A simulation study of impacts of error structure on modelling stock-recruitment data using generalized linear models

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Abstract

Stock-recruitment (S-R) models are commonly fitted to S-R 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 S-R 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 S-R parameter estimation, measured by estimation errors, can be influenced by the realism of error structure assumed in an estimation, the number of S-R data points, and the number of outliers in modeling. A small number of S-R data points and the presence of outliers in S-R data could increase the difficulty in identifying an appropriate error structure in modeling, which might lead to large biases in the S-R parameter estimation. This study shows that generalized linear model methods can help identify an appropriate error distribution in S-R modeling, leading to an improved estimation of parameters even when there are outliers and the number of S-R data points is small. We recommend the generalized linear model be used for quantifying stock-recruitment relationships.