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    Simulations to evaluate model performance for Antarctic toothfish stock assessment in the Amundsen Sea region

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    Document Number:
    WG-SAM-17/40
    Author(s):
    S. Mormede and S. Parker
    Submitted By:
    Mr Alistair Dunn (New Zealand)
    Approved By:
    Mr Alistair Dunn (New Zealand)
    Abstract

    The Amundsen Sea region (ASR) is currently managed under a research plan, which has shown promising results to date, with 8 interseason tags recaptured in the South area in 2016 and 12 (preliminary recaptures) in 2017. The aim of this paper is to ascertain when the Amundsen Sea region stock assessment model is likely to provide management advice, and to investigate the effects of using either two independent single-area models or a two-area model.

    We used simulations to investigate the expected precision in estimates of biomass, the likely biases, and to set expectations for mark-recapture data for the next few years to predict progress towards a robust assessment model using the two-area model. The models showed that using two independent models lead to higher estimates of biomass in the North and lower estimates of biomass in the South, but that the two independent models fitted the data poorly. Simulations suggest that the current research plan with the current levels of catch and tagging rate is likely to deliver the information that would generate a robust estimate of biomass in the ASR in the future. We also found that it is likely that we would observe very few tags moving between the areas and that increasing the tagging rate in the South would result in an increase in bias but no improvement in the precision of the estimation of stock biomass.

    We therefore recommend (i) future work using independent methods to understand toothfish movement within the ASR (such as PSAT tags or otolith microchemistry), and (ii) the current research plan be continued for the next season, and (iii) a two-area model continue to be developed to assess the ASR for 2018, as recommended by Scientific Committee in 2016.