Fisheries Digital Data Framework: A workshop to share vision, evolve requirements for fisheries data
For FRDC to further investment into the fisheries data space, it must first understand industry needs as well as concerns. It is hoped that this workshop will devlop a way forward for FRDC in the data-space, informing a plan that enables both industry and commercial entities to benefit.
Using information for 'data-rich' species to inform assessments of 'data-poor' species through Bayesian stock assessment methods
Over 300 species are caught in the SEF, of which around 100 have commercial value. Twenty five species comprise around 90% of the landed catch. Each year, however, quotas are set for only around 17 species. There are 10 of these species for which there is (or has been) some formal stock assessment (that may not occur every year). For all of the remaining quota species and some of the more important non-quota species, no formal assessment is undertaken and the only assessment that can be made is based on investigation of trends in catch and effort and size distribution and anecdotal input from scientists and industry. There is simply not enough resources to undertake formal stock assessments for the wide range of commercial species landed in the SEF. Yet, each of these species is an important component of the catch of fishers. If the fishery is to continue to operate in its current form and meet the strategic assessments required under the EPBC Act, some form of formal assessment is required.
A recently completed ARF project (Production parameters from the fisheries literature for SEF-like species - Project no R99/0308) demonstrated the utility of using information for "similar" species when conducting assessments for SEF species. Using key parameters such as the virgin biomass, the rate of natural mortality, and the “steepness” of the stock-relationship relationship, a simple formula was developed for identifying “similar” stocks / species and an algorithm was developed for constructing prior probability distributions for these parameters. The resultant distributions can be used in Bayesian stock assessments and as the basis for sensitivity tests when applying other methods of stock assessments. The current project will refine the prior distributions for the production parameters and develop and test methods of stock assessment that use the results of assessments for well-studied species in a formal manner to inform assessments of ‘data-poor’ species. If successful, the methods developed would lead to significant benefits not only for the assessment and management of "data poor" SEF low priority, by-product and by-catch species, but also for a range of new and developing fisheries in Australia.
Final report
Progressing the National Fisheries Digital Data Framework - Industry consultation
Australian fisheries data is currently stored in a segregated manner and connectivity is minimal between sources, leaving data to be relatively inaccessible. The majority of Australia's wild catch fishers continue to complete their catch and effort reporting via paper log books that then require data entry capacity to ensure these can be utilised by the appropriate users - stock assessments, SAFS, etc... The move to electronic, real time data reporting would enable a higher level of data to be collected but would also allow fine scale management of fishing operation and ultimately greater sustainability of fish stocks.
As a result of this, corresponding jurisdictional management agency investment in improving infrastructure is also often segregated. Harmonising fisheries digital data could derive not only efficiencies in the data use (data can be collected once and used many times) but also in infrastructure investment. Harmonised investment in infrastructure as well as innovative change in regards to how a range of services and information are utilised in fishing and aquaculture could deliver greater profit and improve timeliness of decision making. It is however important that and framework proposed has the confidence and support of both government and industry. This project seeks to ensure that industry is involved in progressing the data framework, and that suit a framework suits their needs.