Perspective: Let's simplify stock assessment by replacing tuning algorithms with statistics

摘要

Stock assessments are important to sustainable ocean management, but developing assessments remains time-consuming despite increased computation power and access to shared software. Improved efficiency in developing stock assessments could allow an increased rate of new assessments, increased attention to biological mechanisms in existing assessments, or accelerated testing of existing methods. I argue that the efficiency of the stock-assessment enterprise is hindered by a reliance upon ad hoc "tuning algorithms" that are conducted independently of standard parameter estimation. I present three examples where tuning algorithms are widely used: (1) determining the variance of recruitment, (2) bias-correcting recruitment deviations, and (3) determining the effective sample size for compositional data, and summarize why each tuning algorithm was originally developed. I then review recent research showing that each task can be replaced with parameter estimation involving random effects. Finally, I explain how model development, peer-review, and model testing would each be improved if tuning algorithms were replaced by parameter estimation, and outline the steps required to transition existing stock assessments to modern parameter estimation involving mixed effects.