The SOMLIT-Antioche observation station, located at 5 nautical miles from Chef de Baie harbor (La Rochelle) is part of the French monitoring network SOMLIT (https://www.somlit.fr/), accredited by the INSU-CNRS as a national Earth Science Observatory (Service National d’Observation : SNO), which comprises 12 observation stations distributed throughout France in coastal locations. It aims to detect long-term changes  of these ecosystems under both natural and anthropogenic forcings. SOMLIT is part of the national research infrastructure for coastal ocean observation ILICO (https://www.ir-ilico.fr/?PagePrincipale&lang=en). The SOMLIT-Antioche station (46.0842 °N, 1.30833 °W) is located in the north-eastern part of the Bay of Biscay, halfway between the islands of Ré and Oléron, at the centre of what is commonly known as the Pertuis Charentais area, which correspond to a semi-enclosed shallow basin and includes four islands (Ré, Oléron, Aix and Madame) and three Pertuis (i.e., detroit) (Breton, Antioche and Maumusson). This 40m-deep site, with muddy to sandy marine bottoms, is submitted to a macro-tidal regime and is largely open to the prevailing westerly swells. It remains under a dominant oceanic/neritic influence, even though its winter/spring hydrological context is influenced by the diluted plumes of the Charente, Gironde and Loire rivers, but not by those of too small estuaries (Lay, Seudre and Sèvre Niortaise). SOMLIT-Antioche hydrological monitoring has been carried out by the LIENSs/OASU laboratory on a fortnightly basis since June 2011. Surface water samples are collected  at high-tide during intermediate tides (70 ± 10 in SHOM units) on board the research  vessel ‘L’Estran’ owned by La Rochelle University. Samples are analyzed for more than 16 core parameters: temperature, salinity, dissolved oxygen, pH, ammonia, nitrates, nitrites, phosphates, silicates, suspended matter, particulate organic carbone, particulate organic nitrogen, chlorophyll, delta15N, delta13C; pico- and nano- plankton. Measurements are carried out in accordance with the ISO/IEC 17025:2017 standard. Simultaneous monitoring of the micro-phytoplankton community (since 2013, SNO PHYTOBS: https://www.phytobs.fr/en) and monitoring of prokaryotic communities (Bacteria and Archaea) are also carried out on a monthly basis. Since 2019, seasonal observations of benthic invertebrate communities (SNO BenthObs : https://www.benthobs.fr/) have also been carried out. This monitoring is complementary to that carried out at hydrological stations in the pre-existing REPHY and DCE networks, some of which are located near marine farming areas (oyster and mussel farms).

This folder contains two examples of PAGURE datasets, corresponding to three surveys: -CGFS conducted in 2018 in the English Channel (Northeast Atlantic) -EPIBENGOL conducted in 2019 in the Gulf of Lion (Western Mediterranean) -EVHOE conducted in 2020 in the Bay of Biscay and Celtic Shelf (Northeast Atlantic) Files include metadata for the sampling stations, annotation files. A readme tex file contains the links to the voyage metadata This folder is aimed at providing an example of documented underwater imagery dataset. These data are part of the data exchange conducted in the QuatreA collaboration between the French Research Institute for the Exploitation of the Sea (Ifremer), the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and the University of Tasmania (UTAS).

In October 2019 we chose 15 sites from the 2019 EVHOE survey for environmental DNA (eDNA) sampling. The French international EVHOE bottom trawl survey is carried out annually during autumn in the BoB to monitor demersal fish resources. At each site, we sampled seawater using Niskin bottles deployed with a circular rosette. There were nine bottles on the rosette, each of them able to hold ∼5 l of water. At each site, we first cleaned the circular rosette and bottles with freshwater, then lowered the rosette (with bottles open) to 5 m above the sea bottom, and finally closed the bottles remotely from the boat. The 45 l of sampled water was transferred to four disposable and sterilized plastic bags of 11.25 l each to perform the filtration on-board in a laboratory dedicated to the processing of eDNA samples. To speed up the filtration process, we used two identical filtration devices, each composed of an Athena® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.0 l min–1 ), a VigiDNA 0.20 μm filtration capsule (SPYGEN, le Bourget du Lac, France), and disposable sterile tubing. Each filtration device filtered the water contained in two plastic bags (22.5 l), which represent two replicates per sampling site. We followed a rigorous protocol to avoid contamination during fieldwork, using disposable gloves and single-use filtration equipment and plastic bags to process each water sample. At the end of each filtration, we emptied the water inside the capsule that we replaced by 80 ml of CL1 conservation buffer and stored the samples at room temperature following the specifications of the manufacturer (SPYGEN, Le Bourget du Lac, France). We processed the eDNA capsules at SPYGEN, following the protocol proposed by Polanco-Fernández et al., (2020). Half of the extracted DNA was processed by Sinsoma using newly developped ddPCR assays for European seabass (Dicentrachus labrax), European hake (Merluccius merluccius) and blackspot seabream (Pagellus bogaraveo).  The other half of the extracted DNA was analysed using metabarcoding with teleo primer. The raw metabarcoding data set is available at https://www.doi.org/10.16904/envidat.442 Bottom trawling using a GOV trawl was carried out before or after water sampling. The catch was sorted by species and catches in numbers and weight were recorded. No blackspot seabream individuals were caught.   Data content: * ddPCR/: contains the ddPCR counts and DNA concentrations for each sample and species. * SampleInfo/: contains the filter volume for each eDNA sample. * StationInfo/: contains metadata related to the data collected in the field for each filter. * Metabarcoding/: contains metabarcoding results for teleoprimer. * Trawldata/: contains catch data in numbers and weight (kg).      

Three saltmarshes, Aiguillon, Brouage, Fier d'Ars, located in the Pertuis-Charentais Sea along the south-west coast of France, were studied to evaluate their sediment and mass accumulation rates (SAR; MAR) based on 210Pb and 137Cs profiles in sediments. Coastal saltmarshes play indeed an essential role in providing services such as coastal protection and supporting biodiversity. Saltmarshes are also critical environments for the accumulation of sedimentary organic carbon (blue carbon). However, the number of studies on saltmarshes remains underrepresented compared to studies on mangroves and seagrass. This work is a contribution to the effort to document sediment and mass accumulation rates of saltmarshes.A total of 16 1m sediment cores were collected in the three saltmarshes (Aiguillon, Brouage, Fier d'Ars) in 2021 and 2022 using an Eijkelkamp stainless steel peat sampler. Each sediment core was sampled every 1 cm from the top to the bottom of the core. The sediment layers were used to determine dry bulk density and selected radioisotope activities (210Pb, 226Ra, 137Cs, 228Th, 137Cs). Combining excess 210Pb and 137Cs has allowed to establish a reliable chronology of sediment deposition on a multidecadal timescale.

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 125/25 ml glass bottles until 2022/since then 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). Since then, all water samples have been analyzed on this device. 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) CONFLUENCE (1993-1994, stn)   - AO: station and surface data in the Arctic Ocean and nearby seas that include cruises: GREENFEEDBACK (2024, stn&surf) TCA (2024, stn) REFUGE ARCTIC (2024, 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)

Understanding the spatial and temporal preferences of toxic phytoplankton species is of paramount importance in managing and predicting harmful events in aquatic ecosystems. In this study we address the realised niche of the species Alexandrium minutum, Pseudo-nitzschia fraudulenta and P. australis. We used them to highlight distribution patterns at different scales and determine possible drivers. To achieve this, we have developed original procedures coupling niche theory and habitat suitability modelling using abundance data in four consecutive steps: 1) Estimate the realised niche applying kernel functions. 2) Assess differences between the species’ niche as a whole and at the local level. 3) Develop habitat and temporal suitability models using niche overlap procedures. 4) Explore species temporal and spatial distributions to highlight possible drivers. Data used are species abundance and environmental variables collected over 27 years (1988-2014) and include 139 coastal water sampling sites along the French Atlantic coast. Results show that A. minutum and P. australis niches are very different, although both species have preference for warmer months. They both respond to decadal summer NAO but in the opposite way. P. fraudulenta realised niche lies in between the two other species niches. It also prefers warmer months but does not respond to decadal summer NAO. The Brittany peninsula is now classified as an area of prevalence for the three species. The methodology used here will allow to anticipate species distribution in the event of future environmental challenges resulting from climate change scenarios.

The willingness to pay (WTP) of people to protect animal populations can be used as a tool for these populations’ conservation. The WTP reflects the non-use value of animals, which can be significant for charismatic species. This value can be used as an economic criterion for decision-makers in order to recommend protective measures. The definition of the WTP to protect a species is challenging, as valuation methods are time-consuming and expensive. To overcome these limitations, we built a benefit transfer function based on 112 valuation studies and apply it to 440 Mediterranean marine species. We extracted these species from the IUCN database and retrieved some required parameters from, amongst others, the FishBase database. Marine mammals appear to have the highest WTP value followed in order by sea turtles, sharks and rays, and ray-finned fishes. Commercial fish species appear to have the highest values amongst the fish class.

The Commission for the Conservation Southern Bluefin Tuna collects a variety of data types from its Members and Cooperating Non-Members, including total catch, catch and effort data, and catch at size data. Catch, size and trade information is also collected through the Commission's Catch Documentation Scheme, Japanese import statistics, and other monitoring programs. Annual catches provided on this page are reported on a calendar year basis. CCSBT Members use quota years (not calendar years) for managing catching limits, but quota years differ between Members, so calendar years are used to provide catches on a common timescale. Relevant subsets and summaries of these data are provided below. All figures are subject to change as improved data or estimates become available. In particular, reviews of SBT data in 2006 indicated that southern bluefin tuna catches may have been substantially under-reported over the previous 10-20 years and the data presented here do not include estimates for this unreported catch. Also, data for the last reported year of catch (2020) are preliminary and are subject to revision. Any latitudes and longitudes presented in these summaries represent the north western corner of the relevant grid, which is a 5*5 grid unless otherwise specified. Other information on Members and Cooperating Non-Members fishing activities appears in the reports of the Extended Scientific Committee, Compliance Committee and Extended Commission.

Long-term time series of coliform bacteria concentration (fecal coliform or Escherichia coli) in shellfish in four submarine areas (North Sea/Channel, Britany, Atlantic, Mediterranean).

############# # Data description # #############   This dataset have been constructed and used for scientific purpose, available in the paper "Detecting the effects of inter-annual and seasonal changes of environmental factors on the the striped red mullet population in the Bay of Biscay" authored by  Kermorvant C., Caill-Milly N., Sous D., Paradinas I., Lissardy M. and Liquet B. and published in Journal of Sea Research. This file is an extraction from the SACROIS fisheries database created by Ifremer (for more information see https://sextant.ifremer.fr/record/3e177f76-96b0-42e2-8007-62210767dc07/) and from the Copernicus database. Biochemestry comes from the product GLOBAL_ANALYSIS_FORECAST_BIO_001_028 (https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_ANALYSIS_FORECAST_BIO_001_028). Temperature and salinity comes from GLOBAL_ANALYSIS_FORECAST_PHY_001_024 product (https://resources.marine.copernicus.eu/?option=com_csw&view=details&product_id=GLOBAL_ANALYSIS_FORECAST_PHY_001_024). As fisheries landing per unit of effort is only available per ICES rectangle and by month, environmental data have been aggregated accordingly. ############### # Colomns description # ############### rectangle - The 6 ICES statistical rectangles used in the study. time_m - Time in months, from the beginning to the end of the study. annee = year mois = month (from 1 to 12) Poids = Weight of red mullet landed valeur = Temps_peche = fishing time Nb_sequence = number of fishing sequences Moy / Med / Var / StD Quartil_1 / Quartil_3 / min / max / CV / IQR = statistical descriptors of landing by rectangle and by month log_cpue = log of Med colomn mean_surface_s = mean of surface salinity by month and by rectangle median_surface_s = median of surface salinity by month and by rectangle mean_surface_t = mean of surface temperature by month and by rectangle median_surface_t = median of surface temperature by month and by rectangle si / zeu /po4 / pyc / o2/ nppv / no3 and nh4 mean and median concentration by rectangle and by month pc3 / pc2 / pc1 - projections of previous biochemestry variables on the three first axes of a PCA