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). This dataset contains a set of terrain (static in time) and environmental (dynamic in time) variables were used as candidate predictors of present-day (1951-2000) distribution and to forecast future (2081-2100) changes. All predictor variables were projected with the Albers equal-area conical projection centred in the middle of the study area. The terrain variable depth was extracted from a bathymetry grid built from two data sources: the EMODnet Digital Terrain Model (EMODnet, 2018) and the General Bathymetric Chart of the Oceans (GEBCO 2014; Weatherall et al., 2015). Slope (in degrees) was derived from the final bathymetry grid using the Raster package in R (Hijmans, 2016) and the Bathymetric Position Index (BPI) was computed using the Benthic Terrain Model 3.0 tool in ArcGIS 10.1 with an inner radius of 3 and an outer radius of 25 grid cells. In order to avoid extreme values, BPI was standardized using the scale function from the Raster package. Environmental variables of present-day and future conditions, including particulate organic carbon (POC) flux at 100-m depth (epc100, mg C m-2 d-1), bottom water dissolved oxygen concentration (µmol kg-1), pH, and potential temperature (°K) were downloaded from the Earth System Grid Federation (ESGF) Peer-to-Peer (P2P) enterprise system. The epc100 was converted to export POC flux at the seafloor using the Martin curve (Martin, Knauer, Karl, & Broenkow, 1987) following the equation: epc = epc100*(water depth/export depth)-0.858, and setting the export depth to 100 m. Near seafloor aragonite (Ωar) and calcite (Ωcal) saturation were also used as candidate predictors for habitat suitability of cold-water coral species. These saturation states were computed by dividing the bottom water carbonate ion concentration (mol m-3) by the bottom water carbonate ion concentration (mol m-3) for seawater in equilibrium with pure aragonite and calcite. Yearly means of these parameters were calculated for the periods 1951-2000 (historical simulation) and 2081-2100 (RCP8.5 or business-as-usual scenario) using the average values obtained from the Geophysical Fluid Dynamics Laboratory's ESM 2G model (GFDL-ESM-2G; Dunne et al., 2012), the Institut Pierre Simon Laplace's CM6-MR model (IPSL-CM5A-MR; Dufresne et al., 2013) and Max Planck Institute's ESM-MR model (MPI-ESM-MR; Giorgetta et al., 2013) within the Coupled Models Intercomparison Project Phase 5 (CMIP5) for each grid cell of the present study area.

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: We developed habitat suitability models for 14 vulnerable and foundation CWC taxa of the Azores employing an original combination of traditional and novel modelling techniques. We introduced the term ecoscape to identify a sensu stricto environmental filter that delimits the potential distribution of coexisting species. --- The published data include: 1. GAM and Maxent habitat suitability predictions classified as high (3), medium (2) or low (1) confidence. Confidence in habitat suitability prediction was estimated with a bootstrap process and depended on the frequency individual raster cells were classified as suitable based on sensitivity‐specificity sum maximization thresholds. Based on this process habitat suitability predictions were categorized as low [1-50%), medium [50-90%) or high [90-100%] confidence. 2. Combined Suitability Maps. GAM and Maxent predictions were combined and each raster cell predicted as suitable was classified based on local fuzzy matching and bootstrap frequencies as follow: value of 1.0 in .tif files: high confidence suitable cells, raster cells predicted as suitable with high confidence by GAM or Maxent, or both and with a local fuzzy similarity greater than 0.5; value of 0.5 in .tif files: medium confidence suitable cells, raster cells predicted as suitable with medium confidence by both GAM and Maxent OR raster cells predicted as suitable with high confidence by GAM or Maxent and with a local fuzzy similarity not equal to zero; value of 0.0 in .tif files: low confidence suitable cell, any other cell predicted as suitable by GAM or Maxent, or both. 3. Overlapping habitat suitability predictions. The .tif file shows the number of taxa predicted as suitable for each raster cell. 4. Regional ecoscapes. Ecoscapes were classified as shallow areas (1), upper slopes (2) and lower slopes (3). 5. Environmetal clusters used to define regional ecoscapes. Clusters were derived using the X-means algorithm.

Description: Description: We developed predictive distribution models of deep-sea elasmobranchs for up to 2000 m depth in the Azores EEZ and neighboring seamounts, from approximately 33°N to 43°N and 20°W to 36°W. Georeferenced presence, absence, and abundance data were obtained from scientific surveys and commercial operations reporting at least one deep-sea elasmobranch capture. A 20-year 'survey dataset' (1996-2017) was compiled from annual scientific demersal surveys using two types of bottom longlines (types LLA and LLB), and an 'observer dataset' (2004-2018) from observer programs covering commercial fisheries operations using bottom longline (similar to type LLA) and vertical handline ('gorazeira'). We used the most ecologically relevant candidate environmental predictors for explaining the spatial distribution of deep-sea elasmobranch in the Azores: depth, slope, northness, eastness, Bathymetric Position Index (BPI), nitrates, and near bottom currents. We merged existing multibeam data for the Azores EEZ with bathymetry data extracted from EMODNET (EMODnet Bathymetry Consortium 2018) to calculate depth values (down to 2000m). All variables were projected with the Albers equal-area conical projection centered in the middle of the study area and were rescaled using bilinear interpolation to a final grid cell resolution of 1.12 x1.12 km (i.e., 0.012°). Slope, northness, and eastness were computed from the depth raster using the function terrain in the R package raster. BPI was derived from the rescaled depth with an inner radius of 3 and an outer radius of 25 grid cells using the Benthic Terrain Model 3.0 tool in ArcGIS 10.1. Nitrates were extracted from Amorim et al. (2017). Near-bottom current speed (m·s-1) average values were based on a MOHID hydrodynamic model application (Viegas et al., 2018) with an original resolution of 0.054°. Besides the environmental variables, we also included three operational predictors in the analysis: year, fishing effort (number of hooks) and gear type (longline LLA and LLB, and gorazeira).

Description: We developed habitat suitability models for 14 vulnerable and foundation cold-water coral (CWC) taxa of the Azores (NE Atlantic) using GAM and MAXENT models. The modelled taxa are: Acanthogorgia spp., Callogorgia verticillata, Coralliidae spp., Dentomuricea aff. meteor, Desmophyllum pertusum, Errina dabneyi, Leiopathes cf. expansa, Madrepora oculata, Narella bellissima, Narella versluysi, Paracalyptrophora josephinae, Paragorgia johnsoni, Solenosmilia variabilis and Viminella flagellum. Models were built using a model grid having a cell size of a 1.13 x 1.11 km (i.e. about 0.01° in the UTM zone 26N projection). This resolution was considered a good compromise between the original resolution of occurrence and environmental data and our capacity to resolve suitable and unsuitable areas within the same geomorphological feature using model predictions. Study area and model background were limited to depths shallower than 2000 m where most of the sampling events took place. Predictors variables included bathymetric position indexes (5 km and 20 km radii), slope, particulate organic carbon flux, seawater chemistry (principal component of dissolved near-seafloor nutrient concentration and calcite/aragonite saturation levels) and near seafloor values of current speed, oxygen saturation and temperature. Presence records were obtained from two different sources: species annotations from underwater imagery (76%) and longline and handline bycatch records (24 %). The published data include: 1. Binary GAM and Maxent habitat suitability predictions. A bootstrap process (n = 100) evaluated the local confidence of model predictions. Each bootstrap iteration sampled occurrence data with replacement, fitted HSMs models and produced binary suitability maps based on sensitivity‐specificity sum maximization thresholds. Depending on the number of times individual raster cells were predicted as suitable they were classified as: low [1-30%), medium [30-70%) or high [70-100%] confidence suitable cells. This process was repeated independently for GAM and Maxent models. In raster layers: (3) identifies high-confidence suitable cells, (2) medium-confidence suitable cells, (1) low-confidence suitable cells and NAs unsuitable cells. 2. Local fuzzy matching of GAM and Maxent habitat suitability predictions. The level of similarity between the spatial distribution of GAM and Maxent binary predictions (low, medium and high confidence suitable cells) at a local (i.e. cell) level was measured considering two membership functions: category similarity, which assumed that some categories were more similar than others; distance decay, which defined the fuzzy similarity of two cells as (i) identical if they matched perfectly, (ii) linearly decreasing with distance if the matching category was found within a 2-cell radius (~2 km) or (iii) totally different when no matching category was found within a 2-cell radius. After combining the two membership functions similarity scores ranged from 0 (totally different) to 1 (identical). Values of similarity greater than 0.5 indicate raster cells that are more similar than different. 3. Combined habitat suitability maps. Suitable raster cells of combined habitat suitability maps were classified as follows: (i) high confidence suitable cell (3 in raster layers), raster cell predicted as suitable with high-confidence by both GAM and Maxent models; (ii) medium confidence suitable cell (2 in raster layers), raster cell predicted as suitable with medium or high confidence by GAM, Maxent or both and with a local fuzzy similarity greater than 0.5; (iii) low confidence suitable cell (1 in raster layers), any other cell predicted as suitable by GAM and/or Maxent. 4. Cold water coral richness based on habitat suitability predictions. The .tif file shows the number of taxa predicted as suitable for each raster cell. Note that only high confidence suitable cells of combined habitat suitability maps are considered.

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: Obtaining a comprehensive knowledge of the spatial variation of deep-sea benthic ecosystems is essential for conservation and management purposes. Here we assembled publicly available information on the positions of vulnerable marine ecosystem indicator species from public databases (OBIS, NOAA and ICES), the published literature and from focused efforts from the Logachev Mounds (NE Atlantic), Tropic Seamount (NE tropical Atlantic) and Bermuda for depths below 200 m. Taxa included hexacorals, octocorals, hydroids, sponges, hydrothermal vents associated species (bivalves, decapods), crinoids and xenophyophores.

Description: It is increasingly recognised that deep-sea mining of seafloor massive sulphides (SMS) could become an important source of mineral resources. These operations will remove the targeted substrate and produce potentially sediment toxic plumes from in situ seabed excavation and from the return water pumped back down to the seafloor. However, the spatial extent of the impacts of deep-sea mining plumes is still uncertain because few field experiments and models of plumes dispersion have been conducted. Morato et al. (2022) used three-dimensional hydrodynamic models of the Azores region together with a theoretical commercial mining operation of polymetallic SMS to simulate the potential dispersal of sediment plumes originating from different phases of mining operations and to assess the magnitude of potential impacts. The areas used in the modelling work were (from North to South): Cavala seamount (38.265, -30.710), Lucky Strike Hole (37.503, -31.955), Menez Hom (37.109, -32.618), Famous (37.001, -33.039), Saldanha (36.658, -33.420), and Rainbow (36.262 -33.824). The datasets published here contain all the model outputs, namely for 1) the in situ excavation sediment plume, 2) the return water discharge plume, and 3) the return sediments discharge plume: 1) The concentration of solids and of the discharge water in each horizontal 2-dimensional space cell is calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during each trimester and during a 12-months simulation. 1.1) Concentration of sediments produced during the in situ excavation sediment plume calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation. Sediments were composed of six classes of different particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and 〉2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1. 1.2) Concentration of return water discharge plume (shown in dilution folds) in six study areas calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation and assuming a control temperature as the annual minimum temperature of each location (T1). The salinity of discharge was calculated assuming the MOHID salinity of 83.3% surface water and 16.7% of seafloor water. 1.3) Concentration of sediments in the return sediment discharge plume, calculated as the maximum concentration in the 50 vertical layers of each 2-dimensional cell, for each output time step (3 hours), averaged over all time steps during a 12-months simulation. The average particle diameter was assumed to be 4 µm with an average particle density of 3,780 kg·m-3 and a resultant settling velocity of 0.002 cm·s-1. 2) The proportion of simulated time (temporal frequency) that a specific 2-dimensional space contained plume concentrations higher than the adopted thresholds; 1.2 mg·L-1 for sediment solids and 5,000 fold dilution for discharge water. Those cells whose temporal frequency above the thresholds was greater than 50%, i.e. 6 months out of 12 months, were considered as cells with persistent plumes. 2.1) Proportion of simulated time (temporal frequency) that a specific a 2-dimensional space cell, in six study areas, contained in situ excavation sediment plume above a 1.2 mg·L-1 concentration threshold, during a 12-months simulation, assuming six classes of particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and 〉2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1. 2.2) Proportion of simulated time (temporal frequency) that a specific 2-dimensional space, in six study areas, contained return water discharge plume concentrations higher than the adopted thresholds (i.e., 5,000 fold dilution), during a 12-months simulation and assuming a control temperature as the annual minimum temperature of each location (T1). The salinity of discharge was calculated assuming the MOHID salinity of 83.3% surface water and 16.7% of seafloor water. 2.3) Proportion of simulated time (temporal frequency) that a specific 2-dimensional space cell, in six study areas, contained return sediments discharge plume above a 1.2 mg·L-1 concentration threshold, during a 12-months simulation, assuming an average particle diameter of 4 µm, an average particle density of 3,780 kg·m-3, and a resultant settling velocity of 0.002 cm·s-1. 3) In addition to the thresholds and targets described above, the datasets also present the model results for Cavala seamount and Lucky Strike Hole against other thresholds: 5 mg·L-1, 10 mg·L-1 and 25 mg·L-1 for sediments and 1,000, 600, 300 and 200 fold dilution for discharge water. 4) Seasonal variations in the model outputs for plumes dispersal are also presented for Cavala seamount and Lucky Strike Hole by computing the probability of concentration above thresholds for four periods of three months (January-March, April-June, July-September, and October-December). In these scenarios, the model run duration was approximately 90 days. 5) The sediment thickness of the settled sediments from the discharge sediment and excavation. 5.1) Bottom thickness of settled sediments produced during the in situ excavation sediment plume assuming six classes of particle diameter (0-10 μm, 10-50 μm, 50-100 μm, 100-200 μm, 200-2,000 μm, and 〉2,000 μm), an average particle density of 3,780 kg·m-3, and resultant settling velocities ranging from 75.1 cm·s-1 to 0.002 cm·s-1. The duration of the simulation is one year. 5.2) Bottom thickness of settled sediments from the return sediment discharge plume modelled assuming an average particle diameter of 4 µm, an average particle density of 3,780 kg·m-3, and a resultant settling velocity of 0.002 cm·s-1. The duration of the simulation is one year.

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.