Modeling the microbial loop: An estuarine modeler's perspective.

Contemporary models of the microbial loop should be critically evaluated relative to their compartmental structures, flow networks, and treatment of upper ocean physics (vertical and horizontal boundary conditions). The development of new models that include better representation of the autotrophic components and mixotrophy/competitive interactions is warranted. The mathematical structures should reflect contemporary knowledge of controls (e.g., multiple resource limitation), thresholds and limits, and be based on testable assumptions. Stochastic processes must at some point be included to evaluate known patterns of spatial and temporal variability.

Synergism between research and simulation models of estuarine microbial food webs.

Construction of mathematical simulation models helps to organize current information and extend inferences from available data. During the past two decades, microbial ecology has undergone rapid developments in both quantity and quality of available data. In particular, considerable advances have been made in our knowledge of microbial food web dynamics in the Duplin River watershed at Sapelo Island, Georgia. Here we provide examples of how modeling and microbial ecology have interfaced. In the early 1970s, construction of a 14-compartment model of carbon flow through a salt marsh ecosystem aided in directing method development and field experiments on the sediment microbial community. In turn, the results of field experiments corroborated the model's postulated controls on the community. Also, during the past 12 years we have developed a series of simulation models reflecting the growing information on the aquatic microbial food web. Early models provided evidence for the microbial loop but illustrated the paucity of knowledge concerning controls for bacterial growth on detritus. Results from newer methods in microbial ecology and studies from the Duplin River have allowed us to construct a model which provides realistic simulations but is also highly sensitive to certain parameter value changes (e.g., in organic matter availability and grazing by protozoans). Thus improvements in model structure and corroboration of the models with extant data have been closely tied to methodological and conceptual advances in microbial ecology. The relationship is viewed as synergistic, as needs for model parameter values and equation forms have directed further development of methods, experimentation, and field observations.