Optimal uptake kinetics: physiological acclimation explains the pattern of nitrate uptake by phytoplankton in the ocean

Phytoplankton supply the base of the marine food web and drive the biogeochemical cycles of carbon and nutrients. Over much of the ocean, their growth is limited by their uptake of nitrogen (as nitrate), which has most commonly been described by the hyperbolic Michaelis-Menten (MM) equation. However, the lack of a theory to explain variations in MM constants has hindered our ability to predict the response of marine ecosystems to changes in environmental conditions. The MM equation fits data from short-term experiments well, but does not agree with steady-state experiments over wide ranges of nutrient concentrations. In contrast, the recently developed optimal uptake kinetics (OU) does agree with the latter and can also describe the observed pattern of MM half-saturation constants from field. experiments. OU kinetics explains the observed pattern of N uptake as the result of a general physiological trade-off between nutrient uptake capacity and affinity. The existence of a general trade-off would imply a relatively high degree of predictability in the response of nutrient uptake to changing nutrient concentrations and thus provide a basis for predicting effects of climate change on marine ecosystems and biogeochemical cycles

Adaptive model of DOM dynamics in the surface ocean

The bulk of marine dissolved organic matter (DOM) forms a large, old, refractory carbon pool in the deep ocean, yet a small fraction in the surface ocean is actively involved in the global carbon cycle and may contribute significantly to the biological pump. We argue that present models of plankton and DOM in the surface ocean are incompatible with current knowledge of marine DOM dynamics. We present a plankton model with an adaptive formulation of bacteria-DOM interactions which is more consistent with observations. Our model reproduces net accumulation of DOM and is the first to reconcile the prevailing reports of net consumption of inorganic nitrogen by bacteria with commonly found DOC:DON ratios in the surface ocean. Our model predicts that factors governing DOM production by phytoplankton and zooplankton have little influence on DOM accumulation in the surface ocean. Long-term accumulation, eventual export of DOM, and hence its contribution to the biological pump appear to be primarily controlled by characteristics of bacterial DOM utilization. The model implies a negative relation between temperature and DOM accumulation, which can be obscured, however, by the impact of temperature on water-column stability. On longer time scales, this negative relation could indicate a positive feedback between temperature and CO2. DOM can accumulate independently of inorganic nutrient content of the surface ocean. Therefore, the predicted positive feedback is potentially very strong and could help explain the large variations in atmospheric CO between glacial and interglacial periods

Effects of Inoculation of LAB on Fermentation Pattern and Clostridia Spores in Easily Ensilable Grass Silages

Clostridia can damage the protein quality of grass silages. They cause high gas losses during the fermentation process and quality problems in dairy products like semi-hard cheeses. In comparison to the effect of chemicals such as nitrite on undesirable clostridia in grass silages the respective inhibitory mechanism of LAB requires further investigation. The objective of this experiment was to study under laboratory conditions novel isolates of lactic acid bacteria (LAB), selected for their inhibitory effect on clostridia in grass silages

Global monthly sea surface nitrate fields estimated from remotely sensed sea surface temperature, chlorophyll, and modeled mixed layer depth

Information about oceanic nitrate is crucial for making inferences about marine biological production and the efficiency of the biological carbon pump. While there are no optical properties that allow direct estimation of inorganic nitrogen, its correlation with other biogeochemical variables may permit its inference from satellite data. Here we report a new method for estimating monthly mean surface nitrate concentrations employing local multiple linear regressions on a global 1° by 1° resolution grid, using satellite-derived sea surface temperature, chlorophyll, and modeled mixed layer depth. Our method is able to reproduce the interannual variability of independent in situ nitrate observations at the Bermuda Atlantic Time Series, the Hawaii Ocean Time series, the California coast, and the southern New Zealand region. Our new method is shown to be more accurate than previous algorithms and thus can provide improved information on temporal and spatial nutrient variations beyond the climatological mean at regional and global scales

The Bouguer-Beer-Lambert Law: Shining Light on the Obscure

The Beer-Lambert law is unquestionably the most important law in optical spectroscopy and indispensable for the qualitative and quantitative interpretation of spectroscopic data. As such, every spectroscopist should know its limits and potential pitfalls, arising from its application, by heart. It is the goal of this work to review these limits and pitfalls, as well as to provide solutions and explanations to guide the reader. This guidance will allow a deeper understanding of spectral features, which cannot be explained by the Beer-Lambert law, because they arise from electromagnetic effects/the wave nature of light. Those features include band shifts and intensity changes based exclusively upon optical conditions, i. e. the method chosen to record the spectra, the substrate and the form of the sample. As such, the review will be an essential tool towards a full understanding of optical spectra and their quantitative interpretation based not only on oscillator positions, but also on their strengths and damping constants

What prevents nitrogen depletion in the oxygen minimum zone of the eastern tropical South Pacific?

Local coupling between nitrogen fixation and denitrification in current biogeochemical models could result in runaway feedback in open-ocean oxygen minimum zones (OMZs), eventually stripping OMZ waters of all fixed nitrogen. This feedback does not seem to operate at full strength in the ocean, as nitrate does not generally become depleted in open-ocean OMZs. To explore in detail the possible mechanisms that prevent nitrogen depletion in the OMZ of the eastern tropical South Pacific (ETSP), we develop a box model with fully prognostic cycles of carbon, nutrients and oxygen in the upwelling region and its adjacent open ocean. Ocean circulation is calibrated with Δ14C data of the ETSP. The sensitivity of the simulated nitrogen cycle to nutrient and oxygen exchange and ventilation from outside the model domain and to remineralization scales inside an OMZ is analysed. For the entire range of model configurations explored, we find that the fixed-N inventory can be stabilized at non-zero levels in the ETSP OMZ only if the remineralization rate via denitrification is slower than that via aerobic respiration. In our optimum model configuration, lateral oxygen supply into the model domain is required at rates sufficient to oxidize at least about one fifth of the export production in the model domain to prevent anoxia in the deep ocean. Under these conditions, our model is in line with the view of phosphate as the ultimate limiting nutrient for phytoplankton, and implies that for the current notion of nitrogen fixation being favoured in N-deficit waters, the water column of the ETSP could even be a small net source of nitrate

Identifying and reducing model structure uncertainty based on analysis of parameter interaction

International audienceMulti-objective optimization algorithms are widely used for the calibration of conceptual hydrological models. Such algorithms yield a set of Pareto-optimal solutions, reflecting the model structure uncertainty. In this study, a multi-objective optimization strategy is suggested, which aims at reducing the model structure uncertainty by considering parameter interaction within Pareto-optimal solutions. The approach has been used to develop a nested setup of a rainfall-runoff model, which is integrated in a probabilistic meso-/macroscale flood forecasting system. The optimization strategy aided in determining the best combination of a lumped (computationally efficient in operational real time forecasting) and a semi-distributed parameterization of the hydrological model. First results are shown for two subbasins of the Mulde catchment in Germany. The different phenomena of parameter interaction were analysed in this case study to reduce the model structure uncertainties