Adding splines to the SAM model improves stock assessment

The stock assessment model SAM contains multiple age-dependent parameters that must be manually grouped together to obtain robust inference. This can make the model selection process slow, non-extensive and highly subjective, while producing unrealistic parameter estimates with discrete jumps. We propose to model age-dependent SAM parameters using spline functions, which can produce smoother parameter estimates, while making the model selection process faster, more automatic and less subjective. We develop a SAM spline model and compare it, using simulation studies and cross- and forward-validation methods, with published SAM models for 17 different fish stocks. The results show that our automated spline models overall outcompete the final accepted SAM models from stockassessment.org. We also demonstrate how our proposed spline model can be employed as a diagnostics tool for improving and better understanding properties of other SAM models.