Modernising fish and shark growth curves with Bayesian length-at-age models.

Growth modelling is a fundamental component of fisheries assessments but is often hindered by poor quality data from biased sampling. Several methods have attempted to account for sample bias in growth analyses. However, in many cases this bias is not overcome, especially when large individuals are under-sampled. In growth models, two key parameters have a direct biological interpretation: L0, which should correspond to length-at-birth and L∞, which should approximate the average length of full-grown individuals. Here, we present an approach of fitting Bayesian growth models using Markov Chain Monte Carlo (MCMC), with informative priors on these parameters to improve the biological plausibility of growth estimates. A generalised framework is provided in an R package 'BayesGrowth', which removes the hurdle of programming an MCMC model for new users. Four case studies representing different sampling scenarios as well as three simulations with different selectivity functions were used to compare this Bayesian framework to standard frequentist growth models. The Bayesian models either outperformed or matched the results of frequentist growth models in all examples, demonstrating the broad benefits offered by this approach. This study highlights the impact that Bayesian models could provide in age and growth studies if applied more routinely rather than being limited to only complex or sophisticated applications.

Using in situ hybridization to expand the daily egg production method to new fish species.

The capacity to reliably identify fish eggs is critical in the application of the daily egg production method (DEPM) to estimate biomass of commercially important species. This application has largely been confined to species that have easily identifiable eggs. Various molecular strategies have been used to extend the DEPM to a broader range of species, with recent approaches like in situ hybridization (ISH) that preserves the integrity of whole eggs, embryos or larvae recommended as a suitable alternative over destructive procedures like PCR. Here, we designed and validated an ISH approach for the identification of whole eggs and larvae from Snapper (Chrysophrys auratus) from environmental samples using the mitochondrial 16S rRNA gene as a target for specific horseradish peroxidase (HRP)-conjugated oligonucleotide probes. This colorimetric assay allowed the highly specific detection of positive hybridization signals from intact C. auratus larvae and eggs from mixed-species samples comprising closely related taxa. Furthermore, evaluation of whole eggs across a range of developmental stages revealed the sensitivity of the approach for discerning early stages, thereby guiding staging and the identification of otherwise indistinguishable eggs from environmental samples. This approach represents a major advance from current molecular-based strategies as it is nondestructive and allows for the simultaneous identification and staging of fish eggs (and larvae). The resultant 100% egg identification certainty we have achieved allows the DEPM to be applied to a wider array of fish species and is particularly applicable to species in areas where morphologically similar eggs are being spawned at the same time.