Standardization of commercial catch and effort data is important in fisheries where standardized abundance indices based on fishery-dependent data are a fundamental input to stock assessments. The goal of the standardization is then to minimize bias due to the confounding of apparent abundance patterns with fishing power. There is a high risk of confounding between fishing power and abundance in fisheries where the fleet has altered their fishing technology over the years. Also, the spatial aspects and the fishing history can be so heterogeneous that any standardization really involves an extrapolation, for example to a hypothetical standard vessel. When the standardization involves an extrapolation, then the appropriate modeling strategy is to build a so-called estimation model, rather than a predictive model. Strategies to build such an estimation model from fishery-dependent data include: pay careful attention to subject matter, and collect information about potential confounding effects to include in the model (putting a high value on the acquisition of data on covariates); model variable catchability at a highly disaggregated scale; aim for realistic coefficients when fitting the model and pay relatively less attention to achieving precision or maximizing explained variance; adopt modern statistical methods to combine data from different sources; and if data are deficient, then apply precautionary allowances. These strategies offer some protection against bias due to confounding, in the absence of formal criteria for identifying the best model.
Reviews in Fish Biology and Fisheries – Springer Journals
Published: Aug 16, 2006
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