Here, our study aimed to first assess the influence of plastic on the bacterial community belonging to water, plastic and the microbiome of the giant clam and on the organism's physiology of this putative sentinel species. Our second objective was to identify bacteria whose abundance varies significantly with plastic concentration. Overall, this study will fill the gap towards a better understanding of the impact of plastic pollution on bacterial community assemblages in both inert and living environments.

2bRAD genotyping will be used to estimate genetic diversity and connectivity among populations of Sabellaria alveolata. We will relate population genetic parameters with reef state characteristics.

Présentation des entreprises, cartographie du risque et consignes en cas d'alerte pour les populations des communes de la Presqu'île d'Ambès. Plaquettes 4 ou 8 pages (avec cartographie)

Metagenomic analysis of clams from Sanaga river in Cameroon to describe the virome

This dataset presents the resulting assessment grid (based on the EEA reference grid) with the classification of chemical status of the transitional, coastal and marine waters in the context of the Water Framework Directive (WFD) and the Marine Strategy Framework Directive (MSFD). This classification has been performed using the CHASE+ tool, with classifications of the matrices ‘water’, ‘sediment’ and ‘biota’ and indicators of ‘biological effects’, as well as an integrated classification of chemical status, combining results of all matrices. The chemical status is evaluated in five classes, where NPAhigh and NPAgood are recognised as ‘non-problem areas’ and PAmoderate, PApoor and PAbad are recognised as ‘problem areas’. The overall area of interest used is based on the marine regions and subregions under the Marine Strategy Framework Directive. Additionally, Norwegian (Barent Sea and Norwegian Sea) and Icelandic waters (’Iceland Sea’) have been added (see Surrounding seas of Europe). Note that within the North East Atlantic region only the subregions within EEZ boundaries (~200 nm) have been included. This dataset underpins the findings and cartographic representations published in the report "Contaminants in Europe's Seas" (EEA, 2019): https://www.eea.europa.eu/publications/contaminants-in-europes-seas.

Analysis of tuna stomach contents

UWWTD Discharge Points, Jan. 2022 is one of the datasets produced within the frame of the reporting under 11th UWWTD Art.15 reporting period (UWWTD data call 2019). The Urban Waste Water Treatment Directive (UWWTD) (91/271/EEC) obliges Member States to report data on the implementation of the Directive upon request from the European Commission bi-annually. Reported data include receiving areas as designated under UWWTD, agglomerations, urban waste water treatment plants serving the agglomerations and points of discharges. Dataset UWWTD_DischargePoints contains information on individual points of discharge from urban waste water treatment plants or collecting systems, including their coordinates of discharge, link to specific treatment plant, type of receiving area into which the effluent / wastewater is discharged, related waterbody (or river basin), information on the discharge on land and potential reuse of the treated waste water. This dataset includes the reported discharge points which are displayed on the UWWTD maps (https://www.eea.europa.eu/themes/water/european-waters/water-use-and-environmental-pressures/uwwtd/interactive-maps/urban-waste-water-treatment-maps-3). The active discharge points with correct coordinates in the reported data were selected from the source European UWWTD tabular dataset, which is available on the download link https://www.eea.europa.eu/data-and-maps/data/waterbase-uwwtd-urban-waste-water-treatment-directive-7. The definition of the UWWTD Discharge Points dataset attributes (fields) is available on the link https://dd.eionet.europa.eu/datasets/latest/UWWTDArt15/tables/DischargePoints The full (internal) dataset including inactive discharge points is available under "Urban Waste Water Treatment Directive, Discharge points reported under UWWTD data call 2019 - INTERNAL VERSION, Jan. 2022". In comparison to the previous version (Nov. 2020), late redeliveries and corrections provided by several countries during 2021 are included in current revision. Next, the dataset is provided in GeoPackage and ESRI File geodatabase formats instead of shapefile used up to now, to avoid truncation of attribute names.

In European sea bass like in other animals, the tongue plays a fundamental role in the mechanics of food ingestion. It is composed from the surface in depth of mucosa, submucosa, musculature and fibro cartilaginous skeleton. The tunica mucosa exhibits a stratified epithelium interrupted by numerous teeth differently distributed that erupt more or less completely from the layers below. The European sea bass tongue is composed of canine-like teeth, surrounded by taste buds and numerous fungiform and conical papillae. The tongue beeing directly in contact with external environment, the success of the adaptation of fishes to different environments in the context of global change, depends oamong other on the modifications occurring on the tongue structures. The present study investigates the potential effect of ocean acidification on the lingual transcriptome.

Whole genome pooled sequencing of individuals from 4 populations and 3 different color phenotype in order to uncover the genetic variants linked to color expression in the pearl oyster P. margaritifera.

Shom manages a network of permanent digital coastal tide-gauges on French coasts: the RONIM Sea Level Observation Network. Most tide-gauge observatories are partnered with one or more local partners.<br /><br /> Four main types of data are available for download:<br /> - “Raw high frequency" data: raw observations neither validated nor evaluated, obtained directly from the sensor. 1-minute measurement; integration time 15 seconds (on the minute); sampling period: 1 second.<br /><br /> - "Raw non-real time" data: raw observations neither validated nor evaluated, obtained directly from the sensor. 10-minute measurement; integration time 121 seconds (around every 10 minutes); sampling period: 1 second.<br /><br /> - "Validated non-real time" data: observations checked and validated by Shom from the "Raw non-real time" data. 10-minute measurement; integration time 121 seconds (around every 10 minutes); sampling period: 1 second.<br /><br /> - "Validated hourly" data: observations checked and validated by Shom, generated from "Validated non-real time" data. Hourly measurement obtained from the Vondrak filter with triangular weighting. The hourly height cannot be calculated in the event of an observation gap greater than 1.5 hours.