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The presence-absence data for macrobenthic fauna that has been collected in Mingulay Reef Complex (Scotland, UK) across 79 stations over the years 2003, 2005, 2009, 2010 and 2011. The collection of the benthic samples has been carried out using a Van-Veen grab, mainly from hard habitats (e.g. live and dead coral framework). About 60% of the macrofaunal specimens have been identified at species level using high quality taxonomic keys and advice from taxonomy experts. Most common taxonomic groups analysed here are molluscs, polychaetes, arthropods, bryozoans, anthozoans, tunicates and brachiopods. The collection of the specimens is now deposited at the National Museums of Scotland (see the attached excel file for details). The enviromental data contains information about coordinates and environmental settings at stations where macrobenthic samples mentioned above, were collected. The environmental settings that are included in the file refer to the years 2003, 2005, 2009, 2010, 2011. For more information on the environmental variables have a look in Henry et al. 2010 (doi:10.1007/s00338-009-0577-6) and Henry et al. 2013 (doi:10.5194/bg-10-2737-2013). The environmental variables included in the excel file are: type of macrohabitat (i.e. muddy sand, rubble, rock, live coral, dead framework, live & dead framework), depth (m), slope, ruggedness, broad-scale bathymetric position index, fine-scale bathymetric position index, average current speed (m/s), maximum current speed (m/s), northness, eastness, winter North Atlantic Oscillation Index (same year), winter North Atlantic Oscillation Index (previous year), annual average bottom temperature (same year), annual average bottom salinity (same year). Extraction of bathymetric (depth) and topographic data [slope, aspect, northness, eastness, ruggedness, standardised broad-scale bathymetric position index (BPI; with an inner radius of 1 cell and an outer radius of 5 cells), fine-scale BPI (with an inner radius of 1 cell and an outer radius of 3 cells)] was based on multibeam echosounder data, using the Spatial Analyst and Benthic Terrain Modeler toolboxes in ArcGIS v.10.6.1 Average and maximum current speed values (m/s) were extracted by the ArcGIS v. 10.6.1 Spatial Analyst toolbox using data generated by a high-resolution 3D ocean model created for the MRC by Moreno-Navas et al. (2014). Data for the winter NAOI (DJFM) (Hurrell et al., 2003) were downloaded from the National Center for Atmospheric Research/University Corporation for Atmospheric Research website (climatedataguide.ucar.edu; data accessed on 28/02/2019).

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.

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass. The research leading to these results received (partial/full) funding from the European Commission FP7 EURO-BASIN (European Basin-Scale Anal ysis, Synthesis, and Integration; Grant Agreement 264 933).

This data set provides spatially explicit estimates of the area directly used for surface mining on a global scale. It contains more than 21,000 polygons of activities related to mining, mainly of coal and metal ores. Several data sources were compiled to identify the approximate location of mines active at any time between the years 2000 to 2017. This data set does not cover all existing mining locations across the globe. The polygons were delineated by experts using Sentinel-2 cloudless (https://s2maps.eu by EOX IT Services GmbH (contains modified Copernicus Sentinel data 2017 & 2018)) and very high-resolution satellite images available from Google Satellite and Bing Imagery. The derived polygons cover the direct land used by mining activities, including open cuts, tailing dams, waste rock dumps, water ponds, and processing infrastructure. The main data set consists of a GeoPackage (GPKG) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometres, FID with the feature ID, and geom in geographical coordinates WGS84. The summary of the mining area per country is available in comma-separated values (CSV) file, including the following variables: ISO3_CODE, COUNTRY_NAME, AREA in squared kilometers, and N_FEATURES number of mapped features. Grid data sets with the mining area per cell were derived from the polygons. The grid data is available at 30 arc-second resolution (approximately 1x1 km at the equator), 5 arc-minute (approximately 10x10 km at the equator), and 30 arc-minute resolution (approximately 55x55 km at the equator). We performed an independent validation of the mining data set using control points. For that, we draw a 1,000 random samples stratified between two classes: mine and no-mine. The control points are also available as a GPKG file, including the variables: MAPPED, REFERENCE, FID with the feature ID, and geom in geographical coordinates WGS84. The overall accuracy calculated from the control points was 88.4%, other accuracy metrics are shown below. Confusion Matrix and Statistics ReferencePrediction Mine No-mine Mine 394 106 No-mine 10 490 Accuracy : 0.88495% CI : (0.8625, 0.9032)No Information Rate : 0.596P-Value [Acc > NIR] : < 2.2e-16 Kappa : 0.768 Mcnemar's Test P-Value : < 2.2e-16 Sensitivity : 0.9752Specificity : 0.8221Pos Pred Value : 0.7880Neg Pred Value : 0.9800Precision : 0.7880Recall : 0.9752F1 : 0.8717Prevalence : 0.4040Detection Rate : 0.3940Detection Prevalence : 0.5000Balanced Accuracy : 0.8987This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme grant number 725525 FINEPRINT project (https://www.fineprint.global/).

Video showing the gas release experiment, which has been conducted at ~80 m water depth in the vicinity of the Sleipner CO2 storage site in the North Sea. The gas discharge was observed in situ during a 4 hour dive with GEOMAR's remotely operated vehicle ROV Kiel 6000 equipped with HD camera/video device.