ALBERT

Description: The dataset contains polychaete abundance data collected from boxcores samples (BC; 0.25 m2) collected in the eastern Clarion Clipperton fracture Zone (northeast Pacific), an area currently being explored for polymetallic nodules. Macrobenthic samples were collected onboard RV Sonne during expedition SO239 in 2015. Four exploration contract areas (BGR, IOM, GSR and Ifremer) and one “Area of Particular Environmental Interest” (APEI#3) were sampled. Between 3 and 8 quantitative box cores were collected in each area. Boxcore samples were sliced in three layers (0-3, 3-5 and 5-10 cm depth) and sieved on a 300 µm mesh. Polychaetes have been counted, sequenced and identified. Identifications were realised based on morphology and DNA (COI, 16S and 18S genes) leading to morphotype in most cases (species-level). DNA sequences are available in Genbank or BOLD databases with their respective codes in this dataset.

Description: The dataset contains polychaete abundance data belonging to the family Polynoidae sampled using epibenthic sledge, ROV and box corer (BC; 0.25 m2) within the eastern Clarion Clipperton fracture Zone (northeast Pacific), an area currently being explored for polymetallic nodules. Macrobenthic samples were collected onboard RV Sonne during expedition SO239 in 2015. Four exploration contract areas (BGR, IOM, GSR and Ifremer) and one "Area of Particular Environmental Interest" (APEI#3) were sampled. Eight out of twelve epibenthic sledges samples were fully processed. All polynoids heads have been counted, sequenced and identified. Identifications were realized using morphology and DNA (COI, 16S and 18S genes) leading to morphospecies in most cases (species-level). DNA sequences are available in GenBank or BOLD databases with their respective codes in this dataset.

Description: Standardized video transects, 17 in total, were performed one meter above the seafloor using the Remote Operated Vehicle (ROV) Kiel 6000 to identify composition and densities of both sessile and mobile megabenthic epifauna (excluding fish, crustaceans and large protozoans) in areas with dense nodule concentrations (〉15% cover) and areas with very few or no obvious surface nodules (〈1%).

Description: We used environmental niche modelling along with the best available species occurrence data and environmental parameters to model habitat suitability for key cold-water coral and commercially important deep-sea fish species under present-day (1951-2000) environmental conditions and to forecast changes under severe, high emissions future (2081-2100) climate projections (RCP8.5 scenario) for the North Atlantic Ocean (from 18°N to 76°N and 36°E to 98°W). The VME indicator taxa included Lophelia pertusa , Madrepora oculata, Desmophyllum dianthus, Acanela arbuscula, Acanthogorgia armata, and Paragorgia arborea. The six deep-sea fish species selected were: Coryphaenoides rupestris, Gadus morhua, blackbelly Helicolenus dactylopterus, Hippoglossoides platessoides, Reinhardtius hippoglossoides, and Sebastes mentella. We used an ensemble modelling approach employing three widely-used modelling methods: the Maxent maximum entropy model, Generalized Additive Models, and Random Forest. This dataset contains: 1) Predicted habitat suitability index under present-day (1951-2000) and future (2081-2100; RCP8.5) environmental conditions for twelve deep-sea species in the North Atlantic Ocean, using an ensemble modelling approach.  2) Climate-induced changes in the suitable habitat of twelve deep-sea species in the North Atlantic Ocean, as determined by binary maps built with an ensemble modelling approach and the 10-percentile training presence logistic (10th percentile) threshold. 3) Forecasted present-day suitable habitat loss (value=-1), gain (value=1), and acting as climate refugia (value=2) areas under future (2081-2100; RCP8.5) environmental conditions for twelve deep-sea species in the North Atlantic Ocean. Areas were identified from binary maps built with an ensemble modelling approach and two thresholds: 10-percentile training presence logistic threshold (10th percentile) and maximum sensitivity and specificity (MSS). Refugia areas are those areas predicted as suitable both under present-day and future conditions. All predictions were projected with the Albers equal-area conical projection centred in the middle of the study area. The grid cell resolution is of 3x3 km.

Description: This data is showing the outcomes of the analysis done by ATLAS researchers on the environmental status of nine deep-sea areas in the northeast Atlantic. These results are part of the ATLAS work facilitating the implementation of the European Commission's Marine Strategy Framework Directive in the deep waters of the North Atlantic. The nine study areas that were examined are: 1) LoVe Ocean Observatory, 2) Faroe-Shetland Channel, 3) Reykjanes Ridge, 4) Rockall Bank, 5) Mingulay Reef Complex, 6) Porcupine Seabight, 7) Bay of Biscay, 8) Azores, 9) Gulf of Cádiz. The analyses were carried out using the Nested Environmental status Assessment Tool (NEAT). The environmental status outcomes are shown for the total study area, the designated spatial assessment units (SAUs), the ecosystem components ("Benthic invertebrates", "Fish", "Benthos") and the habitats ("Aggregations of L. pertusa & M. oculata on soft sediments", "Aggregations of sea pens & alcyonaceans on soft sediments", "Aggregations of L. pertusa & M. oculata on hard substrates", "Aggregations of Antipatharians and alcyonaceans on hard substrates", "Benthic", "Rocky", "Sedimentary").

Description: This 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.

Description: This 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.

Description: This 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. No additional information to that used in the EUSeaMap was available for region 1. Therefore, shapefiles were not created for region 1.