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    Recruitment modelling for Euphausia superba stock assessments considering the recurrence of years with low recruitment

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    Número de documento:
    C. Pavez, S. Wotherspoon, D. Maschette, K. Reid and K. Swadling
    Presentado por:
    Mr Dale Maschette (Australia)
    Aprobado por:
    Dr Philippe Ziegler (Australia)


    For GYM assessments with proportional recruitment, we recommend to use:

    • Formula-based methods for low variance in proportional recruitment;

    • Simulation modelling for high variance in proportional recruitment to accurately reproduce mean and standard deviation;

    as described in this paper and implemented in the Grym (open source package).


    Krill are a keystone species in the Southern Ocean food-web, and, as such, it is crucial to effectively manage the krill fishery to ensure its long-term sustainability. To assess the impacts of current harvesting pressures, evaluations rely on sampling and population modelling. Krill stock projections are developed with the Generalised Yield Model (GYM), which provides an assessment for stock status under current harvesting scenarios and various levels of uncertainties. One of the fundamental components of the GYM is the simulation of recruitment. De la Mare (1994) presents a proportional recruitment function for estimating krill recruitment based on estimates from field surveys. The De la Mare (1994) function uses estimates of the mean and variance of recruitment from survey data to determine the scaling of natural mortality and the distribution of random recruits that reproduce the observed mean and variance estimates. We evaluated De la Mare’s (1994) proportional recruitment function and found that for large variations in recruitment the function does not reproduce the observed mean proportion of recruits and its variance accurately. We review the deficiencies within the model and provide two alternative methods, which can support a wider range of values and possible extreme scenarios, such as years of low recruitment.