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Managing fisheries to achieve ecological, economic and social sustainability is complex. The use of dynamic bioeconomic models can be and have been used to assist in determining management targets. However, optimizing profits over time can result in large reductions in fishing effort in the short term with adverse social consequences. There exist other benefits from maintaining fishing effort even in adverse conditions (e.g., maintain crew and fleet capacity). For this reason, many bioeconomic models have included some form of minimum effort, catch or short-term profit constraint. In this paper, we consider a range of approaches to assess an appropriate minimum fishing effort, including the estimation of fishery breakeven effort levels, and approaches based on historical fishing levels. These are tested using a bioeconomic model currently used for fishery management. We find that breakeven approaches tend to result in the most conservative effort levels and the highest net present value of profits. In contrast, using a proportion of the moving average of the observed fishing effort results in less conservative change in effort, while resulting in positive changes in the net present value of fishery profits. The approach also has the advantage of being dynamic, adjusting with recent fishery conditions. [ABSTRACT FROM AUTHOR]

Using length frequency distribution data (LFD) is cost-effective for estimating somatic growth in fish or invertebrates as length data are relatively easy to obtain. The recently developed R packages TropFishR and fishboot extend classic ELEFAN (Electronic LEngth Frequency ANalysis) programs and include more powerful optimization procedures and a bootstrap method for estimating uncertainties. Yet, the fundamental functions require users to provide search conditions (e.g. upper and lower limits for each parameter, length-class size, number of length-classes for the calculation of moving average), which can significantly affect the results. In this paper, we compare the ELEFAN approach with a Bayesian approach in analysing LFD, employing both standard and seasonal von Bertalanffy growth functions. We apply both approaches to a commercially valuable but poorly studied red endeavour prawn (Metapenaeus ensis) harvested in Australia's Northern Prawn Fishery. Sensitivity tests on ELEFAN confirm that any change in search settings would affect the results. Simulation studies on Bayesian growth models show that Linf and K can be accurately obtained even with modal progression of only one year-class and using non-informative priors. However, age information, including the theoretical age at length zero (t0), is difficult to estimate and requires LFD from multiple age classes and informative priors. The Bayesian models yield mean parameters of: Linf = 36.56 mm (carapace length), K = 2.74 yr–1, and t0 = -0.02 yr for the males, and Linf = 51.81 mm, K = 1.94 yr–1, and t0 = -0.02 yr for the females. Seasonal oscillation models fit the LFD better, but the improvement is small and the estimated season-related parameters have large variances. [ABSTRACT FROM AUTHOR]

Understanding recruitment variability in marine fisheries has benefits for the stock management and dependent fishers' ability to plan their income. Here, we overview past and new research on the complex recruitment dynamics of redleg banana prawns Penaeus indicus in Australia's Joseph Bonaparte Gulf to assess themes dating back to the time of Hjort and identify new challenges. During 2015 and 2016, redleg prawn catches and catch per unit effort decreased to anomalously low levels, suggesting a substantial decrease in prawn biomass. We hypothesized that low catches could be explained by temporary drops in sea level and rainfall potentially reducing the ability of postlarvae to reach their nursery ground. We contend that very bad prawn catch years may be predicted using two variables that are possible drivers of recruitment—the January Southern Oscillation Index and the combined January to February cumulative rainfall. However, due to challenges in verifying and defining such environmental relationships for inclusion in a stock assessment, we propose development of a harvest strategy framework to support management recommendations. Our study highlights the increasing role of anthropogenic climate change in exaggerating the impacts of environmental drivers on recruitment processes and the need to also focus on multidisciplinary research. [ABSTRACT FROM AUTHOR]

Analysis of spawning biomass per‐recruit has been widely adopted in fisheries management. Fishing mortality expressed as spawning potential ratio (SPR) often requires a reference point as an appropriate proxy for the fishing mortality that supports a maximum sustainable yield—FMSY. To date, a single generic level between F30% and F40% is routinely used. Using records from stock assessments in the RAM Legacy Database (RAMLD), we confirm that SPR at MSY (SPRMSY) is a declining function of stock productivity quantified by FMSY. We then use general linear models (GLM) and Bayesian errors‐in‐variables models (BEIVM) to show that SPRMSY can be predicted from life‐history parameters (LHPs, including maximum lifespan, age‐ and length‐at‐maturation, growth parameters, natural mortality, and taxonomic Class) as well as gear selectivity. The calculated SPRMSY ranges from about 13% to 95% with a mean of 47%. About 64% of the stocks in the RAMLD require SPRMSY > 40%. Modelling SPRMSY reveals that LHPs plus Class explain 61% of the deviance in SPRMSY. Faster‐growing, low‐survival, and short‐lived species generally require a high SPR. With equal LHPs, elasmobranchs require about 20% higher SPRMSY than teleosts. When FMSY is estimated from fisheries that harvest older fish, increasing the vulnerable age by one year leads to about an 8% increase in SPRMSY. The BEIVM yields smaller variance and bias than the GLM. The models developed in this study could be used to predict SPRMSY reference points for new stocks using the same LHPs for calculating Fx%, but without knowledge of the stock‐recruitment parameters. [ABSTRACT FROM AUTHOR]

• Fisheries stock-recruitment specification has important management implications. • Sensitivity of management reference points should be tested or dynamic form used. • Explicit inclusion of environmental correlates needs rigorous assessment. • Harvest Control Rules effectively evaluate robustness to recruitment variability. • Empirical rules with survey inputs circumvents needing mechanistic understanding. The representation and parameterisation of the stock-recruitment relationship is highly influential in fisheries stock assessments. We overview important management implications arising from choices and assumptions made when modelling recruitment and propose pragmatic solutions. An age-structured population model is used to highlight some additional considerations including (1) incorrect assumptions about the source of recruits and (2) ways to account for environmental correlates in stock assessment models, either directly or indirectly. Existing stationary approaches ignore the sometimes considerable influence of the environment on population processes such as recruitment, growth and mortality, all of which are expected to shift under changing climate. We show that where environmental relationships are explicitly included in models, they need to be rigorously assessed and model parameterization carefully evaluated. Variability in annual recruitment estimates (specified by the standard deviation σ R) must be adjusted to control the partitioning of total variation into environmental and unexplained variation, as reference point estimates are particularly sensitive to the choice of σ R. Finally, we evaluate two recommended solutions to these issues: Harvest Control Rules (HCRs) that are robust to recruitment uncertainty and variability, and empirical HCRs that use pre-season survey inputs as a way to more directly provide forecasts without needing to quantify the complex underlying details of the environmental-recruitment relationships. [ABSTRACT FROM AUTHOR]

• Redleg banana prawns are caught in a remote part of the northern prawn fishery. • Fishing effort on the species has varied considerably in recent years. • We model the key drivers of fishing effort on the sub-fishery. • We find that economic conditions in the main part of the fishery is a key driver. Recent low levels of fishing effort in the redleg banana prawn (Penaeus indicus) component of the Australian Northern Prawn Fishery have highlighted challenges in assessing the stock status of the species and deriving appropriate management responses. Usually, low levels of fishing effort are associated with low stock abundance, but economic factors also affect fishing activity. In this study, we consider the key drivers of fishing effort in the sub-fishery, and conclude that fishing effort is driven by expectations of catch based on environmental drivers, realised revenue per unit of fishing effort, and the opportunity cost of fishing in other areas. In the case of the recent low effort levels, it is the opportunity cost that has been most influential, highlighting the importance of considering economic drivers when managing fisheries. [ABSTRACT FROM AUTHOR]