Description: Deep-water ecosystems are characterized by relatively low carbonate concentration values and, due to ocean acidification (OA), these habitats might be among the first to be exposed to undersaturated conditions in the forthcoming years. However, until now, very few studies have been conducted to test how cold-water coral (CWC) species react to such changes in the seawater chemistry. The present work aims to investigate the mid-term effect of decreased pH on calcification of the two branching CWC species most widely distributed in the Mediterranean, Lophelia pertusa and Madrepora oculata. No significant effects were observed in the skeletal growth rate, microdensity and porosity of both species after 6 months of exposure. However, while the calcification rate of M. oculata was similar for all colony fragments, a heterogeneous skeletal growth pattern was observed in L. pertusa, the younger nubbins showing higher growth rates than the older ones. A higher energy demand is expected in these young, fast-growing fragments and, therefore, a reduction in calcification might be noticed earlier during long-term exposure to acidified conditions.

Description: Cold-water coral (CWC) reefs constitute one of the most complex deep-sea habitats harboring a vast diversity of associated species. Like other tropical or temperate framework builders, these systems are facing an uncertain future due to several threats, such as global warming and ocean acidification. In the case of Mediterranean CWC communities, the effect may be exacerbated due to the greater capacity of these waters to absorb atmospheric CO2 compared to the global ocean. Calcification in these organisms is an energy-demanding process, and it is expected that energy requirements will be greater as seawater pH and the availability of carbonate ions decrease. Therefore, studies assessing the effect of a pH decrease in skeletal growth, and metabolic balance are critical to fully understand the potential responses of these organisms under a changing scenario. In this context, the present work aims to investigate the medium- to long-term effect of a low pH scenario on calcification and the biochemical composition of two CWCs from the Mediterranean, Dendrophyllia cornigera and Desmophyllum dianthus. After 314 d of exposure to acidified conditions, a significant decrease of 70 % was observed in Desmophyllum dianthus skeletal growth rate, while Dendrophyllia cornigera showed no differences between treatments. Instead, only subtle differences between treatments were observed in the organic matter amount, lipid content, skeletal microdensity, or porosity in both species, although due to the high variability of the results, these differences were not statistically significant. Our results also confirmed a heterogeneous effect of low pH on the skeletal growth rate of the organisms depending on their initial weight, suggesting that those specimens with high calcification rates may be the most susceptible to the negative effects of acidification.

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: 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: 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: 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.

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.