<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>The large databases of EMODNET Biology only store confirmed presences of taxon. However, when mapping taxon distribution, it is also important where the taxon 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. There is important meta-information at two different levels: the level of the data set, and the level of the taxon. Datasets can contain implicit information on absences when they have uniformly searched for the same taxon over a number of sample locations.</p>