Geochemistry of oceanic crust of the North Atlantic

87Sr/a6Sr ratios, Sr, K, Rb and Cs contents and the petrology of basalts and secondary phases recovered from deep basement drilling at DSDP Sites 332B, 417A, 417D and 418A show that the oceanic crust alters in four distinct stages characterized by formation of palagonite, smectite and calcite (Stages I, II and III, respectively). Stage IV represents the final compaction of the crust, including a dehydration of the crust without major chemical changes. Isotopic age determinations by Hart and Staudigel (1978, doi:10.1029/GL005i012p01009) and Richardson et al. (doi:10.1029/JB085iB12p07195), show that, at least for Atlantic-type crustal regimes, Stage I and II last for a maximum of 3 m.y., and Stage III lasts beyond Stage I and II, but is probably completed in significantly less than 10 m.y. Stage IV is long-lived and may still be active at 100 m.y. Stages I and II, the phase of halmyrolysis, include geochemically significant interactions between seawater and basalt for the upper 500 m of layer II and involve volumes of seawater containing a large basaltic component. Stage III solutions show evidence of less seawater-basalt interaction, at least to depths of 500 m; calcites deposited from these solutions have Sr isotopic ratios close to seawater values; but also have very low Sr/Ca ratios indicating a large basalt Ca component in the solutions. Smectite formation is the result of the interaction of seawater and basalt. The initial 87Sr/a6Sr ratios of smectites represent the Sr isotopic composition of the solution when the smectite is being formed. Thereafter, alkalies may be continuously added to interlayer positions in the smectite in order of decreasing hydration energy (Cs is more enriched than Rb, Rb more than K). The later-formed carbonates have very low alkali concentrations, and 87Sr/86Sr ratios identical to contemporaneous seawater. Therefore, since the alkali concentrations in a whole rock sample are affected by different alteration processes, the alkali concentrations alone are not reliable indicators of the degree of alteration.

Global macrozoobenthos production and energy budget data base V150731, with link to database

I developed a new model for estimating annual production-to-biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self-learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy-to-measure abiotic and biotic parameters in 1252 data sets of population production. Based on log-transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back-transformation to natural scale introduces a bias in the model predictions, i.e., an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.