The OSPAR EUNIS Combined map is a full-coverage map displaying the best available habitat data within the reporting region of the North-East Atlantic, classifying habitats to EUNIS 2007 - 2011 level 3 where possible. The product integrates fine, medium and broad-scale habitat maps from survey and fills in any gaps using EUSeaMap 2021.

EUNIS 2007-11 habitat map created from data collected on the CEND2213 2013/11/04 survey to North Norfolk Sandbank and Saturn Reef. Sublittoral sediments defined using acoustic and groundtruth data. Survey Techniques: 40 transects. Sidescan sonar and multibeam echosounder, dropcam video tows and still photography. Infaunal sample and particle size analysis (PSA) carried out on 0.1m2 Hamon grab samples. The habitat map was produced using EUNIS classes obtained from PSA data and interpretations from still images, to inform the semi-automated object-based imagery analysis (OBIA). The OBIA divides the bathymetry and backscatter data into meaningful objects based on their spectral and spatial characteristics, which can then be classified using the ground-truthed data.

Habitat map created from data collected on the NLV Polestar 2012 survey to Stanton Banks. Sublittoral sediments defined using acoustic and groundtruth data. Initially classified in the MNCR system and subsequently translated in to EUNIS 20017-11. Survey Techniques: Multibeam echosounder, dropcam video tows and still photography. Infaunal sample and particle size analysis (PSA) carried out on 0.1m2 Day grab samples. The habitat map was produced using EUNIS classes obtained from PSA data and interpretations from still images.

Habitat map created from data collected on the RV Cefas Endeavour CEND19x12 2012 survey to Braemar Pockmarks SAC. Sublittoral sediments defined using acoustic and groundtruth data. Classified in EUNIS 20017-11. Survey Techniques: Multibeam echosounder, sidescan sonar, dropcam video tows and still photography. Infaunal sample and particle size analysis (PSA) carried out on 0.1m2 Day and Hamon grab samples. The habitat map was produced using EUNIS classes obtained from PSA data and interpretations from still images.

Habitat map created from data collected on the RV Cefas Endeavour CEND19x12 2012 survey to Scanner Pockmark SAC. Sublittoral sediments defined using acoustic and groundtruth data. Classified in EUNIS 20017-11. Survey Techniques: Multibeam echosounder, sidescan sonar, dropcam video tows and still photography. Infaunal sample and particle size analysis (PSA) carried out on 0.1m2 Day and Hamon grab samples. The habitat map was produced using EUNIS classes obtained from PSA data and interpretations from still images.

This layer shows the current known extent of biogenic substrate in European waters, collated by EMODnet Seabed Habitats in 2021. The purpose was to produce a data product that would add a new class of substrate into the EUSeaMap substrate layer for EUSeaMap 2021. This was required in order to classify the EUSeaMap broad-scale habitat map according to the 2019 version of the EUNIS habitat classification system, which includes a biogenic substrate category.

This layer shows the current known extent and distribution of Coralligenous and other calcareous bioconcretions in the Mediterranean, collated by EMODnet Seabed Habitats. The purpose was to produce a data product that would provide the best compilation of evidence for this habitat, as described in the "Action Plan for the protection of the coralligenous and other calcareous bio-concretions in the Mediterranean". This data product contains large data gaps and should be viewed as incomplete.

This dataset was produced using an ensemble modelling approach, utilising the random forest algorithm to predict habitat suitability of Zostera marina beds across the UK. Habitat suitability models require two types of input data, presence/absence data (also known as response variables) and environmental datasets (also known as predictor variables). The output of the model describes the probability of habitat occurrence as a percentage, with the overall resolution of the dataset being 300 x 30 0m. Another output shows the standard deviation of the predictive values of the habitat suitability model. This describes the variability of the habitat suitability models derived from fifty iterations using a random forests approach. The standard deviation can be used to assess the level of confidence, however this does not provide a full picture and should only be used as an indicator. The predictor variables used within the model include: Depth to seabed Slope of seabed PAR at the Seabed Kinetic energy at the seabed due to currents Kinetic energy at the seabed due to waves Seabed Substrate Mean of annual minima temperature at the seabed (over 30-year period) Absolute minimum of seasonal salinity

This dataset was produced using an ensemble modelling approach, utilising the random forest algorithm to predict habitat suitability of Modiolus modiolus beds across the UK. Habitat suitability models require two types of input data, presence/absence data (also known as response variables) and environmental datasets (also known as predictor variables). The output of the model describes the probability of habitat occurrence as a percentage, with the overall resolution of the dataset being 300x300m. Another output shows the standard deviation of the predictive values of the habitat suitability model. This describes the variability of the habitat suitability models derived from fifty iterations using a random forests approach. The standard deviation can be used to assess the level of confidence, however this does not provide a full picture and should only be used as an indicator. The predictor variables used within the model include: Depth to seabed Slope of seabed Kinetic energy at the seabed due to currents Kinetic energy at the seabed due to waves Seabed Substrate Absolute maximum of annual temperatures at the seabed (over 30-year period) Absolute minimum of seasonal salinity

This dataset was produced using an ensemble modelling approach, utilising the random forest algorithm to predict habitat suitability of Sabellaria spinulosa across the UK. Habitat suitability models require two types of input data, presence/absence data (also known as response variables) and environmental datasets (also known as predictor variables). The output of the model describes the probability of habitat occurrence as a percentage, with the overall resolution of the dataset being 300 x 300 m. Another output shows the standard deviation of the predictive values of the habitat suitability model. This describes the variability of the habitat suitability models derived from fifty iterations using a random forests approach. The standard deviation can be used to assess the level of confidence, however this does not provide a full picture and should only be used as an indicator. The predictor variables used within the model include: Depth to seabed Slope of seabed Light attenuation coefficient of photosynthetic active radiation (Kd(PAR)) Kinetic energy at the seabed due to currents Kinetic energy at the seabed due to waves Seabed Substrate Mean of annual temperature at the seabed (over 30-year period) Absolute minimum of seasonal salinity