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  • Fish stomach content datasets from the Northeast Atlantic based on the "Year of Stomach" and "Stomach Tender" projects as well as some historical Latvian data

  • 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.

  • 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.

  • The feasibility study for the foreseen Asinara Is. MPA, was funded by the Italian Environmental Ministry in order to collect environmental and socio-economic information needed for the implementation of the MPA management plan. The biocenotic maps of the archipelago was elaborated using new field integrated with available data. Data were collected both using acoustic and seismic surveys (Side scan sonar, sub-bottom profiler) together with ground truth data (scuba dive transects, grab and dredge samples).

  • The feasibility study for the foreseen Maddalena Is. MPA, was funded by the Italian Environmental Ministry in order to collect environmental and socio-economic information needed for the implementation of the MPA management plan. The biocenotic maps of the archipelago was elaborated using new field integrated with available data. Data were collected both using acoustic and seismic surveys (Side scan sonar, sub-bottom profiler) together with ground truth data (scuba dive transects, grab and dredge samples).

  • The elaboration of this map started in 1999 within a project funded by the Italian Environmental Ministry aimed to identify the distribution of the Posidonia meadows along the Sardinia coasts. During the first phase of the project, different field activities were carried out by using Side Scan Sonar, ROV and scuba diver. In this phase also biological samples were collected. Furthermore, remote sensing surveys were conducted using hyperspectral scanner and aerial photo. Collected data were elaborated for the creation of the Posidonia meadows distribution. Maps show the upper limits and a number of different Posidonia meadows with distinction of different facies (i.e. Posidonia on rock, Posidona on sand, Posidonia on matte etc).

  • The elaboration of this map started in 2002 within a project funded by the Italian Environmental Ministry aimed to identify the distribution of the Posidonia meadows along the Sardinia coasts. During the first phase of the project, different field activities were carried out by using Side Scan Sonar, ROV and scuba diver. In this phase also biological samples were collected. Furthermore, remote sensing surveys were conducted using hyperspectral scanner and aerial photo. Collected data were elaborated for the creation of the Posidonia meadows distribution. Maps show the upper limits and a number of different Posidonia meadows with distinction of different facies (i.e. Posidonia on rock, Posidona on sand, Posidonia on matte etc).