Competitive Round Call for Expressions of Interest now open. Closes 27 September 2019



A re-examination of underlying model assumptions and resulting abundance indices of the Fishery Independent Survey (FIS) in Australia’s SESSF

Project Number:



CSIRO Oceans and Atmosphere Hobart

Principal Investigator:

Miriana Sporcic

Project Status:


FRDC Expenditure:



Communities, Industry


It is over 10 years since the original model design, which was conditioned on Commonwealth logbook data over 2001 – 2005, was introduced and much has changed in the SESSF over this period, such as marked changes to the fishing fleet resulting from government buy-outs during 2006 and potential climate change on fisheries and fish distribution, particularly in south-east Australia. This approach may now be outdated by potential changes to the relative abundance of different SESSF species and changes in species behaviour. In an effort to find efficiencies in the sampling design, it is timely to re-examine underlying model assumptions (e.g., depth preference, day-night preference, and species range limits). This will provide updated model-based CVs using more recent Commonwealth logbook data, and should provide more reliable fishery independent abundance indices for selected SESSF species. The FIS was originally intended to support management of SESSF species, providing an abundance series that is free of the influence of fisher behaviour, market forces, closed areas, management regulations and changes in gear used. Given the investment by the fishing industry and Government in the five completed surveys over the last 10 years, it is appropriate to consider how FIS abundance indices can be used as an input to manage the SESSF. To date, these indices have been incorporated in Tier 1 stock assessments for only three species, where it does not appear to be influential. It is not clear how these estimates could be used for non-Tier 1 species.


1. re-examine some of the underlying assumptions of the survey;

2. update data that conditions the model and find efficiencies in sampling design; and,

3. use a data simulation exercise to examine the utility of the estimates given the process and sampling errors that have been observed.