A 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.

Item Type:	Refereed Article
Keywords:	Bayesian inference, macroalgae, IMTA, Sequential Monte Carlo, particle filter, aquaculture
Research Division:	Biological Sciences
Research Group:	Ecology
Research Field:	Marine and Estuarine Ecology (incl. Marine Ichthyology)
Objective Division:	Environment
Objective Group:	Ecosystem Assessment and Management
Objective Field:	Ecosystem Assessment and Management of Marine Environments
UTAS Author:	Hadley, S (Mr Scott Hadley)
UTAS Author:	Johnson, C (Professor Craig Johnson)
UTAS Author:	Macleod, C (Associate Professor Catriona MacLeod)
ID Code:	108107
Year Published:	2016
Web of Science® Times Cited:	3
Deposited By:	Sustainable Marine Research Collaboration
Deposited On:	2016-04-06
Last Modified:	2017-11-02
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