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  • A habitat distribution modelling approach was used to model the spatial distribution of the two main species of kelp forests along Molène archipelagos (France). Lineage: Data represents percentage cover of kelp forest as well as biomass of the 2 main species: L. digitata and L. hyperoborea. The used approach consists in firstly establishing surveys and appropriate processing methods in order to provide a detailed underwater topography of the area and to accurately delineate hard substrates (bedrock) potentially colonized by kelp. Secondly, a habitat suitability model is fitted for each species on some carefully selected field stations, measuring kelp presence/absence and biomass. Predictive maps are produced, based on hard substrate areas previously delineated. Type of occurrence data used: In situ data were acquired in the period from late summer to early autumn. Information on the presence/absence of Laminaria species was acquired by towing a high definition video. Species-specific biomass were sampled at low tide for the intertidal areas and by Scuba diving for the sub-tidal areas. Environmental covariates/explanatory variables: Presence–absence of L. digitata distribution was best determined through the combined effects of depth, sediment proximity along current direction, benthic position index (BPI), immersion rate and winter temperature. The sub-model for biomass of L. digitata where present, was predicted using the additional contribution of several variables, with light being the most important (55.62% of deviance explained) and its interactions withwave exposure and spring temperature. Total suspended matter contributed little and only then through the interaction with light. The best model that explained 78.89% of deviance for the presence of L. hyperborea included depth, winter temperature, sediment proximity along current direction and BPI Biomass where L. hyperborea was present was mainly modeled by the same predictors as presence/absence Algorithm/modelling approach: Kelp biological response (presence/absence or biomass) was estimated using Generalized Additive Models (GAM)

  • Global habitat suitability for Stolonifera cold water octocoral

  • Predictive habitat model showing distribution of Pheronema carpenteri in Irish waters

  • Model describes the potential distribution range of Zostera marina in the Finnish coast. Model was produced using extensive data (~140,000 samples) on the Finnish Inventory Programme for Underwater Marine Environment (VELMU). Model was built using Boosted regression trees (BRT), and resulting models describe the probability of detecting a habitat-forming species in a cell. Environmental predictors include for instance (and are not only restricted to): bathymetry, euphotic depth, salinity, substrate, and wave exposure. As more accurate information is gained by diving than from video methods, dive data was used as the primary source for modelling with 75–90% for model training and 10–25% for validation. The secondary source, video data, was used only for species clearly identifiable from videos with additional subsets (25%) from targeted inventories. Dive and video data are limited to rather shallow depths (typically 20–30 m), leading to a situation where there are not enough samples from deep areas (below 50 m). To avoid artefacts in the models, a randomized absence dataset for areas deeper than 50 m was used during the modelling process. These points were used only as absences in macrophytes models, based on the knowledge that macrophytes do not live at such depths in the Baltic Sea due to habitat constraints and lack of light.

  • Data represents the abundance of benthic community with Echinocyamus pusillus, Harmothoe, Bittium reticulatum, Oligochaeta, Alitta virens, Turritella communis, Asterias rubens in the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServer. For EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article.

  • Data represents the abundance of benthic community with Hydrobiidae, Pygospio elegans, Cerastoderma glaucumin the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServerFor EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article.

  • Mapping and classifying the seabed of the West Greenland continental shelf. Marine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60°N to 72°N in depths of 61–725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200–700 m below 72°N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists.

  • Model describes the potential distribution range of Potamogeton perfoliatus in the Finnish coast. Model was produced using extensive data (~140,000 samples) on the Finnish Inventory Programme for Underwater Marine Environment (VELMU). Model was built using Boosted regression trees (BRT), and resulting models describe the probability of detecting a habitat-forming species in a cell. Environmental predictors include for instance (and are not only restricted to): bathymetry, euphotic depth, salinity, substrate, and wave exposure. As more accurate information is gained by diving than from video methods, dive data was used as the primary source for modelling with 75–90% for model training and 10–25% for validation. The secondary source, video data, was used only for species clearly identifiable from videos with additional subsets (25%) from targeted inventories. Dive and video data are limited to rather shallow depths (typically 20–30 m), leading to a situation where there are not enough samples from deep areas (below 50 m). To avoid artefacts in the models, a randomized absence dataset for areas deeper than 50 m was used during the modelling process. These points were used only as absences in macrophytes models, based on the knowledge that macrophytes do not live at such depths in the Baltic Sea due to habitat constraints and lack of light.

  • Global habitat suitability for Sessiliflorae cold water octocoral

  • Data represents the abundance of benthic community with Bylgides sarsi, Pontoporeia femoratain the Baltic Sea. Baltic wide benthic community analysis is done based on the abundance and biomass data averaged for all sampling events in within 5 km grid cell based on the harmonised dataset that comprises data at over 7000 locations mostly sampled in period 2000-2013. Random Forest model was used for spatial interpolation.Original data can be downloaded: http://gis.ices.dk/gis/rest/services/ExternalDatasets/Benthic/MapServer. For EMODnet Seabed habitat portal the original dataset was separated as presence-absence grids per identified benthic community in the original article.