Bayesian state-space models with multiple CPUE data: the case of a mullet fishery

Rodrigo Sant’Ana,Jorge Pablo Castello,Laura Villwock De Miranda,João Paes Vieira,Paul Gerhard Kinas,Paulo Ricardo Schwingel

doi:10.3989/scimar.04461.11a

Rodrigo Sant’Ana, Jorge Pablo Castello + Show 4 more

Open Access

https://doi.org/10.3989/scimar.04461.11a

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Abstract

We propose a novel Bayesian hierarchical structure of state-space surplus production models that accommodate multiple catch per unit effort (CPUE) data of various fisheries exploiting the same stock. The advantage of this approach in data-limited stock assessment is the possibility of borrowing strength among different data sources to estimate reference points useful for management decisions. The model is applied to thirteen years of data from seven fisheries of the lebranche mullet (Mugil liza) southern population, distributed along the southern and southeastern shelf regions of Brazil. The results indicate that this modelling strategy is useful and has room for extensions. There are reasons for concern about the sustainability of the mullet stock, although the wide posterior credibility intervals for key reference points preclude conclusive statistical evidence at this time

Highlights

Stochastic versions of biomass dynamic models are called state-space models
Summary: We propose a novel Bayesian hierarchical structure of state-space surplus production models that accommodate multiple catch per unit effort (CPUE) data of various fisheries exploiting the same stock
The advantage of this approach in data-limited stock assessment is the possibility of borrowing strength among different data sources to estimate reference points useful for management decisions

Summary

Introduction

Stochastic versions of biomass dynamic models are called state-space models. These models have a hierarchical structure which simultaneously accounts for uncertainties in the time and space dynamics of biomass production and for errors in the observational process of some abundance indices (e.g. Catch per Unit Effort, CPUE) that relate data to the (unknown or latent) biomass.Surplus Production (SP) models are simple but robust non-linear models for stock assessment that are362 R. Stochastic versions of biomass dynamic models are called state-space models. Widely used to model biomass dynamics in state-space models for exploited fish populations (Millar and Meyer 2000). A useful feature of SP models is that they do not require analytic detailing about specific biological characteristics of target stocks under survey (Gulland 1983). This is important because detailed information about population dynamics may not be available for analysis of stock sustainability (Hilborn and Walters 1992, Chen and Andrew 1998). Application of SP models seeks to determine optimal levels of fishing effort that can reach predefined goals within a scenario of sustainability (Gulland 1983, Hilborn and Walters 1992, Sparre and Venema 1997)

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