Drawing strength from each other: simulation testing of Australia’s abalone harvest strategies
Cathy Dichmont Consulting
Cathy M. Dichmont
All Australian abalone harvest strategies use empirical approaches heavily supported by CPUE based indicators that reflect tensions between producing harvest strategies that work at both larger management scales and that account for local scale population dynamics. All harvest strategies apply a weight of evidence approach that is generally not clearly codified. However, the different harvest strategies lead to very disparate ways of setting catches, with some adjusting catch based on relative performance of indicators, while others assume a direct relationship between CPUE and sustainable catch. These contrasting approaches have developed despite managing essentially the same group of species caught with the same gear type and from similar reef environments. This project will therefore test each of the presently used harvest strategies to draw out their strengths and weaknesses in a common platform. Only the Victorian Western Zone and Tasmanian harvest strategies have been simulation tested. The MSE models used in each, due to funding and time constraints, have been developed in a way that means that portability across jurisdiction is time consuming and costly. As such, this project will address the need to write MSE code that will be usable for the future and in other jurisdictions. It will then test this code on two abalone stocks, one blacklip and one greenlip, to assist in this need for code generality. The final product will be freely available on a version control site such as GitHub with detailed guides on how it is best used.
1. Undertake Management Strategy Evaluation testing of each jurisdiction’s current abalone harvest strategies in Australia.
2. Contrast harvest strategy performance under a common dynamic range of stock types, with and without conflicting indicators
3. Provide guidance on what constitutes best approaches to using empirical abalone harvest strategies
4. Provide fully documented open-source R package for other MSE expert’s use
5. Provide advice on how best to include additional indicators