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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 MacLeod
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.

History

Publication title

Environmental Modelling and Software

Volume

78

Pagination

120-133

ISSN

1364-8152

Department/School

Institute for Marine and Antarctic Studies

Publisher

Elsevier Sci Ltd

Place of publication

The Boulevard, Langford Lane, Kidlington, Oxford, England, Oxon, Ox5 1Gb

Rights statement

Copyright 2016 Elsevier Ltd.

Repository Status

  • Restricted

Socio-economic Objectives

Assessment and management of terrestrial ecosystems

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