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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.
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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 20 most common species of (phyto)plankton in the Greater Baltic Sea.
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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.
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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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<p>This dataset includes the data used in in the HELCOM/OSPAR Ballast Water Exemptions Decision Support Tool. This tool was developed in 2013 based on the overall IMO framework, the 21 Baltic and North-East Atlantic coastal states and the EU. It allows user to define 'low risk' routes, as well as other necessary steps in granting exemptions under regulation A-4 of the IMO Ballast Water Management Convention (BWM</p>
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<p>Due to fishing and other human activities, reef forming species have almost completely disappeared over roughly the past century. They are important structures that accommodate juvenile fish and other small organisms. For protection of areas where such reefs could possibly be reintroduced, it is important to define areas that are suitable habitats. This product aims to classify areas in the North Sea based on current occurrence in combination with environmental variables that are particularly suitable for these organisms.</p>
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<p>Loggerhead turtles (<i>Caretta caretta</i>) are a globally vulnerable species of marine turtle. The Mediterranean Sea subpopulation, which ranges throughout the entire Mediterranean basin, is listed as least concern by the International Union for the Conservation of Nature (IUCN), but experiences significant threats in the region including bycatch in fisheries, climate change, coastal development, and marine pollution. Broad-scale patterns of distribution and abundance can provide regional managers a tool to effectively conserve and manage this species at basin and sub-basin scales.</p>
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<p>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.</p><p>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.</p>
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<p>This product builds on the EMODnet Biology data product <a href="https://www.vliz.be/en/imis?module=dataset-amp;dasid=8216">Presence/absence data of macrozoobenthos in the European Seas</a> to derive estimates of temporal turnover in benthic communities on a spatial grid across European seas. This product only uses species-level records, and only uses sampling events where the full macrobenthic community was surveyed (i.e. where there are no 'NA' values in the presence/absence dataset for any species). Six time periods are considered, based on data availability: before 1990, 1990-1999, 2000-2004, 2005-2009, 2010-2014, and 2015 and after. A 1 degree grid is used to obtain reasonable numbers of repeat samples per grid cell. The code below could be adapted to set different time periods and/or a different grid resolution. This readme describes the product structure, including the workflow to generate the required derived datasets and the process for turning them into gridded maps of community turnover.</p>
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<p>Biodiversity loss due to human activities is an increasing threat for marine ecosystems and the services we obtain from them. As biodiversity is directly related to the resilience of ecosystems to temporary disturbance, biodiversity monitoring is a vital task for areas subjected to conservation goals. Environmental factors often control the community composition and biodiversity of marine microplankton, such as the pronounced salinity gradient in the Baltic Sea (e.g. Hu et al. 2016). Time series data of biodiversity can therefore provide an indication of changes in community composition due to environmental stressors, such as climate change or eutrophication.</p><p>As many biodiversity estimates are biased by sampling effort, caution must be taken when interpreting alpha diversity from microscopy phytoplankton counts. By rarefaction and evenness estimation, these biases can be reduced, but not ignored.</p>