Improving ballast water risk tables

Project ID: 1501C (1301C)
CEBRA Project Leader: Andrew Robinson
DA Sponsor: Andrew Cupit
DA Project Leader: Peter Stoutjesdijk
DA Division: Biosecurity Animal
MPI Project Manager: N/A
Collaborators: ABARES

Previous ACERA and CEBRA work identified several shortcomings in the modelling approach we used to develop Ballast Water Risk Tables.  The risk tables rely on current temperature data.  The risk tables currently used are generated using a restricted temperature dataset.  Research will be conducted to identify sources of sea surface temperature data, and assess them to identify the best source for the needs of BWRA.  There will be further work required to incorporate sea-surface temperature data into the BWRA.

Further, the risk of translocation does not presently incorporate the number of transits between ports (based on shipping movement data).  Vessel transit data can be sourced from a number of sources, including: the Australian Maritime Safety Authority (AMSA), Lloyds, Port Authorities and the Bureau of Infrastructure, Transport and Regional Economics (BITRE).  These data sources will underpin an assessment of vessel traffic to and from ports to identify if the approach can effectively assess the cumulative risk of establishment for each port, and subsequently identify areas where departmental resources should be targeted for compliance activities.  The MPU has obtained Lloyds shipping data up to the end of 2014.  The Lloyds data will be available for this project.

Finally, the tables also rely on up-to-date species presence and range data, which is provided by the States and Northern Territory through port monitoring surveys.  As well as understanding cumulative risk for ports and taking into account current pest presence data, this research should be able to provide information to industry (and others) about which port surveys, if carried out and resulting in pest species freedom, would provide the most financial benefit to the shipping industry.

Final Report 1301C

Project Documents

Research Program

Data Mining

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