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A probabilistic process model for pelagic marine ecosystems informed by Bayesian inverse analysis
Citation:
Hosack, G. R. AND P. M. ELDRIDGE. A probabilistic process model for pelagic marine ecosystems informed by Bayesian inverse analysis. Presented at Ocean Science Meeting, Nice, FRANCE, January 25 - 30, 2009.
Impact/Purpose:
Marine ecosystems are complex systems with multiple pathways that produce feedback cycles, which may lead to unanticipated effects.
Description:
Marine ecosystems are complex systems with multiple pathways that produce feedback cycles, which may lead to unanticipated effects. Models abstract this complexity and allow us to predict, understand, and hypothesize. In ecological models, however, the paucity of empirical data usually prohibits direct estimation of all parameters. Monte Carlo models typically specify unknown parameters a priori, but here we propose a new approach that uses inverse analysis to parameterize the predictive process model based on biogeochemical flux estimates derived from empirical data, expert opinion, and ecological theory. Previous inverse analyses that use an optimization protocol have not fully addressed the uncertainty in empirical data. Furthermore, an optimized inverse solution fails to adequately represent the uncertainty inherent to the system. Hence, we have developed a Bayesian approach that explicitly addresses observation error and use it to construct a probabilistic process model. Results for a tropical coral atoll study, which was originally described by other researchers using optimized inverse analysis, show that fluxes involving DOC affect the ecosystem’s long-term response to anthropogenic or environmental inputs, whereas the microbial loop affects the short-term response.