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The Stock Synthesis Approach

The Stock Synthesis Approach. Based on many of the ideas proposed in Fournier and Archibald (1982), Methot developed a stock assessment approach and computer program called Stock Synthesis. It has the following features:. Multiple fisheries and surveys, each with its own selectivity curve.

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The Stock Synthesis Approach

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  1. The Stock Synthesis Approach Based on many of the ideas proposed in Fournier and Archibald (1982), Methot developed a stock assessment approach and computer program called Stock Synthesis. It has the following features: • Multiple fisheries and surveys, each with its own selectivity curve. • Multinomial errors assumed for the observed age composition data (fisheries and surveys). • The analysis is tuned using multiple biomass or abundance indices (surveys, fishery effort or CPUE), assumed to have log-normal errors. FW599 Winter 2008

  2. Stock Synthesis (continued) • Catch biomass values are assumed to be known exactly and are removed mid-period. (There is no explicit fishing mortality coefficient.) • Selection can be a function of length or age (or both) and can differ by sex. • Mean weight-at-age (by sex) is derived from a growth model and a length-weight relationship. • Most parameters can be configured to vary with time (e.g., changing selection coefficients). • Unlike VPA or Cohort Analysis, Synthesis does not require complete catch-at-age data matrices. FW599 Winter 2008

  3. Stock Synthesis (continued) • Seasons for seasonal fisheries or seasonal growth. • Transition matrices can be used to create predicted distributions (e.g., age compositions with error). • Synthesis can accommodate numerous kinds of data: • Observations of discarded amounts or percentages. • Age or length composition data for retained, discarded, or total catch. • Mean length-at-age data by fishery and survey. • Age composition within specified length ranges. • Mean body weight by fishery (retained or discarded). FW599 Winter 2008

  4. Stock Synthesis (continued) • The maximum likelihood method is used for estimating the model parameters. • log( Like. ) = lj * S log( Like.Component j ) • Parameters can be constrained by including penalty functions as log-likelihood components and mimic a Bayesian estimation approach. • Synthesis II uses Auto-Diff Model Builder (ADMB) routines to find the parameter estimates. • ADMB allows Synthesis II to produce variance estimates for all estimated parameters and for quantities derived from the estimated parameters. FW599 Winter 2008

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  6. Retention – small fish are often discarded FW599 Winter 2008

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