Original Articles

Development of Bayesian stock assessment methods for Namibian orange roughy Hoplostethus atlanticus


Bayesian methods are useful in fisheries stock assessment because they provide a conceptually elegant and statistically rigorous approach to making decisions under uncertainty. The application of Bayesian stock assessment methods in the management of Namibian orange roughy Hoplosthethus atlanticus within the 200 mile EEZ of Namibia is reviewed. Time-series of relative abundance are short and their reliability in indicating abundance trends is uncertain. The development of informative prior probability density functions (pdfs) for the constants of proportionality (q) for hydro-acoustic, commercial trawl swept area, and research trawl swept area indices produced statistically consistent prior estimates of absolute abundance for each of the three grounds where more than one index of abundance was available. The posterior pdfs for stock assessment model parameters were used to account for uncertainty in evaluations of the potential consequences of alternative harvesting policies under a stock reduction model in which catch removals were assumed to account for any declines. It appears that all orange roughy stocks off Namibia have been depleted below the limit reference point (50% of long-term unfished biomass). However, the stock reduction model could not easily account for the large declines in indices on the four fishing grounds over the period from 1995 until 1999 when the informative priors for q were applied. In the 2000 stock assessment, the Bayesian procedure was updated to account formally for uncertainty in model structures that could explain the decline in abundance. The possibility of very low stock abundance could still not be discounted when these uncertainties were accounted for. Although this most recent methodology applies more statistical rigour, its complexity has hindered its acceptance in Namibia. However, if it is worth quantifying risks and uncertainties in future stock assessments for the provision of precautionary management advice, it is proposed that the assessment protocols adopted be probabilistic to account for uncertainty in model parameters, that careful attention be given to subjective judgements about their inputs and the representation of uncertainty within them, and that, where appropriate, alternative hypotheses about stock abundance and mechanisms for catchability and stock decline be taken into account.

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