

relations for any combination of water quality factors observed (past or present) 

 in nature. 



Simulation Modeling Structure 



We employed two distinctly different strategies for simulation modeling 

 which were central to thee overall SAV research program. One strategy was 

 directed primarily toward understanding the dynamic behavior of the seagrass 

 ecosystem including energy flux, predator-prey interactions, nutrient cycling 

 and trophic structure. As before (in the broad design of our research program), we 

 utilized a hierarchical perception to decompose a detailed SAV ecosystem model 

 into a cluster of subsystem models. This allowed us to maintain sufficient 

 ecological detail against the limits of conceptual and computational tract- 

 ability. The other approach in our modeling program emphasized the role of 

 these plant communities in a larger context of the entire estuarine system in- 

 cluding socio-economic considerations. Here, we developed an aggregated version 

 of the SAV ecosystem model (i.e., combined submodels) and placed it into a 

 sequence of cascading connections of influence, which lead from human uses of 

 the estuary for waste disposal, through the SAV ecosystems, to human uses of the 

 estuary as a source of fisheries harvest and other recreational activities. 

 In this paper we describe the structure and the logic behind this dual modeling 

 framework, and we provide a few selected results from these models to indicate 

 briefly the breadth of research questions which were addressed. 



SAV ECOSYSTEM MODEL 



Ecosystem Modeling Framework 



The initial step in developing a simulation model of the SAV ecosystem 

 involved identification of the level of aggregation and essential state variables. 

 Obviously, there are certain misleading consequences of reduced dimensionality 

 such as artifically conferred stability (e.g., Schaffer 1981). However, we have 

 taken cognizance of population time-constants (Goodall 1974, Schaffer 1981), as 

 well as life histories, trophic relations and habitats (Boling et al., 1975) in 

 defining aggregated biological state variables. We have reduced the number of 

 chemical variables (e.g., plant nutrients) by recognizing basic principles of 

 chemical kinetics whereby biochemical rates are determined by a single rate- 

 limiting step or substrate (e.g., Brezonik 1972). In all we defined 37 state 

 variables to be included in this model. There are a few published examples of 

 analytical or simulation models for seagrasses or other submerged macrophytes 

 (Titus et al., 1975, Belyaev et al., 1977, Short 1980, Weber et al . , 1981, Verhagen 

 and Nienhuis 1983, Adams et al. , 1979). However, all but one of these dealt with 

 plant production only, and none contained more than 8 state variables. It 

 was decided that this many (37) variables in one model would produce a virtually 

 unmanagable system of equations, particularly given the necessary high degree 

 of connectivity. 



A hierarchical scheme of six subsystem models was used to define the SAV 

 ecosystem (fig. 2). Other modelers have similarly utilized hierarchical 

 approaches (Goodall 1974, Overton 1975, Mclntire and Colby 1978), and various 

 methods have been suggested for interconnecting subsystem models. We elected to 

 simulate subsystem models independently and then to use outputs of each as 



136 





