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Most of the stock assessments conducted in the USA and in New Zealand are based on packages that have been developed for generic use, are well documented, and have been tested using simulation. However, this is not the case for assessments conducted in Australia and many other countries. This paper reviews all of the model-based stock assessments for Australian fisheries to evaluate how many of these assessments could have been conducted using the publicly-available stock assessment packages used widely in the USA and New Zealand. The 76 model-based assessments reflect 37% of the 2013 catch recorded in Australia's Status for Key Australian Fish Stocks Reports (or 34% of the total catch in 2013). All but 18 (or 24 if full rather than approximate age-size-structured models need to be used) of the stock assessments could have been conducted using stock assessment packages used in the United States and New Zealand. Adoption and use of packages for more stocks in Australia should increase the likelihood that results are based on correctly-coded models whose estimation performance is widely understood, reduce the time needed to conduct assessments, and speed up the peer-review process. The availability of training, manuals, and example data sets for stock assessment packages should partially address their additional complexity. Additional benefits, in terms of numbers of assessed stocks, could occur if Australian stock assessment scientists develop a forum to collaborate and share methods. These results are applicable to many other jurisdictions that undertake stock assessments. [ABSTRACT FROM AUTHOR]

The harvest control rules for many fish and invertebrate stocks, managed using stock assessments based on fitting population dynamics models to monitoring data, rely on biological reference points. These reference points are often related to unfished conditions (' B 0 ') and are calculated assuming that biological parameters and their associated functional forms (e.g., unfished recruitment (' R 0 '), natural mortality, growth) are stationary. However, there is increasing evidence that the assumption of stationarity is untenable in the face of environmental change. In principle, non-stationarity can be addressed by defining stock status (i.e., spawning biomass relative to unfished spawning biomass) using 'dynamic B 0 ' (the spawning biomass that would be expected in the absence of fishing). We show how catch limits (Recommended Biological Catches) for stocks in Australia's Southern and Eastern Scalefish and Shark Fishery would have differed had management been based on dynamic B 0. We also explore the performance of static and dynamic B 0 -based harvest control rules using simulations where various biological parameters (R 0 , B 0 , L ∞ , κ, natural mortality, and stock-recruitment steepness) exhibit trends over time. The results confirm previous work that the implications of adopting a dynamic B 0 approach would differ among species, with quite major changes in stock status and catch limits for some species and negligible changes for others. In terms of management performance in projections, there are species-specific trade-offs, with a dynamic B 0 approach tending to lead to higher catches, lower biomass on occasion and less inter-annual variation in catches than a static B 0 approach. The implications of harvest control rules based on static and dynamic B 0 also differ depending on which biological parameter is changing over time. [ABSTRACT FROM AUTHOR]

Capacity reduction programmes, in the form of buybacks or decommissioning, have had relatively widespread application in fisheries in the US, Europe and Australia. A common criticism of such programmes is that they remove the least efficient vessels first, resulting in an increase in average efficiency of the remaining fleet, which tends to increase the effective fishing power of the remaining fleet. In this paper, the effects of a buyback programme on average technical efficiency in Australia's Northern Prawn Fishery are examined using a multi-output production function approach with an explicit inefficiency model. As expected, the results indicate that average efficiency of the remaining vessels was generally greater than that of the removed vessels. Further, there was some evidence of an increase in average scale efficiency in the fleet as the remaining vessels were closer, on average, to the optimal scale. Key factors affecting technical efficiency included company structure and the number of vessels fishing. In regard to fleet size, our model suggests positive externalities associated with more boats fishing at any point in time (due to information sharing and reduced search costs), but also negative externalities due to crowding, with the latter effect dominating the former. Hence, the buyback resulted in a net increase in the individual efficiency of the remaining vessels due to reduced crowding, as well as raising average efficiency through removal of less efficient vessels. [ABSTRACT FROM AUTHOR]

In Australian fisheries, explicit harvest control rules are increasingly applied to achieve bioeconomic objectives. Enhancing economic return, an objective valued by both regional communities and the fishing industry, can often be achieved by increasing stock abundance, consequently supporting the second principal management objective of sustainability. A harvest strategy proposed by the fishing industry for the Southern Zone rock lobster fishery in South Australia implemented in 2011 was based on a table that, given CPUE from the preceding season, specified the following year’s catch quota (total allowable commercial catch, TACC). A guiding principle in constructing the 2011 harvest control rule was to target a constant exploitation rate. Here we report on bioeconomic modeling undertaken to evaluate and improve this harvest strategy. A length-based stock assessment model was extended to forward project biological and economic time series to evaluate candidate harvest strategies. Key performance indicators were the 10-year projected discounted profit, biomass, egg production and catch stability. These simulations imply that the 2011 harvest strategy required only moderate change: (1) current or moderately lower levels of exploitation are economically optimal; (2) narrower CPUE band widths for the TACC-vs-CPUE decision rule table enhance yearly catch stability; and (3) for depressed levels of stock abundance, exploitation rates were set to decrease linearly down to a lower limit reference point of CPUE below which the fishery is closed. A final harvest strategy incorporating these features was adopted in 2014 for yearly quota setting. [ABSTRACT FROM AUTHOR]