L’étalement urbain est une forme d’urbanisation qui s’est développée autour des agglomérations. Il entraine une artificialisation des sols qui produit des impacts sur l’environnement, sur le paysage et sur l’organisation des territoires. La lutte contre l’étalement urbain et l’artificialisation des sols au profit de formes urbaines denses et compactes est une priorité régionale qui implique de mettre en oeuvre une politique globale de maîtrise de la consommation des espaces naturels, agricoles et forestiers. L’Aquitaine est une région vaste et davantage artificialisée que la France métropolitaine. La densité de population est faible et l’Aquitaine arrive en 8ème position des régions occupant le plus d’espace artificialisé par habitant. Son fort dynamisme démographique accroît les pressions sur le foncier disponible, notamment autour des pôles d’emploi. Dans plusieurs aires urbaines, les sols s’artificialisent à un rythme bien supérieur à l’évolution de la population.

Specification of the desirable and recomended product attributes for generating spatial layers of sea surface temperature temperature trend for the last 10, 50, 100 years for the Mediterranean basin and for each NUTS3 region along the coast.

Specification of the desirable and recommended products attributes for generating spatial layers of sea surface temperature for the last 10, 50 and 100 years for the Mediterranean basin and for each NUTS3 region along the coast.

Première utilisation du sol, devant l'agriculture et loin devant l'urbanisme, la forêt couvre 45 % du territoire aquitain. La région se caractérise par la domination d'une essence, le pin maritime. Celui-ci couvre plus de la moitié de la surface forestière régionale. Outre sa valeur patrimoniale, cette forêt génère une activité économique qui représente environ 3 milliards d'euros. Ce secteur forêt-bois est donc un formidable gisement d'emplois, principalement en milieu rural. Cet espace occupé par la forêt attise néanmoins des convoitises pour différents types d'usage: l'urbanisation, les installations photovoltaïques ou encore l'agriculture.

Metabolome of of the marine diatom Haslea ostrearia. Bacteria were isolated from Haslea ostrearia isolates cultivated in ES 1/3 medium in laboratory conditions over a 3-month period. These microalgal isolates were recovered from four sites on the French Atlantic coast: Bouin , La Barre-de-Monts (46.90 N; 2.11°W), Isle de Ré (46.22 N; 1.45°W), and La Tremblade (45.80 N; 1.15°W) . Data processing and statistical analysis of the metabolic profiles were performed on an LC/MS Metabolomics Discovery Workflow using Mass Profiler Professional Software and an Agilent 1290 Infinity II LC system coupled to an Agilent 6540 UHD Accurate-Mass QTOF hybrid mass spectrometer (Agilent Technologies, Waldbronn, Germany) equipped with a dual electrospray ionization (ESI) source. The full history (tools, parameters, input and output data files) is publicly available on http://dx.doi.org/10.12770/046e1e6a-864e-48a6-944b-d8613d67de0f

The vision of the AtlantOS project was to improve and innovate Atlantic observing by using the Framework of Ocean Observing to obtain an international, more sustainable, more efficient, more integrated, and fit-for-purpose system contributing to the Trans-Atlantic Research Alliance, the GEO (Group on Earth Observations) global initiative Blue Planet, and GOOS (Global Ocean Observing Systems). Hence, the AtlantOS project will have a long-lasting and sustainable contribution to the societal, economic and scientific benefit arising from this integrated approach. This will be achieved by improving the value for money, extent, completeness, quality and ease of access to Atlantic Ocean data required by industries, product supplying agencies, scientists and citizens. The overarching target of the AtlantOS initiative was to deliver an advanced framework for the development of an integrated Atlantic Ocean Observing System that goes beyond the state-of–the-art, and leaves a legacy of sustainability after the life of the project (see AtlantOS High-Level Strategy and find out more about the AtlantOS program). The legacy derived from the AtlantOS aims: - to improve international collaboration in the design, implementation and benefit sharing of ocean observing, - to promote engagement and innovation in all aspects of ocean observing, - to facilitate free and open access to ocean data and information, - to enable and disseminate methods of achieving quality and authority of ocean information, - to strengthen the Global Ocean Observing System (GOOS) and to sustain observing systems that are critical for the Copernicus Marine Environment Monitoring Service and its applications and - to contribute to the aims of the Galway Statement on Atlantic Ocean Cooperation The project was organized along work packages on: i) observing system requirements and design studies, ii) enhancement of ship-based and autonomous observing networks, iii) interfaces with coastal ocean observing systems, iv) integration of regional observing systems, v) cross-cutting issues and emerging networks, vi) data flow and data integration, vii) societal benefits from observing /information systems, viii) system evaluation and resource sustainability. Engagement with wider stakeholders including end-users of Atlantic Ocean observation products and services was also key throughout the project. The AtlantOS initiative contributed to achieving the aims of the Galway Statement on Atlantic Ocean Cooperation that was signed in 2013 by the EU, Canada and the US, launching a Transatlantic Ocean Research Alliance to enhance collaboration to better understand the Atlantic Ocean and sustainably manage and use its resources.

This gridded product visualizes 1960 - 2014 water body ammonium concentration (umol/l) in the North Sea domain, for each season (winter: December – February; spring: March – May; summer: June – August; autumn: September – November). It is produced as a Diva 4D analysis, version 4.6.9: a reference field of all seasonal data between 1960-2014 was used; results were logit transformed to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. Every step of the time dimension corresponds to a 10-year moving average for each season. The depth dimension allows visualizing the gridded field at various depths.

This gridded product visualizes 1960 - 2014 water body total nitrogen concentration (umol/l) in the North Sea domain, for each season (winter: December – February; spring: March – May; summer: June – August; autumn: September – November). It is produced as a Diva 4D analysis, version 4.6.11: a reference field of all seasonal data between 1960-2014 was used; results were logit transformed to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. Every step of the time dimension corresponds to a 10-year moving average for each season. The depth dimension allows visualizing the gridded field at various depths.

Moving 10-years analysis of nitrates at Mediterranean Sea for each season : - winter (January-March), - spring (April-June), - summer (July-September), - autumn (October-December). Every year of the time dimension corresponds to the 10-year centered average of each season. Decades span from 1960-1969 until 2004-2013. Observational data span from 1960 to 2013. Depth range (IODE standard depths): -1500.0, -1400.0, -1300.0, -1200.0, -1100.0, -1000.0, -900.0, -800.0, -700.0, -600.0, -500.0, -400.0, -300.0, -250.0, -200.0, -150.0, -125.0, -100.0, -75.0, -50.0, -30.0, -20.0, -10.0, -5.0, -0.0. Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVA (Data-Interpolating Variational Analysis) tool. Profiles were interpolated at standard depths using weighted parabolic interpolation algorithm (Reiniger and Ross, 1968). GEBCO 1min topography is used for the contouring preparation. Analyzed filed masked using relative error threshold 0.3 and 0.5. DIVA settings: A constant value for signal-to-noise ratio was used equal to 3. Correlation length was optimized and filtered vertically and a seasonally-averaged profile was used. Logarithmic transformation applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data. Detrending of data: no. Advection constraint applied: no. Originators of Italian data sets-List of contributors • Brunetti Fabio (OGS) • Cardin Vanessa, Bensi Manuel doi:10.6092/36728450-4296-4e6a-967d-d5b6da55f306 • Cardin Vanessa, Bensi Manuel, Ursella Laura, Siena Giuseppe doi:10.6092/f8e6d18e-f877-4aa5-a983-a03b06ccb987 • Cataletto Bruno (OGS) • Cinzia Comici Cinzia (OGS) • Civitarese Giuseppe (OGS) • DeVittor Cinzia (OGS) • Giani Michele (OGS) • Kovacevic Vedrana (OGS) • Mosetti Renzo (OGS) • Solidoro C.,Beran A.,Cataletto B.,Celussi M.,Cibic T.,Comici C.,Del Negro P.,De Vittor C.,Minocci M.,Monti M.,Fabbro C.,Falconi C.,Franzo A.,Libralato S.,Lipizer M.,Negussanti J.S.,Russel H.,Valli G., doi:10.6092/e5518899-b914-43b0-8139-023718aa63f5 • Celio Massimo (ARPA FVG) • Malaguti Antonella (ENEA) • Fonda Umani Serena (UNITS) • Bignami Francesco (ISAC/CNR) • Boldrini Alfredo (ISMAR/CNR) • Marini Mauro (ISMAR/CNR) • Miserocchi Stefano (ISMAR/CNR) • Zaccone Renata (IAMC/CNR) • Lavezza, R., Dubroca, L. F. C., Ludicone, D., Kress, N., Herut, B., Civitarese, G., Cruzado, A., Lefèvre, D., Souvermezoglou, E., Yilmaz, A., Tugrul, S., and Ribera d’Alcala, M.: Compilation of quality controlled nutrient profiles from the Mediterranean Sea, doi:10.1594/PANGAEA.771907, 2011. Units: umol/l

This gridded product visualizes 1960 - 2014 water body dissolved oxygen concentration (umol/l) in the North Sea domain, for each season (winter: December – February; spring: March – May; summer: June – August; autumn: September – November). It is produced as a Diva 4D analysis, version 4.6.11: a reference field of all seasonal data between 1960-2014 was used; results were logit transformed to avoid negative/underestimated values in the interpolated results; error threshold masks L1 (0.3) and L2 (0.5) are included as well as the unmasked field. Every step of the time dimension corresponds to a 10-year moving average for each season. The depth dimension allows visualizing the gridded field at various depths.