The dynamics of fish stocks are significantly influenced by environmental and oceanographic factors. Although this is now recognised by industry and scientists alike, there is increasing frustration with the application of single species stock assessments or CPUE analyses that do not incorporate any information about the broader environmental/oceanographic factors. There are clear examples in the SESSF of cyclic patterns in recruitment and availability and indications of regime shifts, but there has been little support for compiling these data and incorporating them in a quantitative manner into stock assessments of fisheries in SE Australia. Much of this information about the influence of environmental factors is in the heads of experienced fishermen but needs to be formally (and quantitatively) incorporated into the assessments/analyses that underpin the TAC setting process for the fishery. Clearly, a better understanding of the influences of the environment will improve several aspects of the assessment and management processes. Including environmental factors in the standardization of catch rates has the potential to remove a significant source of uncertainty that can lead to misleading population estimates. There is also a need to include environmental factors directly into the assessment, for example through models of environmentally-driven cycles (eg blue grenadier). As outputs from the assessments flow directly into management decisions, for example through the TAC setting process and appropriately chosen harvest strategies, the project will enhance management’s confidence in the decisions being made, and also improve industry’s faith in the assessment/management process. Industry members are currently getting disillusioned with assessments that do not take environmental factors of fishery dynamics into account to the point where they are beginning to walk out on the fishery assessment process. This only further decreases the relevance and applicability of these assessments. This trend can be turned around if industry is listened to and empowered with the capability of bringing quantitative information into the stock assessment process (rather than anecdotal).

From information passed down through generations and decades of their own experiences, good fishermen have an informed understanding of the influence of environmental and oceanographic on fish stocks. Industry and scientists would both appreciate the means to incorporate environmental/oceanographic data into the stock assessment process in a formal manner. Subsequent benefits to the management process will ensue through the provision of better developed harvest strategies that can explicitly account for environmental fluctuations in key fishery parameters (eg projections of cyclic patterns in availability and recruitment), and an exploration of flexible multi-year TACs. Also, short-term predictions of environmentally driven changes in biomass (either increases or decreases), that have led to unnecessary changes to TACs, may be offset by an increased ability to forecast biomass changes and thereby enable management to respond in a manner that does not unduly impact the stock or the financial stability of the industry. This project provides the datasets and models that would enable this to occur.

Most importantly, this project is the first step in the process of getting fishers to collect the information that is so needed to manage the fish stocks. With the burden of increasing costs of fishery monitoring, data collection and analysis, the fishing industry is looking towards cost effective alternatives to this work always being undertaken by government agencies. Industry members are already purchasing software that will enable them to collect and analyse much of this information themselves. There is a need for this to be a coordinated process which will ultimately empower the industry to bring valuable interpretations and analysis into the stock assessment process in a quantitative manner. Using the resources from this project to begin with, we aim to assess whether industry can be self-sufficient in collecting these data by the end of this three year project.