The SeaDataCloud TS historical data collection V2 for the North Atlantic Ocean, includes open access in situ data on temperature and salinity of water column in the North Atlantic Ocean from 10°N to 62°N, including the Labrador Sea, The data were retrieved from the SeaDataNet infrastructure at summer 2019. The dataset format is Ocean Data View (ODV - http://odv.awi.de/) binary collection. The quality control of the data has been performed with the help of ODV software. Data Quality Flags have been revised and set up using the elaborated by SeaDataNet2 project QC procedures in conjunction with the visual expert check. The final number of the Temperature and Salinity profiles (stations) in the collection is 10119755. For data access please register at http://www.marine-id.org/.

The SDC_NAT_DP1 product contains the North Atlantic Ocean monthly climatology for mixed layer depth (MLD) based on temperature climatology spanning 60 years (1955-2015). The MLD fields have spatial resolution 1/4°. The profiles of temperature combines data from 2 major sources, the SeaDataNet infrastructure and a part of data of the Coriolis Ocean Dataset for Reanalysis (CORA). The used climatology is the SeaDataCloud North Atlantic Ocean Temperature Climatology V1 (https://doi.org/10.13155/61810) done with the DIVA software, version 4.7.2. The product was developed in framework of the SeaDataCloud project. This product must be considered as feasibility study for the next phases, it is a beta-version and that further research needs to be done before its usage from users.

All Member States are requested by the Habitats Directive (92/43/EEC) to monitor habitat types and species listed in its annexes and send a report every 6 years following an agreed format. The assessment of conservation status is based on information about the status and trends of species populations and of habitats at the level of the biogeographical or marine region. The spatial dataset contains habitat and species distribution data (10km grid cells) as reported by Member States for the 2013-2018 period. This metadata refers to the public dataset, without sensitive species. The data sets are divided in two sets for species and two sets for habitat types. Species: ART17 species distribution MS (by Member State) ART17 species distribution EU (European Union aggregate) Habitats: ART17 habitats distribution MS (by Member State) ART17 habitats distribution EU (European Union aggregate) Both MS datasets are aggregated by habitat/species code, country and biogeographical /marine region [CO_MS_RE]. Using this attribute [CO_MS_RE] the tabular conservation status, which is available in the table, per biogeographical/marine region of the Member State level (MS) can be joined directly to the spatial dataset. Both EU datasets are aggregated by habitat/species code and biogeographical /marine region [CO_RE]. Using this attribute [CO_RE] the tabular conservation status, which is available in the table, of the biogeographical/marine region of the EU-28 level (EU) can be directly joined to the spatial dataset. Further description of the Article 17 tabular and spatial dataset and a Article 17 web tool can be accessed with the download data.

Itinéraires de randonnée et pistes cyclables du Département des Landes. Le Département des Landes propose 3 500 km d’itinéraires inscrits au Plan départemental des itinéraires de promenade et de randonnée (PDIPR) et près de 2 500 km d’itinéraires cyclables. Ces circuits sont entretenus et balisés avec des niveaux de difficultés mentionnés sur chaque parcours.

In integrated multi-trophic aquaculture (IMTA), multiple aquatic species from different trophic levels are farmed together. Thus, waste from one species can be used as input (fertiliser and food) for another species. The EU-funded ASTRAL project will develop IMTA production chains for the Atlantic markets. Focusing on a regional challenge-based perspective, it will bring together labs in Ireland and Scotland (open offshore labs), South Africa (flow-through inshore) and Brazil (recirculation inshore) as well as Argentina (prospective IMTA lab). The aim is to increase circularity by as much as 60 % compared to monoculture baseline aquaculture and to boost revenue diversification for aquaculture producers. ASTRAL will share, integrate, and co-generate knowledge, technology and best practices fostering a collaborative ecosystem along the Atlantic.

This metadata refers to a dataset that shows the percentage of cities' administrative area (core city based on the Urban Morphological Zones dataset) inundated by the sea level rise of 2 metres, without any coastal flooding defences present for a series of individual coastal European cities (included in Urban Audit). The dataset has been computed using the CReSIS (Centre for Remote Sensing of Ice Sheets) dataset for 2018.

'''DEFINITION''' The CMEMS IBI_OMI_tempsal_extreme_var_temp_mean_and_anomaly OMI indicator is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different CMEMS products are used to compute the indicator: The Iberia-Biscay-Ireland Multi Year Product (IBI_MULTIYEAR_PHY_005_002) and the Analysis product (IBI_ANALYSISFORECAST_PHY_005_001). Two parameters have been considered for this OMI: • Map of the 99th mean percentile: It is obtained from the Multi Year Product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2021). • Anomaly of the 99th percentile in 2022: The 99th percentile of the year 2022 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2022 percentile. This indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (Pérez Gómez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (Pérez Gómez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). '''CONTEXT''' The Sea Surface Temperature is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018). The IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The Sea Surface Temperature field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the Iberia-Biscay-Ireland region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003). Also relevant in the region are the upwelling processes taking place in the coastal margins. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980). '''CMEMS KEY FINDINGS''' In the IBI region, the 99th mean percentile for 1993-2021 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in Álvarez Fanjul (2019) for the period 1993-2016. The analysis of the 99th percentile anomaly in the year 2023 shows that this period has been affected by a severe impact of maximum SST values. Anomalies exceeding the standard deviation affect almost the entire IBI domain, and regions impacted by thermal anomalies surpassing twice the standard deviation are also widespread below the 43ºN parallel. Extreme SST values exceeding twice the standard deviation affect not only the open ocean waters but also the easter boundary upwelling areas such as the northern half of Portugal, the Spanish Atlantic coast up to Cape Ortegal, and the African coast south of Cape Aguer. It is worth noting the impact of anomalies that exceed twice the standard deviation is widespread throughout the entire Mediterranean region included in this analysis. '''Figure caption''' Iberia-Biscay-Ireland Surface Temperature extreme variability: Map of the 99th mean percentile computed from the Multi Year Product (left panel) and anomaly of the 99th percentile in 2022 computed from the Analysis product (right panel). Transparent grey areas (if any) represent regions where anomaly exceeds the climatic standard deviation (light grey) and twice the climatic standard deviation (dark grey). '''DOI (product):''' https://doi.org/10.48670/moi-00254

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

Pôles de la CAPB correspondant aux anciens EPCI

Cette couche recense les Zones d’Activités Economiques (ZAE) présentes sur le département de la Charente. Initialement crée par Charente Développement, il s'agit d'un surfacique qui permet d'identifier précisément le contour de ces zones en se calant sur les données du Cadastre.