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Gridded spatial fields (raster objects) containing the species distribution models (SDMs) projections of mean annual plankton total plankton, phytoplankton and zooplankton species diversity from Benedetti et al. (2021). The present .grd file ('rasterStack' object in R) contain the fields of mean annual surface plankton/phytoplankton/zooplankton species diversity for the contemporary (2012-2031) and future (2081-2100) conditions of the global open ocean (i.e., data underlying those maps in Figure 1 and Figure 3 of Benedetti et al., 2021). Layers quantifying the uncertainty (i.e., the variablity across models projections estimated through the standard deviation) in ensemble projections were also added (i.e., data underlying the maps in Supplementary Figure 4). See the Methods section of Benedetti et al. (2021) for a full description of the methodology and the ensemble SDMs forecasting framework. The raster layers follow the 1°x1° cell grid of the World Ocean Atlas (https://www.ncei.noaa.gov/). In short, we empirically modelled the monthly and mean annual diversity patterns stemming from the distribution of 860 plankton species (336 phytoplankton, 524 zooplankton) spanning 13 phyla, 71 orders and 324 genera through an ensemble approach based on SDMs. The considered species cover a wide range of traits and functions, representing 10 major plankton functional groups (PFGs; three phytoplankton and seven zooplankton groups). We compiled the species occurrence records from various data sources (available here: https://zenodo.org/record/5101349#.YO7Dqm469lM) and aggregated them onto a monthly-resolved 1°x1° grid, excluding observations from regions where the seafloor is shallower than 200 m. We matched these binned open ocean records with observation-based climatologies of environmental predictors (temperature, dissolved oxygen concentration, solar irradiance, macronutrients concentration, chlorophyll a concentration) that reflect the climatic and biogeochemical conditions of the surface open ocean. Four types of SDMs (generalized linear models, generalized additive models, artificial neural networks, and random forests) were fitted to model the species’ current environmental habitat suitability patterns. For each SDMs, we used four alternative pools of predictors. Assuming niche conservatism, we projected each of the 16 resulting species-level habitat suitability models into the future using outputs from five ESMs belonging to the Coupled Model Intercomparison Project 5 (CMIP5) that were forced by the Representative Concentration Pathway 8.5 (RCP8.5) scenario of high greenhouse gas concentrations. To this end, we first computed the modelled monthly climatologies of the selected predictors for the 2012-2031 and 2081-2100 periods, and derive the future monthly anomalies from the differences between these two time periods. These anomalies were added to the observation-based monthly climatologies (i.e., those used to train the SDMs) to estimate the future environmental conditions of the ocean, and projected the SDMs in these future conditions. Finally, we estimated the mean annual present and future alpha diversity (species richness; SR) and beta diversity (species turnover through time) patterns for both trophic levels, for each cell, from the ensemble of SDMs. SR ensembles are estimated as the sum of all species’ habitat suitability patterns averaged across all 80 possible combinations (i.e., "ensemble members") of SDMs (n = 4), ESMs (n = 5) and predictor pools (n = 4). To assess the uncertainties of our diversity projections based on the ensemble members, we compute the interquartile range of the 80 ensemble members SR projections. We calculate species turnover as the change in mean annual species composition between present and future time based on Jaccard’s dissimilarity index and by decomposing this total turnover into the true species turnover (ST, also known as species replacement) and the nestedness (SR change) components. Numerous tests are conducted to ensure the robustness of the results with regard to the spatially and temporally highly uneven sampling effort as well as with regard to the relative role of different predictors. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein. {"references": ["Benedetti, F., Vogt, M., Hofmann-Elizondo, U., Righetti, D., Zimmermann, N.E., Gruber, N. Major restructuring of marine plankton assemblages under global warming. Nature Communications. 2021."]} # To read the raster layers in R use: library("raster"); library("rgdal") raster <- raster::stack("Nat.Comms.2021.Benedetti_et_al._Fig1+Fig2_species_richness+composition.grd")

High resolution flow speed reconstructions of two core sites located on Gardar Drift in the northeast Atlantic Basin and Orphan Knoll in the northwest Atlantic Basin reveal a long-term decrease in flow speed of Northeast Atlantic Deep Water (NEADW) after 6,500 years. Benthic foraminiferal oxygen isotopes of sites currently bathed in NEADW show a 0.2per mil depletion after 6,500 years, shortly after the start of the development of a carbon isotope gradient between NEADW and Norwegian Sea Deep Water. We consider these changes in near-bottom flow vigor and benthic foraminiferal isotope records to mark a significant reorganization of the Holocene deep ocean circulation, and attribute the changes to a weakening of NEADW flow during the mid to late Holocene that allowed the shoaling of Lower Deep Water and deeper eastward advection of Labrador Sea Water into the northeast Atlantic Basin. MD99-2251 originally published in Ellsion et al., (2006). Conversion to calendar years before present (cal. yrs BP) with CALIB 501 (Stuiver et al., 1993) using marine04 as calibration data set. Mean ages are the arithmetic means of the age ranges given by the program. Supplement to: Hoogakker, Babette A A; Chapman, Mark R; McCave, I Nick; Hillaire-Marcel, Claude; Ellison, Christopher RW; Hall, Ian R; Telford, Richard J (2011): Dynamics of North Atlantic Deep Water masses during the Holocene. Paleoceanography, 26(4), PA4214

On behalf of the Ministry of Agriculture, Nature and Food Quality (LNV), Wageningen Food Safety Research analyses samples of agricultural products of animal origin for dioxins, PCBs, brominated flame retardants (BFRs) and poly- and perfluorinated alkyl substances (PFASs). This includes meat, milk, eggs and fish.The samples are taken at the primary production or processing stage (e.g. in slaughterhouses or raw milk collection services). For dioxin-like compounds, 350 samples are first screened with the DR CALUX�� method. Samples giving a signal indicating a level above the lowest action level are regarded as suspected. These samples are further examined using GC/HRMS as confirmatory method. Concerning fish, shellfish and crustaceans, approx. 25 samples are collected at sea by research vessels, at the fish auction, or from whole-sale traders (farmed fish).

Figure 8 - Distribution of Harpacticella species. A Harpacticella amurensis B Harpacticella inopinata C Harpacticella paradoxa D Harpacticella lacustris E Harpacticella oceanica F Harpacticella itoi G Harpacticella jejuensis sp. n.

The Tara Oceans Expedition (2009-2013) was a global survey of ocean ecosystems aboard the Sailing Vessel Tara. It carried out extensive measurements of evironmental conditions and collected plankton (viruses, bacteria, protists and metazoans) for later analysis using modern sequencing and state-of-the-art imaging technologies. Tara Oceans Data are particularly suited to study the genetic, morphological and functional diversity of plankton. The present data set includes properties of seawater, particulate matter and dissolved matter that were measured from discrete water samples collected with Niskin bottles during the 2009-2013 Tara Oceans expedition. Properties include pigment concentrations from HPLC analysis (10 depths per vertical profile, 25 pigments per depth), the carbonate system (Surface and 400m; pH (total scale), CO2, pCO2, fCO2, HCO3, CO3, Total alkalinity, Total carbon, OmegaAragonite, OmegaCalcite, and dosage Flags), nutrients (10 depths per vertical profile; NO2, PO4, N02/NO3, SI, quality Flags), DOC, CDOM, and dissolved oxygen isotopes. The Service National d'Analyse des Paramètres Océaniques du CO2, at the Université Pierre et Marie Curie, determined CT and AT potentiometrically. More than 200 vertical profiles of these properties were made across the world ocean. DOC, CDOM and dissolved oxygen isotopes are available only for the Arctic Ocean and Arctic Seas (2013). marc.picheral@obs-vlfr.fr>2014-10-03T13:23:32>D:\process_ctd_data\Tara_validated_CTD_data_20121011\Tara_Oceans_Data_RVSS-NISKIN_2009-2013_20141003>

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