(Table 2) Distribution of alkenone unsaturation index UK37 of the eastern North Atlantic

This paper reports the concentrations and within-class distributions of long-chain alkenones and alkyl alkenoates in the surface waters (0-50 m) of the eastern North Atlantic, and correlates their abundance and distribution with those of source organisms and with water temperature and other environmental variables. We collected these samples of >0.8 µm particulate material from the euphotic zone along the JGOFS 20°W longitude transect, from 61°N to 24°N, during seven cruises of the UK-JGOFS Biogeochemical Ocean Flux Study (BOFS) in 1989-1991; the biogeographical range of our 53 samples extends from the cold (<10°C), nutrient-rich and highly productive subarctic waters of the Iceland Basin to the warm (>25°C) oligotrophic subtropical waters off Africa. Surface water concentrations of total alkenone and alkenoates ranged from <50 ng/l in oligotrophic waters below 40°N to 2000-4500 ng/l in high latitude E. huxleyi blooms, and were well correlated with E. huxleyi cell densities, supporting the assumption that E. huxleyi is the predominant source of these compounds in the present day North Atlantic. The within-class distribution of the C37 and C38 alkenones and C36 alkenoates varied strongly as a function of temperature, and was largely unaffected by nutrient concentration, bloom status and other surface water properties. The biosynthetic response of the source organisms to growth temperature differed between the cold (<16°C) waters above 47°N and the warmer waters to the south. In cold (<16°C) waters above 47°N, the relative amounts of alkenoates and C38 alkenones synthesized was a strong function of growth temperature, while the unsaturation ratio of the alkenones (C37 and C38) was uncorrelated with temperature. Conversely, in warm (>16°C) waters below 47°N, the relative proportions of alkenoates and alkenones synthesized remained constant with increasing temperature while the unsaturation ratios of the C37 and C38 methyl alkenones (Uk37 and Uk38Me, respectively) increased linearly. The fitted regressions of Uk37 and Uk38Me versus temperature for waters >16°C were both highly significant (r**2 > 0.96) and had identical slopes (0.057) that were 50% higher than the slope (0.034) of the temperature calibration of Uk37 reported by Prahl and Wakeham (1987; doi:10.1038/330367a0) over the same temperature range. These observations suggest either a physiological adjustment in biochemical response to growth temperature above a 16-17°C threshold and/or variation between different E. huxleyi strains and/or related species inhabiting the cold and warm water regions of the eastern North Atlantic. Using our North Atlantic data set, we have produced multivariate temperature calibrations incorporating all major features of the alkenone and alkenoate data set. Predicted temperatures using multivariate calibrations are largely unbiased, with a standard error of approximately ±1°C over the entire data range. In contrast, simpler calibration models cannot adequately incorporate regional diversity and nonlinear trends with temperature. Our results indicate that calibrations based upon single variables, such as Uk37, can be strongly biased by unknown systematic errors arising from natural variability in the biosynthetic response of the source organisms to growth temperature. Multivariate temperature calibration can be expected to give more precise estimates of Integrated Production Temperatures (IPT) in the sedimentary record over a wider range of paleoenvironmental conditions, when derived using a calibration data set incorporating a similar range of natural variability in biosynthetic response.

Global distributions of diazotrophs abundance, biomass and nitrogen fixation rates - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. This is a gridded data product about diazotrophic organisms . There are 6 variables. Each variable is gridded on a dimension of 360 (longitude) * 180 (latitude) * 33 (depth) * 12 (month). The first group of 3 variables are: (1) number of biomass observations, (2) biomass, and (3) special nifH-gene-based biomass. The second group of 3 variables is same as the first group except that it only grids non-zero data. We have constructed a database on diazotrophic organisms in the global pelagic upper ocean by compiling more than 11,000 direct field measurements including 3 sub-databases: (1) nitrogen fixation rates, (2) cyanobacterial diazotroph abundances from cell counts and (3) cyanobacterial diazotroph abundances from qPCR assays targeting nifH genes. Biomass conversion factors are estimated based on cell sizes to convert abundance data to diazotrophic biomass. Data are assigned to 3 groups including Trichodesmium, unicellular diazotrophic cyanobacteria (group A, B and C when applicable) and heterocystous cyanobacteria (Richelia and Calothrix). Total nitrogen fixation rates and diazotrophic biomass are calculated by summing the values from all the groups. Some of nitrogen fixation rates are whole seawater measurements and are used as total nitrogen fixation rates. Both volumetric and depth-integrated values were reported. Depth-integrated values are also calculated for those vertical profiles with values at 3 or more depths.