WMS The EMODnet Biology products include a set of gridded map layers showing the average abundance of marine species for different time windows (seasonal, annual) using geospatial modelling. The spatial modelling tool used to calculate the gridded abundance maps is based on DIVA. DIVA (Data-Interpolating Variational Analysis) is a tool to create gridded data sets from discrete point measurements of the ocean. For the representation of time dynamics, it was decided to produce gridded maps for sliding time windows. WFS WMS GEOSERVER EMODnet Biology team Flanders Marine Institute (VLIZ)

Oostende 8400 Belgium bio@emodnet.eu NONE NONE text/xml image/png application/atom+xml application/json;type=utfgrid application/pdf application/rss+xml application/vnd.google-earth.kml+xml application/vnd.google-earth.kml+xml;mode=networklink application/vnd.google-earth.kmz image/geotiff image/geotiff8 image/gif image/jpeg image/png; mode=8bit image/svg+xml image/tiff image/tiff8 image/vnd.jpeg-png image/vnd.jpeg-png8 text/html; subtype=openlayers text/html; subtype=openlayers2 text/html; subtype=openlayers3 text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml; subtype=gml/3.1.1 text/html application/json XML INIMAGE BLANK JSON https://emodnet.ec.europa.eu/geonetwork/srv/api/records/09c23ed4385da9c22f8247b75731f1b49582c2b7/formatters/xml application/vnd.iso.19139+xml eng eng The EMODnet Biology products include a set of gridded map layers showing the average abundance of marine species for different time windows (seasonal, annual) using geospatial modelling. The spatial modelling tool used to calculate the gridded abundance maps is based on DIVA. DIVA (Data-Interpolating Variational Analysis) is a tool to create gridded data sets from discrete point measurements of the ocean. For the representation of time dynamics, it was decided to produce gridded maps for sliding time windows. 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EPSG:69036405 -180.000006104363 180.000006104363 -90.0 90.0000076306659 abundance_benthos_North_Baltic_Seas 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. abundance WCS NetCDF EPSG:4326 CRS:84 -16.0 36.0 40.0 74.0 text/xml text/xml url download_url imisdasid abundance_loggerheads Loggerhead turtles (Caretta caretta) 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. In 2020, a spatial density model estimating loggerhead density, abundance, and distribution across the Mediterranean Sea was generated as a long-term annual average. The model was adjusted for availability bias using dive data from loggerhead turtles tagged with time depth recorders from multiple regions within the Mediterranean Sea. Geographic extrapolation in areas near surveys was undertaken with caution. Mean abundance for the long-term average model was estimated as 1,201,845 (CV 0.22). This estimate represents the first basin-wide estimate of abundance for this species in the Mediterranean not based on demographic models. This work was funded by the U.S. Navy to assist with assessing risk to protected marine species resulting from military readiness activities. The data are an annual estimated density of loggerhead turtles, expressed as the number of individuals per square kilometer, within the Mediterranean Sea. The study area was gridded into 5x5 kilometer blocks, the resolution at which density modeling was performed. The study area encompasses all major subregions of the Mediterranean Sea, except the Sea of Marmara, which loggerhead turtles do not frequent. The DENSITY attribute contains the estimated density (individuals per square kilometer) and the UNCERTAINTY attribute contains the estimated coefficient of variation (CV) for the density estimate. The density estimates represent the mean throughout year, averaged for the period 2003-2018. For most of the study area, density was estimated using a habitat-based density model that correlated local abundance observed on systematic line transect surveys with environmental conditions observed at that same location and time. For unsurveyed areas and times, density was estimated by extrapolation. Density was not estimated for nearshore locations and inshore waters as they were often not covered by surveys or lacked reliable remoted sensed covariate coverage. See Sparks and DiMatteo (2020) for more information about the modeling process. The metadata documentation has more information regarding individual attributes. The shoreline and other geographic features were delinated by NAVFAC from a database provided by the National Geospatial-Intelligence Agency (NGA). These data have been re-projected from the original published data presented in DiMatteo et al. 2024, in order to meet EMODnet technical requirements. Small spatial mismatches and value discrepancies between the original model and the version available here may exist but have not been formally assessed. abundance WCS NetCDF EPSG:4326 CRS:84 -75.5092731029212 -13.835957466013047 39.82705136375553 73.53996152495834 text/xml text/xml download_url url alpha_diversity_shannon 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. shannon WCS NetCDF EPSG:4326 CRS:84 10.291228707624114 24.0605432179078 54.81334288967846 65.98905153837853 2000-01-15T00:00:00.000Z/2021-12-15T00:00:00.000Z/PT1S text/xml text/xml download_url axis_score This product enables users to assess spatial and temporal change in fish functional feeding traits across the northeast Atlantic shelf seas. Values are based on non-metric multidimensional axis scores, weighted by biomass observed in otter trawl surveys. This product is the result of collating and analyzing fish stomach contents data from the North Atlantic and Arctic Oceans to classify fish into feeding guilds. The feeding guilds identified include planktivores, benthivores, bentho-piscivores, and piscivores. Non-metric multidimensional scaling (NMDS) analysis was used to provide a more nuanced understanding of different predator feeding traits in relation to others. The NMDS analysis presents axis scores that offer a bounded data perspective, as opposed to categorical, of different predator feeding traits. This analysis is part of the process to classify feeding guilds based on predator stomach contents data. See figure S4 in https://essd.copernicus.org/preprints/essd-2024-102/essd-2024-102-supplement.pdf for a pictorial depiction. Uses attributes recommended by http://cfconventions.org axis_score WCS NetCDF EPSG:4326 CRS:84 -16.0 13.0 36.0 61.5 1997-01-01T00:00:00.000Z/2020-01-01T00:00:00.000Z/PT1S text/xml text/xml donwload_url coefficient_variation_loggerheads Loggerhead turtles (Caretta caretta) 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. In 2020, a spatial density model estimating loggerhead density, abundance, and distribution across the Mediterranean Sea was generated as a long-term annual average. The model was adjusted for availability bias using dive data from loggerhead turtles tagged with time depth recorders from multiple regions within the Mediterranean Sea. Geographic extrapolation in areas near surveys was undertaken with caution. Mean abundance for the long-term average model was estimated as 1,201,845 (CV 0.22). This estimate represents the first basin-wide estimate of abundance for this species in the Mediterranean not based on demographic models. This work was funded by the U.S. Navy to assist with assessing risk to protected marine species resulting from military readiness activities. The data are an annual estimated density of loggerhead turtles, expressed as the number of individuals per square kilometer, within the Mediterranean Sea. The study area was gridded into 5x5 kilometer blocks, the resolution at which density modeling was performed. The study area encompasses all major subregions of the Mediterranean Sea, except the Sea of Marmara, which loggerhead turtles do not frequent. The DENSITY attribute contains the estimated density (individuals per square kilometer) and the UNCERTAINTY attribute contains the estimated coefficient of variation (CV) for the density estimate. The density estimates represent the mean throughout year, averaged for the period 2003-2018. For most of the study area, density was estimated using a habitat-based density model that correlated local abundance observed on systematic line transect surveys with environmental conditions observed at that same location and time. For unsurveyed areas and times, density was estimated by extrapolation. Density was not estimated for nearshore locations and inshore waters as they were often not covered by surveys or lacked reliable remoted sensed covariate coverage. See Sparks and DiMatteo (2020) for more information about the modeling process. The metadata documentation has more information regarding individual attributes. The shoreline and other geographic features were delinated by NAVFAC from a database provided by the National Geospatial-Intelligence Agency (NGA). These data have been re-projected from the original published data presented in DiMatteo et al. 2024, in order to meet EMODnet technical requirements. Small spatial mismatches and value discrepancies between the original model and the version available here may exist but have not been formally assessed. coefficient_of_variation WCS NetCDF EPSG:3035 CRS:84 -75.5092731029212 -13.835957466013047 39.82705136375553 73.53996152495834 text/xml text/xml download_url url eusm_benthos_n_species This product is built from two existing EMODnet data products. It uses the EUSeaMap product created by EMODNet Seabed Habitats and the Presence/absence data of macrozoobenthos in the European Seas product created by EMODnet Biology to obtain lists of all macrobenthic species occurring in all EUNIS seabed habitat types. For the purposes of this product the seabed habitat map is summarised on a grid at approx. 500m resolution, with each grid cell assigned to the dominant EUNIS code present within it. Summaries of benthic diversity are created for each grid cell, as well as more synthetic summaries by EUNIS category. The full matched benthos community data was written to file in the derived data folder as benthos_full_matched in parquet format with gzip compression. eusm_benthos_n_species WCS NetCDF EPSG:4326 CRS:84 -35.97180141999996 43.00059858000003 23.680472180000038 81.54087218000004 text/xml text/xml download_url eusm_benthos_n_surveys This product is built from two existing EMODnet data products. It uses the EUSeaMap product created by EMODNet Seabed Habitats and the Presence/absence data of macrozoobenthos in the European Seas product created by EMODnet Biology to obtain lists of all macrobenthic species occurring in all EUNIS seabed habitat types. For the purposes of this product the seabed habitat map is summarised on a grid at approx. 500m resolution, with each grid cell assigned to the dominant EUNIS code present within it. Summaries of benthic diversity are created for each grid cell, as well as more synthetic summaries by EUNIS category. The full matched benthos community data was written to file in the derived data folder as benthos_full_matched in parquet format with gzip compression. eusm_benthos_n_surveys WCS NetCDF EPSG:4326 CRS:84 -35.97180141999996 43.00059858000003 23.680472180000038 81.54087218000004 text/xml text/xml download_url fraction_mixoplankton 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. fraction_mixoplankton WCS NetCDF EPSG:4326 CRS:84 -16.0 9.0 46.0 66.0 1,2,3,4 text/xml text/xml url url imisdasid habitat_suitability Due to fishing and other human activities, reef forming species have almost completely disappeared over roughly the past century. They are important structures that accomodate 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 occurence in combination with environmental variables that are particularly suitable for these organisms. habitat_suitability WCS NetCDF EPSG:4326 CRS:84 -2.84806123504988 9.05746876495012 50.8754356582586 57.60983565825861 text/xml text/xml download_url url habitat_suitability_porpoise The dataset includes 6 layers showing modelled habitat suitability for harbour porpoises (Phocoena phocoena) across the North Sea–Baltic Sea transition zone over a 25-year period (1997–2004; 2005-2012; 2013-2022) during winter (Nov–Apr) and summer (May–Oct). Habitat suitability was estimated using the maximum entropy (MaxEnt) algorithm, based on satellite tag data of 111 individuals and a range of dynamic and static environmental conditions as predictor variables. Habitat suitability models and maps were developed by researchers of the Department of Ecoscience, Aarhus University to quantify potential spatiotemporal changes in the overlap between suitable habitats and an existing network of 62 MPAs dedicated to harbour porpoise protection. The habitat suitability rasters can inform effective design of forthcoming MPAs under the EU Biodiversity Strategy for 2030. Harbour porpoise habitat suitability layers cover a seascape area of 70,145 km2. Habitat suitability layers are available for the periods: 1997–2004; 2005-2012; 2013-2022 and for both winter (Nov–Apr) and summer (May–Oct). All layers are projected using the EEA reference grid (ETRS89-LAEA Europe, EPSG:3035), have a 1x1 km spatial resolution (cell size) and a spatial extent of 4275000, 4545000, 3415000, 4105000 (xmin, xmax, ymin, ymax). Habitat suitability values (probabilities) range between 1 and 0 with values closer to 1 indicating habitat being highly suitable for harbour porpoises while values closer to 0 indicate habitat that is poorly suitable for harbour porpoises. Location data underlying the habitat suitability models were collected from 111 harbour porpoises tracked over the period 1997–2022 as part of a long-term satellite telemetry monitoring program in Denmark. Location and meta data are freely available at Zenodo (https://doi.org/10.5281/zenodo.13968991).  Harbour porpoise habitat suitability layers were produced as part of the projects: GES4SEAS (grant agreement no. 101059877) and OBAMA-NEXT (101091642)) habitat_suitability WCS NetCDF EPSG:4326 CRS:84 9.094689276840526 14.080289276840528 53.71659362409777 60.12646362409777 text/xml text/xml download_url macroalgae_habitat_suitability Ocean warming is expected to drive quick shifts in marine species distributions modifying coastal communities. Previous research has shown how the southern Bay of Biscay is particularly affected and the canopy-forming subtidal and intertidal macroalgal assemblages are expected to be replaced by turf-forming Mediterranean-like communities with warmer affinities by the end of the century. The effect of temperature on macroalgal communities could be assessed using thermal metrics that can target temperature effects alone. An example is the Community Temperature Index (CTI) which is an abundance-weighted mean of the species’ optimal temperature. It has been previously used to understand the thermal preferences of different organisms such us birds, fish or invertebrates. habitat_suitability WCS NetCDF EPSG:4326 CRS:84 -180.000006104363 180.000006104363 -89.99999847369712 90.0000076306659 text/xml text/xml download_url macroalgae_presence_absence Ocean warming is expected to drive quick shifts in marine species distributions modifying coastal communities. Previous research has shown how the southern Bay of Biscay is particularly affected and the canopy-forming subtidal and intertidal macroalgal assemblages are expected to be replaced by turf-forming Mediterranean-like communities with warmer affinities by the end of the century. The effect of temperature on macroalgal communities could be assessed using thermal metrics that can target temperature effects alone. An example is the Community Temperature Index (CTI) which is an abundance-weighted mean of the species’ optimal temperature. It has been previously used to understand the thermal preferences of different organisms such us birds, fish or invertebrates. presence_absence WCS NetCDF EPSG:4326 CRS:84 -180.000006104363 180.000006104363 -89.99999847369712 90.0000076306659 text/xml text/xml download_url mod_hab_comm_fish_prob_occurrence 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. The data is based on the output of an ensemble of 5 Environmental Niche Modeling techniques: Maximum Entropy (MaxEnt), Generalised Linear Model (GLM), Support Vector Machine (SVM), Random Forest and BIOCLIM. Each model was trained to capture the environmental requirements of individual fish species, using the 20 year-average in the period 1997-2016 of the environmental variables: near-bed sea temperature, sea surface temperature, near-bed salinity, sea surface salinity, the difference in near-bed and surface salinity and temperature (an approximation of stratification), and depth. Once the models were trained, they were used to study future habitat suitability for the 49 commercially-important fish species in 20-year averages centered in 2020, 2030, 2040, 2050 and 2060. To produce maps of the suitable habitat, the model projections were converted to binary presence/absence data using the thresholds that optimized the True Skill Statistics (TSS) of each model. The data shows the model agreement, normalised to 1 ("1" denotes all ENMs agree an area is suitable, "0" denotes all ENMs agree an area is not suitable). The number of ENMs considered for each combination of fish species and emission scenario depends on model performance. A ENM model was considered if its Area Under the Curve (AUC) score of the Receiver Operator Characteristic (ROC) curve was at least 0.7. Model performance is listed in the file "Model_performance_AUC.xlsx". Model input data: Climate change projection data from Met Office and Plymouth Marine Laboratory (PML) and species presence and abundance records from online databases (Ocean Biodiversity Information System (OBIS), Global Biodiversity Information System (GBIF) and Marine Scotland (Moriarty, M., Greenstreet, S.P.R. and Rasmussen, J. (2017) Derivation of Groundfish Survey Monitoring and Assessment Data Product for the Northeast Atlantic Area. Scottish Marine and Freshwater Science, 8, 240pp. DOI: 10.7489/1984-1). Models were trained using the R and Maxent softwares. Study region: northeast Atlantic shelf. Latitudes between -17° and 9.25°; longitudes between 44° and 65°. These datasets were produced for the study in the article "Climate change projections of commercial fish distribution and suitable habitat around northwestern Europe", by Bryony L. Townhill, Elena Couce, Jonathan Tinker, Susan Kay, John K. Pinnegar, in Fish and Fisheries (2023; http://doi.org/10.1111/faf.12773). probability_of_occurrence WCS NetCDF EPSG:4326 CRS:84 -17.1666574557696 9.166677367145837 43.8333484883211 64.83334967621568 text/xml text/xml download_url imisdasid url mod_habi_feeding_guilds_prob_occurrence 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 et al. (2020). The purpose of this study was to predict how climate change could affect the species richness of consumers lower in the food web (planktivores), compared to those intermediate (benthivores) and higher up (piscivores). In brief, a collation of trophic interactions spanning the northeast Atlantic shelf seas has been applied to define feeding guilds by grouping fish species size classes (we use the taxonomic level of Gobiidae and Ammodytes for taxa that are not consistently identified to species) that have prey taxa in common, and whose prey differentiate them from other predator guilds based on cluster analysis. Taxon-specific size categories were defined as: <3 cm as larvae; small juvenile fish between 3 cm and half of length at maturity; juvenile-medium fish from half of length at maturity to length at maturity; medium fish from length at maturity to half-maximum length; and all remaining larger fish as large. Taxon-specific length at maturity and maximum length (i.e., asymptotic length at infinity) were estimated using the R package Fishlife (Thorson et al., 2017). Environmental projections were derived from the coupled marine ecosystem models POLCOMS/NEMO-ERSEM. We focus on two emission scenarios (representative concentration pathways, RCPs) developed for the Intergovernmental Panel on Climate Change (IPCC)’s fifth phase of the Coupled Model Intercomparison Project (CMIP5): RCP4.5, the “stabilization scenario”, characterised by medium emissions and high mitigations, and the “no mitigation policy” scenario RCP8.5, derived from high fossil fuel emission and low mitigations. We use projections of temperature, salinity, pH, nitrate, phosphate, dissolved oxygen, current velocity, chlorophyll, gross primary production, non-living organic carbon, zooplankton carbon concentration and secondary carbon production by zooplankton in our predictive habitat models. For temperature, salinity, current velocity, dissolved oxygen, and pH both surface and bottom mean annual averages were considered, and in the case of temperature, also the difference between bottom and surface values, to account for stratification. For chlorophyll, gross primary production, non-living organic carbon, phosphate, nitrate, zooplankton carbon concentration and secondary carbon production by zooplankton the total across the water column was used, rather than surface or bottom values. We also include depth (from the General Bathymetric Chart of the Oceans GEBCO; www.gebco.net, at 15 second resolution), distance to coast and substrate composition (median grain size and percentages of mud, sand and gravel from Wilson et al., 2018) to capture key spatial gradients that affect habitat suitability for fish. All environmental data was processed onto a 10 km by 10 km grid, and because an annual mean of, e.g., temperature, does not capture the environmental variability that ultimately determines the thresholds within which biota must survive; for temperature, salinity, pH, oxygen and current speed we also include the standard deviation of the 12 monthly means in each year, for all locations within a radius of 75 km of each grid cell, in order to provide a measure of spatio-temporal heterogeneity. For the environmental variables where surface and bottom values were extracted, we use sea surface values to model habitat suitability for planktivores, which are largely pelagic species, and seabed values for the benthivores and piscivores which are largely demersal species. Pairwise Pearson correlation coefficients were computed for the set of environmental variables used to model the habitat of planktivores and of non-planktivores separately, to assess multicollinearity. Variables were removed if they correlated with another >0.7. Additionally we provide data on model performance and presence/absence thresholds, in the file "Model performance and threshold.xlsx". Model performance is measured via the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) plot, the AUC of the Precision-Recall (PR) plot (He & Garcia, 2009), and the Miller slope (Miller et al., 1991). We assess model performance in both space and time. Spatial performance was analysed via 8-fold block cross validation using the R library “blockCV” (Valavi et al., 2019). Temporal performance of the models was assessed by training a new model that excluded the last 5 years of survey data (2015 to 2019), using the resulting model to predict to those 5 years, a “novel time period” for that model. These data were produced for the study in the article "Climate change affects the distribution of diversity across marine food webs", by Murray S. A. Thompson, Elena Couce, Michaela Schratzberger and Christopher P. Lynam, in Global Change Biology (in press). probability_of_occurrence WCS NetCDF EPSG:32629 CRS:84 -19.94240986660436 21.34871875890968 41.33388978486105 64.99715255196696 text/xml text/xml imisdasid url download_url occurrence_probability_fish 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. occurrence_probability WCS NetCDF EPSG:4326 CRS:84 -98.0 68.5 -83.0 90.0 text/xml text/xml download_url url imisdasid porpoise_habitat_suitability_Baltic The dataset includes 6 layers showing modelled habitat suitability for harbour porpoises (Phocoena phocoena) across the North Sea–Baltic Sea transition zone over a 25-year period (1997–2004; 2005-2012; 2013-2022) during winter (Nov–Apr) and summer (May–Oct). Habitat suitability was estimated using the maximum entropy (MaxEnt) algorithm, based on satellite tag data of 111 individuals and a range of dynamic and static environmental conditions as predictor variables. Habitat suitability models and maps were developed by researchers of the Department of Ecoscience, Aarhus University to quantify potential spatiotemporal changes in the overlap between suitable habitats and an existing network of 62 MPAs dedicated to harbour porpoise protection. The habitat suitability rasters can inform effective design of forthcoming MPAs under the EU Biodiversity Strategy for 2030. habitat_suitability WCS NetCDF EPSG:4326 CRS:84 9.094689276840526 14.080289276840528 53.71659362409777 60.12646362409777 text/xml text/xml text/html probability 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. probability WCS NetCDF EPSG:4326 CRS:84 -16.05 9.05 44.95 66.05000000000001 text/xml text/xml url download_url imisdasid probability_occurrence_benthos_northsea 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. probability_occurrence WCS NetCDF EPSG:4326 CRS:84 -16.0 8.999999999999993 46.000000000000014 66.0 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text/xml text/xml url download_url imisdasid probability_occurrence_phyto_baltic 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. probability_occurrence WCS NetCDF EPSG:4326 CRS:84 8.999999999999996 37.000000000000036 51.999999999999964 68.00000000000003 text/xml text/xml download_url probability_occurrence_phyto_northsea 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. probability_occurrence WCS NetCDF EPSG:4326 CRS:84 -16.0 9.0 46.0 66.0 17651,17657,101195,106283,106285,106287,106303,106306,109462,109466,109470,109473,109475,109476,109477,109479,109485,109491,109500,109506,109515,109519,109528,109540,109541,109543,109545,109553,109564,109566,109567,109603,109604,109637,109876,109885,109889,109921,109955,110152,110208,110210,110217,110245,110257,110303,110316,115086,115088,115090,115106,118014,118028,118051,119077,134529,134533,148912,148917,148931,148940,148942,148944,148947,148953,148955,148956,148963,148967,148969,148985,148992,149003,149004,149018,149022,149023,149028,149033,149038,149039,149042,149045,149050,149054,149055,149059,149060,149065,149066,149069,149070,149073,149074,149077,149094,149102,149106,149109,149114,149115,149116,149118,149120,149121,149123,149126,149130,149131,149134,149135,149142,149150,149165,149167,149168,149181,149186,149208,149219,149224,149236,149241,149305,149306,149308,149309,149310,149314,149321,149322,149324,149328,149335,149342,149352,149629,149630,156505,156632,157081,157083,157240,157258,157438,160527,160530,160595,160599,160602,162810,162969,163030,163243,163261,163288,163728,163931,163932,179320,231788,231887,232069,232376,233024,233619,254445,291409,341501,345464,345484,369960,370366,376667,480248,494057,495363,601993,604918,605105,611858,699394,837453,837459,840626,840627,841259,841260,960230,961694,964216,964778,968792,971564,985165,985570,1310442,1322470,1322474,1346683 1,2,3,4 text/xml text/xml url download_url imisdasid seabirds_gridded_count The data product consists of a netCDF file containing the gridded field obtained by the interpolation of the observations of more than 400 sea bird species. The field is computed for 5 time periods: 1970-1979, 1980-1989, 1990-1999, 2000-2009 and 2010-2019. Along with the gridded field, an error field is also provided. It allows user to only consider the regions where the analysis can be trusted, i.e. where the error field is lower than a given threshold (0.25 for example). gridded_count WCS NetCDF EPSG:4326 CRS:84 -180.0 180.0 -90.0 90.0 text/xml text/xml download_url seabirds_gridded_count_error The data product consists of a netCDF file containing the gridded field obtained by the interpolation of the observations of more than 400 sea bird species. The field is computed for 5 time periods: 1970-1979, 1980-1989, 1990-1999, 2000-2009 and 2010-2019. Along with the gridded field, an error field is also provided. It allows user to only consider the regions where the analysis can be trusted, i.e. where the error field is lower than a given threshold (0.25 for example). gridded_count_error WCS NetCDF EPSG:4326 CRS:84 -55.25 21.25 13.75 72.25 text/xml text/xml download_url zooplankton_baltic_shannon 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 plankton, 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. As many biodiversity estimates are biased by sampling effort, caution must be taken when interpreting alpha diversity from microscopy counts. By rarefaction and evenness estimation, these biases can be reduced, but not ignored. This data product provides a spatial-temporal interpolation of zooplankton alpha diversity in the greater Baltic Sea area (including Skagerrak and Kattegat). Using observed Shannon diversity indices from environmental monitoring data, the product employs the DIVAnd (Data-Interpolating Variational Analysis in n-Dimensions) to create a three-dimensional interpolation across longitude, latitude, and seasonal time dimensions. The output includes interpolated diversity values, an associated error map, and metadata on the spatial-temporal grid resolution. It is tailored for ecological and oceanographic studies, aiding in the visualization and analysis of diversity trends over time and space. shannon WCS NetCDF EPSG:4326 CRS:84 9.396464646464647 30.103535353535353 53.44266055045872 66.05733944954129 2006-10-02T00:00:00.000Z/2022-10-02T00:00:00.000Z/PT1S text/xml text/xml download_url