Project number: 2002-094
Project Status:
Budget expenditure: $176,237.00
Principal Investigator: David Smith
Organisation: Agriculture Victoria
Project start/end date: 29 Sep 2002 - 30 Nov 2005


Over 300 species are caught in the SEF, of which around 100 have commercial value. Twenty five species comprise around 90% of the landed catch. Each year, however, quotas are set for only around 17 species. There are 10 of these species for which there is (or has been) some formal stock assessment (that may not occur every year). For all of the remaining quota species and some of the more important non-quota species, no formal assessment is undertaken and the only assessment that can be made is based on investigation of trends in catch and effort and size distribution and anecdotal input from scientists and industry. There is simply not enough resources to undertake formal stock assessments for the wide range of commercial species landed in the SEF. Yet, each of these species is an important component of the catch of fishers. If the fishery is to continue to operate in its current form and meet the strategic assessments required under the EPBC Act, some form of formal assessment is required.

A recently completed ARF project (Production parameters from the fisheries literature for SEF-like species - Project no R99/0308) demonstrated the utility of using information for "similar" species when conducting assessments for SEF species. Using key parameters such as the virgin biomass, the rate of natural mortality, and the “steepness” of the stock-relationship relationship, a simple formula was developed for identifying “similar” stocks / species and an algorithm was developed for constructing prior probability distributions for these parameters. The resultant distributions can be used in Bayesian stock assessments and as the basis for sensitivity tests when applying other methods of stock assessments. The current project will refine the prior distributions for the production parameters and develop and test methods of stock assessment that use the results of assessments for well-studied species in a formal manner to inform assessments of ‘data-poor’ species. If successful, the methods developed would lead to significant benefits not only for the assessment and management of "data poor" SEF low priority, by-product and by-catch species, but also for a range of new and developing fisheries in Australia.


1. Expand the database of production parameters for SEF-like species
2. Develop prior probability distributions for steepness and the coefficient of variation about the stock-recruitment relationship using Bayesian meta-analysis
3. Develop a Bayesian framework within which the results for data-rich species can 'inform' assessments for data-poor species.
4. Apply the framework to three case-studies to determine the robustness of the framework.
5. Test the framework by means on Monte Carlo simulation

Final report

ISBN: 1-74146-524-9
Author: David Smith

Related research