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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]

The majority of catch per unit effort (cpue) standardizations use generalized linear models (GLMs) or generalized additive models (GAMs). We develop geostatistical models that model catch locations as continuous Gaussian random fields (GRFs) and apply them to standardizing cpue in Australia's Eastern Tuna and Billfish Fishery (ETBF). The results are compared with the traditional GLMs currently used in ETBF assessments as well as GAMs. Specifically, we compare seven models in three groups: two GLMs, two GAMs, and three GRF models. Within each group, one model treats spatial and temporal variables independently, while the other model(s) treats them together as an interaction term. The two spatio-temporal GRF models differ in treating the spatial–temporal interaction: either as a random process or as an autoregressive process. We simulate catch rate data for five pelagic species based on real fishery catch rates so that the simulated data reflect real fishery patterns while the "true" annual abundance levels are known. The results show that within each group, the model with a spatial–temporal interaction term significantly outperforms the other model treating spatial and temporal variables independently. For spatial–temporal models between the three groups, prediction accuracy tends to improve from GLM to GAM and to the GRF models. Overall, the spatio-temporal GRF autoregressive model reduces mean relative predictive error by 43.4% from the GLM, 33.9% from the GAM, and reduces mean absolute predictive error by 23.5% from the GLM and 3.3% from the GAM, respectively. The comparison suggests a promising direction for further developing the geostatistical models for the ETBF. [ABSTRACT FROM AUTHOR]

To assess fishing effects on data-poor species, impact can be derived from spatial overlap between species distribution and fishing effort and gear catchability. Here, we enhance the existing sustainability assessment for fishing effect method by estimating gear efficiency and heterogeneous density from sporadic catch data. We apply the method to two chondrichthyan bycatch species, Bight Skate and Draughtboard Shark in Australia, to assess cumulative fishing mortality (F cum) from multiple fisheries. Gear efficiency is estimated from a Bayesian mixture distribution model and fish density is predicted by a generalized additive model. These results, combined with actual fishing effort, allow estimation of fishing mortality in each sector and subsequently, the F cum. Risk is quantified by comparing F cum with reference points based on life history parameters. When only the point estimates were considered, our result indicates that for the period 2009 and 2010 Bight Skate caught in 14 fisheries was at high cumulative risk (F cum ≥ F lim) while Draughtboard Shark caught by 19 fisheries was at low cumulative risk (F cum ≤ F msy). Because of the high cost of conducting cumulative risk assessments, we recommend examining the distribution of fishing effort across fisheries before carrying out the assessments. [ABSTRACT FROM AUTHOR]

Catchability and natural mortality are key quantities in fisheries stock assessment. However, it is difficult to estimate these two parameters simultaneously using only fishery catch and effort data. A Bayesian state-space modified delay-difference model is outlined that can estimate time series of catchability by fleet as well as natural mortality. This model, and three variants thereof, is fitted to data for grooved tiger prawns () in Australia's Northern Prawn Fishery during the period of the year when there is little recruitment. A model that allows for both observation and process error and estimates natural mortality is best, in terms of model selection criteria as well as fit diagnostics. The posterior median estimate for catchability for the primary target fleet ranges from 6.15 × 10−4 to 1.09 × 10−4 during 1980-2007, while the posterior median estimate for catchability for a fleet with P. semisulcatus as its byproduct is about 20% of that for the primary fleet. Fishing efficiency increased at approximately 2% annually during 1980-2007, while the weekly natural mortality is estimated to be 0.053 week-1. [ABSTRACT FROM AUTHOR]

Abstract: We present a quantitative approach to the ecological Sustainability Assessment for Fishing Effects (SAFE) on diverse and data-poor bycatch assemblages. The method estimates fishing impact and compares the impact to sustainability reference points based on basic life-history parameters. We demonstrate the effectiveness of this method by assessing the impact of Australia''s Northern Prawn Fishery (NPF) on the sustainability of 51 elasmobranch bycatch species. We estimated the proportion of the population distributed within trawled areas, from detection–nondetection data collected from scientific surveys. This estimate of species’ abundance was then included in a model incorporating catch rate and escapement probability to give an estimate of the fishing mortality rate of each species. To guide management of bycatch species, we established two reference points based on natural mortality rate and growth rate: maximum sustainable fishing mortality and minimum unsustainable fishing mortality. The proportion of the 51 species’ populations distributed within the fished area ranged between 0.02 and 1.00 (mean 0.36±S.D. 0.31). Our results indicated that fishing impacts may have exceeded the maximum sustainable fishing mortality for 19 species, and exceeded the minimum unsustainable fishing mortality for 9 species. However, the estimates were highly uncertain for some species. SAFE can also be used by scientists and fishery managers to focus monitoring programs on potentially at-risk species to obtain additional data for further sustainability evaluation. Because the framework of SAFE is compatible with the management of target species, it can be incorporated into existing fishery management strategies, and may fulfill emerging ecosystem-based fishery management objectives. [Copyright &y& Elsevier]

Abstract: Banana prawns, Penaeus merguiensis, in the Gulf of Carpentaria, Australia typically form dense aggregations during a fishing season. It has been speculated that catchability decreases significantly as the fishing season progresses and stock size decreases. We used commercial catch effort data from 1987 to 2004 for three stocks in the Gulf to investigate whether density-dependent catchability exists in this species of aggregating penaeid. We developed two stochastic models based on an improved depletion method, one assuming a linear relationship between catchability and abundance and the other assuming a nonlinear power function between catchability and abundance. A stock-specific annual catchability coefficient, initial biomass, and a shape parameter of the power function were estimated using maximum likelihood or hierarchical Bayesian approach (for density-independent catchability models). For the majority of the datasets, the two models result in similar estimates. Although a weak but statistically significant density-dependent catchability, either positive or negative, was detected in about one fifth of the datasets, there is no clear pattern that points to positive density-dependence as suggested by previous studies. With all years and stocks combined, the density-dependent parameter in the second model has an overall mean of −0.03 and a standard deviation of 0.57 from all datasets, and its distribution looks approximately normal. However, a between year negative power function relationship between catchability and abundance appears to exist in this prawn species. [Copyright &y& Elsevier]

The Northern Prawn Fishery (NPF) is one of Australia's most valuable federally managed fisheries. It has also been a focus for modelling in Australia, with over 40 years of development of bioeconomic, stock assessment and management strategy evaluation models that have been used to varying degrees to inform management. The current management model has been in operation for over 10 years and is used to set the total allowable effort (TAE) for the tiger prawn (Penaeus semisulcatus and P. esculentus) component of the fishery, historically undertaken after a mid-year closure. The success of management has resulted in increased stocks of tiger prawns, and a fishery before the mid-year closure during which fishing for banana prawns was historically the main activity. While the current management model includes the consequences of fishing for tiger prawns during the first season in terms of which weeks are available for fishing for tiger prawns when estimating the TAE, the lack of explicit inclusion of banana prawns in the bioeconomic modelling has resulted in concerns that the TAE may be biased, as effort is allocated in the model to tiger prawns that, in reality, should be applied to banana prawns. The current model was consequently extended to include an explicit banana prawn component, and a range of scenarios undertaken to explore the impact of this on the management advice. It was found that the current model overestimates the optimal level of effort to be applied to tiger prawns except during periods of very low banana prawn abundance. Banana prawns, which vary substantially inter-annually due largely to environmental drivers, were also found to be the key driver of the level of overall fishery profit, suggesting that greater attention to this component of the fishery may be necessary to achieve the objective of maximizing net economic returns from the NPF. [ABSTRACT FROM AUTHOR]

Fish and seafood consumption in Australia has been growing, yet the implications of this trend across the food system, from both a health and sustainability perspective have not been fully explored. This paper aims to map out the fish and seafood food system in Australia, linking production and consumption, to articulate challenges and opportunities for enhancing the sector's contribution to future healthy and sustainable diets. We conducted a secondary analysis of publicly available datasets on fish and seafood production and consumption, triangulated and supplemented with peer-reviewed and grey literature on environmental, economic and social sustainability issues throughout the food system. A key challenge for health is the high proportion of fish and seafood consumed as discretionary food, particularly among children. Key challenges for sustainability include the narrow focus on environmental sustainability (with little consideration of the other domains), and the focus on production with little consideration for sustainability throughout post-harvest handling, processing, retail, distribution and consumption. Key opportunities for health and sustainability include the innovative use of processing and packaging technology to maximise nutritional quality; creation of markets and supply chains for a greater diversity of underutilised fish and seafood species and processing by-products; and reductions in waste and loss throughout the entire supply chain.