JGOFS/SMP: Data-Based Models of Food Web Structure and Export Flux

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Data-Based Models of Plankton Community Structure and Export FluxIntensive JGOFS investigations over the past decade have indicated strong links among physical-chemical environmental forcing, plankton community structure, and the fate of carbon production (remineralization or export). However, these relationships are crudely portrayed in existing plankton food web models. It is presently unclear how much information on size and taxonomic composition is needed for adequate model predictions and to what extent community dynamics and biogeochemical fluxes in different water masses are linked by common organizational principles and quantitative relationships.The overall goal of the proposed research is to contribute to the mechanistic understanding of the factors that regulate "euphotic zone production and export of carbon and related biologically active substances". To accomplish this, the PIs will develop and evaluate data-based models of plankton community structure and export flux for tropical/subtropical open-ocean ecosystems. This project will focus on the tropical and subtropical oceans, areas most strongly represented in JGOFS Process and Time-series studies (EqPac, Arabian Sea, HOT, BATS) and presently most developed in terms of mature databases and supporting experimental studies. The specific objectives are: 1) to develop data-constrained representations of planktonic community structure and trophic interactions; 2) to construct models of temporal plankton community dynamics and associated biogeochemical fluxes at the Hawaii Ocean Time-series (HOT) site and following iron perturbation in the equatorial Pacific (IronEx II); 3) to compare and test simple parameterizations, for lower trophic level interactions and export fluxes for use in 3-D coupled models; 4) to develop a general tropical ocean model that includes appropriate responses to nutrient supply (Arabian Sea) and iron-limitation (Equatorial Pacific); and 5) to examine model structures and strategies for predicting biologically realistic responses in computationally intensive Global Circulation Models (GCMs). Based on observed similarities in the stocks and process rates in diverse tropical oceanic ecosystems, the PIs hypothesize that the data will lend themselves to representation in a general tropical ocean model with common structure and parameters. To investigate the minimum level of complexity required to capture essential biological realities in global models, they will statistically examine an interrelated spectrum of models, all calibrated to common data sets but differing in structural complexity.