Model phytoplankton uptake

Fig. 9.8. A. Model simulation for Cs. The driving function is an atmospheric fallout during month 24, with a peak value of 25 kBq/m. Curves 7, 2, 3 and 4 show the model-predicted concentrations in water, phytoplankton, prey and predatory fish. Curve 5 gives the assumed time-dependent outflow rate from the catchment. Default Kd=0.1 default phytoplankton uptake rate 68.3 10 (1/month)...

Changes in surface temperature elsewhere in the globe are likely to have a lesser impact on carbon or DMS production. For example, the warming that a doubling of atmospheric COj could produce in the Southern Ocean has been modelled to lead to decreased carbon uptake, but enhanced biological productivity, due to the temperature effect on phytoplankton growth." This would lead to an approximately 5% increase in DMS production and a lesser increase in CCN. There is thus a negative feedback here, but only of minor impact. 

Although the details of the equilibrium model are still uncertain, the general trends are likely reliable. As shown in Figme 5.16, most of the Fe(III) in seawater is predicted to be in the form of the FeL complex. The equilibrium model also predicts that this degree of complexation should enhance iron solubility such that 10 to 50% of the iron delivered to the ocean as dust will eventually become dissolved if equilibrimn is attained. If this model is a reasonable representation for iron speciation in seawater, uptake of [Fe(III)]jQjgj by phytoplankton should induce a spontaneous dissolution of additional particulate iron so as to drive the dissolved iron concentrations back toward their equilibrium values. 

The accumulation of hydrophobic contaminants in phytoplankton plays a significant role in the transport and fate of these potentially toxic compounds. However, the limited amount of available field data indicate that partitioning models fail to adequately predict the distribution of these compounds in the water column. Several hypotheses have been proposed to explain these differences. In this chapter we propose additional explanations for these differences. We hypothesize that assumptions in the partitioning model about the rate of uptake, mechanism of uptake, and effect of phytoplankton growth also contribute to these deviations. 

The Richards model reduces the unexplained statistical variation in the accumulation of PCBs by phytoplankton, but it does not provide any information about the mechanisms responsible for the observed pattern. Numerous causes are possible for deviation from the classical pattern of accumulation. However, violations of assumptions associated with the classical model (i.e., constant uptake rate, instantaneous mixing within a single compartment, and a time-independent probability of depuration) are most likely the cause. With phytoplankton, several physiological mechanisms can potentially contribute to a sigmoidal accumulation curve. 

Although the Richards model does not provide mechanistic information, it does point out a need for further study and understanding of the uptake mechanisms. Brisbin et al. (30) reported that one consequence of sigmoidal accumulation is that there must be a period of accelerated or enhanced accumulation after the lag period in order to attain equilibrium levels similar to the classical model. In contrast to reports that cellular processes play no role in the accumulation of contaminants by phytoplankton (23, 25, 34-36), a sigmoidal accumulation curve may indicate that cellular processes such as the cycling of materials within the cell may enhance the rate of accumulation or depuration to a level above that which is attainable by diffusion alone. 

The appropriate interfacing of chemical with biologic and hydrologic models is a rather difficult problem. For example, the prediction of trace-element bioaccumulation by phytoplankton may require in some instances that the uptake rates and the compartmentalized loss rates for various solute species of the element present in the system be known. The effect, if any, on compartmentalized loss rates of the particular solute species taken up (e.g. HgCH3" " vs Hg " ") also needs to be known. The interaction effect of the concentration of one element upon the uptake and loss rates of another element, such as Hg on Se (33, 34, 35), also need to be known. In many instances, hydrodynamic models may have to be linked with,or otherwise incorporated, into the biologic and chemical models to permit predictions of, for example, increased trace-element levels in oysters resulting from increased anthropogenic inputs to an estuary. 

Description. The model organism is a free-floating unicellular sphere with characteristics selected, where possible, to match those of a phytoplankton cell. The organism and its environment (Figure Ic) are divided into four concentric zones -the bulk solution, the diffusion layer, the containing membrane and the cell concents. We will assume that the species taken up by the cell is the free metal ion since most of the studies of the uptake of B-subgroup metals by organisms support this hypothesis Z . 5 steady-state transport processes are... 

Armstrong, R. (1999). An optimization-based model of iron-light-ammoninm co-limitation of nitrate uptake and phytoplankton growth. Limnol. Oceanogr. 44, 1436—1446. 

Zimmerman, R. C., Kremer, J. N., and Dugdale, R. C. (1987). Acceleration of nutrient uptake by phytoplankton in a coastal upweUing ecosystem—A modeling analysis. Limnol. Oceanogr. 32, 359-367. 

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