We genotyped 1680 thornback ray Raja clavata sampled in the Bay of Biscay using a DNA chip described in Le Cam et al. (2019). After quality control 4604 SNPs were retained for identifying potential sex-linked SNPs using three methods: i) identification of excess of heterozygotes in one sex, ii) FST outlier analysis between the two sexes and iii) neuronal net modelling. Genotype coding: 0 homozygous for major allele, 1 heterozygous, 2 homozygous for minor allele. Flanking DNA sequences of SNPs identified with methods i) and ii) are also provided.  

The PHYTOBS-MARCOBOLO dataset comprises long-term time series on marine microphytoplankton, from 2003 to 2021, along the entire French metropolitan coastline. Microphytoplankton data cover microscopic taxonomic identifications and counts. The PHYTOBS-MARCOBOLO dataset corresponds to a dataset extracted from the PHYTOBS network (DOI:10.17882/85178). The PHYTOBS network provides the scientific community and stakeholders with validated and qualified data on the biomass, abundance and composition of marine microphytoplankton in coastal and lagoon waters, with the aim of supporting scientific research. PHYTOBS-MARCOBOLO is a dataset used as part of the Horizon Europe MARCO-BOLO project (https://marcobolo-project.eu/), in which we are currently working to understand and analyze multi-decadal trends in coastal and marine biodiversity on a European scale. The PHYTOBS-MARCOBOLO dataset gathers data from 18 sampling sites, selected from the PHYTOBS-Network dataset according to requirements of time series quality and geographical location of sampling sites established as part of the MARCO-COLO project. This dataset was also formatted according to a template imposed for the European project.

############# # 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

The dataset dcm_dtb.txt contains bio-optical measurements and environmental parameters associated with  Deep Chlorophyll Maxima (DCM) acquired by BGC-Argo profiling floats. For each BGC-Argo profile the data files includes the World Meteorological Organization (WMO) and profile numbers, the Data Assembly Center (DAC), the geographical position (LON and LAT), the date of the profile in Julian Day (JULD) and in YYYY-MM-DD format; the region of the profile (REGION, acronyms detailed in the region.txt file), the DCM zonal attribution (ZONE, acronyms detailed in the zone.txt file), the vertical resolution of measurements of the concentration of the chlorophyll a [Chla] and of the backscattering coefficient (bbp) within the 250 first meters, the Mixed Layer Depth (MLD, m), the qualification of the vertical profile (DCM_TYPE) as Deep Biomass Maximum (3), Deep photoAcclimation Maximum (2), or presenting no DCM (1); the depth of the DCM (DCM_DEPTH); the chlorophyll a concentration (CHLA_DCM, mg chla m-3 ) the backscattering coefficient (BBP_DCM, m-1), and the Brunt-Vaisala frequency (N2_DCM) at the DCM depth; the nitracline depth (NCLINE_DEPTH, m) and steepness (NCLINE_STEEP, µmol NO3 m-3 m-1), the mean nitrate concentration within the Mixed Layer (NO3_MEAN_MLD, µmol NO3 m-3), the mean daily Photosynthetically Available Radiation in the Mixed Layer (MEAN_IPAR_MLD, E m -1 d -1), the daily Photosynthetically Available Radiation at the nitracline depth (IPAR_NCLINE, E m-2 d-1);  and the [Chla] measured by satellite (CHLA_SAT, mg chla m-3). The dataset shape_NASTG_ASEW.txt contains the seasonal median, the first and third quartiles of the [Chla] and of the bbp profiles for the North Atlantic Subtropical Gyre and Atlantic SubEquatorial Waters regions. The dataset climato_NASTG_ASEW.txt contains the monthly mean and standard deviations of the DCM depth (DCM_depth), the isolume depth of daily Photosynthetically Available Radiation of 20 E m-2 d-1 (iPAR_20), the nitracline depth, and the Mixed Layer Depth (MLD) for the profiles within the North Atlantic Subtropical Gyre and Atlantic SubEquatorial Waters regions.  The qualification and processing of the BGC-Argo profiles, as well as the DCM detection (DCM_TYPE) and the estimation of the environmental parameters, were applied as described from Cornec, M., Claustre, H., Mignot, A., Guidi, L., Lacour, L., Poteau, A., D’Ortenzio, F.,Gentili, B., Schmechtig, C., (to be updated.) Deep Chlorophyll Maxima in the global ocean: occurrences, drivers and characteristics. Global Biogeochemical Cycles, to be updated The [Chla] satellite variable was obtained by the match of each BGC-Argo profile with a L3S [Chla] product from the Ocean Colour-Climate Change Initiative v4.0 database merging observations from MERIS, MODIS, VIIRS and SeaWiFs, at a monthly and 4x4-km-pixel resolution, up to December 31, 2019 (ftp://oc-cci-data:ELaiWai8ae@oceancolour.org/occci-v4.2/).

Data collected by the Spindrift 2 Sails of Change vessel during its attempt at the round-the-world sailing record, the Jules Verne Trophy. More information at https://spindrift-racing.com/fr/.

Tracking data of 7 grey seals were obtained from the deployment of Fastloc GPS/GSM tags developed by the Sea Mammal Research Unit (UK). Full tag description is available at: http://www.smru.st-andrews.ac.uk/Instrumentation/GPSPhoneTag/. The tags include a wet-dry sensor from which haulout events are recorded, a pressure sensor providing detailed dive data, as well as a Fastloc GPS recording irregular locations when the seal is not underwater. Data is stored onboard and transmitted via the GSM network when the seal is in the reception range. The data provided here are the individual GPS locations of the seals fitted with these tags for an average duration of 135 days.

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.

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 network was initiated by IFREMER from 1993 to 2009 (under the acronym REMORA) to study the rearing performance of the Pacific oyster Crassostrea gigas at a national scale. To do so, the network monitored annually the mortality and growth of standardized batches of 18-month-old oysters. Starting in 1995, the monitoring of the rearing performance of 6-month-old oyster spat was integrated into this network. These sentinel batches were distributed simultaneously each year on 43 sites and were monitored quarterly. These sites were distributed over the main French oyster farming areas and allowed a national coverage of the multiannual evolution of oyster farming performances. Most of the sites were located on the foreshore at comparable levels of immersion. Field studies were carried out by the "Laboratoires Environnement Ressources" (LER) for the sites included in their geographical area of investigation. Following the increase in spat mortality in 2008, the network evolved in 2009 (under the acronym RESCO). From this date, the network selected 13 sites among the 43 sites previously monitored in order to increase the frequency of visits (twice a month) and the number of sentinel batches. More precisely, sentinel batches of oysters corresponding to different origins (wild or hatchery, diploid or triploid) and to two rearing age classes (spat or 18-month-old adults) were selected. The monitoring of environmental variables (temperature, salinity) associated with the 13 sites was also implemented. The actions of the network have thus contributed to disentangle the biotic and abiotic parameters involved in mortality phenomena, taking into account the different compartments (environment / host / infectious agents) likely to interact with the evolution of oyster rearing performance. Finally, since 2015, the network has merged the RESCO and VELYGER networks to adopt the acronym ECOSCOPA. The general objective of this current network is to analyze the causes of spatio-temporal variability of the main life traits (Larval stage - Recruitment - Reproduction - Growth - Survival - Cytogenetic abnormalities) of the cupped oyster in France and to follow their evolution on the long term in the context of climate change. To do this, the network proposes a regular spatio-temporal monitoring of the major proxies of the life cycle of the oyster, organized in three major thematic groups: (1) proxies related to growth, physiological tolerance and survival of experimental sentinel populations over 3 age classes: (2) proxies related to reproduction, larval phase and recruitment of the species throughout its natural range in France, and: (3) proxies related to environmental parameters essential to the species (weather conditions, temperature, salinity, pH, turbidity, chlorophyll a and phytoplankton) at daily or sub-hourly frequencies. Working in a geographical network associating several laboratories, ECOSCOPA provide these monitoring within 8 sites selected among the previous ones to ensure the continuity of the data acquisition. Today, these 8 sites are considered as ecosystems of common interest, contrasted, namely : - The Thau lagoon - The Arcachon basin - The Marennes Oléron basin - The Bourgneuf Bay - The bay of Vilaine - The bay of Brest - The bay of Mont Saint Michel - The bay of Veys The ECOSCOPA network is therefore one of the relevant monitoring tools on a national scale, allowing to objectively measure through different proxies the general state of health of cultivated and wild oyster populations, and this for the different sensitive phases of their life cycle. This network aims at allowing a better evaluation, on the long term, of the biological risks incurred by the sector but also by the ecosystems, in particular under the increasing constraint of climatic and anthropic changes. Figure : Sites monitored by the ECOSCOPA network