Abstract
Stockrecruitment (SR) models are commonly fitted to SR data with a least-squares method. Errors in modeling are usually assumed to be normal or lognormal, regardless of whether such an assumption is realistic. A Monte Carlo simulation approach was used to evaluate the impact of the assumption of error structure on SR modeling. The generalized linear model, which can readily deal with different error structures, was used in estimating parameters. This study suggests that the quality of SR parameter estimation, measured by estimation errors, can be influenced by the realism of error structure assumed in an estimation, the number of SR data points, and the number of outliers in modeling. A small number of SR data points and the presence of outliers in SR data could increase the difficulty in identifying an appropriate error structure in modeling, which might lead to large biases in the SR param eter estimation. This study shows that generalized linear model methods can help identify an appropriate error distribution in SR modeling, leading to an improved estimation of parameters even when there are outliers and the number of SR data points is small. We recommend the generalized linear model be used for quantifying stockrecruitment relationships.
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More From: Canadian Journal of Fisheries and Aquatic Sciences
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