LOCEAN has been in charge of analyzing the isotopic composition of the dissolved inorganic carbon (DIC) in sea water collected during a series of cruises or ships of opportunity mostly in the southern Indian Ocean , the North Atlantic, and the equatorial Atlantic, but also in the Mediterranean Sea and in the equatorial Pacific. The LOCEAN sea-water samples for δ13CDIC were collected in 125 ml glass bottles and poisoned with HgCl2 (1 ml of saturated solution) before storage in a dark room à 4°C until their measurement. The DIC was extracted from the seawater by acidification with phosphoric acid (H3PO4 85%) and CO2 gas that was produced was collected in a vacuum system following the procedure described by Kroopnick (1974). The isotopic composition of CO2 was determined using a dual inlet-isotopic ratio mass spectrometer (SIRA9-VG) by comparing the 13C/12C ratio of the sample to the 13C/12C ratio of a reference material, the Vienna-Pee Dee Belemnite (V-PDB). The isotopic composition is expressed in the δ-unit defined by Craig (1957)(method type 2).  Experience showed that samples older than 3-4 years are likely to have experienced conservation issues and have been dismissed. The mass spectrometer has worked very well until 2014-2015. Afterwards, its aging as well as the aging of the preparation line resulted in more data loss, and often less accurate results. The preparation line was renovated in 2019, and analyses in 2020 were run manually, often repeating the measurement a second time for each sample. Up to 2007-2008, δ13CDIC values have a precision of±0.01 ‰ (Vangriesheim et al.,2009) and a reproducibility of±0.02 ‰. After an interlaboratory comparison exercise led by Claire Normandeau (Dalhousie  University),  results  suggest  that  recent  LOCEAN  samples have a slightly poorer reproducibility (±0.04 ‰ ) as well as an offset of -0.13‰ (details available in Reverdin et al., ESSD 2018) that is confirmed by Becker et al. 2016 work by comparison with other cruises after removing the anthropogenic signal. Recent comparisons in early May 2021 with Orsay GEOPS facility samples suggest that the current offset is much smaller and might be +0.03‰. LOCEAN has installed in 2021 a new measurement device by coupling a Picarro G2131-I cavity ring down spectrometer (CRDS) with a CO2 extractor (Apollo SciTech) that will measure at the same time DIC (method type 3) (Leseurre, 2022). Part of the data set, as well as a scientific context and publications are also presented on the WEB site https://www.locean-ipsl.upmc.fr/oceans13c. Individual files correspond to regional subsets of the whole dataset. The file names are based on two letters for the region followed by (-) the cruise or project name (see below) followed by –DICisotopes, followed by either -s (surface data) or -b (subsurface data), and a version number (-V0, …): example SI-OISO-DICisotopes-s-V0; the highest version number corresponds to the latest update of the cruise/project data set, and can be directly downloaded. Earlier versions can be obtained on request, but are not recommended. The region two letters are the followings:   - SI: station and surface data in the Southern Indian Ocean that include cruises : INDIGO I (1985 – stn) (https://doi.org/10.17600/85000111) CIVA I (1993 – stn & surf) (https://doi.org/10.17600/93000870) (Archambeau et al., JMS 1998) ANTARES (1993 – stn & surf) (https://doi.org/10.17600/93000600) OISO (*) (since 1998 – stn & surf) (https://doi.org/10.18142/228) (Racapé et al., Tellus 2010, Leseurre, 2022)   - EA: station and surface data in the Tropical Atlantic Ocean that include cruises : EQUALANT (1999 & 2000 – surf) (https://doi.org/10.18142/98) EGEE (2005 to 2007 – stn & surf) (https://doi.org/10.18142/95) PIRATA (since 2013 – stn & surf) (https://doi.org/10.18142/14) EUMELI 2 (1991 – stn) (https://doi.org/10.17600/91004011)  (Pierre et al., JMS 1994) BIOZAIRE 3 (2003 – stn & surf ) (https://doi.org/10.17600/3010120) (Vangriesheim et al., DSRII, 2009) TARA-Microbiomes (2021 - stn & surf)   - NA : station and surface data in the North Atlantic Subpolar gyre that include cruises : OVIDE (**) (since 2002 – stn & surf) (https://doi.org/10.17882/46448) (Racapé et al., 2013) RREX (2017 – stn & surf) (https://doi.org/10.17600/17001400) SURATLANT (since 2010 - surf) (https://doi.org/10.17882/54517) (Racapé et al., BG 2014 ; Reverdin et al., ESSD 2018, Leseurre, 2022) NUKATUKUMA (since 2017- surf)   - MS: station data in the Mediterranean sea that include cruises : ALMOFRONT 1 (1991 – stn) (https://doi.org/10.17600/91004211) VICOMED 3 (1990 – stn) (https://doi.org/10.17600/90000711)   - PO: tropical Pacific that include cruises : PANDORA (2012 – stn) (https://doi.org/10.17600/12010050) ALIZE2 (1991 – stn & surf) (https://doi.org/10.17600/91002711) (Laube-Lenfant and Pierre, Oceanologica Acta 1994)   - SO: station and surface data in the Southern Ocean (except OISO) that include cruises: TARA-Microbiomes (2021-2022, stn & surf) AGULHASII-072022 (2022, stn)   (*) The values for cruises OISO19, 21 and 22 are doubtful (for some, too low) and will require further investigation to find whether adjusted values can be proposed. (**) Some of the OVIDE cruises are also referred to as or GEOVIDE (in 2014), and BOCATS (in 2016). CATARINA, BOCATS1 and BOCATS2 (PID2019-104279GB-C21/AEI/10.13039/501100011033) cruises were funded by the Spanish Research Agency  The values of the OVIDE 2010 stations are doubtful (too low), but no particular error was found, and they have been left in the files.   Data The files are in csv format reported as: - Cruise name, station id, (bottle number), day, month, year, hour, minute, longitude, latitude, pressure (db), depth (m), temperature (°C), temperature qc, salinity (pss-78), salinity qc, d13CDIC, d13CDIC qc, method type - Temperature is an in situ temperature - Salinity is a practical salinity - Method type (1) acid CO2 extraction from helium stripping technique coupled to mass spectrometer, (2) acid CO2 extraction in a vacuum system coupled to mass spectrometer,(3) CO2 extractor (Apollo SciTech) coupled to CRDS measurements. Temperature qc, salinity qc, d13CDIC qc are quality indices equal to: - 0 no quality check (but presumably good data) - 1 probably good data - 2 good data - 3 probably bad data - 4 certainly bad data - 9 missing data (and the missing data are reported with an unlikely missing value)

'''DEFINITION''' Significant wave height (SWH), expressed in metres, is the average height of the highest one-third of waves. This OMI provides time series of seasonal mean and extreme SWH values in three oceanic regions as well as their trends from 2002 to 2020, computed from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum of SWH over the chosen period and region. The 95th percentile represents the value below which 95% of the data points fall, indicating higher wave heights than usual. The mean and the 95th percentile of SWH are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February, and March, and July, August, and September) and are in m while the trends are in cm/yr. '''CONTEXT''' Grasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends. '''KEY FINDINGS''' Over the period from 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere. The 95th percentile of wave heights (q95), increases more rapidly than the average values, indicating that extreme waves are growing faster than the average wave height. In the North Atlantic, SWH has increased in summertime (July August September) and decreased during the wintertime: the trend for the 95th percentile SWH is decreasing by 2.1 ± 3.3 cm/year, while the mean SWH shows a decreasing trend of 2.2 ± 1.76 cm/year. In the south of Australia, in boreal winter, the 95th percentile SWH is increasing at a rate of 2.6 ± 1.5 cm/year (a), with the mean SWH increasing by 0.7 ± 0.64 cm/year (b). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 ± 2.15 cm/year (a) and the mean SWH trend is 1.4 ± 0.82 cm/year (b). This variation highlights that waves evolve differently across different basins and seasons, illustrating the complex and region-specific nature of wave height trends. A full discussion regarding this OMI can be found in A. Laloue et al. (2024). '''DOI (product):''' https://doi.org/10.48670/mds-00352

Serveur wms public de l'Ifremer, Accès aux données du Sismer

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 eutrophication and acidity, and covers the Mediterranean Sea. Data were aggregated and quality controlled by the 'Hellenic Centre for Marine Research, Hellenic National Oceanographic Data Centre (HCMR/HNODC)' in Greece. 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. Regional datasets concerning eutrophication and acidity 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/. 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 1.25 kg/L. The aggregated dataset can also be downloaded as an ODV collection and spreadsheet, which is composed of a metadata header followed by tab separated values. This spreadsheet can be imported to ODV Software for visualisation (more information can be found at: https://www.seadatanet.org/Software/ODV).

The ReZoEnv field campaign was conducted at 9 sites distributed within contrasted seagrass (Zostera notlei) meadows in the Arcachon Bay. This multi-parameter survey was conducted during one year (November 2015 – November 2016). Water levels, temperature and light were recorded every 10 minutes. While bed sediment characteristics (granulometry, water content, organic matter content), seagrass characteristics (coverage, biometry, chlorophyll and CNP content) were measured monthly. Additionally, wind-wave parameters were obtained from high frequency pressure sensor at 4 sites, every 20 minutes. List of sites : - ANDE : 44.745091 N, 1.121366 O - FONT : 44.722631 N, 1.080133 O - GAIL : 44.662573 N, 1.099575 O - GARR : 44.705132 N, 1.121562 O - HAUT : 44.729331 N, 1.15608 O - ILE : 44.683117 N, 1.162716 O - JACQ : 44.724563 N, 1.181109 O - PASS : 44.689927 N, 1.089491 O - ROCH : 44.648529 N, 1.127736 O

Zostera marina (Linnaeus, 1753) is a flowering marine plant that occurs from temperate to subantarctic regions (Green and Short, 2003), forming meadows that are recognized as being among the most important ecosystems on the planet (Costanza et al., 1997; Duffy, 2006; Duarte et al., 2008; Dewsbury et al., 2016). Eelgrass is a foundation species, providing essential functions and services including coastal protection, erosion control, nutrient cycling, water purification, carbon sequestration, as well as food and habitat for a variety of species (Duarte 2002; Heck et al. 2003; Healey & Hovel 2004, Orth et al. 2006; Barbier et al., 2011; Fourqurean et al. 2012; Cullen-Unsworth & Unsworth 2013; Schmidt et al. 2011, 2016). Eelgrass can have a strong influence on the spatial distribution of associated fauna by altering the hydrodynamics of the marine environment (Fonseca and Fisher 1986), stabilizing sediments (Orth et al. 2006), providing abundant resources, available surface area, and increased ecological niches. Meadows also provide protection from predation by providing greater habitat complexity both above and below ground (Heck and Wetstone 1977; Orth et al. 1984; Gartner et al. 2013, Reynolds et al., 2018). Local patterns and regional differences in the taxonomic and functional diversity of assemblages associated with five Zostera marina meadows occurring over a distance of 800 km along the coast of France were investigated with the objective of determining which factors control community composition within this habitat. To this end, we examined - and -diversity of species- and trait-based descriptors, focused on polychaetes; bivalves and gastropods, three diverse groups exhibiting a wide range of ecological strategies (Jumars, Dorgan, & Lindsay, 2015) and having central roles in ecosystem functioning through activities such as bioturbation or trophic regime (Queirós et al., 2013, Duffy et al., 2015). Here we present the abundance (Table 1) and the functional trait database (Table 2) used for the benthic macrofauna found to live in association with eelgrass meadows in Chausey, Dinard, Sainte-Marguerite, Ile d’Yeu and Arcachon, sampled in the fall of 2019. Eight biological traits (divided into 32 modalities, Table S1) were selected, providing information linked to the ecological functions performed by the associated macrofauna. The selected traits provide information on: (i) resource use and availability (by the trophic group of species, e.g. Thrush et al. 2006); (ii) secondary production and the amount of energy and organic matter (OM) produced based on the life cycle of the organisms (including longevity, maximum size and mode of reproduction, e.g. (Cusson and Bourget, 2005; Thrush et al., 2006) and; (iii) the behavior of the species in general [i.e. how these species occupy the environment and contribute to biogeochemical fluxes through habitat, movement, and bioturbation activity, e.g. (Solan et al., 2004; Thrush et al., 2006; Queirós et al., 2013). Species were scored for each trait modality based on their affinity using a fuzzy coding approach (Chevenet et al., 1994), where multiple modalities can be attributed to a species if appropriate, and allowed for the incorporation of intraspecific variability in trait expression. Information for polychaetes was primarily extracted from Fauchald et al (1979), Jumars et al (2015), and Boyé et al (2019). Information for mollusks was obtained either from biological trait databases (www.marlin.ac.uk/biotic, www.univie.ac.at/arctictraits, Bacouillard et al 2020) or from publications (e.g. Queiros et al. 2013; Thrush et al, 2006; Caine, 1977). Information was collected at the lowest possible taxonomic level and when missing was based on data available in other species of the genus, or in some cases, in the same family (only for traits with low variability for these families).   Figure 1. Map indicating the locations of the 5 study sites of Zostera marina meadows in France: three in the the English Channel, and two in the Bay of Biscay (all sites were sampled in 6 different stations).  

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

These data are outputs of a spatio-temporal model inferring fish distribution. The maps are based on high-resolution catch data (VMS-logbook). They have a montly time resolution and a 0.05° spatial resolution. Four demersal species of the Bay of Biscay are available in the dataset: common sole (Solea solea), megrim (Lepidorhombus whiffiagonis), anglerfish (Lophius spp) and thornback ray (Raja clavata). Maps are provided for year 2008 to 2018 ; they were produced in the context of the MACCO project (https://www.macco.fr/en/accueil-english/), an Ifremer project that aims at proposing alternative management strategies for the mixed demersal fisheries of the Bay of Biscay.

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

Based on the consolidation of the Ifremer networks RESCO (https://doi.org/10.17882/53007) and VELYGER (https://doi.org/10.17882/41888), the general objective of the ECOSCOPA project is to analyze the causes of spatio-temporal variability of the main life traits (Larval stage - Recruitment - Reproduction - Growth – Survival – Cytogenetic anomalies) of the Pacific oyster in France and follow their evolution over the long term in the context of climate change. The high frequency environmental data are monitored since 2010 at several stations next to oyster farm areas in eight bays of the French coast (from south to north): Thau Lagoon and bays of Arcachon, Marennes Oléron, Bourgneuf, Vilaine, Brest, Mont Saint-Michel and Veys (see map below). Sea temperature and practical salinity are recorded at 15-mins frequency. For several sites, fluorescence and turbidity data are also available. Data are acquired with automatic probes directly put in oyster bags or fixed on metallic structure at 50 cm over the sediment bottom, except for Thau Lagoon whose probes are deployed at 2m below sea surface. Since 2010, several types of probes were used: STP2, STPS, SMATCH or WiSens CTD from NKE (www.nke-instrumentation.fr) and recently ECO FLNTU (www.seabird.com). The probes are regularly qualified by calibrations in the Ifremer coastal laboratories. Precision estimated of the complete data collection process is: temperature (±0.1°C), salinity (±0.5psu), in vivo fluorescence (±10%), turbidity (±10%). The data are qualified into several levels: 0-No Quality Check performed, 1-Good data, 2-Probably good data, 3-Probably bad data, 4-Bad data, 5-Value changed, 7-Nominal value, 8-Interpolated value, 9-Missing value.