Phytoplankton concentration, modeling

Although the adjustment of model phytoplankton concentrations takes places every month, not all shallow water locations are affected by it every month. Since MERIS is an optical sensor, light availability limits its ability to measure ocean colour. Hence solar angle and clouds determine, whether assimilation is possible or not. The number of months in which is assimilation is possible is higher for locations close to the equator than locations at higher latitudes (Figure 2. lb)). 

Figure 33-7 Basin-wide contour plots of the Fasham et al. (1993) model solution of (A) modeled average phytoplankton concentration (mmol N m ) at 5 m during April, (B) modeled average nitrate concentration (mmol N m ) at 5 m during April, (C) and (D) as for Figures (a) and (b) but for August. Concentrations greater than 1 mmol N m are shaded. The positions of Bermuda Station S and Ocean Weather Station India are indicated by the letters S and I, respectively. Reproduced with permission from Fasham etal, 1993.

The influence of the biological pump on the distribution of DlC carbon in the ocean may serve as a rough model for the influence it has on the distribution of a score of other elements. Low concentrations of DlC are observed in surface waters (Figure 1) due to the uptake of dissolved CO2 (and perhaps HCO/ Raven, 1997) by phytoplankton. Concentrations of... 

The modeling of algal coagulation demonstrated that such coagulation would be consistent with oceanic conditions. Effects of such coagulation would include the facts that aggregation could constrain the maximum phytoplankton concentration, that it would enhance transport of material out of the euphotic zone, and that the transition to an aggregation-dominated state could be very rapid. 

We have seen that chemical and biological interactions lead to mathematical models displaying a variety of linear and nonlinear behavior relaxation to fixed points, multistability, excitability, oscillations, chaos, etc. Despite the different origin of the models, and the diverse nature of the variables they represent (chemical concentrations, population numbers, or even membrane electric potentials) the mathematical structures are quite similar, and it is possible to understand some aspects of the dynamics in one field (e.g. the chemical oscillations in the BZ reaction) with the help of models from other fields (for example the FN model of neurophysiology, or a phytoplankton-zooplankton model). This possibility of common mathematical description will be used in the rest of the book to highlight the similarities and relationships between chemical and biological dynamics when occurring in fluid flows. 

Truscott and Brindley (1994) have shown that some plankton population models can produce excitable dynamics, and they suggested this as a possible explanation for the observed plankton blooms. The phytoplankton population plays the role of the fast component while zooplankton responds on a slower timescale to increased phytoplankton concentration. This allows for a transient plankton bloom, that can be triggered by various changes in the environment. 

Since the late 1980s, Sampayo et al. [163] observed an inverse relationship between mussel toxin levels and total phytoplankton concentrations at similar concentrations of Dinophysis spp., that is, toxin content in shellfish depended on the ratio between toxic phytoplankton cells and the total phytoplankton population. Further studies of Blanco et al. [31,164] showed the importance of intrinsic factors linked to bivalve physiology, and developed kinetic models that take environmental conditions (temperature, salinity, water column stability) into account, and the quantity and quality of food (chlorophyll concentration, seston) available to the bivalves. Concentrations of toxigenic cells and toxin content per cell are important parameters in these kinetic models of intoxication and detoxification. Obviously, in waters low in particulate organic matter, filter-feeders need to filter larger volumes to fulfill their nutritional needs. Blanco et al. [164] introdnced a new parameter, toxic quality of food, by analogy with the term food qnality commonly employed in assimilation models for bivalves. Dahl and Johannessen [165] recommended the use of the ratio between... 

In this way, the near-linear chlorophyll-phosphorus relationship in lakes depends upon the outcome of a large number of interactive processes occurring in each one of the component systems in the model. One of the most intriguing aspects of those components is that the chlorophyll models do not need to take account of the species composition of the phytoplankton in which chlorophyll is a constituent. The development of blooms of potentially toxic cyanobacteria is associated with eutrophication and phosphorus concentration, yet it is not apparent that the yield of cyanobacterial biomass requires any more mass-specific contribution from phosphorus. The explanation for this paradox is not well understood, but it is extremely important to understand that it is a matter of dynamics. The bloom-forming cyanobacteria are among the slowest-growing and most light-sensitive members of the phytoplankton. ... 

Fig. 2.5 Timeseries of daily phytoplankton, zooplankton, dissolved organic carbon, detritus, and phosphorus concentration, and photosyntesis over one model year at two location the shelf seas of the Pacific Ocean, 170 E 65 N and 140 E 10 S.

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. 

Reliable chronic toxicity data were available for 21 species of plants (13 phytoplankton and 8 macrophytes) and 15 species of animals. The species sensitivity distributions (SSDs) for atrazine chronic toxicity (no observed effect concentrations [NOECs]) to plants and animals are shown in Figure 4.4. A log-normal distribution model was fitted to each SSD by least-squares regression. 

DOM is derived from autochthonous sources such as phytoplankton and photosynthetic bacteria (16) at Big Soda Lake near Fallon, Nevada. This lake is alkaline (pH 9.7) and chemically stratified. It contains DOC concentrations as high as 60 mg/L and dissolved salt concentrations as high as 88,000 mg/ L (17). The DOM in this lake is colorless. The fulvic acid fraction was isolated by adsorption chromatography (Amberlite XAD-8 resin) (18) and by zeo-trophic distillation of water from N,N-dimethylformamide (19). Average molecular model synthesis was achieved in a manner similar to that used for fulvic acid from the Suwannee River. The characterization data are presented in Table I and the structural model is presented in Structure 2. 

The accumulation of HOCs in phytoplankton plays an important role in food-web bioaccumulation. Both the increased times to steady state and the effect of dilution decrease accumulation in phytoplankton. This decrease results in a lower phytoplankton body burden and a decreased exposure in higher organisms. As a result, an equilibrium-based model will tend to overestimate concentrations in phytoplankton, and this overestimate will be evident throughout the food web. 

Like algae, phytoplankton, and macrophytes, an equilibrium partitioning model commonly is used to estimate the chemical concentration in zooplankton (Cz, g chemical/kg organism). Hence, Cz is the product of the freely dissolved chemical concentration in the water (CVVI), g chemical/L water), the lipid content of zooplankton (Lz, kg lipid/kg organism), and the lipid-water partition coefficient (KL, L water/kg lipid), which the octanol-water partition coefficient approximates (Clayton et al. 1977) ... 

Gobas (1993) published a foodweb bioaccumulation model to predict chemical concentrations in phytoplankton, macrophytes, zooplankton, benthic invertebrates, and fish, based... 

The model applies equilibrium partitioning to estimate chemical concentrations in phytoplankton, macrophytes, zooplankton, and benthic invertebrates. Chemical concentrations in sediment and water, along with environmental and trophodynamic information, are used to quantify chemical concentrations in all aquatic biota. This model can be applied to many aquatic food webs and relies on a relatively small set of input parameters which are readily accessible. 

Phytoplankton production RpA in environment A is a function of solar radiation Ea, concentration of nutrients nA, temperature TA, phytoplankton biomass pA, and concentration of pollutants A. There are many models that describe the photosynthesis process (Legendre and Legendre, 1998 Legendre and Krapivin, 1992). For the description of this function in the present study, an equation of Michaelis-Menten type is used (block MFB) ... 

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