transects between 2000 and 1400 m depth at Cadamosto Seamount were converted into presence-absence data points. Similar data were not available for the seamounts explored during M80/3 Meteor. However, all the available images and short video clips from that expedition were analysed to identify presence and absence points for each of the four target CWC taxa. All the available presence/absence data from the two expeditions was transformed into one point per grid cell of a 100 m resolution bathymetry grid, with the prevalence of the presence records over the absence records, in grid cells where both categories overlapped.Atlantic OceaniAtlanticfeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeMetallogorgia_CaboVerdeSeamountscold-water coralCabo Verdedistribution modellingDeep-seaurn:ogc:def:crs:EPSG::4326-25.5401744842529 14.6344890594482-21.8747367858887 17.240291595459geonode:Paramuricea_CaboVerdeSeamountsPresence-absence records for four cold-water coral (CWC) taxa (Enallopsammia rostrata, Acanella arbuscula, Metallogorgia spp. and Paramuricea spp.) were gathered to conduct distribution models on seamounts (Cadamosto, Nola, Senghor and Cabo Verde) of the Cabo Verde archipelago (NW Africa), covering a bathymetric range from 2100 to 750 m water depth. Data were extracted from video footage collected with Remotely Operated Vehicles during the M80/3 Meteor (2010) and the iMirabilis2 (2021) research expeditions. Video data from the iMirabilis2 expedition was analysed, quantitively, using the open-source software BIIGLE (Langenkämper et al. 2017). Observations from five continuous 1 to 2 km-long video transects between 2000 and 1400 m depth at Cadamosto Seamount were converted into presence-absence data points. Similar data were not available for the seamounts explored during M80/3 Meteor. However, all the available images and short video clips from that expedition were analysed to identify presence and absence points for each of the four target CWC taxa. All the available presence/absence data from the two expeditions was transformed into one point per grid cell of a 100 m resolution bathymetry grid, with the prevalence of the presence records over the absence records, in grid cells where both categories overlapped.Atlantic OceaniAtlanticfeaturesIntegrated Assessment of Atlantic Marine Ecosystems in Space and Timecold-water coralCabo Verdedistribution modellingDeep-seaParamuricea_CaboVerdeSeamountsurn:ogc:def:crs:EPSG::4326-25.5401744842529 14.6344890594482-21.8753318786621 17.2402362823486geonode:Regional_Fisheries_Management_OrganziationsGeographic Area of Competence of Northwest Atlantic Fisheries Organization (NAFO). Within this area, NAFO may only regulate fishing activity beyond Coastal States’ EEZ. Geographic Area of Competence of North-East Atlantic Fisheries Commission (NEAFC). Geographic Area of Competence of South East Atlantic Fisheries Organisation (SEAFO).Regional_Fisheries_Management_Organziationsfeaturesurn:ogc:def:crs:EPSG::4326-180.000015258789 -88.0180.000015258789 90.0027084350586geonode:RidgesThe ridge geomorphic feature layer represents the spatial extent of the ridges of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Ridges in this study are confined to “an isolated (or group of) elongated narrow elevation(s) of varying complexity having steep sides, often separating basin features” (IHO 2008). In this study “ridges” were confined to features greater than 1,000 m in relief and do not include the mid-ocean ridges, which was mapped as a separate feature.Downloadable Dataseafloor, geomorphic features, habitatsfeaturesRidgesurn:ogc:def:crs:EPSG::4326-180.0 -72.4003143310547180.0 89.5500640869141geonode:Rift_valleysThe rift valley geomorphic feature layer represents the spatial extent of the rift valleys of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Rift valleys were mapped as separate features in the present study where they are clearly evident in SRTM30_PLUS bathymetric data. Rift valleys are confined to the central axis of mid-ocean spreading ridges; they are elongate, local depressions flanked generally on both sides by ridges (Macdonald, 2001). They were mapped by hand based on 100 m contours.Rift_valleysDownloadable Dataseafloor, geomorphic features, habitatsfeaturesurn:ogc:def:crs:EPSG::4326-178.939178466797 -67.8448867797852177.685150146484 87.0678329467773geonode:RisesThe rise geomorphic feature layer represents the spatial extent of the rises of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). The continental rise was digitised by hand at a nominal spatial scale of 1:3,000,000 in ArcGIS based on 100 m contours. A map of global ocean sediment thickness (Divins, 2003) was used to assist with identifying potential rise areas. In general the rise was confined to areas of sediment thickness >300 m. Criteria for identification of continental rises included the occurrence of a smooth sloping seabed as indicated by evenly-spaced, slope-parallel contours (Curray et al., 2002; Dowdeswell et al., 2008; Covault et al., 2011). In this study, the term “Rise” was restricted to features that abut continental margins and does not include the mid-ocean ridge (or “rise”), which was mapped as a separate feature. The GEBCO Gazetteer of geographic names of undersea features (IHO-IOC, 2012) was used to ensure all named features were included.Downloadable DataRisesseafloor, geomorphic features, habitatsfeaturesurn:ogc:def:crs:EPSG::4326-180.0 -76.4211654663086180.000015258789 84.537467956543geonode:SEAFO_Bottom_Fishing_AreaThe vulnerable marine ecosystem (VME) concept emerged from discussions at the United Nations General Assembly (UNGA) and gained momentum after UNGA Resolution 61/105. The FAO International Guidelines for the Management of Deep-sea Fisheries in the High Seas (FAO DSF Guidelines) build on the resolution and provide details on the VME concept for fisheries management. VMEs are now firmly embedded in regimes for the management of deep-sea fisheries in the areas beyond national jurisdiction (ABNJ). VMEs are groups of species, communities or habitats that may be vulnerable to impacts from fishing activities. The vulnerability of an ecosystem is related to the vulnerability of its constituent population, communities or habitats. Specific criteria are included in the FAO DSF Guidelines to assist States in defining VMEs and how to identify them. Deep-sea fishing activities sometimes employ types of fishing gears that can, in the normal course of operation, come into contact with the sea floor. This can have a negative effect on both living marine resources and ecosystems and damage can occur, thereby increasing the physical vulnerability of the ecosystem. Another concern is overfishing and the resulting vulnerability of target stocks, associated species and habitats. Selective removal of a species may change the manner in which the ecosystem functions, making the ecosystem functionally vulnerable. Significant adverse impacts (SAIs) to an ecosystem can occur as a result of fishing activities. Once a VME has been designated and potential SAIs assessed, the FAO DSF Guidelines recommend specific conservation and management measures.SEAFO_Bottom_Fishing_Areafeaturesurn:ogc:def:crs:EPSG::4326-14.0 -50.012.0000009536743 -5.99999952316284geonode:Shannon_Diversity_IndexNo abstract providedfeaturesShannon_Diversity_Indexurn:ogc:def:crs:EPSG::4326-84.3250350952148 -61.52741622924822.7042293548584 81.4339065551758geonode:AtlanticSeabedAreas_WGS84This data set presents the results of an automated cluster analysis using Gaussian mixture models of the entire Atlantic seafloor environment. The analysis was based on eight global datasets and their derivatives: Bathymetry, slope, terrain ruggedness index, topographic position index, sediment thickness, POC flux, salinity, dissolved oxygen, temperature, current velocity, and phytoplankton abundance in surface waters along with seasonal variabilities. We obtained nine seabed areas (SBAs) that portray the Atlantic seafloor that are shown as polygons in the data set. The attribute table holds short descriptions of each SBA as well as about the colours used in the accompanying paper publication. Data sets like this can be used for further analysis like e.g. for landscape ecology metrics to identify regions of interest. The compressed file further contains a style file that can be used to directly load the correct style in the QGIS software package.Atlantic OceanmultivariateAbyssal_Classificationfeatureslandscape metricscluster analysisAtlanticSeabedAreas_WGS84_06_2020_12_31ecology metricslandscapeurn:ogc:def:crs:EPSG::4326-97.9525680541992 -60.215827941894522.3946094512939 74.3884963989258geonode:ShelfThe shelf base layer represents the spatial extent of the shelf areas of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). The continental shelf is defined by IHO (2008)as “a zone adjacent to a continent (or around an island) and extending from the low water line to a depth at which there is usually a marked increase of slope towards oceanic depths”. The low-water mark is taken in this study as the 0 m depth contour. The shelf break (i.e. the line along which there is marked increase of slope at the seaward margin of a shelf) was digitised manually at a nominal spatial scale of 1:500,000 in ArcGIS based on 10 m, 50 m and 100 m contours, depending on the slope and bathymetric profile of the region. In most cases 100 m contours were sufficient at the selected scale of 1:500,000 to identify the shelf break. However, where there was a gradual break in slope over a broad area, more closely spaced contours were used. Floating ice shelves cover large sections of the Antarctic continental shelf and these areas were simply left blank.D1: Coastal benthic habitatsfeaturesShelfDownloadable Dataseafloor depthseafloor, geomorphic features, habitatsurn:ogc:def:crs:EPSG::4326-180.0 -78.6869735717773180.000015258789 84.2073593139648geonode:Shelf_valleysThe shelf valley geomorphic feature layer represents the spatial extent of the shelf valleys of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Valleys incised more than 10 m into the continental shelf were digitised by hand. To qualify for inclusion in this study, shelf valleys had to be greater than 10 km in length and >10 m in depth overall. Only features that had a definite elongate shape were included as valleys, nominally more than 4 times greater in length than width. Features that intersected the shelf break and extended both onto the shelf and down-slope (where they become submarine canyons) were also included.Shelf_valleysDownloadable Dataseafloor, geomorphic features, habitatsfeaturesurn:ogc:def:crs:EPSG::4326-180.0 -78.6016616821289179.999938964844 83.7960891723633geonode:SillsThe sill geomorphic feature layer represents the spatial extent of the sills of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Sills are “a sea floor barrier of relatively shallow depth restricting water movement between basins” (IHO, 2008). Thus every basin has a sill, over which fluid would escape if the basin were filled to overflowing. The identification of sills in this study is based on selecting contours at a specified interval of 10 m (shelf except for Antarctica), 50 m (Antarctic shelf) or 100 m (all other areas) depending upon the location. Selecting the most shoal, closed contour defines the basin; one contour interval above this typically identifies a discrete location where contours “escape” from the basin and join into the regional bathymetry. This location is mapped as the sill. Sills were mapped for all of the major ocean basins and seas and for the larger basins perched on the continental shelf; sills were not mapped for the smaller basins perched on the slope or shelf or for the smaller abyssal basins.Downloadable Dataseafloor, geomorphic features, habitatsfeaturesSillsurn:ogc:def:crs:EPSG::4326-176.84065246582 -76.7535095214844178.681686401367 88.4415969848633geonode:Simpson_Diversity_IndexNo abstract providedfeaturesSimpson_Diversity_Indexurn:ogc:def:crs:EPSG::4326-84.3250350952148 -61.52741622924822.7042293548584 81.4339065551758geonode:SipunculaBenthic species records of deep-sea fauna distributed along the Brazilian Continental Margin (BCM), synthesized from published databases, were mapped in ArcGIS and the here included shapefiles for each phylum were annexed. Two existing biogeographic schemes for the South Atlantic bathyal and abyssal depths (Spalding et al., 2007; Watling et al., 2013) were tested using the distribution data of benthic species along the BCM. A third biogeographic scheme was tested to assess the relationship between benthic fauna and deep water masses within the Brazilian EEZ. Species occurrences were assigned to the biogeographical units of each biogeographical scheme from which the three occurrences databases (watling, hybrid, water masses), included here, were generated. The original data in PANGAEA can be accessed at https://doi.org/10.1594/PANGAEA.950138.biogeographyBrazilBiodiversityfeaturesSipunculaIntegrated Assessment of Atlantic Marine Ecosystems in Space and TimeSouth Atlanticwater massesiAtlanticDeep-seaBenthosurn:ogc:def:crs:EPSG::4326-48.1590003967285 -29.6160011291504-44.1149978637695 -24.3469982147217geonode:SlopeThe slope base layer represents the spatial extent of the slope areas of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). The slope is “the deepening sea floor out from the shelf edge to the upper limit of the continental rise, or the point where there is a general decrease in steepness” (IHO, 2008). In this study, the foot of slope was digitised manually at a nominal spatial scale of 1:500,000 in ArcGIS based on 100 m contours and 3D viewing. ArcGIS was used to highlight zones of abrupt changes in seabed gradient (contour spacing) which suggests the foot of slope in many areas. In areas where marginal plateaus abut the margin, the foot of slope was allowed to extend offshore to encompass the plateau feature, where a clear seaward dipping gradient was apparent. Otherwise the first significant decrease in gradient encountered in a seaward direction from the shelf break was selected as the foot of slope. Note our foot of slope locations are based only on bathymetric data and our interpretation is not intended to define the foot of slope under Article 76 of the 1982 United Nations Convention on the Law of the Sea, particularly in areas of geomorphologically complex, continent-ocean transition.SlopeD1: Coastal benthic habitatsfeaturesDownloadable Dataseafloor depthseafloor, geomorphic features, habitatsurn:ogc:def:crs:EPSG::4326-180.0 -76.516487121582180.000015258789 84.5408172607422geonode:Spreading_ridgesThe spreading ridge geomorphic feature layer represents the spatial extent of the spreading ridges of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Mid-ocean spreading ridges are “the linked major mid-oceanic mountain systems of global extent” (IHO, 2008). Spreading ridges are distinguished from other ridges in this study (see definition of ridges below). They were mapped by hand based on their appearance as ridge-like features that coincide with the youngest ocean crust as mapped by Müller et al. (1997) in their “EarthByte” digital age grid of the ocean floor. Spreading ridges that were not visible in the SRTM30_PLUS bathymetry (100 m contours) were not included in our interpretation, but there is otherwise no size limitation on spreading ridges.Downloadable DataSpreading_ridgesseafloor, geomorphic features, habitatsfeaturesurn:ogc:def:crs:EPSG::4326-178.985504150391 -68.023551940918178.138381958008 87.8884963989258geonode:Submarine_CanyonThe aim of this study is to assess the global occurrence of large submarine canyons to provide context and guidance for discussions regarding canyon occurrence, distribution, geological and oceanographic significance and conservation. Based on an analysis of the ETOPO1 data set, this study has compiled the first inventory of 5849 separate large submarine canyons in the world ocean. Active continental margins contain 15% more canyons (2586, equal to 44.2% of all canyons) than passive margins (2244, equal to 38.4%) and the canyons are steeper, shorter, more dendritic and more closely spaced on active than on passive continental margins. This study confirms observations of earlier workers that a relationship exists between canyon slope and canyon spacing (increased canyon slope correlates with closer canyon spacing). The greatest canyon spacing occurs in the Arctic and the Antarctic whereas canyons are more closely spaced in the Mediterranean than in other areas. River-associated, shelf-incising canyons are more numerous on active continental margins (n = 119) than on passive margins (n = 34). They are most common on the western margins of South and North America where they comprise 11.7% and 8.6% of canyons respectively, but are absent from the margins of Australia and Antarctica. Geographic areas having relatively high rates of sediment export to continental margins, from either glacial or fluvial sources operating over geologic timescales, have greater numbers of shelf-incising canyons than geographic areas having relatively low rates of sediment export to continental margins. This observation is consistent with the origins of some canyons being related to erosive turbidity flows derived from fluvial and shelf sediment sources. Other workers have shown that benthic ecosystems in shelf-incising canyons contain greater diversity and biomass than non-incising canyons, and that ecosystems located above 1500 m water depth are more vulnerable to destructive fishing pracfeaturesSubmarine_Canyonurn:ogc:def:crs:EPSG::4326-180.0 -76.528205871582179.998733520508 87.1120223999023geonode:Surface_EMU_ClustersEcological Marine Units (EMUs) for the entire Ocean are discoverable through this ArcMap Map Package. Users are encouraged to download and explore this dataset using ArcMap. Alternately, ArcGIS Pro Project Packages (available within this Group) are available for download for use in ArcGIS Pro. A group on GeoNet is available for user collaboration and feedback.featuresSurface_EMU_Clustersurn:ogc:def:crs:EPSG::4326-97.625 -59.87519.8750019073486 67.6250076293945geonode:TerracesThe terrace geomorphic feature layer represents the spatial extent of the terraces of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Terraces are “An isolated (or group of) relatively flat horizontal or gently inclined surface(s), sometimes long and narrow, which is (are) bounded by a steeper ascending slope on one side and by a steeper descending slope on the opposite side” (IHO, 2008). In this study terraces (broad steps) were calculated based on the gradient of the SRTM30_PLUS model. The SRTM30_PLUS model was masked using the slope feature layer (i.e. terraces we only mapped on the continental slope).Downloadable Dataseafloor, geomorphic features, habitatsfeaturesTerracesurn:ogc:def:crs:EPSG::4326-179.999938964844 -74.6868133544922179.999969482422 88.8600997924805geonode:BenBIO_databaseBenthic fauna refers to all fauna that live in or on the seafloor, which researchers typically divide into size classes meiobenthos (32/ 64 µm – 0.5/ 1 mm), macrobenthos (250 µm – 1 cm), and megabenthos (> 1 cm). Benthic fauna play important roles in bioturbation activity, mineralization of organic matter, and in marine food webs. Evaluating their role in these ecosystem functions requires knowledge of their global distribution and biomass. We therefore established the BenBioDen database, the largest open-access database for marine benthic biomass and density data compiled so far. In total, it includes 11,792 georeferenced benthic biomass and 51,559 benthic density records from 384 and 600 studies, respectively. We selected all references following the procedure for systematic reviews and meta-analyses, and report biomass records as grams of wet mass, dry mass, or ash-free dry mass, or carbon per m2 and as abundance records as individuals per m2. This database provides a point of reference for future studies on the distribution and biomass of benthic fauna. Citation: Stratmann, Tanja et al. (2021), The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities, Dryad, Dataset, https://doi.org/10.5061/dryad.gb5mkkwm6BenBioDen databaseurn:ogc:def:crs:EPSG::4326-179.872009277344 -78.2070007324219179.998001098633 89.9830017089844geonode:BenDEN_databaseBenthic fauna refers to all fauna that live in or on the seafloor, which researchers typically divide into size classes meiobenthos (32/ 64 µm – 0.5/ 1 mm), macrobenthos (250 µm – 1 cm), and megabenthos (> 1 cm). Benthic fauna play important roles in bioturbation activity, mineralization of organic matter, and in marine food webs. Evaluating their role in these ecosystem functions requires knowledge of their global distribution and biomass. We therefore established the BenBioDen database, the largest open-access database for marine benthic biomass and density data compiled so far. In total, it includes 11,792 georeferenced benthic biomass and 51,559 benthic density records from 384 and 600 studies, respectively. We selected all references following the procedure for systematic reviews and meta-analyses, and report biomass records as grams of wet mass, dry mass, or ash-free dry mass, or carbon per m2 and as abundance records as individuals per m2. This database provides a point of reference for future studies on the distribution and biomass of benthic fauna. Citation: Stratmann, Tanja et al. (2021), The BenBioDen database, a global database for meio-, macro- and megabenthic biomass and densities, Dryad, Dataset, https://doi.org/10.5061/dryad.gb5mkkwm6BenBioDen databaseurn:ogc:def:crs:EPSG::4326-179.872009277344 -75.2620010375977179.998001098633 90.0120010375977geonode:SCOC_database_version_1Sediment community oxygen consumption (SCOC) rates provide important information about biogeochemical processes in marine sediments and the activity of benthic microorganisms and fauna. Therefore, several databases of sediment community oxygen consumption data have been compiled since the mid-1990s. However, these earlier databases contained much less data records and were not freely available. Additionally, the databases were not transparent in their selection procedure, so that other researchers could not assess the quality of the data. Here, we present the largest, best documented, and freely available database of SCOC data compiled to date. The database is comprised of 2,936 georeferenced SCOC records from 208 studies that were selected following the procedure for systematic reviews and meta-analyses. Each data record states whether the oxygen consumption was measured ex situ or in situ, as total oxygen uptake or diffusive oxygen uptake, and which measurement device was used. The database will be curated and updated annually to secure and maintain an up-to-date global database of SCOC data. Citation: Stratmann, Tanja et al. (2019), Data from: The SCOC database – a large, open and global database with sediment community oxygen consumption rates, Dryad, Dataset, https://doi.org/10.5061/dryad.25nd083diffusive oxygen uptake (DOU)total oxygen uptake (TOU)benthic respirationin situ/ ex situadvective oxygen uptake (AOU)sediment community oxygen consumptionurn:ogc:def:crs:EPSG::4326-179.450012207031 -72.0500030517578255.736709594727 88.8000030517578geonode:TrenchesThe trench geomorphic feature layer represents the spatial extent of the trenches of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). Trenches are “a long narrow, characteristically very deep and asymmetrical depression of the sea floor, with relatively steep sides” (IHO, 2008). Trenches are generally distinguished from troughs by their “V” shape in cross section (in contrast with flat-bottomed troughs). In this study trenches were mapped by selecting closed bathymetric contours that defined basins contained within the trench feature, and then joining the basin segments together by hand digitizing along more elevated sections. In this way, bridge features were also identified (as coinciding with infilled sections of trenches; see section on “bridges”).Downloadable Dataseafloor, geomorphic features, habitatsfeaturesTrenchesurn:ogc:def:crs:EPSG::4326-180.0 -61.0626792907715180.0 55.5264549255371geonode:TroughsThe trough geomorphic feature layer represents the spatial extent of the troughs of the worlds oceans based on interpretation of the SRTM30 plus v7 global bathymetry model. The layer is one of the 25 layers that make up the global seafloor geomorphic features map (Harris et.al. 2014). The IHO (IHO, 2008) definition of a trough is “a long depression of the sea floor characteristically flat bottomed and steep sided and normally shallower than a trench”. In this study we found that troughs are also commonly open at one end (i.e. not defined by closed bathymetric contours) and their broad, flat floors may exhibit a continuous gradient along a thalweg. Troughs may originate from glacial erosion processes or have formed through tectonic processes. In this study, glacial troughs incised into the shelf are a separate category; here we include all troughs not of a glacial origin, typically superimposed on the slope and/or abyssal base layers. Trenches that have been infilled with sediment may evolve into troughs, as appears to have occurred in troughs adjacent to North and South America, for example. Slumping on the sides of some troughs has formed a bridge across the trough, thereby dividing it into two separate sections (see “bridges” below). In this study all troughs were digitised by hand based on the interpretation of 100 m bathymetric contours.Downloadable Dataseafloor, geomorphic features, habitatsfeaturesTroughsurn:ogc:def:crs:EPSG::4326-180.0 -71.905143737793180.0 89.0493850708008geonode:a__05_2021_04_1411featuresa__05_2021_04_141urn:ogc:def:crs:EPSG::4326-78.1688919067383 22.1159038543701-43.6129264831543 38.5420303344727geonode:iAtlantic_OceanBasiniAtlantic will use and develop state-of-the-art technologies for ocean monitoring, ecosystem assessment and conservation planning at ocean basin scale to provide managers with the new knowledge and tools to enhance the effectiveness of current monitoring activities integrated across the N and S Atlantic.Atlantic OceaniAtlanticocean basin scalestudy areaurn:ogc:def:crs:EPSG::4326-98.0539245605469 -60.025.7886505126953 74.00439453125geonode:longhurstfeatureslonghursturn:ogc:def:crs:EPSG::4326-180.000015258789 -78.5001602172852180.0 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