The shapefile corresponds to areas where predicted bioregions were extrapolated for lack of benthic in-situ observations.

The ESA Sea State Climate Change Initiative (CCI) project has produced global multi-sensor time-series of along-track satellite altimeter significant wave height data (referred to as Level 4 (L4) data) with a particular focus for use in climate studies. This dataset contains the Version 4 Remote Sensing Significant Wave Height product, gridded over a global regular cylindrical projection (1°x1° resolution), averaging valid and good measurements from all available altimeters on a monthly basis (using the L2P products also available). These L4 products are meant for statistics and visualization. The altimeter data used in the Sea State CCI dataset v3 come from multiple satellite missions spanning from 1992 to 2023 (ERS-1, ERS-2,TOPEX-Poseidon, Envisat, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL, Sentinel-3 A, Sentinel-3 B, Sentinel-6 A), therefore spanning over a wider time range than the previous version 3. The missions already retracked (with WHALES) in version 3 were not reprocessed, but extended when applicable. Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).

The ICES Working Group on Fisheries Benthic Impact and Trade-offs (WGFBIT) has developed an assessment framework based on the life history trait longevity, to evaluate the benthic impact of fisheries at the regional scale. In order to apply this framework to the Mediterranean sea, several Mediterranean longevity databases were merged together with existing North-East Atlantic ones to develop a common database. Longevity was fuzzy coded into four longevity classes: <1, 1-3, 3-10 and >10 years. Both benthic mega and macrofauna organisms are included in this dataset. Further details about both the purpose and the methodology may be found in ICES (2022) and Cuyvers et al. (2023). The result of the final dataset merging is one dataset containing the fuzzy coded average longevity (and standard deviation) for 2264 taxa and for each, the number of databases used. 

Moving 6-year analysis and visualization of Water body dissolved oxygen concentration in the North Sea. Four seasons (December-February, March-May, June-August, September-November). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.12. results were subjected to the minfield option in DIVAnd to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. The depth dimension allows visualizing the gridded field at various depths.

Moving 6-year analysis of Water body silicate in the Mediterranean Sea for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centered average of the season. 6-years periods span from 1970-1975 until 2018-2023. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.12, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is dx=dy=0.125 degrees (around 13.5km and 10.9km accordingly). The vertical resolution is 25 depth levels: [0.,5.,10.,20.,30.,50.,75.,100.,125.,150.,200.,250.,300.,400.,500.,600.,700.,800.,900.,1000.,1100.,1200.,1300.,1400.,1500.]. The horizontal correlation length is 200km. The vertical correlation length (in meters) was set twices the vertical resolution: [10.,10.,20.,20.,40.,50.,50.,50.,50.,100.,100.,100.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.]. Duplicates check was performed using the following criteria for space and time: dlon=0.001deg., dlat=0.001deg., ddepth=1m, dtime=1hour, dvalue=0.1. The error variance (epsilon2) was set equal to 1 for profiles and 10 for time series to reduce the influence of close data near the coasts. An anamorphosis transformation was applied to the data (function DIVAnd.Anam.loglin) to avoid unrealistic negative values: threshold value=200. A background analysis field was used for all years (1970-2023) with correlation length equal to 600km and error variance (epsilon2) equal to 20. Quality control of the observations was applied using the interpolated field (QCMETHOD=3). Residuals (differences between the observations and the analysis (interpolated linearly to the location of the observations) were calculated. Observations with residuals outside the minimum and maximum values of the 99% quantile were discarded from the analysis. Originators of Italian data sets-List of contributors: - Brunetti Fabio (OGS) - Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306 - Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987 - Cataletto Bruno (OGS) - Cinzia Comici Cinzia (OGS) - Civitarese Giuseppe (OGS) - DeVittor Cinzia (OGS) - Giani Michele (OGS) - Kovacevic Vedrana (OGS) - Mosetti Renzo (OGS) - Solidoro C.,Beran A.,Cataletto B.,Celussi M.,Cibic T.,Comici C.,Del Negro P.,De Vittor C.,Minocci M.,Monti M.,Fabbro C.,Falconi C.,Franzo A.,Libralato S.,Lipizer M.,Negussanti J.S.,Russel H.,Valli G., doi:10.6092/e5518899-b914-43b0-8139-023718aa63f5 - Celio Massimo (ARPA FVG) - Malaguti Antonella (ENEA) - Fonda Umani Serena (UNITS) - Bignami Francesco (ISAC/CNR) - Boldrini Alfredo (ISMAR/CNR) - Marini Mauro (ISMAR/CNR) - Miserocchi Stefano (ISMAR/CNR) - Zaccone Renata (IAMC/CNR) - Lavezza, R., Dubroca, L. F. C., Ludicone, D., Kress, N., Herut, B., Civitarese, G., Cruzado, A., Lefèvre, D.,Souvermezoglou, E., Yilmaz, A., Tugrul, S., and Ribera d'Alcala, M.: Compilation of quality controlled nutrient profiles from the Mediterranean Sea, doi:10.1594/PANGAEA.771907, 2011.

This visualization product displays the spatial distribution of fishing related items density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during bottom trawl surveys. In cases where the wingspread and/or number of items were/was unknown, it was not possible to use the data because these fields are needed to calculate the density. Data collected before 2011 are concerned by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using the following formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area was calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities were calculated on each trawl and year using the following computation: Density of fishing related items (number of items per km²) = ∑Number of fishing related items / Swept area (km²) Then a grid with 30km x 30km cells was used to calculate the weighted mean of densities in each cell from the formula : Weighted mean (number of items per km²) = ∑ (Distance (km) * Density (number of items per km²)) / ∑ Distance (km) Percentiles 50, 75, 95 & 99 were calculated taking into account data for all years. More information on data processing and calculation are detailed in the attached methodology document. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area. This work is based on the work presented in the following scientific article: O. Gerigny, M. Brun, M.C. Fabri, C. Tomasino, M. Le Moigne, A. Jadaud, F. Galgani, Seafloor litter from the continental shelf and canyons in French Mediterranean Water: Distribution, typologies and trends, Marine Pollution Bulletin, Volume 146, 2019, Pages 653-666, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2019.07.030.

Worldwide, shellfish aquaculture and fisheries in coastal ecosystems represent crucial activities for human feeding. But these biological productions are under the pressure of climate variability and global change. Anticipating the biological processes affected by climate hazards remains a vital objective for species conservation strategies and human activities that rely on. Within marine species, filter feeders like oysters are real key species in coastal ecosystems due to their economic and societal value (fishing and aquaculture) but also due to their ecological importance. Indeed oysters populations in good health play the role of ecosystem engineers that can give many ecosystem services at several scales: building reef habitats that contribute to biodiversity, benthic-pelagic coupling and phytoplankton bloom control through water filtration, living shorelines against coastal erosion… The Pacific oyster, Crassostrea gigas (Thunberg, 1793), which is currently widespread worldwide, was introduced into the Atlantic European coasts at the end of the 19th century for shellfish culture purposes and becomes the main marine species farmed in France (around 100 000 tons) despite severe mortalities crisis. But in the same time and because of warming, natural oysters beds has spread significantly along the French coast and are supposed to have reach approximately 500 000 tons. In that context, Pacific oyster populations (natural and cultivated) in France are the subjects of many scientific projects. Among them, a specific long-term biological monitoring focuses on the reproduction of these populations at a national scale: the VELYGER national program. With more than 8 years of weekly data at many stations in France, this field-monitoring program offers a valuable dataset for studying processes underpinning reproduction cycle of this key-species in relation to environmental parameters, water quality and climate change.   Database content: Larval concentration (number of individuals per 1.5 m3) monitored, since 2008, at several stations in six bays of the French coast (from south to north): Thau Lagoon and bays of Arcachon, Marennes Oléron, Bourgneuf, Vilaine and Brest (see map below).   Methods used to monitor larval concentration: An important volume of seawater (1.5 m3) is pumped twice a week throughout the spawning season (june-september), at one meter below the surface at high tide (+/- 2h) in several sites within each VELYGER ecosystem. Water is filtered trough plankton net fitted with 40 µm mesh. After a proper rinsing of the net, the retained material is transferred into a polyethylene bottle (1 liter) and fixed with alcohol. At laboratory, sample is then gently filtered and rinse again and transferred into eprouvette. Two sub-samples of 1 mL are then taken using a pipette and examined on a graticule slide for microscope. The microscopic examination is made with a conventional binocular optical microscope with micrometer stage at a magnification of 10 X (or above). During the counting, a special care is necessary as larvae of other bivalves are also collected and confusion is possible. Larvae of C. gigas are also classified into four stage of development: - Stage I = D-shaped straight hinge larvae (shell length <105 µm) - Stage II = Early umbo evolved larvae (shell length between 105 and 150 µm) - Stage III = Medium umbo larvae (shell length between 150 and 235 µm) - Stage IV*= Large umbo eyed pediveliger larvae (shell length > 235 µm) * Larvae that are very closed to settle are sometimes identified into a separated 5th stage, but generally this stage is included in stage IV.   Illustrations: Location of the different Velyger sites along the French coast. From south to north: Thau Lagoon and bays of Arcachon, Marennes Oléron, Bourgneuf, Vilaine and Brest.   Legend: Pacific Oyster Larvae (left side) and Natural oyster bed (right side). Photos : © S. Pouvreau/Ifremer

This visualization product displays the type of litter in percent per net per year from research and monitoring protocols. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10). A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter. European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization. A filter is applied to represent only micro-litter sampled according to research and monitoring protocols as MSFD monitoring. To calculate percentages for each type, formula applied is: Type (%) = (∑number of particles of each type)*100 / (∑number of particles of all type) When the number of micro-litters was not filled or was equal zero, it was not possible to calculate the percentage. Standard vocabularies for micro-litter types are taken from Seadatanet's H01 library (https://vocab.seadatanet.org/v_bodc_vocab_v2/search.asp?lib=H01 ). Some morphological types of micro-litters may have been sampled but were not defined by the protocole applied during the survey. They are represented as « undefined micro-litter items ». Warnings: - the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area. - since 03/07/2023, the preferred label « Undefined micro-litter items » has been integrated into the H01 library whereas the labels « microplastic items », « non-plastic man-made micro-particles (e.g. glass, metal, tar) » and «non-plastic filaments (natural fibres, rubber) » have been deprecated. When defined, the material or polymer type can be checked directly in the source data.

This dataset comprises energy density and proximal composition (water, ash, lipid and protein contents) for anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) from the Bay of Biscay, the English Channel and the southern North Sea between 2014 and 2017. Fish were sampled throughout various seasons thanks to the PELGAS (May, Bay of Biscay), EVHOE (October-November, Bay of Biscay), CGFS and CAMANOC (September-October, English Channel) surveys conducted by IFREMER on the RV “Thalassa”, to the JUVENA (September-October, Bay of Biscay) survey conducted by AZTI, and from commercial landings within the European Data Collection Framework (DCF) during the CAPTAIN project (France Filière Pêche). During the surveys, pelagic (PELGAS and JUVENA) or demersal (EVHOE and CGFS) trawl hauls are undertaken to identify species and measure individual fish traits. Professional sampling was performed from pelagic trawl or purse-seine catches. From the various surveys, a sub-sampling of the trawls was performed to cover as much as possible the spatial extent of the surveys along the french coast. From the various selected trawls, a sub-sampling of 5 fish per size class (when possible) was performed to cover the size range of each species, based on the following size classes : sardine (1 : <15 cm ; 2 : 15-20 cm ; 3 : >20cm), anchovy (1 : <10cm ; 2 : 10-14 cm ; 3 : >14cm). Each fish was individually measured to the nearest tenth of a centimeter and weighted to the nearest tenth of a gram. These measurements were taken either at sea or later in the laboratory. The collected fish were frozen individually at -20°C before laboratory processing. In the laboratory, maturity stages were determined following ICES guidelines (ICES, 2008) based on macroscopic gonads observation and using a six-stage key as follows: stages 1 & 2 indicate immature and developing individuals, stages 3–5 indicate three steps of increasing gonad development and the spawning period (stage 3: partial pre-spawning; stage 4: spawning (hydrated); stage 5: partial post-spawning), and stage 6 features the final post-spawning period. Fish characterised by maturity stages 3, 4 or 5 were considered as being in an active reproductive period as opposed to fish in stages 1, 2 or 6. Fish were then ground and freeze-dried during at least 48 hours. Water content of the entire fish was determined from dry mass and wet mass ratio. Then, fish were ground again to obtain fine homogeneous dry powder for subsequent analysis. Energy density measurements were performed following the protocols of Gatti et al. (2018). Two subsamples of fish powder were placed in an adiabatic bomb calorimeter (IKA C-4000 adiabatic bomb calorimeter, IKA-WerkeGmbh & co. KG) for energy measurements. The energy density (ED, kJ.g-1 dry mass) was determined by measuring the heat released through the combustion of a small subsample, approximately 200 mg. If the coefficient of variation between the two measurements exceeded 3%, a third measurement was made. Finally, ED subsamples measurements were averaged and assigned to each individual fish. Energy density analyses were conducted on 503 individuals for anchovy and 976 individuals for sardine. Ash content was determined gravimetrically by combusting dried tissue in a muffle furnace at 550°C for six hours. Lipids and proteins were analysed by a certified laboratory (Labocea, Plouzané, France). Protein content was estimated using the Kjeldahl method. It consists in first determining the quantity of nitrogen contained in the sample, and to convert it into protein content using a conversion factor (6.25 here). Lipid content was determined by hydrolysis, using petroleum ether as an organic solvent. Carbohydrates represent less than 1% of fish mass and were thus neglected. Protein, lipid and ash content did not exactly sum to 1 in DW (anchovy: mean = 0.91, sd = 0.04; sardine: mean = 0.90, sd = 0.04). This discrepancy may arise from residual water, measurement uncertainties, or to a lesser extent the exclusion of carbohydrates. Body component contents have been normalised by dividing each component by the sum of lipids, proteins and ash content, to sum to one, enabling comparisons between fishes, assuming proportional errors across the components. A total of 116 and 104 proximate composition analyses were performed for anchovy and sardine, respectively.

This visualization product displays fishing related items density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during bottom trawl surveys. In cases where the wingspread and/or the number of items were/was unknown, it was not possible to use the data because these fields are needed to calculate the density. Data collected before 2011 are concerned by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using the following formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area was calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities were calculated on each trawl using the following computation: Density of fishing related items (number of items per km²) = ∑Number of fishing related items / Swept area (km²) Percentiles 50, 75, 95 & 99 were calculated taking into account data for all years. The list of selected items for this product is attached to this metadata. Information on data processing and calculation is detailed in the attached methodology document. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.