Diatom biostratigraphy, Argon ages and chronology of ODP Hole 183-1138A

A thick Neogene section was recovered in the upper ~300 m of Ocean Drilling Program Hole 1138A, drilled on the Central Kerguelen Plateau in the Indian sector of the Southern Ocean. Sediment lithologies consist primarily of mixed carbonate and biosiliceous clays and oozes, with several thin (1-3 cm) tephra horizons. The tephras are glass rich, well sorted, and dominantly trachytic to rhyolitic in composition. Volcaniclastic material in these horizons is interpreted to have originated from Heard Island, 180 km northwest of Site 1138, and was likely emplaced through both primary ash fall and turbiditic, submarine flows. A Neogene age-depth model for Hole 1138A is constructed primarily from 36 diatom biostratigraphic datums. Nannofossil and planktonic foraminifer biostratigraphy provides supporting age information. Additionally, four high-precision 40Ar-39Ar ages are derived from ash and tephra horizons, and these radiometric ages are in close agreement with the biostratigraphic ages. The integrated age-depth model reveals a reasonably complete lower Miocene to upper Pleistocene section in Hole 1138A, with the exception of a ~1-m.y. hiatus at the Miocene/Pliocene boundary. Another possible hiatus is also identified at the Oligocene/Miocene boundary. High Neogene sedimentation rates and the presence of both calcareous and siliceous microfossils, combined with datable tephra horizons, establish Site 1138 as a suitable target for future drilling legs with paleoceanographic objectives. This report also proposes two new diatom species, Fragilariopsis heardensis and Azpeitia harwoodii, from Pliocene strata of Hole 1138A.

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.