Giant Crabs are a long, lived, slow reproducing xanthoid crab distributed from southern Western Australia to central New South Wales that are considered to be a single stock across southern Australia.
The giant crab fishery (GCF) across southern Australia has a small tonnage of large, individually valuable animals. Despite the implementation of harvest strategies and management plans guiding the setting of conservative TACC’s, declining trends are evident across the shared Southern Australian resource.
The fishery has had ongoing problems collecting quality stock assessment data, leading to uncertainty in the assessment and management. Owing to the size of the fishery, and the remote nature of the fishing operations, assessments now rely on fisher dependant catch rate data with an inherent high level of volatility due to the small number of operators.
Attempts to improve the collection of fishery data over the years have been challenging, in particular for fisher-based collection of length-frequency data from volunteer measuring programs.
Innovative data collection methods for small scale fisheries such as the GCF are required to improve the monitoring of stock status of this important commercial fishery and enhance long-term sustainability of the Giant Crab resource.
A length based model has previously been developed and was designed to integrate assessments across the jurisdictions. The model for giant crab and was being used for South Australia and Tasmania but was not applied to Victoria due to data limitations. In recent years the modelling has discontinued in SA and Tas because of insufficient length frequency data, which compounded uncertainty present due to weak growth data. The lack of length data in previous years was the critical change that has forced the model to be discontinued.
So our ability to understand changes in the stock has become weakest at precisely the period in the history of the fishery when information is most needed. This project is designed specifically in response to this need to address the lack of data through development of an efficient method to collect LF information, with minimal burden on fishers to improve accuracy of stock assessments.
