Extensive research has been undertaken to develop biological reference points (BRPs) for fisheries management. Traditionally, management aims to obtain maximum sustainable yield (MSY) from a stock, so MSY-relative quantities have been the most soughtafter BRPs and are considered "best practice" (Sainsbury, 2008).MSY-based BRPs can be derived from surplus production models, spawner-recruitment relationships (SRR) for semelparous species, delay-difference models or age-structured models fitted to monitoring data (Quinn & Deriso, 1999). However, many fish populations do not have sufficient data for reliable population dynamics modelling. This has led to widespread use of proxy reference points for data-poor species.One group of proxy reference points is based on per-recruit analysis. This approach follows a single cohort through its entire life accounting for growth, maturation, natural and fishing mortality.As the approach focuses on a single cohort, it does not consider a

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.

Most sharks (class Chondrichthyes, subclass Elasmobranchii) have a long lifespan, slow growth rate and low fecundity, leading to low productivity compared to teleosts, and many are also high trophic level predators with naturally low abundance. As such, fishing impacts on elasmobranchs have caused increasing concern in fisheries management and biodiversity conservation (Dulvy et al., 2014;Stein et al., 2018). Lack of biological and fisheries data for elasmobranchs has hindered development of management advice based on the outputs of traditional quantitative stock assessments (such as surplus production models, statistical catch-at-age models, stock-recruitment models, delay-difference models and virtual population analysis models). Furthermore, even where sufficient data are available, traditional