Using GPS technology to improve fishery dependent data collection in abalone fisheries

Project Number:



University of Tasmania (UTAS)

Principal Investigator:

Craig Mundy

Project Status:


FRDC Expenditure:





Strategic R&D Plans TasFRAB 2005 Theme 3: Improving the scale of data collection and development of performance measures. Tasmanian Abalone Strategic Research Plan (2005 – 2009) - Need for fine-scale data on fishing effort. Catch and effort data are either important components of model-based stock assessment (NSW, VIC, SA, NZ) or form the primary basis for trend-based stock assessment (TAS). Because of the current low quality and resolution of effort reporting in abalone fisheries, CPUE data are insensitive to serial depletion. Low resolution catch effort data decreases the ability to identify stock declines, and increases the risk that stocks will collapse, or be diminished for long periods. Low resolution data will also increase the risk that major management intervention is required because of late confirmation a fishery is in decline. Acquisition of fine-scale data on fishing location is an essential component of flexible management for abalone fisheries, and provides managers and industry with the capacity to continue broad scale management at larger scales (zones), but also to manage elements of the fishery at a fine scale if required. A flexible scale of management will enable the current natural dynamic of fishing effort within regions to continue. CPUE is the primary fishery dependent indicator variable that is used to measure performance. Because CPUE is not linearly related to stock abundance, there is an important need for alternate indicator variables. This need could be resolved through the development of new technology derived indicator variables that can be calculated using the combined GPS and DTR data. High resolution location and effort data based on GPS/DTR data will increase the precision of stock assessments by improving quality of CPUE data, and by development of additional indicator variables.


1. Develop protocols and/or tools to automate conversion and interpretation of high resolution data.

2. Develop and test technology derived indicator variables.

3. Evaluate high resolution data for assessment of spatially-structured abalone populations.

4. Commence mapping commercially productive abalone populations

5. Preliminary investigations of spatial dynamics of abalone fisheries.

6. Incorporation of electronically derived indicator variables into the Tasmanian Abalone Management Plan.