Donnees publiques de la Directive Cadre Strategie pour le Milieu Marin (DCSMM)

A compliant implementation of WMS plus most of the SLD extension (dynamic styling). Can also generate PDF, SVG, KML, GeoRSS

Serveur wms public de l'Ifremer - projet REPAMO

Web Feature Service for Emodnet Chemistry

This visualization product displays the density of floating micro-litter per net normalized per m³ 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. Densities were calculated for each net using the following calculation: Density (number of particles per m³) = Micro-litter count / Sampling effort (m³) When the number of micro-litters was not filled, it was not possible to calculate the density. Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years. 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 National Oceanographic Data Centre (NODC) for this area.

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.

'''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

The flat oyster Ostrea edulis is a European native species that once covered vast areas in the North Sea, on the Atlantic coast and in other European coastal waters including the Mediterranean region. All these populations have been heavily fished by dredging over the last three centuries. More recently, the emergence of parasites combined with the proliferation of various predators and many human-induced additional stressors have caused a dramatic decrease in the last remaining flat oyster populations. Today, this species has disappeared from many locations in Europe and is registered on the OSPAR (Oslo-Paris Convention for the Protection of the Marine environment of the North-East Atlantic) list of threatened and/or declining species (see https://www.ospar.org/work-areas/bdc/species-habitats/list-of-threatened-declining-species-habitats). In that context, since 2018, the Flat Oyster REcoVERy project (FOREVER) has been promoting the reestablishment of native oysters in Brittany (France). This multi-partner project, involving the CRC (Comité Régional de la Conchyliculture), IFREMER (Institut Français de Recherche pour l’Exploitation de la Mer), ESITC (École Supérieure d’Ingénieurs des Travaux de la Construction) Caen and Cochet Environnement, has consisted of (1) inventorying and evaluating the status of the main wild flat oyster populations across Brittany, (2) making detailed analysis of the two largest oyster beds in the bays of Brest and Quiberon to improve understanding of flat oyster ecology and recruitment variability and to suggest possible ways of improving recruitment, and (3) proposing practical measures for the management of wild beds in partnership with members of the shellfish industry and marine managers. the final report of this project is available on Archimer : https://doi.org/10.13155/79506. This survey is part of the task 1 of the FOREVER, which took place between 2017-2021. Some previous data, acquired with the same methodology and within the same geographic area have been also added to this dataset. These data were collected during 30 intertidal and diving surveys in various bays and inlets of the coast of Bretagne. The localization of these surveys has been guided by the help of historical maps. In the field, the methodology was simple enough to be easily implemented regardless of the configuration of the sampled site. The intertidal survey was conducted at very low tide (tidal range > 100) to sample the 0-1m level. Sampling was carried out randomly or systematically following the low water line. Where possible (in terms of visibility and accessibility), dive surveys were also carried out (0-10m depth), along 100m transects, using the same methodology of counting in a 1m2 quadrat. As often as possible, geo-referenced photographs were taken to show the appearance, density and habitat where Ostrea edulis was present. All these pictures are available in the image bank file. Overall, this dataset contains a total of 300 georeferenced records, where flat oysters have been observed. The dataset file contains also information concerning the surrounding habitat description and is organized according the OSPAR recommendations. This publication gives also a map, under a kml format showing each occurrence and its characteristics. This work was done in the framework of the following research project: " Inventaire, diagnostic écologique et restauration des principaux bancs d’huitres plates en Bretagne : le projet FOREVER. Contrat FEAMP 17/2215675".

The ICES Working Group on Fisheries Benthic Impact and Trade-offs (WGFBIT) has developed an assessment framework based on the life history trait longevity, to evaluate the benthic impact of fisheries at the regional scale. In order to apply this framework to the Mediterranean sea, several Mediterranean longevity databases were merged together with existing North-East Atlantic ones to develop a common database. Longevity was fuzzy coded into four longevity classes: <1, 1-3, 3-10 and >10 years. Both benthic mega and macrofauna organisms are included in this dataset. Further details about both the purpose and the methodology may be found in ICES (2022) and Cuyvers et al. (2023). The result of the final dataset merging is one dataset containing the fuzzy coded average longevity (and standard deviation) for 2264 taxa and for each, the number of databases used.