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In a state-space framework, temporal variations in fishery-dependentprocesses, such as selectivity and catchability, can be modeled as randomeffects. This makes state-space models (SSMs) powerful tools for data-limitedassessments, especially when conventional CPUE standardization is inapplicable.However, the flexibility of this modeling approach can lead to challenges suchas overfitting and parameter non-identifiability. To demonstrate and addressthese challenges, we developed a state-space length-based age-structured model,which we applied to the Korea chub mackerel ($\textit{Scomber japonicus}$)stock as a case study. The model underwent rigorous scrutiny using variousmodel checking methods to detect potential model mis-specification andnon-identifiability under diverse scenarios. Our results demonstrated thatincorporating temporal variations in fishery-dependent processes through randomeffects resolved model mis-specification, but excessive inclusion of randomeffects rendered the model sensitive to a small number of observations, evenwhen the model was identifiable. For the non-identifiability issue, we employeda non-degenerate estimator, using a gamma distribution as a penalty for thestandard deviation (SD) parameters of observation errors. This approach madethe SD parameters identifiable and facilitated the simultaneous estimation ofboth process and observation error variances with minimal bias, known to be achallenging task in SSMs. These findings underscore the importance of modelchecking in SSMs and emphasize the need for careful consideration ofoverfitting and non-identifiability when developing such models fordata-limited assessments. Additionally, novel assessment results for themackerel stock were presented, and implications for future stock assessment andmanagement were discussed.

Enhancing data-limited assessments: Optimal utilization of  fishery-dependent data through random effects -- A case study on Korea chub  mackerel ($\textit{Scomber japonicus}$)