skipjack


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    Stock assessment of skipjack tuna in the western and central Pacific Ocean: 2022 (29July2022) - Rev.05 C. Castillo Jordán , T. Teears, J. Hampton, N. Davies , J. Scutt Phillips, S. McKechnie, T. Peatman , J.MacDonald, J. Day, A. Magnusson, R. Scott, F. Scott, G. Pilling, and P. Hamer




    Executive Summary

    This paper describes the 2022 stock assessment of skipjack tuna Katsuwonus pelamisin the western and  central  Pacific  Ocean.   An  additional  three  years  of  data  were  available  since  the  previous assessment in 2019, and the model extends through to the end of 2021.  The assessment applies the same 8–region model structure that was used for management advice from the 2019 assessment. New developments to the stock assessment include:

    • Application of a new MFCL catch conditioned approach to the estimation of fishing mortality, plus inclusion of survey fisheries and a likelihood component for the indices from those survey fisheries.
    • Application of a self-scaling approach to estimate effective sample size, the Dirichlet-multinomiallikelihood, with growth estimation within the diagnostic model.
    • Application of variable tag mixing periods for tag release groups based on simulations using individual based modelling of tag mixing processes.
    • Development of an alternative growth model based on tag recapture growth increments and daily aging from otoliths.
    • Development of new CPUE indices based on unassociated (free-school) fishing for the purse seine fisheries in equatorial model regions using a novel travel distance effort metric, truncation of the pole-line-index in Region 8, and grouping of selected CPUE indices to inform regional biomass scaling.

    This assessment is supported by the analysis of catch and effort data for pole-and-line and purse seine fisheries (Teears et al., 2022), a novel approach to estimating tag mixing periods (Scutt Phillips et al., 2022), a review and new analysis of skipjack growth (Macdonald et al., 2022), re-analysis of tag seeding experiments to inform tag reporting rate priors (Peatman, 2022), and a new analysis of tagger effects (Peatman et al., 2022).

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