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.

This dataset gather isotopic ratios (carbon and nitrogen) and concentrations of mercury species (methyl and inorganic mercury) measured in several tissues (muscle, liver and gonad) for three commonly consumed fish species from the south Bay of Biscay (France) in 2017 and 2018.

The diet and stable isotopic (i.e. δ15N and δ13C values) compositions of eels have been studied during each season of 2019 with a fyke net in six estuaries located along the French coast of the eastern English Channel (Slack, Wimereux, Liane, Canche, Authie and Somme estuaries) (10.1371/journal.pone.0270348).

The SOMLIT-SOGIR time-series data characterize the hydrology of the Gironde Estuary ecosystem, located in the South-western France and flowing into the Bay of Biscay. Monthly-like measurements have been undertaken since 1997 by the OASU and EPOC teams (Univ. Bordeaux/CNRS). The SOMLIT-SOGIR time series is a part of the French monitoring network SOMLIT (https://www.somlit.fr/), labelled by the CNRS as a national Earth Science Observatory (Service National d’Observation : SNO). It aims to detect the long-term evolution of monitored ecosystems including both natural and anthropogenic forcings. Implemented at three sites (PK 30: 45.06833°N, 0.63833°W; PK 52: 45.24667°N, 0.725°W; PK 86:  45.5167°N, 0.95°W), the SOMLIT-SOGIR time series is among the oldest long-term coastal observation time series of the French Research Infrastructure dedicated to coastal ocean observations (RI ILICO, https://www.ir-ilico.fr). SOMLIT-SOGIR samples are collected at 1m below the water surface and 1m above the floor, at high and low tide, during slack water. Samples collected are analysed for 15 core parameters: water temperature and salinity, dissolved oxygen, pH, ammonia, nitrate, nitrite, phosphate, silicic acid, suspended particulate matter, particulate organic carbone, particulate nitrogen, chlorophyll a, delta15N and delta13C. CTD-PAR-profile is also performed at site PK86 during high tide. The SOMLIT network quality management system is in line with the ISO/IEC 17025:2017 standard: “General requirements for the competence of testing and calibration laboratories”. Further information on standard operating procedures for sample collection and data acquisition are available at: https://www.somlit.fr/parametres-et-protocoles. For more information on the quality flagging scheme: https://www.somlit.fr/codes-qualite/.

Particularly suited to the purpose of measuring the sensitivity of benthic communities to trawling, a trawl disturbance indicator (de Juan and Demestre, 2012, de Juan et al. 2009) was proposed based on benthic species life history traits to evaluate the sensibility of mega- and epifaunal community to fishing pressure known to have a physical impact on the seafloor (such as dredging and bottom trawling). The selected biological traits were chosen as they determine vulnerability to trawling: mobility, fragility, position on substrata, average size and feeding mode that can easily be related to the fragility, recoverability and vulnerability ecological concepts. Life history traits of species have been defined from the BIOTIC database (MARLIN, 2014) and from information given by Le Pape et al. (2007), Brindamour et al. (2009) and Garcia (2010). For missing life history traits, additional information from literature has been considered. The five categories retained are life history functional traits that were selected based on the knowledge of the response of benthic taxa to trawling disturbance (de Juan and Demestre, 2012). They reflect respectively the possibility to avoid direct gear impact, to benefit from trawling for feeding, to escape gear, to get caught by the net and to resist trawling/dredging action, each of these characteristics being either advantageous or sensitive to trawling. Then, to allow quantitative analysis, a score was assigned to each category: from low vulnerability (0) to high vulnerability (3). The five categories scores were then summed for each taxon (the highly vulnerable taxon could reach the maximum score is 15) and this value may be considered as a species index of sensitivity to trawling disturbance. The scores of 773 taxa commonly found in bottom trawl by-catch in the southern North Sea, English Channel and north-western Mediterranean were described.

This is the FAO Fishery and Aquaculture Reference Data repository: Codes and reference data for fishing gear, species, currencies, commodities, countries and others.

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.

An observation network was initiated in 2021 in the framework of the CocoriCO2 project to monitore carbonate parameters along the French coastal systems. Six sites were selected along the French Atlantic and Mediterranean coastlines based on their importance in terms of shellfish production and the presence of high- and low-frequency monitoring activities. At each site, autonomous pH sensors were deployed both inside and outside shellfish production areas, next to high-frequency CTD (conductivity-temperature-depth) probes operated through two operating monitoring networks (SNO COAST-HF and Ifremer ECOSCOPA). pH sensors were set to an acquisition rate of 15 min and discrete seawater samples were collected biweekly in order to control the quality of pH data (laboratory spectrophotometric measurements) as well as to measure total alkalinity and dissolved inorganic carbon concentrations for full characterization of the carbonate system. While this network has been up and running for more than two years, the acquired dataset has already revealed important differences in terms of pH variations between monitored sites related to the influence of diverse processes (freshwater inputs, tides, temperature, biological processes).