Practical uses of non-parametric methods in fisheries assessment modelling
R. M. Hillary
+ Author Affiliations
- Author Affiliations
CSIRO Marine and Atmospheric Research, Wealth from Oceans National Research Flagship, Castray Esplanade, Hobart, TAS 7000, Australia. Email: rich.hillary@csiro.au
Marine and Freshwater Research 63(7) 606-615 https://doi.org/10.1071/MF12031
Submitted: 31 January 2012 Accepted: 30 April 2012 Published: 29 June 2012
Abstract
The vast majority of fisheries stock assessment modelling is parametric, where specific models are assumed and fitted to data, the results of which are used to assess stock status and provide scientific advice. Often, the assumed models may not acceptably explain the data, or the data are not informative enough to estimate the parameters of even the most simple models. Using a fully inferential statistical framework, artificial neural networks were fitted to example data sets (stock-recruit, catch and relative abundance) and key assessment quantities such as maximum sustainable yield and relative biomass depletion were estimated. The combination of flexibility and statistical rigor suggests there is an as yet under-utilised role for such approaches in stock assessment, and not just in data-poor scenarios.
Additional keywords: non-parametric modelling, neural networks, stock assessment.
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