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  • Global habitat suitability for Calcaxonia 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.

  • Predictive habitat suitability model of Sargassum muticum in the British Isles

  • Model describes the potential distribution range of Fucus spp 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 Holaxonia cold water octocoral

  • Predictive habitat suitability model of Himanthalia elongata in the British Isles

  • Predictive habitat suitability model of Saccharina latissima in the British Isles

  • Predictive habitat suitability model of Fucus vesiculosus in the British Isles

  • Global habitat suitability for Alcyoniina cold water octocoral

  • 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)