Project number: 2001-042
Project Status:
Budget expenditure: $246,572.00
Principal Investigator: Caleb Gardner
Organisation: University of Tasmania (UTAS)
Project start/end date: 29 Jun 2001 - 31 Mar 2008


The ability to conduct stock assessments of the giant crab resource and to evaluate management strategies is fundamental for sustainable development of this resource.

Stock assessment of giant crabs across southern Australia is not formalised although this is a requirement for state management and is also required to meet Federal expectations on export of wildlife under Schedule 4 of the Wildlife Protection Act. Our ability to conduct assessments is limited by the data and analytical tools that are available.

Agencies involved in managing giant crabs require information on the setting of meaningful fishery performance indicators, and they also require ongoing information collection to evaluate these indicators. It is important to have the ability to track changes in biomass (or an index of biomass), recruitment of sized animals into the fishery, and reproductive output. Managers require information to balance the goals of optimising yields and ensuring adequate reproductive output is maintained.

Regardless of the management system implemented, all States require the ability to evaluate different harvest strategies such as the impact of closed seasons, different size limits and different TACs. While egg and yield per recruit analyses have been completed, there is a need for information to set TACs using best available knowledge on the state of the fishery.

An over-riding need for assessment of giant crab fisheries is that it be low cost. This is not a high value fishery and biologists conducting future assessments will have access to only low levels of funding. Placing observers on board vessels is not cost effective for the long term. Future assessments will be reliant on commercial log book data and on industry data collection programs to be developed through this proposal.


1. To develop a low cost, long term, giant crab resource assessment and data collection methodology.
2. To quantify biases in the historical log book data to increase its value for resource assessments.
3. To obtain industry's understanding/observations of basic biological and market processes (eg. moulting, egg-bearing, mating, migration, beach prices influences) and how their fishing practices are adapted to this knowledge (eg. targeting of size classes, seasonality of effort, etc.).
4. To develop the ability to investigate alternative harvest strategies (sustainability of different TACs
closed seasons etc.).

Final report

ISBN: 9781862954311
Author: Caleb Gardner
Final Report • 2008-03-31


The project has developed tools for low cost assessment of the giant crab resource across southern Australia.  

Stock assessment and management response is now increasingly based on biomass estimates from this project.  Risk of poor management decisions is thus reduced, which contributes to greater business certainty.  

This project was constructed with the awareness that the resource was small and the fishery would have little ability to fund expensive data collection systems in the future, beyond catch rate data from logbooks.  Size structure data from the fishery was considered the main data type to be valuable for ongoing assessment, but also expensive to collect.  Fishers have adopted electronic calipers combined with an electronic data logger, the solution developed in this project.  This system has dispensed with paper records so that work at sea is easier, and costs for data management (e.g. data entry) are reduced.

Specialised software was developed for conducting model runs to facilitate altering parameters, running a range of alternative scenarios, and plotting outcomes.

Giant crab stock assessments are utilizing outputs from this model.  The fishery performance indicators in Tasmania are being re-written in a new management plan to formalize the adoption of the model outputs.   

Keywords: giant crab, Pseudocarcinus gigas, harvest strategy, population model, data collection.

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