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Marine habitat mapping

280 record(s)
 
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    This layer was created for the EUSeaMap 2019. It was computed from the CMEMS product "ARCTIC OCEAN - SEA ICE CONCENTRATION CHARTS - SVALBARD" (product identifier: SEAICE_ARC_SEAICE_L4_NRT_OBSERVATIONS_011_002). Daily values were averaged over the year 2018

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    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.

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    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.

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    Model describes the potential distribution range of Mytilus trossulus x edulisin 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.

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    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.

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    Fraction of surface light reaching the seabed (Fr), values between 0 and 1. Derived from KDPAR and depth rasters (EMODnet bathymetry 100m DTM, 2019) using the following equation; Fr = e^-KDPAR*depth Datasets spatial extent covers European Seas including the Azores and Canary Islands, but excluding the eastern Baltic.

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    Raster showing the number of MERIS images that were used to derive PAR values for each pixel. Data was collected by the MERIS satellite between 2005 and 2009 and this layer was created for use in the 2019 EUSeaMap and updated in 2018 in order to cover Iceland and the Barents Sea. Datasets spatial extent covers European Seas including the Azores and Canary Islands, but excluding the eastern Baltic. Created by the EMODnet Seabed Habitats consortium using data from the European Space Agency MERIS instrument.

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    Raster showing the number of MERIS images that were used to derive KDPAR values for each pixel. Data was collected by the MERIS satellite between 2005 and 2009 and created for use in the 2016 EUSeaMap, but updated in 2018 in order to cover Iceland and the Barents Sea and used in EUSeaMap 2019. Datasets spatial extent covers European Seas including the Azores and Canary Islands, but excluding the eastern Baltic. Created by the EMODnet Seabed Habitats consortium using data from the European Space Agency MERIS instrument.

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    Raster showing the photosynthetic active radiation (PAR) reaching the seabed, values in mol.phot.m^-2.d^-1. Derived by multiplying the fraction of surface light reaching the seabed by the amount of light at the sea surface. Layer created for use in the 2019 EUSeaMap, and updated in 2018 in order to cover Iceland and the Barents Sea. Dataset's spatial extent covers European Seas including the Azores and Canary Islands, but excluding the eastern Baltic. Created by the EMODnet Seabed Habitats consortium using data from the European Space Agency MERIS instrument.

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    Raster showing the average diffuse attenuation coefficient of photosynthetic active radiation (KDPAR) between 2005 and 2009, values in metres^-1. Data was collected by the MERIS satellite and this layer was created for use in the 2019 EUSeaMap. In 2018 the coverage was extended to Iceland and Barents Sea. In 2022 the coverage was extended to the Caspian Sea. Dataset's spatial extent covers European Seas including the Azores and Canary Islands, but excluding the eastern Baltic. The spatial resolution of the dataset is around 100m, but that of the original data source is around 250m. Created by the EMODnet Seabed Habitats consortium using data from the European Space Agency MERIS instrument.