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

This visualization product displays the cigarette related items abundance of marine macro-litter (> 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations without UNEP-MARLIN data. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter 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 and reference lists used on a European scale. Preliminary processings were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of surveys from non-MSFD monitoring, cleaning and research operations; - Exclusion of beaches without coordinates; - Selection of cigarette related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata); - Exclusion of surveys without associated length; - Exclusion of surveys referring to the UNEP-MARLIN list: the UNEP-MARLIN protocol differs from the other types of monitoring in that cigarette butts are surveyed in a 10m square. To avoid comparing abundances from very different protocols, the choice has been made to distinguish in two maps the cigarette related items results associated with the UNEP-MARLIN list from the others; - Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula: Number of cigarette related items of the survey (normalized by 100 m) = Number of cigarette related items of the survey x (100 / survey length) Then, this normalized number of cigarette related items is summed to obtain the total normalized number of cigarette related items for each survey. Finally, the median abundance of cigarette related items for each beach and year is calculated from these normalized abundances of cigarette related items per survey. Percentiles 50, 75, 95 & 99 have been calculated taking into account cigarette related items from other sources data (excluding UNEP-MARLIN protocol) for all years. More information is available in the attached documents. 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.

'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density variations, which are subtracted from a reference climatology to obtain the fluctuations from an average field. The products used include three global reanalyses: GLORYS, C-GLORS, ORAS5 (GLOBAL_MULTIYEAR_PHY_ENS_001_031) and two in situ based reprocessed products: CORA5.2 (INSITU_GLO_PHY_TS_OA_MY_013_052) , ARMOR-3D (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012). The regional thermosteric sea level values are then averaged from 60°S-60°N aiming to monitor interannual to long term global sea level variations caused by temperature driven ocean volume changes through thermal expansion as expressed in meters (m). '''CONTEXT''' Most of the interannual variability and trends in regional sea level is caused by changes in steric sea level. At mid and low latitudes, the steric sea level signal is essentially due to temperature changes, i.e. the thermosteric effect (Stammer et al., 2013, Meyssignac et al., 2016). Salinity changes play only a local role. Regional trends of thermosteric sea level can be significantly larger compared to their globally averaged versions (Storto et al., 2018). Except for shallow shelf sea and high latitudes (> 60° latitude), regional thermosteric sea level variations are mostly related to ocean circulation changes, in particular in the tropics where the sea level variations and trends are the most intense over the last two decades. '''CMEMS KEY FINDINGS''' Significant (i.e. when the signal exceeds the noise) regional trends for the period 2005-2023 from the Copernicus Marine Service multi-ensemble approach show a thermosteric sea level rise at rates ranging from the global mean average up to more than 8 mm/year. There are specific regions where a negative trend is observed above noise at rates up to about -5 mm/year such as in the subpolar North Atlantic, or the western tropical Pacific. These areas are characterized by strong year-to-year variability (Dubois et al., 2018; Capotondi et al., 2020). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00241

EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification and contaminants. The chemicals chosen EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity and contaminants. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on potential hazardous substances, despite the fact that some data might not be related to pollution (e.g. collected by deep corer). Temperature, salinity and additional parameters are included when available. It covers the Mediterranean Sea. Data were harmonised and validated by the ‘Hellenic Centre for Marine Research, Hellenic National Oceanographic Data Centre (HCMR/HNODC)’ in Greece. The dataset contains water, sediment and biota profiles and timeseries. The temporal coverage is 1974–2022 for water measurements, 1971–2023 for sediment measurements and 1979-2023 for biota measurements. Regional datasets concerning contaminants are automatically harvested and the resulting collections are harmonised and validated using ODV Software and following a common methodology for all sea regions ( https://doi.org/10.6092/8b52e8d7-dc92-4305-9337-7634a5cae3f4 ). Parameter names are based on P01 vocabulary, which relates to BODC Parameter Usage Vocabulary and is available at: https://vocab.nerc.ac.uk/search_nvs/P01/ . The harmonised dataset can be downloaded as as an ODV spreadsheet, which is composed of a metadata header followed by tab separated values. This spreadsheet can be imported into ODV Software for visualisation (more information can be found at: https://www.seadatanet.org/Software/ODV ). In addition, the same dataset is offered also as a txt file in a long/vertical format, in which each P01 measurement is a record line. Additionally, there are a series of columns that split P01 terms into subcomponents (substance, CAS number, matrix...).This transposed format is more adapted to worksheet applications (e.g. LibreOffice Calc).

This dataset contains the dynamical outputs of a global ocean simulation coupling dynamics and biogeochemistry at ¼° over the year 2019. The simulation has been performed using the coupled circulation/ecosystem model NEMO/PISCES (https://www.nemo-ocean.eu/), which is here enhanced to perform an ensemble simulation with explicit simulation of modeling uncertainties in the physics and in the biogeochemistry. This dataset is one of the 40 members of the ensemble simulation. This study was part of the Horizon Europe project SEAMLESS (https://seamlessproject.org/Home.html), with the general objective of improving the analysis and forecast of ecosystem indicators.   See Popov et al. (https://os.copernicus.org/articles/20/155/2024/) for more details on the study.

Data were collected from the regional program LOUPE (Observation of the habitat and associated communities in the context of the fisheries of the Capbreton Canyon). It consisted in the observations of two métiers practiced around the canyon. The observations were carried out between July 2011 and April 2013 on coastal boats. Observations and interviews were made on board commercial vessels. The longlines used in the hake fishery are semi-pelagic and are deployed on the edge of the Capbreton Canyon. It is an emblematic and major métier benefiting from a particular regulation as they take advantage of a prohibition of net and trawl fishing on their fishing grounds. Between 8 and 14 costal boats practice this métier during the year and the fleet characteristics are homogeneous. Boats lay between 1,200 and 1,800 hooks per day, baited with frozen pilchard (Sardina pilchardus). Two or three men are on board these vessels. Fishing is mostly practiced in spring and summer but a small number of vessels work all year. Generally, trips last between ten and twelve hours; longline is set before sunrise and retrieved three or four hours later. Hake is the main targeted species; other targets are pollack (Pollachius pollachius), red sea bream (Pagellus bogaraveo) and conger (Conger conger). Netting is a major métier in terms of vessels involved and the number of trips. Crew composition varies and depends on boat length (from one to four men on average). This métier is practiced by 30 to 35 boats all year round, but fleet characteristics are less homogeneous than in the case of longliners . The strategy of these netters operating in the coastal area is based on the use of several types of nets (gillnets and trammel nets) targeting several species, often sold directly to consumers on the docks. Gillnets, consisting of a single mesh, target hake, sea bass and sea bream species (Diplodus spp, Sparus aurata, Litognathus mormyrus), while the trammel nets (three meshes) are used to capture benthic fish, such as common sole, monkfish (Lophius spp), turbot and brill (Scophthalmus rhombus). Generally, trips last less than twelve hours for coastal netters (less than 15 m), which predominate in the sector, and a few days for large netters. On average, the coastal vessels set 6000 to 8000 m. nets daily.

EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity, contaminants and marine litter. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data profiles on eutrophication and acidity, and covers: the Artic Ocean, the North East Atlantic, the Greater North Sea and Celtic Seas, the Baltic Sea, the Mediterranean Sea and the Black Sea. ITS-90 water temperature and water body salinity variables have also been included ('as are') to complete the eutrophication and acidity data. If you use these variables for calculations, please refer to SeaDataNet for the quality flags: https://www.seadatanet.org/Products/Aggregated-datasets . This European dataset is the result of the aggregation of the regional datasets concerning eutrophication and acidity present in EMODnet Chemistry. The regional datasets are automatically harvested, and the resulting collections are aggregated and quality controlled using ODV Software and following a common methodology for all sea regions ( https://doi.org/10.13120/8xm0-5m67 ). Parameter names are based on P35 vocabulary, which relates to EMODnet Chemistry aggregated parameter names and is available at: https://vocab.nerc.ac.uk/search_nvs/P35/. This process were regionally performed by: 'Institute of Marine Research - Norwegian Marine Data Centre (NMD)' (Norway), 'IFREMER / IDM / SISMER - Scientific Information Systems for the SEA' (France), 'Aarhus University, Department of Bioscience, Marine Ecology Roskilde' (Denmark), 'Swedish Meteorological and Hydrological Institute (SMHI)' (Sweden), 'Hellenic Centre for Marine Research, Hellenic National Oceanographic Data Centre (HCMR/HNODC)' (Greece) and 'National Institute for Marine Research and Development 'Grigore Antipa' (Romania). When not present in original data, water body nitrate plus nitrite was calculated by summing all nitrate and nitrite parameters. The same procedure was applied for water body dissolved inorganic nitrogen (DIN), which was calculated by summing all nitrate, nitrite, and ammonium parameters. Concentrations per unit mass were converted to a unit volume using a constant density of 1.025 kg/L. The aggregated dataset can be downloaded as an ODV collection.

Particularly suited to the purpose of measuring the sensitivity of benthic communities to trawling, a trawl disturbance indicator (de Juan and Demestre, 2012, de Juan et al. 2009) was proposed based on benthic species life history traits to evaluate the sensibility of mega- and epifaunal community to fishing pressure known to have a physical impact on the seafloor (such as dredging and bottom trawling). The selected biological traits were chosen as they determine vulnerability to trawling: mobility, fragility, position on substrata, average size and feeding mode that can easily be related to the fragility, recoverability and vulnerability ecological concepts. Life history traits of species have been defined from the BIOTIC database (MARLIN, 2014) and from information given by Le Pape et al. (2007), Brindamour et al. (2009) and Garcia (2010). For missing life history traits, additional information from literature has been considered. The five categories retained are life history functional traits that were selected based on the knowledge of the response of benthic taxa to trawling disturbance (de Juan and Demestre, 2012). They reflect respectively the possibility to avoid direct gear impact, to benefit from trawling for feeding, to escape gear, to get caught by the net and to resist trawling/dredging action, each of these characteristics being either advantageous or sensitive to trawling. Then, to allow quantitative analysis, a score was assigned to each category: from low vulnerability (0) to high vulnerability (3). The five categories scores were then summed for each taxon (the highly vulnerable taxon could reach the maximum score is 15) and this value may be considered as a species index of sensitivity to trawling disturbance. The scores of 812 taxa commonly found in bottom trawl by-catch in the southern North Sea, English Channel and north-western Mediterranean were described.

This product contains observations and gridded files from two up-to-date carbon and biogeochemistry community data products: Surface Ocean Carbon ATlas SOCATv2023 and GLobal Ocean Data Analysis Project GLODAPv2.2023. The SOCATv2023-OBS dataset contains >25 million observations of fugacity of CO2 of the surface global ocean from 1957 to early 2023. The quality control procedures are described in Bakker et al. (2016). These observations form the basis of the gridded products included in SOCATv2023-GRIDDED: monthly, yearly and decadal averages of fCO2 over a 1x1 degree grid over the global ocean, and a 0.25x0.25 degree, monthly average for the coastal ocean. GLODAPv2.2023-OBS contains >1 million observations from individual seawater samples of temperature, salinity, oxygen, nutrients, dissolved inorganic carbon, total alkalinity and pH from 1972 to 2021. These data were subjected to an extensive quality control and bias correction described in Olsen et al. (2020). GLODAPv2-GRIDDED contains global climatologies for temperature, salinity, oxygen, nitrate, phosphate, silicate, dissolved inorganic carbon, total alkalinity and pH over a 1x1 degree horizontal grid and 33 standard depths using the observations from the previous major iteration of GLODAP, GLODAPv2. SOCAT and GLODAP are based on community, largely volunteer efforts, and the data providers will appreciate that those who use the data cite the corresponding articles (see References below) in order to support future sustainability of the data products.

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.