The data sets presented here result from the long-term monitoring of individual growth patterns in anchovy and sardine in the Bay of Biscay, from 2000 to 2018. They derived from the PELGAS survey series (Doray et al., 2018), which monitors annually the Bay of Biscay pelagic ecosystem since 2000. The survey is performed in May during the peak spawning of anchovy and main spawning of sardine. Among the many data collected, anchovy and sardine populations are assessed by combining acoustic records with pelagic trawl hauls catches and ICES survey protocoles are used, as detailed in Doray et al. (2021). Briefly, fish acoustic backscatter are recorded along survey transect lines and pelagic trawl hauls undertaken opportunistically to identify echotraces to species and collect fish samples for acquiring biometric data. At each trawl haul and for each species, a random subsample of individuals is taken to establish the species’ length distributions. For anchovy and sardine, this subsample is further subsampled, spanning the whole length range, to take individual fish measurements. These amount to extracting otoliths and measuring individuals’ age, length, weight, sexual maturity and other parameters. Individual measurements are taken on fourty individuals of anchovy and sardine when the species are present in the catch. For each individual fish, the two otolith sagittae are extracted on board, mounted in leukit for age reading on board when permitting and/or on land in the laboratory. Growth patterns in the otoliths were analysed on land with a binocular stereomicroscope under reflected natural light. For anchovy, otoliths’ growth was measured for all individuals in all the hauls. For sardine, trawl hauls were selected and all individual otoliths were measured in each selected haul. The selection was made using the geographical stratification defined in Petitgas et al. (2018) based on the ecosystem spatial structure. An average of two to three hauls in each of the four strata were selected per year. The otoliths mounted in leukit were imaged and growth-at-age in the otoliths was measured with the software TNPC (Traitement numérique des pièces calcifiées: Mahé et al., 2009). Under the binocular microscope and natural light, the otoliths showed hyaline (aragonite-poor) rings corresponding to winter periods of low growth and between the rings, white opaque (aragonite-rich) portions corresponding to annual growth periods. The annual ring determination, the age assignment and the measurement of annual ring diameters followed ICES protocoles and guidelines for anchovy and sardine (ICES, 2010; 2011). The age was estimated as the number of hyaline rings. If the edge was hyaline, it was counted as a ring as a hyaline edge observed within the first half of the year is assumed to represent the last winter. The diameter of each annual ring was measured from middle of the hyaline ring on one side to the middle of the ring on the opposite side along the major elongated axis of the otolith and passing through its centre. The distance was expressed in mm after calibration of the stereomiscroscope and the pixel images. Such diameter corresponded to growth-at-age. Ages 0 to 4 were considered (diameters R1 to R5). The total diameter of the otolith was also measured. The data sets span 19 years, from 2000 to 2018 and comprise 20,186 and 8,624 individual fish analyzed at 535 and 235 trawl hauls for anchovy and sardine, respectively. These data sets were used by Boëns et al. (2021 and 2023) to analyse changes in growth patterns and growth-selective mortality at age in anchovy and sardine under environmental and fishing pressures. References: Doray, M., Boyra, G. and Van Der Kooij, J. (eds) (2021). ICES Survey Protocols – Manual for acoustic surveys coordinated under ICES Working Group on Acoustic and Egg Surveys for Small Pelagic Fish (WGACEGG). 1st Edition. ICES Techniques in Marine Environmental Sciences, 64. https://doi.org/10.17895/ices.pub.7462  Doray, M., Petitgas, P., Romagnan, J.-B., Huret, M., Duhamel, E., Dupuy, Ch., Spitz, J., Authier, M., Sanchez, F., Berger, L., Doremus, G., Bourriau, P., Grellier, P. and Masse, J. (2018). The PELGAS survey: ship-based integrated monitoring of the Bay of Biscay pelagic ecosystem. Progress In Oceanography, 166, 15-29. https://doi.org/10.1016/j.pocean.2017.09.015 ICES (2010). Report of the Workshop on Age reading of European anchovy (WKARA), 9-13 November 2009, Sicily, Italy. ICES CM 2009/ACOM: 43. 122 pp. https://doi.org/10.17895/ices.pub.19280525 ICES (2011). Report of the Workshop on Age Reading of European Atlantic Sardine (WKARAS), 14-18 February 2011, Lisbon, Portugal. ICES CM 2011/ACOM:42. 91 pp. https://doi.org/10.17895/ices.pub.19280855 Petitgas, P., Huret, M., Dupuy, Ch., Spitz, J., Authier, M., Romagnan, J.-B. and Doray, M. (2018). Ecosystem spatial structure revealed by integrated survey data. Progress In Oceanography, 166, 189-198. https://doi.org/10.1016/j.pocean.2017.09.012 Mahe, K., Bellail, R., Dufour, J.-L., Boiron-Leroy, A., Dimeet, J., Duhamel, E., Elleboode, R., Felix, J., Grellier, P., Huet, J., Labastie, J., Le Roy, D., Lizaud, O., Manten, M.-L., Martin, S., Metral, L., Nedelec, D., Verin, Y. and Badts, V. (2009). Synthèse française des procédures d'estimation d'âge / French summary of age estimation procedures. https://archimer.ifremer.fr/doc/00000/7294/ Boëns, A., Grellier, P., Lebigre, Ch. and Petitgas, P. (2021). Determinants of growth and selective mortality in anchovy and sardine in the Bay of Biscay. Fisheries Research, 239, 105947. https://doi.org/10.1016/j.fishres.2021.105947 Boëns, A., Ernande, B., Petitgas, P. and Lebigre, Ch. (2023). Different mechanisms underpin the decline in growth of anchovies and sardines of the Bay of Biscay. Evolutionary Applications, 16: 1393–1411. https://doi.org/10.1111/eva.13564  

'''DEFINITION''' The OMI_EXTREME_SL_IBI_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). It is computed for the variable sea level measured by tide gauges along the coast. The use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. The annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset omi_extreme_sl_ibi_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. This study of extreme variability was first applied to sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (Pérez Gómez et al 2018). '''CONTEXT''' Sea level (SLEV) is one of the Essential Ocean Variables most affected by climate change. Global mean sea level rise has accelerated since the 1990’s (Abram et al., 2019, Legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (WCRP, 2018). Basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one meter by the end of the century (Vousdoukas et al., 2020, Tebaldi et al., 2021). This will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (Boumis et al., 2023). The increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. Note, however, that the methodology used to compute this OMI removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. The Iberian Biscay Ireland region shows positive sea level trend modulated by decadal-to-multidecadal variations driven by ocean dynamics and superposed to the long-term trend (Chafik et al., 2019). '''COPERNICUS MARINE SERVICE KEY FINDINGS''' The completeness index criteria is fulfilled by 62 stations in 2023, five more than those available in 2022 (57), recently added to the multi-year product INSITU_GLO_PHY_SSH_DISCRETE_MY_013_053. The mean 99th percentiles reflect the great tide spatial variability around the UK and the north of France. Minimum values are observed in the Irish eastern coast (e.g.: 0.66 m above mean sea level in Arklow Harbour) and the Canary Islands (e.g.: 0.93 and 0.96 m above mean sea level in Gomera and Hierro, respectively). Maximum values are observed in the Bristol Channel (e.g.: 6.25 and 5.78 m above mean sea level in Newport and Hinkley, respectively), and in the English Channel (e.g.: 5.16 m above mean sea level in St. Helier). The annual 99th percentiles standard deviation reflects the south-north increase of storminess, ranging between 1-3 cm in the Canary Islands to 15 cm in Hinkley (Bristol Channel). Negative or close to zero anomalies of 2023 99th percentile prevail throughout the region this year, reaching < -20 cm in several stations of the UK western coast and the English Channel (e.g.: -22 cm in Newport; -21 cm in St.Helier). Significantly positive anomaly of 2023 99th percentile is only found in Arcklow Harbour, in the eastern Irish coast. '''DOI (product):''' https://doi.org/10.48670/moi-00253

This visualization product displays beaches locations where the Marine Strategy Framework Directive (MSFD) monitoring protocol has been applied to collate data on macrolitter (> 2.5 cm). Reference lists associated with these protocols have been indicated with different colors in the map. 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 MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates; - Some categories & 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). 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.

Seasonal climatology of Water body dissolved oxygen concentration for Loire river for the period 1950-2021 and for the following seasons: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Observation data span from 1950 to 2021. Depth levels (m): [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 15 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.01 degrees. Horizontal correlation length is defined seasonally (in meters): 150000 (winter), 100000 (spring), 70000 (summer), 115000 (autumn). Vertical correlation length was optimized and vertically filtered and a seasonally-averaged profile was used (DIVAnd.fitvertlen). Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.Anam.loglin) was applied to the data prior to the analysis to avoid unrealistic negative values. Background field: the vertically-filtered data mean profile is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: umol/l.

DNA sequencing of Crassostrea gigas Pacific oyster spat experimentally infected with OsHV-1 virus from oyster basin of Marennes-Oleron

Seawater samples (500 mL) were taken during the PIRATA FR-32 cruise to measure surface inorganic carbon and alkalinity. The analyses were realised by potentiometric titration using a closed-cell at the SNAPO-CO2, LOCEAN in Paris.

This product displays for Cadmium, positions with values counts that have been measured per matrix for each year and are present in EMODnet regional contaminants aggregated datasets, v2022. The product displays positions for every available year.

This dataset contains libraries of 3 coral species: Acropora hyacinthus, Porites lobata and Poscillopora acuta. In three islands with contrasting thermal regimes, the three species were sampled and brought back to the laboratory to induce an experimental thermal stress. The different colonies were split into two conditions. One part was placed in tanks filled with seawater at a given control temperature, the other part in tanks where the water temperature was increased. The samples in this dataset correspond to part of the control condition samples from French Polynesia and New Caledonia.

Opportunistic macroalgae blooms (green tides) data are collected during monitoring surveys on the English Channel / Bay of Biscay French coasts since 2008 (Quadrige program code : BLOOMS). Protocols are implemented in the European Water Framework Directive.

Moving 6-year analysis of Water body dissolved oxygen concentration 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 1971-1976 until 2017-2022. Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.9, 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 27 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.,1750.,2000.]. 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.,500.,500.,500.]. 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 (1971-2022) 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.