<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>

<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>

<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 (BWMC)</p>

<p>Environmental Niche Model (ENM) outputs for 49 commercial fish species under climate change until the decade of 2060 around northwestern Europe. A model ensemble of 5 ENMs was used (MaxEnt, Generalised Linear Models, Support Vector Machine, Random Forest and BIOCLIM ), and projections were made under three different emission scenarios: A1B, RCP4.5 and RCP 8.5. The data shows model agreement (normalised to 1) for presence/absence decadal projections from 2020 to 2060. Additionally we provide data on model performance, with the Area Under the Curve (AUC) scores of the Receiver Operator Characteristic (ROC) curve for each of the 5 ENMs trained for each combination of fish species and emission scenario. Only ENMs with an AUC score of at least 0.7 were considered.</p>

<p>These data are Bayesian Additive Regression Tree model annual predictions for habitat suitability of marine fish species across a range of body sizes and belonging to different feeding guilds from 2010 to 2095 in 5 year intervals in the northeast Atlantic shelf seas. Feeding guilds were allocated based on classifications following Thompson <i>et al</i>. (2020).</p>

<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>

<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>

<p>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 (Asparagopsis armata, Caulerpa Taxifolia, Sargassum muticum and Undaria pinnatifida) selecting the best model building process.</p>

<p>Spatial interpolation of Calanus finmarchicus and Calanus helgolandicus observations in the North Sea using the DIVAnd software tool. The objective of this project is twofold: (1) Create gridded maps of Calanus finmarchicus and Calanus helgolandicus abundances and (2) Develop and apply a multivariate approach in the interpolation method.</p>

<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>