Seasonal climatology of Water body dissolved oxygen concentration for Loire river for the period 1950-2021 and for the following seasons: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Observation data span from 1950 to 2021. Depth levels (m): [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 15 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.01 degrees. Horizontal correlation length is defined seasonally (in meters): 150000 (winter), 100000 (spring), 70000 (summer), 115000 (autumn). Vertical correlation length was optimized and vertically filtered and a seasonally-averaged profile was used (DIVAnd.fitvertlen). Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.Anam.loglin) was applied to the data prior to the analysis to avoid unrealistic negative values. Background field: the vertically-filtered data mean profile is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: umol/l.

This product displays for Lead, median values of the last 6 available years that have been measured per matrix and are present in EMODnet regional contaminants aggregated datasets, v2022. The median values ranges are derived from the following percentiles: 0-25%, 25-75%, 75-90%, >90%. Only "good data" are used, namely data with Quality Flag=1, 2, 6, Q (SeaDataNet Quality Flag schema). For water, only surface values are used (0-15 m), for sediment and biota data at all depths are used.

Seasonal climatology of Water body dissolved inorganic nitrogen for Loire river for the period 1971-2021 and for the following seasons: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Observation data span from 1971 to 2021. Depth levels (m): [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0, 110.0, 120.0, 130.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 15 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.01 degrees. Horizontal correlation length is defined seasonally (in meters): 72000 (winter), 43000 (spring), 215000 (summer), 150000 (autumn). Vertical correlation length was optimized and vertically filtered and a seasonally-averaged profile was used (DIVAnd.fitvertlen). Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.Anam.loglin) was applied to the data prior to the analysis to avoid unrealistic negative values. Background field: the vertically-filtered data mean profile is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: umol/l.

Species distribution models (GAM, Maxent and Random Forest ensemble) predicting the distribution of Solitary Scleractinian fields assemblage in the Celtic Sea. This community is considered ecologically coherent according to the cluster analysis conducted by Parry et al. (2015) on image sample. Modelling its distribution complements existing work on their definition and offers a representation of the extent of the areas of the north-east Atlantic where they can occur based on the best available knowledge. This work was performed at the University of Plymouth in 2021.

EMODnet Chemistry aims to provide access to marine chemistry datasets and derived data products concerning eutrophication, acidity and contaminants. The importance of the selected substances and other parameters relates to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on potential hazardous substances, despite the fact that some data might not be related to pollution (e.g. collected by deep corer). Temperature, salinity and additional parameters are included when available. It covers the Northeast Atlantic Ocean (40W). Data were harmonised and validated by the '‘IFREMER / IDM / SISMER - Scientific Information Systems for the SEA’ in France. The dataset contains water (profiles), sediment (profiles and timeseries) and biota (timeseries). The temporal coverage is 1974–2018 for water measurements, 1966–2014 for sediment measurements and 1979–2021 for biota measurements. Regional datasets concerning contaminants are automatically harvested and the resulting collections are harmonised and validated using ODV Software and following a common methodology for all sea regions ( https://doi.org/10.6092/8b52e8d7-dc92-4305-9337-7634a5cae3f4). Parameter names are based on P01 vocabulary, which relates to BODC Parameter Usage Vocabulary and is available at: https://vocab.nerc.ac.uk/search_nvs/P01/. The harmonised dataset can be downloaded as as an ODV spreadsheet, which is composed of a metadata header followed by tab separated values. This spreadsheet can be imported into ODV Software for visualisation (more information can be found at: https://www.seadatanet.org/Software/ODV). In addition, the same dataset is offered also as a txt file in a long/vertical format, in which each P01 measurement is a record line. Additionally, there are a series of columns that split P01 terms into subcomponents (substance, CAS number, matrix...).This transposed format is more adapted to worksheet applications (e.g. LibreOffice Calc).

Moving 6-year analysis of Water body silicate in the NorthEast Atlantic for each season: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December. Every year of the time dimension corresponds to the 6-year centred average of each season. 6-year periods span from 1950/1955 until 2016/2021. Observation data span from 1950 to 2021. Depth levels (IODE standard depths): [0.0, 5.0, 10.0, 20.0, 30.0, 40.0, 50.0, 75.0, 100.0, 125.0, 150.0, 200.0, 250.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1750.0, 2000.0]. Data sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Descrption of DIVAnd analysis: the computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 30 sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps is 0.1 degrees. Horizontal correlation length varies from 300km in open sea regions to 50km at the coast. Vertical correlation length is defined as twice the vertical resolution. Signal-to-noise ratio was fixed to 1 for vertical profiles and 0.1 for time series to account for the redundancy in the time series observations. A logarithmic transformation (DIVAnd.Anam.loglin) was applied to the data prior to the analysis to avoid unrealistic negative values. Background field: a vertically-filtered profile of the seasonal data mean value (including all years) is substracted from the data. Detrending of data: no, advection constraint applied: no. Units: umol/l.

Modelled density of the seapen Kophobelemnon stelliferum in the North East Atlantic. The Random Forest density model trained on data collected by an ROV was constrained by an ensemble of Maxent and Random Forest presence-absence model trained on a larger dataset also collected by an ROV. This species provides structural complexity in an environment where it is lacking and, thus, promotes higher biodiversity where they settle. They are vulnerable to mechanical disturbance of the sediment by fishing gear and a better understanding of their distribution will lead to better management of their population. This work was performed at the University of Plymouth in 2021.

'''This product has been archived''' This dataset provide a times series of gap free map of Sea Surface Temperature (SST) foundation at high resolution on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the Level 4 SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. An optimal interpolation is used to fill in where observations are missing. '''DOI (product) :''' https://doi.org/10.48670/mds-00321

Moving 6-year analysis and visualization of Water body silicate in the North Sea. Four seasons (December-February, March-May, June-August, September-November). Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: Geostatistical data analysis by DIVAnd (Data-Interpolating Variational Analysis) tool, version 2.7.9. results were subjected to the minfield option in DIVAnd 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. The depth dimension allows visualizing the gridded field at various depths.

The data sets presented here result from the long-term monitoring of individual growth patterns in anchovy and sardine in the Bay of Biscay, from 2000 to 2018. They derived from the PELGAS survey series (Doray et al., 2018), which monitors annually the Bay of Biscay pelagic ecosystem since 2000. The survey is performed in May during the peak spawning of anchovy and main spawning of sardine. Among the many data collected, anchovy and sardine populations are assessed by combining acoustic records with pelagic trawl hauls catches and ICES survey protocoles are used, as detailed in Doray et al. (2021). Briefly, fish acoustic backscatter are recorded along survey transect lines and pelagic trawl hauls undertaken opportunistically to identify echotraces to species and collect fish samples for acquiring biometric data. At each trawl haul and for each species, a random subsample of individuals is taken to establish the species’ length distributions. For anchovy and sardine, this subsample is further subsampled, spanning the whole length range, to take individual fish measurements. These amount to extracting otoliths and measuring individuals’ age, length, weight, sexual maturity and other parameters. Individual measurements are taken on fourty individuals of anchovy and sardine when the species are present in the catch. For each individual fish, the two otolith sagittae are extracted on board, mounted in leukit for age reading on board when permitting and/or on land in the laboratory. Growth patterns in the otoliths were analysed on land with a binocular stereomicroscope under reflected natural light. For anchovy, otoliths’ growth was measured for all individuals in all the hauls. For sardine, trawl hauls were selected and all individual otoliths were measured in each selected haul. The selection was made using the geographical stratification defined in Petitgas et al. (2018) based on the ecosystem spatial structure. An average of two to three hauls in each of the four strata were selected per year. The otoliths mounted in leukit were imaged and growth-at-age in the otoliths was measured with the software TNPC (Traitement numérique des pièces calcifiées: Mahé et al., 2009). Under the binocular microscope and natural light, the otoliths showed hyaline (aragonite-poor) rings corresponding to winter periods of low growth and between the rings, white opaque (aragonite-rich) portions corresponding to annual growth periods. The annual ring determination, the age assignment and the measurement of annual ring diameters followed ICES protocoles and guidelines for anchovy and sardine (ICES, 2010; 2011). The age was estimated as the number of hyaline rings. If the edge was hyaline, it was counted as a ring as a hyaline edge observed within the first half of the year is assumed to represent the last winter. The diameter of each annual ring was measured from middle of the hyaline ring on one side to the middle of the ring on the opposite side along the major elongated axis of the otolith and passing through its centre. The distance was expressed in mm after calibration of the stereomiscroscope and the pixel images. Such diameter corresponded to growth-at-age. Ages 0 to 4 were considered (diameters R1 to R5). The total diameter of the otolith was also measured. The data sets span 19 years, from 2000 to 2018 and comprise 20,186 and 8,624 individual fish analyzed at 535 and 235 trawl hauls for anchovy and sardine, respectively. These data sets were used by Boëns et al. (2021 and 2023) to analyse changes in growth patterns and growth-selective mortality at age in anchovy and sardine under environmental and fishing pressures. References: Doray, M., Boyra, G. and Van Der Kooij, J. (eds) (2021). ICES Survey Protocols – Manual for acoustic surveys coordinated under ICES Working Group on Acoustic and Egg Surveys for Small Pelagic Fish (WGACEGG). 1st Edition. ICES Techniques in Marine Environmental Sciences, 64. https://doi.org/10.17895/ices.pub.7462  Doray, M., Petitgas, P., Romagnan, J.-B., Huret, M., Duhamel, E., Dupuy, Ch., Spitz, J., Authier, M., Sanchez, F., Berger, L., Doremus, G., Bourriau, P., Grellier, P. and Masse, J. (2018). The PELGAS survey: ship-based integrated monitoring of the Bay of Biscay pelagic ecosystem. Progress In Oceanography, 166, 15-29. https://doi.org/10.1016/j.pocean.2017.09.015 ICES (2010). Report of the Workshop on Age reading of European anchovy (WKARA), 9-13 November 2009, Sicily, Italy. ICES CM 2009/ACOM: 43. 122 pp. https://doi.org/10.17895/ices.pub.19280525 ICES (2011). Report of the Workshop on Age Reading of European Atlantic Sardine (WKARAS), 14-18 February 2011, Lisbon, Portugal. ICES CM 2011/ACOM:42. 91 pp. https://doi.org/10.17895/ices.pub.19280855 Petitgas, P., Huret, M., Dupuy, Ch., Spitz, J., Authier, M., Romagnan, J.-B. and Doray, M. (2018). Ecosystem spatial structure revealed by integrated survey data. Progress In Oceanography, 166, 189-198. https://doi.org/10.1016/j.pocean.2017.09.012 Mahe, K., Bellail, R., Dufour, J.-L., Boiron-Leroy, A., Dimeet, J., Duhamel, E., Elleboode, R., Felix, J., Grellier, P., Huet, J., Labastie, J., Le Roy, D., Lizaud, O., Manten, M.-L., Martin, S., Metral, L., Nedelec, D., Verin, Y. and Badts, V. (2009). Synthèse française des procédures d'estimation d'âge / French summary of age estimation procedures. https://archimer.ifremer.fr/doc/00000/7294/ Boëns, A., Grellier, P., Lebigre, Ch. and Petitgas, P. (2021). Determinants of growth and selective mortality in anchovy and sardine in the Bay of Biscay. Fisheries Research, 239, 105947. https://doi.org/10.1016/j.fishres.2021.105947 Boëns, A., Ernande, B., Petitgas, P. and Lebigre, Ch. (2023). Different mechanisms underpin the decline in growth of anchovies and sardines of the Bay of Biscay. Evolutionary Applications, 16: 1393–1411. https://doi.org/10.1111/eva.13564