RESULTS AND DISCUSSION 



Simulations with AROSAN1 produced the following results when the carrying 

 capabity for mussels was set to equal the current number (2,000) of rafts. 



Phytoplankton (fig. 2) and zooplankton (fig. 3) show standing stock values 

 and an annual pattern of variation in agreement with data reported by Tenore 

 and Gonzales (1975). The simulated primary production by macrophytes (fig. 4) 

 tracks the annual pattern found by Fuentes (unpublished). Increasing the number 

 of mussel rafts depresses the standing stock of phytoplankton, and because of 

 both direct ingestion and reduction of their food, that of zooplankton as well. 

 Macrophytes, because they grow on the ropes of mussels, increase directly with 

 increase in number of rafts. We emphasize that the model was constructed from 

 experimental and literature data on rates. Thus the measured standing stocks 

 cited above are independent of the construction of the model and are available 

 as a first test of the models' predictive ability. 



Varying the carrying capacity threshold for mussels in order to simulate 

 a change in number of rafts produces a decreasing yield of mussels per raft as 

 the raft number increased (fig. 5). This is due to the reduction in standing 

 stock of phytoplankton and zooplankton. Mussel production is predicted to 

 reach an asymptote of approximately 105,000 Tm (wet weight) yr -1 . 



The increased standing stock of mussels does, however, have an impact on 

 the recycling of N between the sediments and the water. The model predicts 

 (fig. 6) an accumulation of N in the sediments as the number of rafts increases. 

 This is a consequence of the assumption that the saturation threshold for the 

 microbial uptake of the substrate is readily reached and surpassed by the 

 tremendous amount of detritus deposed by the increasing numbers of mussels. 

 The value of that threshold also takes into account the influence of bioturba- 

 tion by meiofauna and infauna that are strongly limited by the oxygen depletion 

 accompanying the increased rain of detritus onto the bottom. 



The model also predicts a pronounced negative impact of mussels on the 

 productivity of oysters. This seems to be due to a difference in the refuge 

 response thresholds for these two species with respect to a shared food. This 

 is an intriguing and wholly unexpected result from the model. If it is supported 

 by further field and modeling work, it could form the basis for some very 

 interesting simulations involving the socio-economics of oyster and mussel 

 production and pricing and lead to a better monetary return from this valuable 

 marine resource, the productive Ria de Arosa. This is the focus of our continuing 

 work with this model. 



CONCLUSIONS 



A crude first-generation ecosystem model of the Ria de Arosa simulates 

 annual patterns for the major biotic components that are in agreement with 

 field data not used in the construction of the model. The predicted response 

 to an increase of mussel rafts over the current 2,000 or so in the estuary was 



1) a decreased return of mussels per raft 



2) an increased and accelerating retention of N in the sediments 



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