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A Bayesian inference approach to account for multiple sources of uncertainty in a macroalgae based integrated multi-trophic aquaculture model
journal contribution
posted on 2023-05-18, 18:37 authored by Scott HadleyScott Hadley, Jones, E, Craig JohnsonCraig Johnson, Wild-Allen, K, Catriona MacLeodCatriona MacLeodA Bayesian inference method was employed to quantify uncertainty in an Integrated Multi-Trophic Aquaculture (IMTA) model. A deterministic model was reformulated as a Bayesian Hierarchical Model (BHM) with uncertainty in the parameters accounted for using “prior” distributions and unresolved time varying processes modelled using auto-regressive processes. Observations of kelp grown in 3 seeding densities around salmon pens were assimilated using a Sequential Monte Carlo method implemented within the LibBi package. This resulted in a considerable reduction in the variability in model output for both the observed and unobserved state variables. A reduction in variance between the prior and posterior was observed for a subset of model parameters which varied with seeding density. Kullback–Liebler (KL) divergence method showed the reduction in variability of the state and parameters was approximately 90%. A low to medium seeding density results in the most efficient removal of excess nutrients in this simple system.
History
Publication title
Environmental Modelling and SoftwareVolume
78Pagination
120-133ISSN
1364-8152Department/School
Institute for Marine and Antarctic StudiesPublisher
Elsevier Sci LtdPlace of publication
The Boulevard, Langford Lane, Kidlington, Oxford, England, Oxon, Ox5 1GbRights statement
Copyright 2016 Elsevier Ltd.Repository Status
- Restricted