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Fish stomach content records extracted from the DAPSTOM 4.5 database (held at the UK Centre for Environment, Fisheries and Aquaculture Science - CEFAS). Data collated as part of the EU Euro-Basin project and specifically concerning herring (Clupea harengus), mackerel (Scomber scombrus), blue whiting (Micromesistius poutassou), albacore (Thunnus alalunga) and bluefin tuna (Thunnus thynnus). The data set consist of 20720 records - collected throughout the northeast Atlantic, between 1906 and 2011 - mostly during routine fisheries monitoring research cruises.

Atmospheric methane (CH4) records reconstructed from polar ice cores represent a integrated view on processes predominantly taking place in the terrestrial biogeosphere. Here we present dual stable isotopic methane records (d13CH4 and dD(CH4)) from four Antarctic ice cores, which provide improved constraints on past changes in natural methane sources. Our isotope data show that tropical wetlands and seasonally inundated floodplains are most likely the controlling sources of atmospheric methane variations for the current and two older interglacials and their preceding glacial maxima. The changes in these sources are steered by variations in temperature, precipitation and the water table, as modulated by insolation, (local) sea level and monsoon intensity. Based on our new dD(CH4) constraint, it appears that geologic emissions of methane may play a steady but only minor role in atmospheric CH4 changes, and that the glacial budget is not dominated by these sources. Superimposed on the glacial/interglacial variations is a marked difference in both isotope records, with systematically higher values during the last 25,000 years compared to older time periods. This shift cannot be explained by climatic changes. Rather, our isotopic methane budget points to a marked increase in fire activity, possibly due to biome changes and accumulation of fuel related to the late Pleistocene megafauna extinction, which took place in the course of the last glacial.

Large herbivores are a major agent in ecosystems, influencing vegetation structure and carbon and nutrient flows. Yet most of the current global dynamic vegetation models (DGVMs) lack explicit representation of large herbivores. Here we incorporated a grazing module in the ORCHIDEE-MICT DGVM based on physiological and demographic equations for wild large grazers, taking into account the feedbacks of large grazers on vegetation. The model was applied globally for present-day and the last glacial maximum (LGM). Three NetCDF files are included, corresponding to the model results for three periods: present-day (1960-2009 average), pre-industrial (1860-1899 average), and the last glacial maximum (ca. 21 ka before present). Variables include the modeled potential grazer biomass/population density, along with the directly relevant outputs: vegetation distribution (i.e. fractional coverage of the plant functional types), and gross and net primary productivity. Detailed model descriptions and the simulation setup can be found in: Zhu et al. (2018).

Quaternary records provide an opportunity to examine the nature of the vegetation and fire responses to rapid past climate changes comparable in velocity and magnitude to those expected in the 21st century. The best documented examples of rapid climate change in the past are the warming events associated with the Dansgaard-Oeschger (D-O) cycles during the last glacial period, which were sufficiently large to have had a potential feedback through changes in albedo and greenhouse gas emissions on climate. Previous reconstructions of vegetation and fire changes during the D-O cycles used independently constructed age models, making it difficult to compare the changes between different sites and regions. Here we present the ACER (Abrupt Climate Changes and Environmental Responses) global database which includes 93 pollen records from the last glacial period (73-15 ka) with a temporal resolution better than 1,000 years, 32 of which also provide charcoal records. A harmonized and consistent chronology based on radiometric dating (14C, 234U/230Th, OSL, 40Ar/39Ar dated tephra layers) has been constructed for 86 of these records, although in some cases additional information was derived using common control points based on event stratigraphy. The ACER database compiles metadata including geospatial and dating information, pollen and charcoal counts and pollen percentages of the characteristic biomes, and is archived in Microsoft ACCESS(TM).

We evaluated the effects of low pH on Corallium rubrum from aquaria experiments. Several colonies of C. rubrum were long-term maintained for 314 days in aquaria at two different pH levels (8.10 and 7.81, pHT). Calcification rate, spicule morphology, major biochemical constituents (protein, carbohydrates and lipids) and fatty acids composition were measured periodically. Exposure to lower pH conditions caused a significant decrease in the skeletal growth rate in comparison to the control treatment. Similarly, the spicule morphology clearly differed between both treatments at the end of the experiment, with aberrant shapes being observed only under the acidified conditions. On the other hand, while total organic matter was significantly higher under low pH conditions, no significant differences were detected between treatments regarding total carbohydrate, lipid, protein and fatty acid composition. However, the lower variability found among samples maintained in acidified conditions relative to controls, suggests a possible effect of pH decrease on the metabolism of the colonies. Our results show, for the first time, evidence of detrimental ocean acidification effects on this valuable and endangered coral species.

Coccolithophores are an important component of the Earth system, and, as calcifiers, their possible susceptibility to ocean acidification is of major concern. Laboratory studies at enhanced pCO2 levels have produced divergent results without overall consensus. However, it has been predicted from these studies that, although calcification may not be depressed in all species, acidification will produce "a transition in dominance from more to less heavily calcified coccolithophores" [Ridgwell A, et al., (2009) Biogeosciences 6:2611-2623]. A recent observational study [Beaufort L, et al., (2011) Nature 476:80-83] also suggested that coccolithophores are less calcified in more acidic conditions. We present the results of a large observational study of coccolithophore morphology in the Bay of Biscay. Samples were collected once a month for over a year, along a 1,000-km-long transect. Our data clearly show that there is a pronounced seasonality in the morphotypes of Emiliania huxleyi, the most abundant coccolithophore species. Whereas pH and CaCO3 saturation are lowest in winter, the E. huxleyi population shifts from 90% (winter) of the heavily calcified form. However, it is unlikely that the shifts in carbonate chemistry alone caused the morphotype shift. Our finding that the most heavily calcified morphotype dominates when conditions are most acidic is contrary to the earlier predictions and raises further questions about the fate of coccolithophores in a high-CO2 world.

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass. The research leading to these results received (partial/full) funding from the European Commission FP7 EURO-BASIN (European Basin-Scale Anal ysis, Synthesis, and Integration; Grant Agreement 264 933).

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass. The research leading to these results received (partial/full) funding from the European Commission FP7 EURO-BASIN (European Basin-Scale Anal ysis, Synthesis, and Integration; Grant Agreement 264 933).

Source data of the North Sea well inventory: United Kingdom (UK) - Oil and Gas Authority (Dec. 2018) - https://data-ogauthority.opendata.arcgis.com/datasets/oga-wells-ed50 Contains information provided by the OGA. Wells are extracted for the area of the PGS data set PGS Mega Survey Plus. We measured the distance between all wells of the test group (n = 43) and all those who are within the seismic data set (n = 1,792; presented here) and their closest bright spot with polarity reversal. Furthermore, we calculated the mean RMS amplitudes and RMS amplitude standard deviation for a buffer radius of 300 m around the well paths for all wells inside the seismic data set and the visited wells as 300 m is the distance below which all of the visited wells of the test group showed gas release in form of flares from the seafloor. We test, if the propensity of a well to leak can be identified by using a logistic regression, which includes regressors such as well activity data and/or derived parameters such as mean RMS amplitude and mean RMS amplitude standard deviation, the distance towards the most proximal bright spot with polarity reversal and age (spud date). In order to identify the most suitable regressor combination best subset selection is employed. The main selection criterion chosen was the prediction accuracy from randomly and repeatedly splitting the visited wells into a training and a test set and then using the fitted logistic regression to predict the test data. The most suitable subset turns out to only employ the distance to polarity reversal, producing a prediction accuracy of 89% and the following logistic regression results: In order to obtain confidence intervals using the normal distribution the distance to bright spot with polarity reversal has to be normally distributed, which it is not. Yet it can be transformed to normality by adding 100 meters to the original distance and then taking the natural logarithm: Logistic regression fit for leakage of all visited wells using distance to bright spot with polarity reversal in meters as a regressor. Please find further information on the applied statistical analyses in the supplementary material. Estimate Std. Error z value Pr(>|z|) Significance Intercept 4,853.946 1,735.128 2.797 0.00515 0.01 Distance −0.007361 0.002700 −2.726 0.00640 0.01 The transformed logistic regression model is then used to predict the probabilities of leakage for the wells within our seismic data set in the Central North Sea (here presented data). In order to obtain confidence bands this logistic regression is performed subtracting and adding two standard deviations from the calculated probability. The point estimate predicts leakage from 926 of the 1,792 wells, where the 95% confidence interval ranges from 719 to 1,058.