Approaches to Age-Structured Separable Sequential Population Analysis

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Abstract
Modern statistical methods are being used more often to perform age-structured separable sequential population analysis (SSPA). This paper describes how some of these methods can be easily understood from a unified point of view. The approach is to begin with the now standard separable age-structured model, and modify some of the basic assumptions. The resulting models are examined using Monte Carlo simulation, with the mean square error of modeled biomass estimates used as the evaluation criterion. Simulation results indicate that nonlinear least squares and multinomial maximum likelihood are both capable of fitting lognormally and multinomially distributed catch-at-age data. It also appears that errors in modeling results introduced by ageing error may be minor, provided ageing error is of modest magnitude and is normally distributed. However, use of a somewhat incorrect functional form for the selectivities can cause greatly increased error in the modeling results, indicating that caution should be exercised when modeling selectivities. Results indicate that length-based SSPA is feasible. And finally, the models are used to provide insight into the old question of "how many fish should be aged?"

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