This visualization product displays the spatial distribution of fishing related items 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 fishing bottom trawl surveys. In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this 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 is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been calculated on each trawl and year using the following computation: Density of fishing related items (number of items per km²) = ∑Number of fishing related items / Swept area (km²) Then a grid with 30km x 30km cells is used to calculate the weighted mean of densities in each cell from the formula : Weighted mean (number of items per km²) = ∑ (Distance (km) * Density (number of items per km²)) / ∑ Distance (km) Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. More information on data processing and calculation are detailed in the document attached. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area. This work is based on the work presented in the following scientific article: O. Gerigny, M. Brun, M.C. Fabri, C. Tomasino, M. Le Moigne, A. Jadaud, F. Galgani, Seafloor litter from the continental shelf and canyons in French Mediterranean Water: Distribution, typologies and trends, Marine Pollution Bulletin, Volume 146, 2019, Pages 653-666, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2019.07.030.

This visualization product displays fishing related items 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 fishing bottom trawl surveys. In cases where the wingspread and/or the number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this 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 is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been calculated on each trawl using the following computation: Density of fishing related items (number of items per km²) = ∑Number of fishing related items / Swept area (km²) Percentiles 50, 75, 95 & 99 have been 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 doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

Moving 6-year analysis of Water body silicate in the Mediterranean Sea for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centered average of the season. 6-years periods span from 1970-1975 until 2017-2022. Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Units: umol/l. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.9, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is dx=dy=0.125 degrees (around 13.5km and 10.9km accordingly). The vertical resolution is 25 depth levels: [0.,5.,10.,20.,30.,50.,75.,100.,125.,150.,200.,250.,300.,400.,500.,600.,700.,800.,900.,1000.,1100.,1200.,1300.,1400.,1500.]. The horizontal correlation length is 200km. The vertical correlation length (in meters) was set twices the vertical resolution: [10.,10.,20.,20.,40.,50.,50.,50.,50.,100.,100.,100.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.,200.]. Duplicates check was performed using the following criteria for space and time: dlon=0.001deg., dlat=0.001deg., ddepth=1m, dtime=1hour, dvalue=0.1. The error variance (epsilon2) was set equal to 1 for profiles and 10 for time series to reduce the influence of close data near the coasts. An anamorphosis transformation was applied to the data (function DIVAnd.Anam.loglin) to avoid unrealistic negative values: threshold value=200. A background analysis field was used for all years (1970-2022) with correlation length equal to 600km and error variance (epsilon2) equal to 20. Quality control of the observations was applied using the interpolated field (QCMETHOD=3). Residuals (differences between the observations and the analysis (interpolated linearly to the location of the observations) were calculated. Observations with residuals outside the minimum and maximum values of the 99% quantile were discarded from the analysis. Originators of Italian data sets-List of contributors: - Brunetti Fabio (OGS) - Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306 - Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987 - Cataletto Bruno (OGS) - Cinzia Comici Cinzia (OGS) - Civitarese Giuseppe (OGS) - DeVittor Cinzia (OGS) - Giani Michele (OGS) - Kovacevic Vedrana (OGS) - Mosetti Renzo (OGS) - Solidoro C.,Beran A.,Cataletto B.,Celussi M.,Cibic T.,Comici C.,Del Negro P.,De Vittor C.,Minocci M.,Monti M.,Fabbro C.,Falconi C.,Franzo A.,Libralato S.,Lipizer M.,Negussanti J.S.,Russel H.,Valli G., doi:10.6092/e5518899-b914-43b0-8139-023718aa63f5 - Celio Massimo (ARPA FVG) - Malaguti Antonella (ENEA) - Fonda Umani Serena (UNITS) - Bignami Francesco (ISAC/CNR) - Boldrini Alfredo (ISMAR/CNR) - Marini Mauro (ISMAR/CNR) - Miserocchi Stefano (ISMAR/CNR) - Zaccone Renata (IAMC/CNR) - Lavezza, R., Dubroca, L. F. C., Ludicone, D., Kress, N., Herut, B., Civitarese, G., Cruzado, A., Lefèvre, D.,Souvermezoglou, E., Yilmaz, A., Tugrul, S., and Ribera d'Alcala, M.: Compilation of quality controlled nutrient profiles from the Mediterranean Sea, doi:10.1594/PANGAEA.771907, 2011.

This visualization product displays the color 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 color, formula applied is: Color (%) = (∑number of particles of each color)*100 / (∑number of particles of all color) When the number of microlitters was not filled or zero, the percentage could not be calculated. Standard vocabularies for microliter colors are taken from Seadatanet's H04 library (https://vocab.seadatanet.org/v_bodc_vocab_v2/search.asp?lib=H04) Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area.

This visualization product displays the location of all the surveys present in the EMODnet marine litter database (MLDB). The different fishing gears used are represented by different colors. 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 fishing bottom trawl surveys. Unlike other EMODnet seafloor litter products, all trawls surveyed since 2006 are included in this map even if the wingspread and/or the distance are unknown. Only surveys with an unknown number of items were excluded from this product. More information on data processing and calculation are detailed in the document attached. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This dataset concerns sequences from the metabarcoding analysis (bacteria & archaea) of 4 hydrothermal sites from the TAG field (2 inactive sites & 2 weakly active sites) collected during the HERMINE2 oceanographic campaign.

Fronts are ubiquitous discrete features of the global ocean often associated with enhanced vertical velocities, in turn boosting primary production and so forth. Fronts thus form dynamical and ephemeral ecosystems where numerous species meet across all trophic levels. Fronts are also targeted by fisheries. Capturing ocean fronts and studying their long-term variability in relation with climate change is thus key for marine resource management and spatial planning. The Mediterranean Sea and the Southwest Indian Ocean are natural laboratories to study front-marine life interactions due to their energetic flow at sub-to-mesoscales, high biodiversity (including endemic and endangered species) and numerous conservation initiatives. Based on remotely-sensed Sea Surface Temperature and Height, we compute thermal fronts (2003-2020) and attracting Lagrangian Coherent Structures (1994-2020), in both regions over several decades. We advocate for the combined use of both thermal fronts and attracting Lagrangian Coherent Structures to study front-marine life interactions. The resulting front database differs from other alternatives by its high spatio-temporal resolution, long time coverage, and relevant thresholds defined for ecological provinces.

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 fishing bottom trawl surveys. In cases where the wingspread and/or number of items were unknown, data could not be used because these fields are needed to calculate the density. Data collected before 2011 are affected by this filter. When the distance reported in the data was null, it was calculated from: - the ground speed and the haul duration using this 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 is calculated from the wingspread (which depends on the fishing gear type) and the distance trawled: Swept area (km²) = Distance (km) * Wingspread (km) Densities have been 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 have been 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 doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

This dataset is composed of 1,153,507 zooplankton individuals, zooplankton parts, non-living particles and imaging artefacts, ranging from 300 µm to 3.39 mm Equivalent Spherical Diameter, individually imaged and measured with the ZooScan (Gorsky et al., 2010). The objects were sorted in 127 taxonomic and morphological groups. The imaged objects originate from samples collected on the Bay of Biscay continental shelf, in spring, from 2004 to 2016 during the PELGAS ecosystemic surveys (Doray et al., 2018). The samples were collected with a WP2 200 µm mesh size fitted with a Hydrobios (back-run stop) mechanical flowmeter, generally from 100 m depth to the surface, or 5 m above the sea floor (if bottom depth less than 100 m) in vertical hauls, at night. From 2004 to 2006, vertical WP2 net tows were performed in the anchovy core distribution area in the southern Bay of Biscay and North of it until the Loire estuary only. Since 2009, WP2 sampling has been carried out at all PELGAS stations, up to the southern coast of Brittany. The samples were preserved in 4% buffered formaldehyde seawater solution directly after collection, until 2019-2020 where they were imaged with the ZooScan, in the lab, on land. Each imaged object is geolocated, associated to a station, a cruise, a year and other metadata that enable the reconstruction of quantitative zooplankton communities for ecological studies (i.e. Grandrémy et al., 2023a). Each object is described by 46 morphological and grey level based features (8 bits encoding, 0 = black, 255 = white), including size, automatically extracted on each individual image by the Zooprocess. Each object was taxonomically identified using the web based application Ecotaxa with built-in, random forest and CNN based, semi-automatic sorting tools followed by expert validation or correction (Picheral et al., 2017). This dataset is intended to be used for ecological studies as well as machine learning applied to plankton studies. The archive contains: - One tab separated file (PELGAS ZooScan zooplankton dataset) containing all data and metadata associated to each imaged and identified object. Metadata and features are in columns (n =71) and objects are in rows (n = 1,153,507). - One comma separated file containing the name, type, definition and unit of each field (column) in the .tsv (dataset_descriptor_zooscan). - One comma separated file containing the taxonomic list of the dataset, with counts and nature of the content of the category, i.e. “T” for taxonomical category, and “M” for morphological category (taxonomy_descriptor_zooscan). - A individual_images directory containing images of each imaged object sorted in subdirectories named according to objects’ identifications object_taxon appended to an Ecotaxa internal taxon numerical id classif_id (i.e. taxon__123456789) across years and sampling stations. Within subdirectories, each object is named after its unique internal Ecotaxa identifier, objid. - A Map of the sampling station location over the 2004-2016 period

Modelled density of the seapen Kophobelemnon stelliferum in the North East Atlantic. The Random Forest density model trained on data collected by an ROV was constrained by an ensemble of Maxent and Random Forest presence-absence model trained on a larger dataset also collected by an ROV. This species provides structural complexity in an environment where it is lacking and, thus, promotes higher biodiversity where they settle. They are vulnerable to mechanical disturbance of the sediment by fishing gear and a better understanding of their distribution will lead to better management of their population. This work was performed at the University of Plymouth in 2021.