These grid data were derived from National Parks and Wildlife Service cetacean surveys within the Irish MSFD area and the EEA-10km GRID. The grid shows the current distribution of Harbour seal (Phoca vitulina) in Irish coastal and marine waters. The data were collected for the purposes of 2019 reporting under Article 17 of the EU Habitats Directive.

These grid data were derived from National Parks and Wildlife Service cetacean surveys within the Irish MSFD area and the EEA-10km GRID. The grid shows the current distribution of Harbour seal (Phoca vitulina) and Grey seal (Halichoreus grypus) in Irish coastal and marine waters. The data were collected for the purposes of 2019 reporting under Article 17 of the EU Habitats Directive.

This dataset contains data collected by the MEDISEH project. It contains amoung other things distribution maps of Coralligenous, mäerl and Seagrass beds along the Mediterranean coasts.

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.

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.

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.

The project aims to produce comprehensive data product of the occurence and absence of (phyto)plankton species. As a basis, data from EMODnet Biology are used. The selection of relevant datasets is optimized in order to find all planktonic species, and exclude all species that are not planktonic. The occurences from EMODnet Biology were complemenented with absence data assuming fixed species lists within each dataset and year. The products are presented as maps of the distribution of the 20 most common species of (phyto)plankton in the Greater Baltic Sea.

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.

Probability of occurrence of different macroinvertebrate benthic species in the North Sea. This product was created using DIVAnd, an interpolation method that takes into account several environmental variables and physical coastlines.

The data product on benthic living modes (Beauchard, 2018), was based on an extensive compilation of data on benthic abundance and biomass. However, this dataset was only present as a data file, without the underlying scripts to reproduce the result. With the present data product, we correct this procedural gap. This dataset differs in details from the file underlying the data product on living modes. Datasets were selected that were sufficiently similar in methods for sampling (either boxcore or grab), sampled surface (in the order of 0.1 square meter, although the exact value is variable - it can be found back in the data files) and sieves (1 mm and 0.5 mm sieves were included). For all datasets, abundance was either used directly from the given abundance in the dataset, or calculated from the given counts and area sampled.