Assessments run at AFWG provide the scientific basis for the management of cod, haddock, saithe, redfish, Greenland halibut and capelin in subareas 1 and 2. Taking the catch values provided by the Norwegian fisheries ministry for Norwegian catches1 and raising the total landed value to the total catches gives an approximate nominal first-hand landed value for the combined AFWG stocks of ca. 20 billion NOK or ca. 2 billion EUR (2018 estimates).

'''DEFINITION''' The CMEMS MEDSEA_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 (MEDSEA_MULTIYEAR_PHY_006_004) and the Analysis product (MEDSEA_ANALYSISFORECAST_PHY_006_013). 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 (1987-2019). * Anomaly of the 99th percentile in 2020: The 99th percentile of the year 2020 is computed from the Near Real Time product. The anomaly is obtained by subtracting the mean percentile from the 2020 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. In recent decades (from mid ‘80s) the Mediterranean Sea showed a trend of increasing temperatures (Ducrocq et al., 2016), which has been observed also by means of the CMEMS SST_MED_SST_L4_REP_OBSERVATIONS_010_021 satellite product and reported in the following CMEMS OMI: MEDSEA_OMI_TEMPSAL_sst_area_averaged_anomalies and MEDSEA_OMI_TEMPSAL_sst_trend. The Mediterranean Sea is a semi-enclosed sea characterized by an annual average surface temperature which varies horizontally from ~14°C in the Northwestern part of the basin to ~23°C in the Southeastern areas. Large-scale temperature variations in the upper layers are mainly related to the heat exchange with the atmosphere and surrounding oceanic regions. The Mediterranean Sea annual 99th percentile presents a significant interannual and multidecadal variability with a significant increase starting from the 80’s as shown in Marbà et al. (2015) which is also in good agreement with the multidecadal change of the mean SST reported in Mariotti et al. (2012). Moreover the spatial variability of the SST 99th percentile shows large differences at regional scale (Darmariaki et al., 2019; Pastor et al. 2018). '''CMEMS KEY FINDINGS''' The Mediterranean mean Sea Surface Temperature 99th percentile evaluated in the period 1987-2019 (upper panel) presents highest values (~ 28-30 °C) in the eastern Mediterranean-Levantine basin and along the Tunisian coasts especially in the area of the Gulf of Gabes, while the lowest (~ 23–25 °C) are found in the Gulf of Lyon (a deep water formation area), in the Alboran Sea (affected by incoming Atlantic waters) and the eastern part of the Aegean Sea (an upwelling region). These results are in agreement with previous findings in Darmariaki et al. (2019) and Pastor et al. (2018) and are consistent with the ones presented in CMEMS OSR3 (Alvarez Fanjul et al., 2019) for the period 1993-2016. The 2020 Sea Surface Temperature 99th percentile anomaly map (bottom panel) shows a general positive pattern up to +3°C in the North-West Mediterranean area while colder anomalies are visible in the Gulf of Lion and North Aegean Sea . This Ocean Monitoring Indicator confirms the continuous warming of the SST and in particular it shows that the year 2020 is characterized by an overall increase of the extreme Sea Surface Temperature values in almost the whole domain with respect to the reference period. This finding can be probably affected by the different dataset used to evaluate this anomaly map: the 2020 Sea Surface Temperature 99th percentile derived from the Near Real Time Analysis product compared to the mean (1987-2019) Sea Surface Temperature 99th percentile evaluated from the Reanalysis product which, among the others, is characterized by different atmospheric forcing). Note: The key findings will be updated annually in November, in line with OMI evolutions. '''DOI (product):''' https://doi.org/10.48670/moi-00266

Pôles de la CAPB correspondant aux anciens EPCI

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 study gathers multi-year environmental sequencing datasets generated within the French ROME pilot observatory network. It includes eDNA metabarcoding and RNA-based analyses from water samples, oyster tissues, and viral fractions collected across four French estuarine ecosystems between 2020 and 2023, supporting integrated monitoring of coastal microbiomes and microbial hazards.

The Ocean Colour Climate Change Initiative project aims to: Develop and validate algorithms to meet the Ocean Colour GCOS ECV requirements for consistent, stable, error-characterized global satellite data products from multi-sensor data archives. Produce and validate, within an R&D context, the most complete and consistent possible time series of multi-sensor global satellite data products for climate research and modelling. Optimize the impact of MERIS data on climate data records. Generate complete specifications for an operational production system. Strengthen inter-disciplinary cooperation between international Earth observation, climate research and modelling communities, in pursuit of scientific excellence. The ESA OC CCI project is following a data reprocessing paradigm of regular re-processings utilising on-going research and developments in atmospheric correction, in-water algorithms, data merging techniques and bias correction. This requires flexibility and rapid turn-around of processing of extensive ocean colour datasets from a number of ESA and NASA missions to both trial new algorithms and methods and undertake the complete data set production. Read more about the Ocean Colour project on ESA's project website. https://climate.esa.int/en/projects/ocean-colour/.

This metadata describes the ICES data on the temporal development of the Lusitanian/Boreal species ratio in the period from 19657 to 2016. Key message: The ratio between the number of Lusitanian (warm-favouring) and Boreal (cool-favouring) species are significantly increasing in several North-East Atlantic marine areas whereas there is no significant changes in all the southern areas. Changes in ratios are most apparent in the North Sea, Irish Sea and West of Scotland. Furthermore, it seems that Lusitanian species have not spread in all northward directions, but have followed two particular routes, through the English Channel and north around Scotland Blue dots indicates L/B ratios below 1 (dominance of Boreal species) Yellow dots indicates L/B ratios >=1 and <2 (dominance of Lusitanian species) Red dots indicates L/B ratios >=2 (high dominance of Lusitanian species) The dataset is derived from the ICES data portal 'DATRAS' (the Database of Trawl Surveys). DATRAS is an online database of trawl surveys with access to standard data products. DATRAS stores data collected primarily from bottom trawl fish surveys coordinated by ICES expert groups. The survey data are covering the Baltic Sea, Skagerrak, Kattegat, North Sea, English Channel, Celtic Sea, Irish Sea, Bay of Biscay and the eastern Atlantic from the Shetlands to Gibraltar. At present, there are more than 56 years of continuous time series data in DATRAS, and survey data are continuously updated by national institutions. The dataset has been used in the EEA Indicator "Changes in fish distribution in European seas" https://www.eea.europa.eu/data-and-maps/indicators/fish-distribution-shifts/assessment-1. The dataset has been used for this static map: https://www.eea.europa.eu/en/analysis/indicators/changes-in-fish-distribution-in/temporal-development-of-the-ratio

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.

This data set corresponds to the global offshore wind farm boundaries with the following attributes for each project: + WindfarmId (ID of the windfarm) + Name (Name of the windfarm) + Country (Country code) + Status (Status code) + WindfarmStatus (Windfarm Status or Project Status) + StatusComments (Comments on the Windfarm Status or Project Status) + CapacityMWMin (Capacity of the windfarm - Min) + CapacityMWMax (Capacity of the windfarm - Max) + NoTurbinesMin (Number of turbines - Min) + NoTurbinesMax (Number of turbines - Max) + Comments (Comments) + TurbineMWMin (Capacity of the turbine (set-up in the windfarm) - Min) + TurbineMWMax (Capacity of the turbine (set-up in the windfarm) - Max) + OtherNames (Other name of the windfarm) + CountryName (Country where the windfarm is set) + Lat (Geographic coordinate - centre latitude) + Lon (Geographic coordinate - centre longitude) + IsEstimatedLocation (This is where we know that a project exists but we don't know its exact location.) + IsOnHold + Developers (Developer(s) of the windfarm) + Owners (Owner of the windfarm) + Operators (Operator of the windfarm) + OffshoreConstructionStarts The frequency of the database release is monthly. This data set corresponds to the release of January 2020. This data set is strictly for internal EEA use as is subjected to a commercial license. Given the limited user subscriptions available, interested users should contact the SDI Team (sdi@eea.europa.eu) to be granted access to the data set.

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.