Modelling catch sampling uncertainty in fisheries stock assessment : the Atlantic-Iberian sardine case
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The statistical assessment of harvested fish populations, such as the Atlantic-Iberian sardine (AIS) stock, needs to deal with uncertainties inherent in fisheries systems. Uncertainties arising from sampling errors and stochasticity in stock dynamics must be incorporated in stock assessment models so that management decisions are based on realistic evaluation of the uncertainty about the status of the stock. The main goal of this study is to develop a stock assessment framework that accounts for some of the uncertainties associated with the AIS stock that are currently not integrated into stock assessment models. In particular, it focuses on accounting for the uncertainty arising from the catch data sampling process. The central innovation the thesis is the development of a Bayesian integrated stock assessment (ISA) model, in which an observation model explicitly links stock dynamics parameters with statistical models for the various types of data observed from catches of the AIS stock. This allows for systematic and statistically consistent propagation of the uncertainty inherent in the catch sampling process across the whole stock assessment model, through to estimates of biomass and stock parameters. The method is tested by simulations and found to provide reliable and accurate estimates of stock parameters and associated uncertainty, while also outperforming existing designed-based and model-based estimation approaches. The method is computationally very demanding and this is an obstacle to its adoption by fisheries bodies. Once this obstacle is overcame, the ISA modelling framework developed and presented in this thesis could provide an important contribution to the improvement in the evaluation of uncertainty in fisheries stock assessments, not only of the AIS stock, but of any other fish stock with similar data and dynamics structure. Furthermore, the models developed in this study establish a solid conceptual platform to allow future development of more complex models of fish population dynamics.
Thesis, PhD Doctor of Philosophy
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