Lake Michigan Mass Balance

Food Web Bioaccumulation Model

A bioaccumulation model simulates chemical accumulation in the food web in response t chemical exposure, based upon chemical mass balances for aquatic biota (living matter). The general form of the bioaccumulation equation is well-defined, and equates the rate of change in chemical concentration within a fish (or other aquatic organism) the sum of chemical fluxes into and out of the animal. These fulxes include direct uptake of chemcal from water, the flux of chemical into the animal through feeding, and the loss of checmialc due to elimination (desorption and excretion) and dilution due to growth. To predict bioaccumulation for top predator fish (the modeling objective here), the bioaccumulation mass balance is repeatedly applie to animals at each trophic level to simulate chemical biomagnification from primary and secondary producers, through forage species to top predators. Food web bioaccumulation models have been successfully applied for PCBs and other HOCs in several large-scale aquatic ecosystems, (Thomann and Connolly, 1984; Connolly and Tonelli, 1985), and, most recently, for the Green Bay Mass Balance Study (Connolly et al., 1992). The model developed for that project, FDCHN, will be adapted for use in Lake Michigan. FDCHN is a time-variable, population-based age class model., incorporating realistic descriptions of bioenergetic, trophodynamic, and toxicokinetic processes. The general features of FDCHN are well-suited to a modeling application such as the [LMMB].

For Lake Michigan, bioaccumulation of PCB congeners and TNC will be modeled for lake trout and coho salmon food webs. Food web bioaccumulation will be simulated for sub-populations of lake trout in three distinct biotic zones. The general structure of the lake trout food web in Lake Michigan is:

In each zone, different food webs support lake trout, including benthic and pelagic food web linkages. Biotic zones are defined by the approximately 50-mile range of movement of lake trout. The coho salmon, in comparison, is strictly pelagic. Although the coho food web is simpler, the bioaccumulation simulation must account for significant migration over the two-year lifetime of this stocked salmonid in Lake Michigan.

It should be recognized the FDCHN, and []all current food web bioaccumulation models, is not predictive in terms of the dynamics of the food web itself. In other words, the food web structure is described as model input. FDCHN does not predict changing forage composition, trophic status, in response to nutrients, exotic species invasion, or fisheries management. Yet such factors have been demonstrated to alter food web structures in the Great Lakes, and these changes have been suggested to affect bioaccumulation in top predators including salmonids. To address the sensitivity of bioaccumulation predictions to food web dynamics, the SIMPLE model (Jones, Koonce, and O'Gorman; 1993), a bioenergetic model for fish population dynamics in the Great Lakes, will be used to construct scenarios for food web change that will then be tested in FDCHN. While less satisfactory than an integrated population dynamics simulation, such testing will demonstrate the sensitivity of bioaccumulation predictions to food web dynamics in comparison to changes in contaminant concentrations in fish due to reducing exposure concentrations.

Atrazine bioaccumulation will not be modeled, because it is not expected to accumulate in biota due to its low hydrophobicity. It is not presently feasible to model bioaccumulation of mercury because a mass balance for the bioaccumulative fraction (the methyl species) is beyond present analytical and modeling capabilities. As identified in Mercury in the Great Lakes; Management and Strategy (Rossmann and Endicott, 1992), the development of such capabilities must initially take place on small, constrained ecosystems as opposed to the Great Lakes. This is consistent with the research approach of Porcella et al. (1992) in developing the EPRI Mercury Cycling Model, which was based upon data gathered from the Little Rock Lake and other bog seepage lakes in Wisconsin.

A number of FDCHN enhancements will be considered in the Lake Michigan application. These include incorporating specialized sub-models for phytoplankton (Swackhamer and Skoglund, 1993) and Diporeia (Landrum et al., 1992), the organisms at the base of the pelagic and benthic food webs. The bioaccumulation press formulations of Gobas (1993), Barber et al. (1991) and Sijm et al. (1992) will be reviewed for possible updating of FDCHN toxicokinetic descriptions. The detailed bioenergetics model of Hewett and Johnson (1987, 1989) which is currently employed in simplified form in FDCHN, may also be more fully incorporated in the model.

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