Modified hierarchical Bayesian biomass dynamics models for assessment of short-lived invertebrates: a comparison for tropical tiger prawns

Abstract

Conventional biomass dynamics models express next year’s biomass as this year’s biomass plus surplus production less catch. These models are typically applied to species with several age-classes but it is unclear how well they perform for short-lived species with low survival and high recruitment variation. Two alternative versions of the standard biomass dynamics model (Standard) were constructed for short-lived species by ignoring the ‘old biomass’ term (Annual), and assuming that the biomass at the start of the next year depends on density-dependent processes that are a function of that biomass (Stock-recruit). These models were fitted to catch and effort data for the grooved tiger prawn Penaeus semisulcatus using a hierarchical Bayesian technique. The results from the biomass dynamics models were compared with those from more complicated weekly delay-difference models. The analyses show that: the Standard model is flexible for short-lived species; the Stock-recruit model provides the most parsimonious fit; simple biomass dynamics models can provide virtually identical results to data-demanding models; and spatial variability in key population dynamics parameters exists for P. semisulacatus. The method outlined in this paper provides a means to conduct quantitative population assessments for data-limited short-lived species.