Variability lake phytoplankton

Prepas E. E., Planas D., Gibson J. J., Vitt D. H., Prowse T. D., Dinsmore W. P., Halsey L. A., McEachern P. M., Paquet S., Scrimgeour G. J., Tonn W. M., Paszkowski C. A., and Wolfstein K. (2001b) Landscape variables influencing nutrients and phytoplankton communities in Boreal Plain lakes of northern Alberta a comparison of wetland- and upland-dominated catchments. Can. J. Fish. Aquat. Sci. 58, 1286-1299. 

Figure 6. Effects of UVR on photosynthesis (total C assimilation) of phytoplankton moved through different mixing depths, presented as per cent photosynthesis in quartz (UVR transparent) relative to glass (partial UVR exclusion) bottles. Measured rates are for bottles that were circulated over the indicated depth ranges at the rate of once per 4 min (0-2 m), once per 8 min (0-3.9 m) and once per 20 min (0-10 and 0-14 m) for a 4 h midday incubation period. The modeled rates are the average of the steady-state (irradiance based) photosynthesis predicted using a biological weighting function and photosynthesis irradiance (BWF/P-I) curve applied to in situ irradiance estimated from recorded surface irradiance, depth of the bottles and measured vertical extinction coefficient. Model and measurements agree within measurement variability (ca. 10%) except for the 0-10 m incubation. Experiments were conducted in Lake Lucerne on September 13,1999 (no asterisks) and September 15,1999 (asterisks, see exposure data in Figure 2). [Modified from Kohler et al. 79.]...

M.A. Xenopoulos, Y.T. Prairie, D.F. Bird (2000). Influence of ultraviolet-B radiation, stratospheric ozone variability, and thermal stratification on the phytoplankton biomass dynamics in a mesohumic lake. Can. J. Fish. Aquat. ScL, 57,600-609. 

Other models have been proposed which follow the outlines of the equations already discussed. Equations with parameters that vary as a function of temperature, sunlight, and nutrient concentration have been presented by Davidson and Clymer (9) and simulated by Cole (10). A set of equations which model the population of phytoplankton, zooplankton, and a species of fish in a large lake have been presented by Parker (II). The application of the techniques of phytoplankton modeling to the problem of eutrophication in rivers and estuaries has been proposed by Chen (12). The interrelations between the nitrogen cycle and the phytoplankton population in the Potomac Estuary has been investigated using a feed-forward-feed-back model of the dependent variables, which interact linearly following first order kinetics (13). 

Fig. 9.4. The new mixed model for metals in lakes. Load (or dose) parameters are related to the input of metals to the lake (direct load and load from the catchment), the metal amount in the lake water is distributed into dissolved and particulate phases by the partition coefficient (Kd). Sedimentation is net sedimentation per unit of time (the calculation unit is set to 1 year for Hg and 1 month for Cs). The sensitivity parameters influence biouptake of metals from water to phytoplankton (but they may also be used in other contexts, e.g., to influence the Kd-values, as illustrated by the dotted line, or the rate of sedimentation). The biological or ecosystem variables include pelagic and benthic uptake, bioaccumulation and retention time in the five compartments (lake water, active sediments, phytoplankton, prey and predator fish). The ejfect parameter is the concentration of the metal in predatory fish (used for human consumption). One panel gives the calculation of concentrations, another the driving parameters (model variables should, preferably, not be altered for different lakes, while environmental variables must be altered for each lake). The arrows between these two panels illustrate the phytoplankton biomass submodel...

The metal in solution (dissolved phase) may be picked up by the phytoplankton (regulated by pelagic uptake rate). The pelagic uptake rate may be influenced by pH, total-P and color and by the specific lake characteristics (water retention time, area, mean depth, etc.). In traditional mass-balance models, one would multiply an amount (kg) by a rate (1/year) to get a flux. Here, we multiply kg (l/year) mod, where mod is a dimensionless moderator which describes empirical knowledge how environmental variables (like pH) influence the given flux. 

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