Times series of water level at four stations of the Gironde Estuary (Pointe de Grave, Le Marquis, Bordeaux and Cadillac) for the the years 1953, 1971, 1982, 1994, 2005 and 2014. The dataset includes all of the time seris used in the publication: To what extent multidecadal changes in morphology and fluvial discharge impact tide in a convergent (turbid) tidal river (2018), Journal of Geophysical Research: Oceans.

Inorganic carbon and alkalinity measurements (in micromoles/kg) from Brazilian cruises in the Western Tropical Atlantic.

The R/V Miguel Oliver was built in 2007 for scientific fisheries assessment and oceanographic research purposes. Owned by the Spanish General Secretariat of Fisheries, it mainly operates around the Iberian Peninsula and Canary Islands waters. Equipped with an automatic weather station, collected data are daily sent to the Spanish Institute of Oceanography (IEO) Data Center / National Oceanographic Data Center (NODC) for delayed-mode quality control and long-term archive and dissemination. Recently, a semi-automatic process has been used to perform quality control and data assessment. The quality controlled data have been stored in MEDAR/MEDATLAS format, and the corresponding quality flag has been added to each individual measurement. This facilitates their reuse for different purposes, from climatology or academic studies to industrial sector needs, among others. In 2023, air temperature, relative humidity, wind speed and direction and solar radiation were collected.

In the framework of the ANR AMORAD project, the METEOR cruises (Grasso, 2017) aimed at deploying the ‘Gironde Estuary Mouth MEasurement Stations’ (GEMMES) to measure hydrodynamics and sediment dynamics at the mouth of the Gironde Estuary and on the West Gironde Mud Patch (SW France, Bay of Biscay). Measurements were carried out between November 2016 and December 2017: i) from a buoy station around 20-m water depth (GEMMES-20), collecting sub-surface data of temperature, salinity and turbidity; and ii) a benthic station around 40-m water depth (GEMMES-40), collecting data of current velocity and turbidity. Bottom and surface water samples were regularly collected to calibrate turbidity measurements to SPM concentrations.

The database displays the field measurements recover during a one-week experiment in October 2021 on the Socoa's rocky platform, France. The general objective of the study was to evaluate the momentum balance over the rocky platform, in particular to quantifiy the combined effect of high seabed roughness and waves on the coupling between circulation, wave force and mean water level. The analysis is performed along a single cross-shore transect.  The data include: - the cross-shore bathymetric profile - the time-series of integrated wave parameters, local mean water depth and wave-averaged currents over successive 30-min bursts - the depth- and wave-averaged momentum fluxes following the formulation proposed by Smith 2006 and Bruneau et al. 2011. The fluxes are median values obtained over significant wave height to depth ratio bins

Inorganic carbon and alkalinity measurements (in micromoles/kg) along the coast of Brazil, 2013-2015.  

Reef-building species are recognized as having an important ecological role and as generally enhancing the diversity of benthic organisms in marine habitats.  However, although these ecosystem engineers have a facilitating role for some species, they may exclude or compete with others. The honeycomb worm Sabellaria alveolata (Linnaeus, 1767) is an important foundation species, commonly found from northwest Ireland to northern Mauritania (Curd et al., 2020), whose reef structures increase the physical complexity of the marine benthos, supporting high levels of biodiversity. Local patterns and regional differences in taxonomic and functional diversity were examined in honeycomb worm reefs from ten sites along the northeastern Atlantic to explore variation in diversity across biogeographic regions and the potential effects of environmental drivers. To characterize the functional diversity at each site, a biological trait analysis (BTA) was conducted (Statzner et al., 1994). Here we present the functional trait database used for the benthic macrofauna found to live in association with honeycomb worm reefs. Eight biological traits (divided into 32 modalities) were selected (Table 1), 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 at different bathymetric levels, 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. The information concerning polychaetes was derived primarily from Fauchald et al (1979) and Jumars et al (2015). Information on other taxonomic groups was obtained either from databases of biological traits (www.marlin.ac.uk/biotic) or publications (Naylor, 1972; King, 1974; Caine, 1977; Lincoln, 1979; Holdich and Jones, 1983; Smaldon et al., 1993; Ingle, 1996; San Martín, 2003; Southward, 2008; Gil, 2011; Leblanc et al., 2011; Rumbold et al., 2012; San Martín and Worsfold, 2015; Jones et al., 2018). Map indicating the locations of the 10 study sites in the UK, France and Portugal within the four biogeographic provinces defined by Dinter (2001). (All sites were sampled in 8 different stations, except for UK4 where 5 stations were sampled).

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows detecting changes (e.g., acceleration) in one or more components. Study of the sea level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled “Regional Sea Level and Coastal Impacts”, an international effort involving the sea level community worldwide has been recently initiated with the objective of assessing the various data sets used to estimate components of the sea level budget during the altimetry era (1993 to present). These data sets are based on the combination of a broad range of space-based and in situ observations, model estimates and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about sixty research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level). The results presented in this paper are a synthesis of the first assessment performed during 2017-2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 +/- 0.3 mm/yr and acceleration of 0.1 mm/yr2 over 1993-present), as well as of the different components of the sea level budget. We further examine closure of the sea level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute by 42%, 21%, 15% and 8% to the global mean sea level over the 1993-present. We also study the sea level budget over 2005-present, using GRACE-based ocean mass estimates instead of sum of individual mass components. Results show closure of the sea level budget within 0.3 mm/yr. Substantial uncertainty remains for the land water storage component, as shown in examining individual mass contributions to sea level.

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

Dataset summaryPlankton and detritus are essential components of the Earth’s oceans influencing biogeochemical cycles and carbon sequestration. Climate change impacts their composition and marine ecosystems as a whole. To improve our understanding of these changes, standardized observation methods and integrated global datasets are needed to enhance the accuracy of ecological and climate models. Here, we present a global dataset for plankton and detritus obtained by two versions of the Underwater Vision Profiler 5 (UVP5). This release contains the images classified in 33 homogenized categories, as well as the metadata associated with them, reaching 3,114 profiles and ca. 8 million objects acquired between 2008-2018 at global scale. The geographical distribution of the dataset is unbalanced, with the Equatorial region (30° S - 30° N) being the most represented, followed by the high latitudes in the northern hemisphere and lastly the high latitudes in the Southern Hemisphere. Detritus is the most abundant category in terms of concentration (90%) and biovolume (95%), although its classification in different morphotypes is still not well established. Copepoda was the most abundant taxa within the plankton, with Trichodesmium colonies being the second most abundant. The two versions of UVP5 (SD and HD) have different imagers, resulting in a different effective size range to analyse plankton and detritus from the images (HD objects >600 µm, SD objects >1 mm) and morphological properties (grey levels, etc.) presenting similar patterns, although the ranges may differ. A large number of images of plankton and detritus will be collected in the future by the UVP5, and the public availability of this dataset will help it being utilized as a training set for machine learning and being improved by the scientific community. This will reduce uncertainty by classifying previously unclassified objects and expand the classification categories, ultimately enhancing biodiversity quantification.Data tablesThe data set is organised according to:- samples : Underwater Vision Profiler 5 profiles, taken at a given point in space and time. - objects : individual UVP images, taken at a given depth along the each profile, on which various morphological features were measured and that where then classified taxonomically in EcoTaxa.samples and objects have unique identifiers. The sample_id is used to link the different tables of the data set together. All files are Tab separated values, UTF8 encoded, gzip compressed.samples.tsv.gz - sample_id        unique sample identifier - sample_name        original sample identifier - project        EcoPart project title - lat, lon        location [decimal degrees] - datetime        date and time of start of profile [ISO 8601: YYYY-MM-DDTHH:MM:SSZ] - pixel_size        size of one pixel [mm] - uvp_model        version of the UVP: SD: standard definition, ZD: zoomed, HD: high definitionsamples_volume.tsv.gzAlong a profile, the UVP takes many images, each of a fixed volume. The profiles are cut into 5 m depth bins in which the number of images taken is recorded and hence the imaged volume is known. This is necessary to compute concentrations. - sample_id        unique sample identifier - mid_depth_bin        middle of the depth bin (2.5 = from 0 to 5 m depth) [m] - water_volume_imaged        volume imaged = number of full images × unit volume [L]objects.tsv.gz - object_id         unique object identifier - object_name         original object identifier - sample_id         unique sample identifier - depth        depth at which the image was taken [m] - mid_depth_bin        corresponding depth bin [m]; to match with samples_volumes - taxon         original taxonomic name as in EcoTaxa; is not consistent across projects - lineage         taxonomic lineage corresponding to that name - classif_author         unique, anonymised identifier of the user who performed this classification - classif_datetime         date and time at which the classification was - group         broader taxonomic name, for which the identification is consistent over the whole dataset - group_lineage         taxonomic lineage corresponding to this broader group - area_mm2        measurements on the object, in real worl units (i.e. comparable across the whole dataset) … - major_mm     - area        measurements on the objet, in [pixels] and therefore not directly comparable among the different UVP models and units - mean     … - skeleton_area    properties_per_bin.tsv.gzThe information above allows to compute concentrations, biovolumes, and average grey level within a given depth bin. The code to do so is in `summarise_objects_properties.R`. - sample_id         unique sample identifier - depth_range         range of depth over which the concentration/biovolume are computed: (start,end], in [m] where `(` means not including, `]` means including - group         broad taxonomic group - concentration        concentration [ind/L] - biovolume        biovolume [mm3/L] - avg_grey        average grey level of particles [no unit; 0 is black, 255 is white]ODV_biovolumes.txt, ODV_concentrations.txt, ODV_grey_levels.txtThis is the same information as above, formatted in a way that Ocean Data View https://odv.awi.de can read. In ODV, go to Import > ODV Spreadsheet and accept all default choices.ImagesThe images are provided in a separate, much larger, zip file. They are stored with the format `sample_id/object_id.jpg`, where `sample_id` and `object_id` are the integer identifiers used in the data tables above.