This visualization product displays plastic bags density per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during bottom trawl surveys. In cases where the wingspread and/or number of items were/was unknown, it was not possible to use the data because these fields are needed to calculate the density. Data collected before 2011 are concerned by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using the following formula: Distance (km) = Haul duration (h) * Ground speed (km/h); - the trawl coordinates if the ground speed and the haul duration were not filled in. The swept area was calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities were calculated on each trawl and year using the following computation: Density of plastic bags (number of items per km²) = ∑Number of plastic bags related items / Swept area (km²) Percentiles 50, 75, 95 & 99 were calculated taking into account data for all years. The list of selected items for this product is attached to this metadata. Information on data processing and calculation is detailed in the attached methodology document. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.

'''DEFINITION''' Significant wave height (SWH), expressed in metres, is the average height of the highest third of waves. This OMI provides global maps of the seasonal mean and trend of significant wave height (SWH), as well as time series in three oceanic regions of the same variables and their trends from 2002 to 2020, calculated from the reprocessed global L4 SWH product (WAVE_GLO_PHY_SWH_L4_MY_014_007). The extreme SWH is defined as the 95th percentile of the daily maximum SWH for the selected period and region. The 95th percentile is the value below which 95% of the data points fall, indicating higher than normal wave heights. The mean and 95th percentile of SWH (in m) are calculated for two seasons of the year to take into account the seasonal variability of waves (January, February and March, and July, August and September). Trends have been obtained using linear regression and are expressed in cm/yr. For the time series, the uncertainty around the trend was obtained from the linear regression, while the uncertainty around the mean and 95th percentile was bootstrapped. For the maps, if the p-value obtained from the linear regression is less than 0.05, the trend is considered significant. '''CONTEXT''' Grasping the nature of global ocean surface waves, their variability, and their long-term interannual shifts is essential for climate research and diverse oceanic and coastal applications. The sixth IPCC Assessment Report underscores the significant role waves play in extreme sea level events (Mentaschi et al., 2017), flooding (Storlazzi et al., 2018), and coastal erosion (Barnard et al., 2017). Additionally, waves impact ocean circulation and mediate interactions between air and sea (Donelan et al., 1997) as well as sea-ice interactions (Thomas et al., 2019). Studying these long-term and interannual changes demands precise time series data spanning several decades. Until now, such records have been available only from global model reanalyses or localised in situ observations. While buoy data are valuable, they offer limited local insights and are especially scarce in the southern hemisphere. In contrast, altimeters deliver global, high-quality measurements of significant wave heights (SWH) (Gommenginger et al., 2002). The growing satellite record of SWH now facilitates more extensive global and long-term analyses. By using SWH data from a multi-mission altimetric product from 2002 to 2020, we can calculate global mean SWH and extreme SWH and evaluate their trends, regionally and globally. '''KEY FINDINGS''' From 2002 to 2020, positive trends in both Significant Wave Height (SWH) and extreme SWH are mostly found in the southern hemisphere (a, b). The 95th percentile of wave heights (q95), increases faster than the average values, indicating that extreme waves are growing more rapidly than average wave height (a, b). Extreme SWH’s global maps highlight heavily storms affected regions, including the western North Pacific, the North Atlantic and the eastern tropical Pacific (a). In the North Atlantic, SWH has increased in summertime (July August September) but decreased in winter. Specifically, the 95th percentile SWH trend is decreasing by 2.1 ± 3.3 cm/year, while the mean SWH shows a decrease of 2.2 ± 1.76 cm/year. In the south of Australia, during boreal winter, the 95th percentile SWH is increasing at 2.6 ± 1.5 cm/year (c), with the mean SWH increasing by 0.5 ± 0.66 cm/year (d). Finally, in the Antarctic Circumpolar Current, also in boreal winter, the 95th percentile SWH trend is 3.2 ± 2.14 cm/year (c) and the mean SWH trend is 1.7 ± 0.84 cm/year (d). These patterns highlight the complex and region-specific nature of wave height trends. Further discussion is available in A. Laloue et al. (2024). '''DOI (product):''' https://doi.org/10.48670/mds-00352

This visualization product displays the marine litter material categories percentage per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during bottom trawl surveys. Unlike other EMODnet seafloor litter products, all trawls surveyed since 2007 are included in this map even if the wingspread and/or the distance are/is unknown. Only surveys with an unknown number of items were excluded from this product. Harmonization of the material categories between ICES and MEDITS lists was performed and the following calculation was applied: Material % = (∑Number of items of each material category*100)/(∑Number of items of all material categories) More information on data processing and calculation are detailed in the attached document. Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.

EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity, contaminants and marine litter. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on floating micro-litter. This dataset is the result of a validation and harmonisation process of the datasets concerning floating micro-litter present in EMODnet Chemistry. The datasets concerning micro-litter are automatically harvested and the resulting collections are harmonised and validated using ODV Software and following a common methodology for all sea regions. Parameter names are based on P01 vocabulary, which relates to BODC Parameter Usage Vocabulary and is available at: https://vocab.nerc.ac.uk/search_nvs/P01/ This process was performed by ‘Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Division of Oceanography (OGS/NODC)’ from Italy. Harmonisation means that: (1) unit conversion is carried out to express variables with a limited set of measurement units and (2) merging of variables described by different “local names”, but corresponding exactly to the same concepts in BODC P01 vocabulary. The harmonised dataset can be downloaded as ODV collection that can be opened with ODV software for visualization (More information can be found at: https://www.seadatanet.org/Software/ODV ). The same dataset is offered as spreadsheet (txt format, tab separated values) where the values of the categories for the following reported parameters (type, shape, size, color, transparency and material) have been uniformed as labelled in the SeaDataNet H01, H02, H03, H04, H05, H06 vocabularies (https://vocab.seadatanet.org/search ). This format is more adapted to worksheet applications (e.g. LibreOffice Calc).

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  

Worldwide, shellfish aquaculture and fisheries in coastal ecosystems represent crucial activities for human feeding. But these biological productions are under the pressure of climate variability and global change. Anticipating the biological processes affected by climate hazards remains a vital objective for species conservation strategies and human activities that rely on. Within marine species, filter feeders like oysters are real key species in coastal ecosystems due to their economic and societal value (fishing and aquaculture) but also due to their ecological importance. Indeed oysters populations in good health play the role of ecosystem engineers that can give many ecosystem services at several scales: building reef habitats that contribute to biodiversity, benthic-pelagic coupling and phytoplankton bloom control through water filtration, living shorelines against coastal erosion… The Pacific oyster, Crassostrea gigas (Thunberg, 1793), which is currently widespread worldwide, was introduced into the Atlantic European coasts at the end of the 19th century for shellfish culture purposes and becomes the main marine species farmed in France (around 100 000 tons) despite severe mortalities crisis. But in the same time and because of warming, natural oysters beds has spread significantly along the French coast and are supposed to have reach approximately 500 000 tons. In that context, Pacific oyster populations (natural and cultivated) in France are the subjects of many scientific projects. Among them, a specific long-term biological monitoring focuses on the reproduction of these populations at a national scale: the VELYGER national program. With more than 8 years of weekly data at many stations in France, this field-monitoring program offers a valuable dataset for studying processes underpinning reproduction cycle of this key-species in relation to environmental parameters, water quality and climate change.   Database content: Larval concentration (number of individuals per 1.5 m3) monitored, since 2008, at several stations in six bays of the French coast (from south to north): Thau Lagoon and bays of Arcachon, Marennes Oléron, Bourgneuf, Vilaine and Brest (see map below).   Methods used to monitor larval concentration: An important volume of seawater (1.5 m3) is pumped twice a week throughout the spawning season (june-september), at one meter below the surface at high tide (+/- 2h) in several sites within each VELYGER ecosystem. Water is filtered trough plankton net fitted with 40 µm mesh. After a proper rinsing of the net, the retained material is transferred into a polyethylene bottle (1 liter) and fixed with alcohol. At laboratory, sample is then gently filtered and rinse again and transferred into eprouvette. Two sub-samples of 1 mL are then taken using a pipette and examined on a graticule slide for microscope. The microscopic examination is made with a conventional binocular optical microscope with micrometer stage at a magnification of 10 X (or above). During the counting, a special care is necessary as larvae of other bivalves are also collected and confusion is possible. Larvae of C. gigas are also classified into four stage of development: - Stage I = D-shaped straight hinge larvae (shell length <105 µm) - Stage II = Early umbo evolved larvae (shell length between 105 and 150 µm) - Stage III = Medium umbo larvae (shell length between 150 and 235 µm) - Stage IV*= Large umbo eyed pediveliger larvae (shell length > 235 µm) * Larvae that are very closed to settle are sometimes identified into a separated 5th stage, but generally this stage is included in stage IV.   Illustrations: Location of the different Velyger sites along the French coast. From south to north: Thau Lagoon and bays of Arcachon, Marennes Oléron, Bourgneuf, Vilaine and Brest.   Legend: Pacific Oyster Larvae (left side) and Natural oyster bed (right side). Photos : © S. Pouvreau/Ifremer

Numerous reef-forming species have declined dramatically in the last century, many of which have been insufficiently documented due to anecdotal or hard-to-access information. One of them, the honeycomb worm Sabellaria alveolata (L.) is a tube-building polychaete that can form large reefs, providing important ecosystem services such as coastal protection and habitat provision. It ranges from Scotland to Morocco, yet little is known about its distribution outside of the United Kingdom, where it is protected and where there is a strong heritage of natural history and sustained observations. As a result, online marine biodiversity information systems currently contain haphazardly distributed records of S. alveolata. One of the objectives of the REEHAB project (http://www.honeycombworms.org) was to combine historical records with contemporary data to document changes in the distribution and abundance of S. alveolata. Here we publish the result of the curation of 446 sources, gathered from literature, targeted surveys, local conservation reports, museum specimens, personal communications by authors and by their research teams, national biodiversity information systems (i.e. the UK National Biodiversity Network (NBN), https://nbn.org.uk/) and validated citizen science observations (i.e. https://www.inaturalist.org/). 80%[ar1]  of these records were not previously referenced in any online information system. Additionally, historic field notebooks from Edouard Fischer-Piette and Gustave Gilson were scanned for S. alveolata information and manually entered. The original taxonomic identification of the 23296 S. alveolata records has been kept. Some identification errors may however be present, particularly in the English Channel and the North Sea where incorrectly identified observations of intertidal Sabellaria spinulosa were recorded. A further 229 observations are recorded as ‘Sabellaria spp.’ as the available information does not allow a species-level identification. Many sources reported abundances based on the semi-quantitative SACFOR scale while others simply noted its presence, and others still verified both its absence and presence. The result is a curated and comprehensive dataset spanning over two centuries on the past and present global distribution and abundance of S. alveolata. Sabellaria alveolata records projected onto a 50km grid. When SACFOR scale abundance scores were given to occurrence records, the highest abundance value per grid cell was retained.

This visualization product displays the type of litter in percent per net per year from specific protocols different from research and monitoring protocols. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10). A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter. European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization. A filter is applied to represent only micro-litter sampled according to a very specific protocol such as the Volvo Ocean Race (VOR) or Oceaneye. To calculate percentages for each type, formula applied is: Type (%) = (∑number of particles of each type)*100 / (∑number of particles of all type) When the number of micro-litters was not filled or was equal to zero, it was not possible to calculate the percentage. Standard vocabularies for micro-litter types are taken from Seadatanet's H01 library (https://vocab.seadatanet.org/v_bodc_vocab_v2/search.asp?lib=H01). Some morphological types of micro-litters may have been sampled but were not defined by the protocole applied during the survey. They are represented as « undefined micro-litter items ». Warnings: - the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area. - since 03/07/2023, the preferred label « Undefined micro-litter items » has been integrated into the H01 library whereas the labels « microplastic items », « non-plastic man-made micro-particles (e.g. glass, metal, tar) » and «non-plastic filaments (natural fibres, rubber) » have been deprecated. When defined, the material or polymer type can be checked directly in the source data.

Observations of Sea surface temperature and salinity are now obtained from voluntary sailing ships using medium or small size sensors. They complement the networks installed on research vessels or commercial ships. The delayed mode dataset proposed here is upgraded annually as a contribution to GOSUD (http://www.gosud.org )

'''DEFINITION''' The temporal evolution of thermosteric sea level in an ocean layer is obtained from an integration of temperature driven ocean density Static product based on Sentinel-2 (images from 202X to 2024). There are 4 bathymetry datasets, three from three different analysis methods and a merged one. Three methods: intertidal, optical (radiation transfer equation), wave kinematics (depth inversion from wave kinematics). It includes 4 datasets, with 2 variables each. The bathymetry for intertidal, optical, wave kinematics methods and the merged of the three, plus the quality indexes for each. '''DOI (product):''' https://doi.org/10.48670/mds-00364