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    Gridded abundance map of Polychaete worm <i>Marenzelleria</i>. This animated gridded abundance map shows how the species invaded the Baltic Sea from Danish waters in the 1990s and became abundant throughout the whole region. This animation shows that probably multiple invasions occurred at the same time. The species is part of the Descriptor 2, Non Indigenous Species of the MSFD and is being monitored under HELCOM. The product was developed with DIVA (Data-Interpolating Variational Analysis) and shows the log-transformed abundance. Data was provided by Sweden (SMH - Swedish Agency for Marine and Water Management; Swedish Meteorological and Hydrological Institute (SMHI); (2015): SHARK - Marine soft bottom macrozoobenthos monitoring in Sweden since 1971)), Finland (SYKE) and Denmark (Aarhus University - Josefson, A.; Rytter, D.; Department of Bioscience - AU, Denmark; (2015): Danish benthic marine monitoring data from ODAM).

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    The International Council for the Exploration of the Sea (ICES), located in Copenhagen is an organisation providing scientific advice in the North Atlantic on the exploitation and stewardship of the marine ecosystem and marine living resources. Within this role, it is developing an integrated ecosystem advice at a regional level which will be appropriate to managers, policy developers and interested stakeholders. As part of this ICES has recently constructed “Ecosystem Overviews” which describe the trends in pressures and state of regional ecosystems. These advice processes require regular inputs of monitoring information on the oceanography and hydrology of the regions, called Operational Oceanographic Products and Services (OOPS).

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    Gridded abundance maps of commercial fish species. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by ICES (IBTS dataset - Fish trawl survey: ICES North Sea International Bottom Trawl Survey for commercial fish species. ICES Database of trawl surveys (DATRAS). The International Council for the Exploration of the Sea, Copenhagen. 2010. Online source: http://ecosystemdata.ices.dk.). The maps show clearly the dramatic stock depletion of the last decades in the North Sea. Scale: log-transformed CPUE.

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    Gridded abundance maps of marine birds from the North Sea. All the bird species of this dataset are indicator species for Descriptor 1 Biodiversity of the MSFD for the North East Atlantic. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by JNCC (Joint Nature Conservation Committee): Dunn, T. 2012. JNCC seabird distribution and abundance data (all trips) from ESAS database. Data downloaded from OBIS-SEAMAP (http://seamap.env.duke.edu/dataset/427).

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    Gridded abundance maps of marine mammals (sperm whale, short beaked common dolphin, common bottlenose dolphin, Atllantic spotted dolphin) around the Azores. This product was developed with DIVA (Data-Interpolating Variational Analysis). Data was provided by the Azores Fisheries Observation Programme (POPA).

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    Gridded abundance maps of selected benthos taxa in the North Sea. This product was developed with DIVA (Data-Interpolating Variational Analysis) and the Scale: log-transformed abundance. Data was derived from the 1986 North Sea Benthos Survey (Craeymeersh J., P. Kingston, E. Rachor, G. Duineveld, Carlo Heip, Edward Vanden Berghe, 1986: North Sea Benthos Survey.).

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    This data product is an R Shiny application that discloses the data collected by the Institute of Oceanography and Fisheries (IZOR) in Croatia, in the Middle Adriatic (Skejic et al., 2015). A time series has been built of observations on the species composition of the plankton. The application shows the evolution over time of abundance of major groups of species, as well as the most frequent species (or other taxonomic units) in the dataset. There is also a multivariate representation based on a PCA of abundances of the most frequent species, which shows the seasonal (monthly) fluctuations and the long-term (yearly) trend, and the contribution of each individual species to the temporal evolution of the community.

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    This data product is a series of gridded abundance maps for 40 zooplankton species from 2007 to 2013 in the Baltic Sea, based on a neural network analysis. As input data a combination of EMODnet Biology datasets were used, together with the environmental variables dissolved oxygen, salinity, temperature, chlorophyll concentration bathymetry and the distance from coast. Additionally the position (latitude and longitude) and the year are provided to the neural network. DIVAnd (n-dimensional Data-Interpolating Variational Analysis) and the neural network library Knet were used in this analysis.

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    This data product is an R Shiny application that discloses the data collected by the National Institute of Oceanography and Experimental Geophysics (OGS) in the North Adriatic-Gulf of Trieste LTER. A time series has been built of observations on the species composition of the plankton. The application shows the evolution over time of abundance of major groups of species, as well as the most frequent species (or other taxonomic units) in the dataset. There is also a multivariate representation based on a PCA of abundances of the most frequent species, which shows the seasonal (monthly) fluctuations and the long-term (yearly) trend, and the contribution of each individual species to the temporal evolution of the community.

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    Mixoplankton (sensu Flynn et al., 2019) is a newly introduced term indicating plankton that is capabable of both photosynthesis and phagotrophy. More details are found in Flynn et al., (2019). The potential trophic state can be seen as an inherent characteristic of plankton species. A literature and expert-knowledge study has provided the classification in either phototrophy or mixotrophy which is submitted as traits data to WoRMS. This analysis makes use of this classification to estimate the spatial and temporal distribution of the fraction of mixoplanktonic species in the Greater North Sea including the Celtic Seas. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 766327.