Stocks (abundance and biomass) of the slipper limpet Crepidula fornicata Linnaeus, 1758 in the Pertuis Charentais shallows were estimated in 2011 and mapped (Sauriau et al., 2011). The influence of the estimate includes the Pertuis Breton, the eastern part Pertuis Antioch Fouras peninsula and the Bay of Marennes-Oléron where the latter earlier estimates are available. Contours and surface areas of the crepidula were previously determined by acoustic imaging performed aboard the VO/Haliotis in 2008 and 2009, interpreted and validated by spot sampling in 2009 (Curti, 2009). Sampling stocks of 2011 is based on a stratified random sample of 40 banks with a proportional distribution of sampling of 200 stations between banks. The listing for each crepidula bank stocks (abundance in millions, biomass in tonnes) both living and dead, these shells may provide support to potential new colonization.

The systematic swath bathymetric mapping of the Mediterranean Sea started in the mid-nineties. This mapping has considerably modified our understanding of the morphology of the Mediterreanean Sea and of the various active geological processes (sedimentary, tectonic, volcanic, bio-geochemical processes) which participate to the seafloor morphology.

Multivariate Inversion of Ocean Surface Topography- Internal Tide (MIOST-IT) Model products. MIOST-IT database includes 4 tidal components for the ocean internal tide surface elevations. Each tidal component is described by 2 grids (amplitude and phase) provided on a cartesian grid 1/10°x1/10°. - The internal tide elevations for the four tidal frequencies M2, S2, K1, O1, are available for download. - The tidal prediction code is also available to download.

An inventory of mäerl ( unfixed red marine algae Corallinaceae with calcified arbuscular thalli) is taken in the Pertuis Charentais using the compilation of historical data and new observations over the period 2006-2012. The geographic extent of the inventory includes Pertuis Breton, Antioch and Maumusson, extends from the Lay estuary in the north to the Bay of Marennes-Oléron in the south and includes the edges of the islands of Ré, Oléron and Aix. Contributing to this inventory are, surveys DCE 2006 DCE, control monitoring DCE 2007 to 2012 under the aegis of Ifremer and Agences de l’Eau Loire-Bretagne and Adour-Garonne, surveys conducted for the Port Atlantique La Rochelle on the site of dredge spoil deposits in Lavardin from 2007 to 2012, surveys in 2007 FONSOLE by Ifremer and the surveys by Ifremer and laboratory LIENSs to map the crepidula stocks in the Pertuis Charentais Sea. Adding specific surveys conducted in 2011 by the ship Estran by dredge on subtidal bottom sediments of Pertuis Breton and intertidal quadrat in 2012 on the periphery of the islands of Ré, Oléron and along the cliffs of Aytré. The positioning of ground truthing stations were identified by GPS or dGPS. Initially identified by samples of the dredged material in October 2009 during the validation of sonar surveys to individualize stocks of crepidula, a live maerl bed is mapped in July 2011 in the harbor of Saint-Martin in Pertuis Breton. Its area is estimated at more than 2 km². Surveys of pedibus jambi conducted at the point of Grouin and Saint-Martin de Ré has confirmed the presence of live maerl scattered on the lower foreshore. This suggests a scattering of live maerl thalli of several tens of km² around the main bed of Saint-Martin. It is also confirmed by the single algological extraction of Phymatolithon calcareum done at the beginning of the twentieth century in Saint-Martin de Ré by Dollfus, probably already on the lower foreshore as observed in 2012 near the Couronneau beacon. Numerous dead maerl thalli in the procress of diagenesis have also been observed in the East and Southeast of Pertuis Breton and Antioch suggesting passive transport by contributions from live beds farther to the West, including Pertuis in Antioch. The presence of live maerl on the lower foreshore is seen very occasionally in the western islands of Oléron (Pointe Chassiron) and Ré (Phare des Baleines) via recent algologiques and fauna surveys. In biogeographic terms, the maerl bed of Pertuis Breton is the easternmost and southernmost of those known in the Bay of Biscay. The nearest mäerl beds known are respectively located in Noirmoutier (Bourgneuf Bay) and Galicia (Spain). These observations complement the atlas mapping of maerl populations in Europe published under the auspices of the OSPAR Convention. They provide a knowledge base for future conservation actions defined locally in the context of Natura 2000 Sea Marine Natural Park of the Gironde estuary and the Pertuis Charentais.

The geographical distribution of the main Sabellaria Hermelles alveolata beds (Linné, 1767) from the coast of the Pertuis Charentais was compiled from 2010 to 2012. The inventory focuses on the tidal beds (+ 6-0 m marine coast) and compiles two cartographic sources from the project CARTHAM Natura 2000 at sea of the Agency of Marine Protected Areas: 1) Hermelles inventories made on 2011 by the Association IODDE on the west of Oleron Island and 2) mapping inventory of intertidal marine habitats by LIENSs (in cooperation with CREOCEAN, Ré Nature Environnement, GEO-Transfert et la LPO Moëze-Oléron). Methodologies used by IODDE on Oléron combine ground truths with reports with Geographic Information System (GIS). The methodologies used by LIENSs to estimate the extension of the intertidal habitats throughout the Pertuis Charentais combine aerial image analysis, remote sensing, field validations by differential GPS and thematic analysis using GIS (Cajeri et al., 2012). It is during these ground truths that the presence of tackles or small Hermelles reefs was noted. The IODDE results have allowed an almost exhaustive inventory of Hermelles reefs west of Oleron. Those of LIENSs have clarified the distribution of Hermelles on most of the Pertuis Charentais foreshores.

Geomorphological analysis of the continental slope of the Mediterranean sea, based on a 100 m Data Terrain Model (DTM). This DTM is a synthesis of data acquired during several surveys. Bibliographic references :

SWOT - the State of the World's Sea Turtles - is a partnership led by Marine Flagship Species Program at the Oceanic Society, Conservation International (CI), the IUCN Marine Turtle Specialist Group (MTSG), and supported by the OBIS-SEAMAP project at the Marine Geospatial Ecology Lab (MGEL), Duke University. This Database allows mapping marine turtle available data. Here is presented - the world distribution by species

Landslides and gravitational features mapped within French metropolitan EEZ at 1 : 250 000 in the framework of work-package 6 (Geological Events and Probabilities) of the european project EMODNet Geology. These datasets were delivered during the phase 2 of the project.

Daily gridded Sargassum area coverage derived from MODIS Terra and Aqua observations over the Atlantic, produced with the AFAI (Alternative Floating Algae Index)-based SAREDA (SArgassum Evolving Distribution in the Atlantic) algorithm.

The geographical distribution of the main sources of wild Pacific oysters (Crassostrea gigas) (Thunberg, 1793) on the coasts of the Pertuis Charentais was compiled from 2010 to 2012. The inventory focuses on natural foreshore deposits (+ 6-0 m sea coast) and compiles two cartographic sources: 1) quantitative inventories made in 2010 by Ifremer from the Tranche sur Mer (Vendée) to Vaux-sur-Mer in 21 representative sites to estimate wild stocks of Pacific oysters and 2) inventory mapping of intertidal marine habitats mainly made by LIENSs (in cooperation with CREOCEAN, IODDE, Ré Nature Environnement and GEO-Transfert) under the project CARTHAM Natura 2000at sea of the Agency of Marine Protected Areas. Methodologies used by Ifremer to estimate the areas colonized combine thematic analysis in Geographic Information System (GIS) and field validations (Soletchnik et al., 2012). The methodologies used to estimate LIENSs extension of the intertidal habitat of the oysters combined aerial image analysis, remote sensing, field validations and thematic analysis using GIS (Cajeri et al., 2012).