Marine protected areas (MPAs) provide biodiversity conservation benefits in a range of marine habitats. Many protected areas are established and governed through top-down or shared governance arrangements, yet little is known about how these governance strategies compare in terms of the protection benefits they provide to MPAs globally. Using an extensive data set of MPA conditions, we developed a set of Bayesian hierarchical models to understand the role of shared governance versus federal governance on reef fish biomass from 218 global MPAs. We find greater reef fish biomass benefits in MPAs with shared governance than with top-down, or federal arrangements. We also find greater benefits in older MPAs and MPAs farther away from shore. Our results highlight the fundamental importance of multi-stakeholder participation for improving conservation outcomes, representing an important conservation opportunity for new or existing MPAs. 

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.

Understanding the dynamics of species interactions for food (prey-predator, competition for resources) and the functioning of trophic networks (dependence on trophic pathways, food chain flows, etc.) has become a thriving ecological research field in recent decades. This empirical knowledge is then used to develop population and ecosystem modelling approaches to support ecosystem-based management. The TrophicCS data set offers spatialized trophic information on a large spatial scale (the entire Celtic Sea continental shelf and upper slope) for a wide range of species. It combines ingested prey (gut content analysis) and a more integrated indicator of food sources (stable isotope analysis). A total of 1337 samples of large epifaunal invertebrates (bivalve mollusks and decapod crustaceans), zooplankton, fish and cephalopods, corresponding to 114 species, were collected and analyzed for stable isotope analysis of their carbon and nitrogen content. Sample size varied between taxa (from 1 to 52), with an average of 11.72 individuals sampled per species, and water depths ranged from 57 to 516 m. The gut contents of 1026 fish belonging to ten commercially important species: black anglerfish (Lophius budegassa), white anglerfish (Lophius piscatorius), blue whiting (Micromesistius poutassou), cod (Gadus morhua), haddock (Melanogrammus aeglefinus), hake (Merluccius merluccius), megrim (Lepidorhombus whiffiagonis), plaice (Pleuronectes platessa), sole (Solea solea) and whiting (Merlangius merlangus) were analyzed. The stomach content data set contains the occurrence of prey in stomach, identified to the lowest taxonomic level possible. To consider potential ontogenetic diet changes, a large size range was sampled. The TrophicCS data set was used to improve understanding of trophic relationships and ecosystem functioning in the Celtic Sea. When you use the data in your publication, we request that you cite this data paper. If you use the present data set (TrophicCS) for the majority of the data analyzed in your study, you may wish to consider inviting at least one author of the core team of this data paper to become a collaborator /coauthor of your paper.

The ICES Working Group on Fisheries Benthic Impact and Trade-offs (WGFBIT) has developed an assessment framework based on the life history trait longevity, to evaluate the benthic impact of fisheries at the regional scale. In order to apply this framework to the Mediterranean sea, several Mediterranean longevity databases were merged together with existing North-East Atlantic ones to develop a common database. Longevity was fuzzy coded into four longevity classes: <1, 1-3, 3-10 and >10 years. Both benthic mega and macrofauna organisms are included in this dataset. Further details about both the purpose and the methodology may be found in ICES (2022) and Cuyvers et al. (2023). The result of the final dataset merging is one dataset containing the fuzzy coded average longevity (and standard deviation) for 2264 taxa and for each, the number of databases used. 

The West Gironde Mud Patch (WGMP) is a mud deposit located 25 km from the mouth of the Gironde Estuary in the Bay of Biscay. This 4-metre-thick clay-silt feature, which extends over an area of 420 km2, is found at depths between 30 and 80 meters. The main objectives of the JERICObent7 cruise, in July 2019, were to characterise the evolution of the WGMP’s benthic ecosystem in terms of its sedimentary, biogeochemical and ecological properties and to reconstruct climate variations and identify potential anthropogenic impacts over the last few centuries. To this end, a precise chronological framework was established for the sedimentary archives of the last few decades using 210Pbxs (T1/2 = 22.3 years). Interface cores were collected at stations 1, 3 and 4 along a cross-shelf transect. Twin Kullenberg cores were collected at sites 3 and 4 for geochemical (KGL) and palaeoceanographic (JB7-ST) investigations. Each interface core was carefully extruded at 0.5 cm intervals from the top of the core to 4 cm, and then at 1 cm intervals until the bottom was reached. Kullenberg cores were only collected at sites 3 and 4. Depending on their intended use, the Kullenberg cores were sampled at different resolutions, the depth of each sediment layer corresponded to the depth from the top of the core. These layers were then used to determine the dry bulk density and radioisotope activities of interest (210Pb, 226Ra,  228Th, 137Cs, 40K). Excess 210Pb was used to establish the realignment and chronological framework of the interface and Kullenberg cores.

The Argo program provides an unprecedented volume of oceanographic data, yet the complexity of its dataset — involving multiple data modes, quality control flags, and metadata conventions — often hinders its direct usage. The EasyOneArgo initiative addresses this challenge by delivering simplified, high-quality subsets of Argo data, specifically designed to streamline user access and integration. The EasyOneArgo data product is comprised of 4 datasets: - EasyOneArgoTS – A curated selection of temperature-salinity profiles filtered by strict quality criteria and optimized across raw, real-time adjusted, and delayed-mode status. - EasyOneArgoTSLite – The vertically interpolated counterpart of EasyOneArgoTS, with standardized pressure levels between 2 and 6000 dbar. - EasyOneArgoBGC – A curated selection of biogeochemical profiles filtered by strict quality criteria. The BGC parameters include DOXY, NITRATE, PH, CHLA, BBP, and various radiometry parameters. All BGC data are adjusted either in real-time or in delayed-mode. Accompanying TS measurements are also included. - EasyOneArgoBGCLite – The vertically interpolated counterpart of EasyOneArgoBGC, with the same standardized pressure levels as EasyOneArgoTSLite. Note: The BGC vertical interpolation process involves pre-filtering the original data before interpolation. Hence the magnitudes of some of the features are modulated in the BGCLite dataset. Each profile in EasyOneArgo is packaged as a standalone CSV file, with metadata and indexes for seamless retrieval. This work represents a user-centric shift in Argo data delivery: no QC flags, no data modes, no data manuals — just good, clean, structured ocean data ready for immediate scientific application. The EasyOneArgo data product is publicly available through monthly FAIR-compliant releases. We invite community feedback for continued refinement. Please send any comments to Annie Wong (apsw.uw@gmail.com) or Thierry Carval (Thierry.Carval@ifremer.fr).

The Arcachon Bay is a unique and ecologically important meso-tidal lagoon on the Atlantic coast of south-west France. The Arcachon Bay has the largest area of dwarf seagrass (Z. noltei) in Europe, the extent of which was stable in their extent between the 1950s and 1990s, but a decline in seagrass was observed in mid-2000. The decline of Zostera (seagrass) may have a significant impact on sedimentation in this coastal ecosystem rich in marine life. Interface cores were collected in September 2022 to determine sediment and mass accumulation rates (SAR, MAR) in the Arcachon Bay. Ten study areas were selected, distributed over most of the areas where seagrass meadows are actually observed. Two sites were visited each time, one with the presence of Zostera noltei in good condition (Healthy) and the other where the sediment was bare (Bare). Maximum water heights during spring tides range from 3.44 m for the deepest site (Garrèche) to 2.09 m for the shallowest site (Fontaines). A total of 20 sediment cores were sampled and carefully extruded 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: DBD, 210Pb, 226Ra, 137Cs, 228Th and 40K expressed as %K). 

Global Fishing Watch is working across the globe to provide governments and authorities with actionable reports and capacity building to help strengthen fisheries monitoring and compliance. Our global team of experts produce analyses to inform monitoring, control and surveillance of fisheries in five key areas: - Illegal, unreported and unregulated fishing - Transshipment - Port controls - Marine protected areas - Operation support Collaboration and information sharing are integral to achieving well-managed fisheries. By working with stakeholders and making analyses available to national, regional and intergovernmental partners, Global Fishing Watch is enabling fisheries agencies to make more informed and cost-efficient decisions. Topics: - Commercial fishing, Global Fishing Watch is harnessing innovative technology to turn transparent data into actionable information and drive tangible change in the way that fisheries are governed. - Transshipment, Through publicly sharing map visualisations and creating data and analysis tools, we seek to inform management and policy efforts and provide a more complete picture of transshipment at sea. - Marine protected areas, Global Fishing Watch is harnessing the data and technology revolution to support the effective design, management and monitoring of marine protected areas.

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).      

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)