This map presents all layers corresponding to "Beverage serving activities" activities in the Atlantic area. For more information about this NACE code : https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=DSP_NOM_DTL_VIEW&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=18514154&IntKey=18514184&StrLayoutCode=HIERARCHIC&IntCurrentPage=1 Indicators collected are : Number of persons employed and number of employees in full time equivalent units per NUTS 3 unit of the Atlantic Area Number of establishments per NUTS3 unit of the Atlantic Area

This map presents all layers corresponding to "Support activities for other mining and quarrying" activities in the Atlantic area. For more information about this NACE code : https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=DSP_NOM_DTL_VIEW&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=18496274&IntKey=18496304&StrLayoutCode=HIERARCHIC&IntCurrentPage=1

'''DEFINITION''' We have derived an annual eutrophication and eutrophication indicator map for the North Atlantic Ocean using satellite-derived chlorophyll concentration. Using the satellite-derived chlorophyll products distributed in the regional North Atlantic CMEMS MY Ocean Colour dataset (OC- CCI), we derived P90 and P10 daily climatologies. The time period selected for the climatology was 1998-2017. For a given pixel, P90 and P10 were defined as dynamic thresholds such as 90% of the 1998-2017 chlorophyll values for that pixel were below the P90 value, and 10% of the chlorophyll values were below the P10 value. To minimise the effect of gaps in the data in the computation of these P90 and P10 climatological values, we imposed a threshold of 25% valid data for the daily climatology. For the 20-year 1998-2017 climatology this means that, for a given pixel and day of the year, at least 5 years must contain valid data for the resulting climatological value to be considered significant. Pixels where the minimum data requirements were met were not considered in further calculations. We compared every valid daily observation over 2021 with the corresponding daily climatology on a pixel-by-pixel basis, to determine if values were above the P90 threshold, below the P10 threshold or within the [P10, P90] range. Values above the P90 threshold or below the P10 were flagged as anomalous. The number of anomalous and total valid observations were stored during this process. We then calculated the percentage of valid anomalous observations (above/below the P90/P10 thresholds) for each pixel, to create percentile anomaly maps in terms of % days per year. Finally, we derived an annual indicator map for eutrophication levels: if 25% of the valid observations for a given pixel and year were above the P90 threshold, the pixel was flagged as eutrophic. Similarly, if 25% of the observations for a given pixel were below the P10 threshold, the pixel was flagged as oligotrophic. '''CONTEXT''' Eutrophication is the process by which an excess of nutrients – mainly phosphorus and nitrogen – in a water body leads to increased growth of plant material in an aquatic body. Anthropogenic activities, such as farming, agriculture, aquaculture and industry, are the main source of nutrient input in problem areas (Jickells, 1998; Schindler, 2006; Galloway et al., 2008). Eutrophication is an issue particularly in coastal regions and areas with restricted water flow, such as lakes and rivers (Howarth and Marino, 2006; Smith, 2003). The impact of eutrophication on aquatic ecosystems is well known: nutrient availability boosts plant growth – particularly algal blooms – resulting in a decrease in water quality (Anderson et al., 2002; Howarth et al.; 2000). This can, in turn, cause death by hypoxia of aquatic organisms (Breitburg et al., 2018), ultimately driving changes in community composition (Van Meerssche et al., 2019). Eutrophication has also been linked to changes in the pH (Cai et al., 2011, Wallace et al. 2014) and depletion of inorganic carbon in the aquatic environment (Balmer and Downing, 2011). Oligotrophication is the opposite of eutrophication, where reduction in some limiting resource leads to a decrease in photosynthesis by aquatic plants, reducing the capacity of the ecosystem to sustain the higher organisms in it. Eutrophication is one of the more long-lasting water quality problems in Europe (OSPAR ICG-EUT, 2017), and is on the forefront of most European Directives on water-protection. Efforts to reduce anthropogenically-induced pollution resulted in the implementation of the Water Framework Directive (WFD) in 2000. '''CMEMS KEY FINDINGS''' The coastal and shelf waters, especially between 30 and 400N that showed active oligotrophication flags for 2020 have reduced in 2021 and a reversal to eutrophic flags can be seen in places. Again, the eutrophication index is positive only for a small number of coastal locations just north of 40oN in 2021, however south of 40oN there has been a significant increase in eutrophic flags, particularly around the Azores. In general, the 2021 indicator map showed an increase in oligotrophic areas in the Northern Atlantic and an increase in eutrophic areas in the Southern Atlantic. The Third Integrated Report on the Eutrophication Status of the OSPAR Maritime Area (OSPAR ICG-EUT, 2017) reported an improvement from 2008 to 2017 in eutrophication status across offshore and outer coastal waters of the Greater North Sea, with a decrease in the size of coastal problem areas in Denmark, France, Germany, Ireland, Norway and the United Kingdom. '''DOI (product):''' https://doi.org/10.48670/moi-00195

In 1967, E.Postel, researcher at the OSTPM (Scientific and Technical Office for Maritime Fisheries) set up a data collection system on albacore catches by French fleets. JC Dao and FX Bard continued this work within CNEXO (National Center for the Exploitation of Oceans) and then at IFREMER from 1984. This information was transmitted by fishing professionals via logbooks, on the basis of volunteering (Dao, 1971, Bard, 1977). In 1982, the European Community put in place a mandatory system of declarations of fishing effort and catches via logbooks (EC Regulation No. 2057 in Sanchez and Santurtun, 2013). As a result, the two systems persisted between 1982 and 1986, with European logbooks gradually supplanting logbooks

This dataset was built to feed a basin-wide spatial conservation planning exercise, targeting the deep sea of the North Atlantic, in the framework of the ATLAS H2020 project. This approach aimed to inform Marine Spatial Planning and conservation initiatives for the deep sea of the North Atlantic, by identifying conservation priority areas for the Vulnerable Marine Ecosystems (VMEs) and deep fish species and discussing the efficiency of the current spatial management context relatively to conservation stakes. This publication provides (1) the links to spatial datasets used as an input, (2) the R scripts used to run the final conservation scenarios together with associated table of targets and connectivity matrix, that can be run on the input data, and (3) the outputs of the final scenarios constructed and computed for ATLAS. Produced by IFREMER.

The Pélagiques Gascogne (PELGAS, Doray et al., 2000) integrated survey aims at assessing the biomass of small pelagic fish and monitoring and studying the dynamics and diversity of the Bay of Biscay pelagic ecosystem in springtime. PELGAS has been conducted within the EU Common Fisheries Policy Data Collection Framework and Ifremer’s Fisheries Information System. Details on survey protocols and data processing methodologies can be found in Doray et al., (2014, 2018a). This dataset comprises the biomass (in metric tons) and abundance (in thousands of individuals) of small pelagic fish estimated during the PELGAS survey in the Bay of Biscay in springtime. The dataset also includes the estimation coefficient of variation, derived based on the hydroacoustic methodology described in Doray et al. (2010), and the survey area. Those estimates have been validated by the ICES WGACEGG group and provided to the ICES WGHANSA group for stock assessment purposes. Data have been used in Doray et al., 2018b.

In the framework of the ANR AMORAD project, the METEOR cruises (Grasso, 2017) aimed at deploying the ‘Gironde Estuary Mouth MEasurement Stations’ (GEMMES) to measure hydrodynamics and sediment dynamics at the mouth of the Gironde Estuary and on the West Gironde Mud Patch (SW France, Bay of Biscay). Measurements were carried out between November 2016 and December 2017: i) from a buoy station around 20-m water depth (GEMMES-20), collecting sub-surface data of temperature, salinity and turbidity; and ii) a benthic station around 40-m water depth (GEMMES-40), collecting data of current velocity and turbidity. Bottom and surface water samples were regularly collected to calibrate turbidity measurements to SPM concentrations.

Ifremer conducts numerous fisheries surveys dedicated to benthic and demersal populations (commercial / non-commercial fishes and invertebrates). For several years, in application of the ecosystem approach, all benthic invertebrate fauna collected in fishing gear has been systematically monitored: megabenthic invertebrates captured have been sorted, identified, counted and weighted. All these surveys are based on fixed or random stratified sampling strategy with varying intensity depending on the covered survey area. These data are stored, in historical access-based databases or for the most recent years in the centralised “Harmonie” database held in the Ifremer Fishery Information Systeme (SIH). The species nomenclature used was standardized using WoRMS database. Taxa caught at least once a year are listed for each monitoring area on the basis of already available data series. In order to facilitate the identification of individuals sampled on board vessels and to improve the training of onboard scientists, the present work aims to define the minimum level of identification for each of them. The analysis identifies taxa that appears recurrently on available historical series or gathers them on less precise taxonomic levels if this is not the case, which may indicate potential identification difficulties. The following procedure was used: all taxa expressed at the species level were first aggregated at genus level if they occurred less 90% of the years over the available time series. For MEDITS, EPIBENGOL and ORHAGO, the occurrence threshold was set to 70% and to only 50% for NOURMONT because the datasets were less than 10 years long. Then to be kept at that taxonomic level, a given genus had to be observed over 90% of the time (for example over at least 9 years if the dataset contains 10 years). Otherwise it was iteratively regrouped into a higher taxonomic level (family, order, class, division) following the same criteria (Foveau et al, 2017). For instance, for the NOURSEINE survey, this resulted into the aggregation of the 103 origin taxa into 35 taxonomic groups. The name of the final taxon after data processing represents the minimum level of identification defined by the analysis. However, these results are very theoretical. This is why they were sent to scientists who embark regularly in order to refine the level of taxonomic identification with field experience. The first dataset is composed of 8 tables relevant to the different vessel surveys. The first column of each table represents the permanent code of the taxon in the Ifremer taxonomic referential, the second the systematic number and the third the species abbreviated code. The other columns are the different taxonomic levels of the taxon. The minimum level of identification at sea defined by the data processing appears in blue. The level determined by feedback of scientist’s field experience, which is the one to use at sea, appears in green. The second dataset summaries the results detailed in the first table and indicates directly for each taxon identified to far, the minimum level of identification required for the benthic invertebrates by-catch of each fisheries surveys studied.

This visualization product displays marine macro-litter (> 2.5cm) material categories percentage per beach per year from non-MSFD monitoring surveys, research & cleaning operations. 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 processing 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; - Exclusion of surveys without associated length; - Some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata); - Exclusion of the "feaces" category: it concerns more exactly the items of dog excrements in bags of the OSPAR (item code: 121) and ITA (item code: IT59) reference lists; - 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 items (normalized by 100 m) = Number of litter per items x (100 / survey length) Then, this normalized number of items is summed to obtain the total normalized number of litter for each survey. To calculate percentages for each material category, formula applied is: Material (%) = (∑number of items (normalized at 100 m) of each material category)*100 / (∑number of items (normalized at 100 m) of all categories) The material categories differ between reference lists (OSPAR, TSG_ML, UNEP, UNEP_MARLIN). In order to apply a common procedure for all the surveys, the material categories have been harmonized. More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This dataset is an aggregation of all availale in situ data from Coriolis and Copernicus in situ data centres, observed in the French DCSMM area. It contains 5167 NetCDF CF files from 1903 to 2017. Each file contains the observations of a specific platform (e.g. vessel, mooring site, sea level station). Observed parameters are temperature, salinity, pressure, oxygen, nitrate, chlorophyll (and other bio-geo-chemicals), current, wave, sea level, river flow.