Much effort has been placed over the last couple of decades on the development of harvest strategies, stock assessments, risk assessments and the strategic use of ecosystem models to facilitate meeting the needs of the Commonwealth’s Harvest Strategy Policy. A focus on modelling to improve fisheries management has required effort towards method development. However, little effort has been made towards revisiting and updating the biological parameters that fundamentally underpin such modelling (e.g. growth rates, age and size at maturity, natural mortality rates, dietary information, mixing rates and stock structure) and the tools or methods used to derive them. As a result, most models now rely on parameters and community dietary data derived from information collected during the 1970s-1990s, (e.g. available maturity ogives for blue-eye trevalla are over 20 years old), or information that is borrowed from other regions or species. Whether such old or borrowed values are now representative for commercial Australian fish species is unknown but many factors point to major changes occurring in our marine environment. Australian waters in the south east and south west are climate hotspots and, overall, Australian waters have warmed faster than the global average. Key components of the productivity of marine fish (growth, maturity, and recruitment) are expected to be undergoing directional changes under a changing climate and it is entirely possible that there have been changes in fundamental productivity parameters for some Australian stocks. The reliance of current assessments on what is likely to be out-of-date information leads to increased uncertainty, which propagates into management decisions. Without an understanding of any changes in biological parameters and how any change might impact assessment frameworks, determining whether current management measures are ensuring sustainability becomes highly uncertain.

The need for cumulative impact assessment (CIA) is increasingly being recognized. The development process for Australia's Harvest and Bycatch Policies, and their associated guidelines have reinforced the need for assessment of cumulative impacts, and the EPBC Act has also explicitly required consideration of cumulative impacts.

Where multiple activities occur or are planned, an understanding of their combined effects on the environment is necessary to address policy requirements and achieve sustainability. The concept of cumulative impact assessment is not new – indeed cumulative assessment has been recognized for many years, and a range of methods have been proposed around the globe. However, no methodology for undertaking cumulative assessments has been accepted nationally or globally. In addition to considering the impacts across all fishing sectors (commercial, recreational, indigenous, as required by recent changes to the Fisheries Administration Act 1991) and all fisheries, there is also an increasing need to consider other users of marine resources and coastal waters (e.g. renewable energy, shipping etc), especially where space crowding may be an issue.

Target species stock assessments typically consider the species of interest as well as other sources of fishing mortality (e.g. discards), but they do not usually consider their effects on other fisheries sectors or the effects of other sectors on the focal fishery. CIA methods therefore need to consider interactive and indirect effects. To date, interactive effects are often viewed as additive (simple linear addition of one impact to another) with little consideration given to synergistic, antagonistic or non-linear effects. While the ERAEF toolbox used for assessment of bycatch and protected species has some potential options for cumulative impacts (e.g. SAFE method), at this stage they are insufficient for moving to the scales and complexities across multiple fishing sectors and fisheries.

Thus, sustainable fisheries management requires new approaches that consider all sectors and all fisheries and how they impact the environment. Such CIAs will be challenging given that empirical data are often lacking - a dedicated research effort is needed.