Analysis of tuna stomach contents

One product and 3 components were developed in order to fulfill the third objectif ATLANTIC_CH02_Product_5 / Distribution of ocean monitoring systems to assess climate change existing into the MPA network • Physical parameter monitoring • Chemical parameter monitoring • Biological parameter monitoring The aim of the product is the identification of ocean monitoring systems to assess climate change in MPAs.

It's a study of MPA connectivity with assessment of : -size -shape -spacing between each MPA

Map at 1 degree resolution of 10 year linear trend in sea water temperature at 3 levels: surface, 500m , bottom

'''This product has been archived'''                For operationnal and online products, please visit https://marine.copernicus.eu '''DEFINITION''' The indicator of the Kuroshio extension phase variations is based on the standardized high frequency altimeter Eddy Kinetic Energy (EKE) averaged in the area 142-149°E and 32-37°N and computed from the DUACS (https://duacs.cls.fr) delayed-time (reprocessed version DT-2021, CMEMS SEALEVEL_GLO_PHY_L4_MY_008_047) and near real-time (CMEMS SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS_008_046) altimeter sea level gridded products. The change in the reprocessed version (previously DT-2018) and the extension of the mean value of the EKE (now 27 years, previously 20 years) induce some slight changes not impacting the general variability of the Kuroshio extension (correlation coefficient of 0.988 for the total period, 0.994 for the delayed time period only). '''CONTEXT''' The long-term mean and trends alone do not give a complete view of the likely changes in position of unstable western boundary current extensions (Kelly et al., 2010). The Kuroshio Extension is an eastward-flowing current in the subtropical western North Pacific after the Kuroshio separates from the coast of Japan at 35°N, 140°E. Being the extension of a wind-driven western boundary current, the Kuroshio Extension is characterized by a strong variability and is rich in large-amplitude meanders and energetic eddies (Niiler et al., 2003; Qiu, 2003, 2002). The Kuroshio Extension region has the largest sea surface height variability on sub-annual and decadal time scales in the extratropical North Pacific Ocean (Jayne et al., 2009; Qiu and Chen, 2010, 2005). Prediction and monitoring of the path of the Kuroshio are of huge importance for local economies as the position of the Kuroshio extension strongly determines the regions where phytoplankton and hence fish are located. '''CMEMS KEY FINDINGS''' The different states of the Kuroshio extension phase have been presented and validated by Bessières et al. (2013) and further reported by Drévillon et al. (2018) in the Copernicus Ocean State Report #2. Two rather different states of the Kuroshio extension are observed: an ‘elongated state’ (also called ‘strong state’) corresponding to a narrow strong steady jet, and a ‘contracted state’ (also called ‘weak state’) in which the jet is weaker and more unsteady, spreading on a wider latitudinal band. When the Kuroshio Extension jet is in a contracted (elongated) state, the upstream Kuroshio Extension path tends to become more (less) variable and regional eddy kinetic energy level tends to be higher (lower). In between these two opposite phases, the Kuroshio extension jet has many intermediate states of transition and presents either progressively weakening or strengthening trends. In 2018, the indicator reveals an elongated state followed by a weakening neutral phase since then. '''DOI (product):''' https://doi.org/10.48670/moi-00222

'''DEFINITION''' Ocean acidification is quantified by decreases in pH, which is a measure of acidity: a decrease in pH value means an increase in acidity, that is, acidification. The observed decrease in ocean pH resulting from increasing concentrations of CO2 is an important indicator of global change. The estimate of global mean pH builds on a reconstruction methodology, * Obtain values for alkalinity based on the so called “locally interpolated alkalinity regression (LIAR)” method after Carter et al., 2016; 2018. * Build on surface ocean partial pressure of carbon dioxide (CMEMS product: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) obtained from an ensemble of Feed-Forward Neural Networks (Chau et al. 2022) which exploit sampling data gathered in the Surface Ocean CO2 Atlas (SOCAT) (https://www.socat.info/) * Derive a gridded field of ocean surface pH based on the van Heuven et al., (2011) CO2 system calculations using reconstructed pCO2 (MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008) and alkalinity. The global mean average of pH at yearly time steps is then calculated from the gridded ocean surface pH field. It is expressed in pH unit on total hydrogen ion scale. In the figure, the amplitude of the uncertainty (1σ ) of yearly mean surface sea water pH varies at a range of (0.0023, 0.0029) pH unit (see Quality Information Document for more details). The trend and uncertainty estimates amount to -0.0017±0.0004e-1 pH units per year. The indicator is derived from in situ observations of CO2 fugacity (SOCAT data base, www.socat.info, Bakker et al., 2016). These observations are still sparse in space and time. Monitoring pH at higher space and time resolutions, as well as in coastal regions will require a denser network of observations and preferably direct pH measurements. A full discussion regarding this OMI can be found in section 2.10 of the Ocean State Report 4 (Gehlen et al., 2020). '''CONTEXT''' The decrease in surface ocean pH is a direct consequence of the uptake by the ocean of carbon dioxide. It is referred to as ocean acidification. The International Panel on Climate Change (IPCC) Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems (2011) defined Ocean Acidification as “a reduction in the pH of the ocean over an extended period, typically decades or longer, which is caused primarily by uptake of carbon dioxide from the atmosphere, but can also be caused by other chemical additions or subtractions from the ocean”. The pH of contemporary surface ocean waters is already 0.1 lower than at pre-industrial times and an additional decrease by 0.33 pH units is projected over the 21st century in response to the high concentration pathway RCP8.5 (Bopp et al., 2013). Ocean acidification will put marine ecosystems at risk (e.g. Orr et al., 2005; Gehlen et al., 2011; Kroeker et al., 2013). The monitoring of surface ocean pH has become a focus of many international scientific initiatives (http://goa-on.org/) and constitutes one target for SDG14 (https://sustainabledevelopment.un.org/sdg14). '''CMEMS KEY FINDINGS''' Since the year 1985, global ocean surface pH is decreasing at a rate of -0.0017±0.019 decade-1 '''DOI (product):''' https://doi.org/10.48670/moi-00224

Map of seasonal averages of dissolved oxygen indicator (mg/l) for eutrophication for the past 10 years (2005-2014) in the Atlantic basin. It will be generated using in situ measurements of the different parameteres required to assess the dissolved oxygen indicator and the OSPAR Convention Common procedure methodology (OSPAR 2013, Common Procedure for the Identification of the Eutrophication Status of the OSPAR Maritime Area. Agreement 2013-08. 67 pp)

This data product selects sample areas of digital bathymetry, chosen for their relevance to marine activities and data sources alternative to GEBCO. The approach for building the digital map of water depth is to use GEBCO as a baseline and look at a set of sample areas where GEBCO could be improved upon. Sample areas have also been selected to be representative of each continent bordering the Atlantic and expected future requirements. Data sources include GEBCO, EMODNET, USGS and CHS.

Maisons éclusières sur les départements de la Gironde et du Lot-et-Garonne.

We took inspiration from a “Matrix of marine activities” (appropriate for each IUCN management category) extracted from IUCN paper, to achieve the first objective by computing 1 product comprising the following 12 components: Product ATLANTIC_CH02_Product_1 / MPA Atlantic network classified in IUCN classification • Traditional fishing area • Sustainable fishing area (industrial) • Leisure fishing area • Leisure activity area (diving, surfing, tourist beaches) • Shipping area (shipping trajectory, aids navigation) • Scientific activity area • Renewable energy generation facility area (ocean energy facilities, wind farms) • Aquaculture area (finfish production, shellfish production) • Shipping infrastructure area (harbours, dredging area...) • Waste discharge area • Mining area (aggregate extraction, hydrocarbon extraction) • Habitation area (urban area) Each geographic information required for the components was compiled into a layer in grid format. These grids were intersected with the MPAs layer to assign each MPA a IUCN category according to the conditional matrix illustrated below : If the MPA area contains : Habitation area (urban area) The IUCN category is :V If the MPA area contains : Mining area (aggregate extraction, hydrocarbon extraction) The IUCN category is V If the MPA area contains : Waste discharge area The IUCN category is : V If the MPA area contains : Shipping infrastructure area (harbours, dredging area...) The IUCN category is IV If the MPA area contains : Aquaculture area (finfish production, shellfish production) The IUCN category is IV If the MPA area contains : Renewable energy generation facility area (ocean energy facilities, wind farms) The IUCN category is IV If the MPA area contains : Leisure fishing area The IUCN category is IV If the MPA area contains : Sustainable fishing area (industrial) The IUCN category is IV If the MPA area contains : Shipping area (shipping trajectory, aids navigation) The IUCN category is II If the MPA area contains : Leisure activity area (diving, surfing, tourist beaches) The IUCN category is Ib If the MPA area contains : Traditional fishing area The IUCN category is Ib If the MPA area contains : Scientific activity area The IUCN category is Ia