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    The large databases of EMODNET Biology only store confirmed presences of species. However, when mapping species distribution, it is also important where the species did not occur: there is at least as much information in absences as in presences. Inferring absences from presence-only databases is difficult and always involves some guesswork. In this product we have used as much meta-information as possible to guide us in inferring absences.

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    The project aims to produce comprehensive data product of the occurrence 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 occurrences from EMODnet Biology were complemented 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.

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    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 100 most common species of (phyto)plankton in the Greater North Sea. This product then is also used for interpolated maps, using the DIVA software.

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    The number of marine seaweeds outside their natural boundaries has increased in the last decades generating impacts on biodiversity and economy. This makes the development of management tools necessary, where species distribution models (SDMs) play a crucial role. SDMs can help in the early detection of invasions and predict the extent of the potential spread. However, modelling non-native marine species distributions is still challenging in terms of model building, evaluation and selection. This product aims to predict the European distribution of four widespread introduced seaweed species selecting the best model building process.

  • A compliant implementation of WMS plus most of the SLD extension (dynamic styling). Can also generate PDF, SVG, KML, GeoRSS

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    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.

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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.

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    3-D habitat suitability maps (HSM) or probability of occurrence maps, built using Shape-Constrained Generalized Additive Models (SC-GAMs) for the 30 main commercial species of the Atlantic region. Predictor variables for each species were selected from: sea water temperature, salinity, nitrate, net primary productivity, distance to seafloor, distance to coast, and relative position to mixed layer depth. Each species HSM contains 47 maps, one per depth level from 0 to 1000 m. Probability values of each map range from 0 (unsuitable habitat) to 1 (optimal habitat). For depth levels below the 0.99 quantile of the depth values found on the species occurrence data, NA values were assigned. Maps have been masked to species native range regions. See Valle et al. (2024) in Ecological Modelling 490:110632 (https://doi.org/10.1016/j.ecolmodel.2024.110632), for more details.

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    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.