On the effects of circulation, sediment resuspension and biological incorporation by diatoms in an ocean model of aluminium, link to model data

The distribution of dissolved aluminium in the West Atlantic Ocean shows a mirror image with that of dissolved silicic acid, hinting at intricate interactions between the ocean cycling of Al and Si. The marine biogeochemistry of Al is of interest because of its potential impact on diatom opal remineralisation, hence Si availability. Furthermore, the dissolved Al concentration at the surface ocean has been used as a tracer for dust input, dust being the most important source of the bio-essential trace element iron to the ocean. Previously, the dissolved concentration of Al was simulated reasonably well with only a dust source, and scavenging by adsorption on settling biogenic debris as the only removal process. Here we explore the impacts of (i) a sediment source of Al in the Northern Hemisphere (especially north of ~ 40° N), (ii) the imposed velocity field, and (iii) biological incorporation of Al on the modelled Al distribution in the ocean. The sediment source clearly improves the model results, and using a different velocity field shows the importance of advection on the simulated Al distribution. Biological incorporation appears to be a potentially important removal process. However, conclusive independent data to constrain the Al / Si incorporation ratio by growing diatoms are missing. Therefore, this study does not provide a definitive answer to the question of the relative importance of Al removal by incorporation compared to removal by adsorptive scavenging.

Particle fluxes and in-situ particle properties measured with sediment traps and a moored camera system from 2008 - 2010 in region off Cape Blanc / NW-Africa

We compared particle data from a moored video camera system with sediment trap derived fluxes at ~1100 m depth in the highly dynamic coastal upwelling system off Cape Blanc, Mauritania. Between spring 2008 and winter 2010 the trap collected settling particles in 9-day intervals, while the camera recorded in-situ particle abundance and size-distribution every third day. Particle fluxes were highly variable (40-1200 mg m**-2 d**-1) and followed distinct seasonal patterns with peaks during spring, summer and fall. The particle flux patterns from the sediment traps correlated to the total particle volume captured by the video camera, which ranged from1 to 22 mm**3 l**-1. The measured increase in total particle volume during periods of high mass flux appeared to be better related to increases in the particle concentrations, rather than to increased average particle size. We observed events that had similar particle fluxes, but showed clear differences in particle abundance and size-distribution, and vice versa. Such observations can only be explained by shifts in the composition of the settling material, with changes both in particle density and chemical composition. For example, the input of wind-blown dust from the Sahara during September 2009 led to the formation of high numbers of comparably small particles in the water column. This suggests that, besides seasonal changes, the composition of marine particles in one region underlies episodical changes. The time between the appearance of high dust concentrations in the atmosphere and the increase lithogenic flux in the 1100 m deep trap suggested an average settling rate of 200 m d**-1, indicating a close and fast coupling between dust input and sedimentation of the material.

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.