DOI: 10.5061/dryad.vx0k6dk17
title: Code from: Enhancing data-limited assessments with random effects: A case study on Korea chub mackerel (Scomber japonicus)

description: In a state-space framework, temporal variations in fishery-dependent
 processes can be modeled as random effects. This modeling flexibility
 makes state-space models (SSMs) powerful tools for data-limited
 assessments. Though SSMs enable the model-based inference of the
 unobserved processes, their flexibility can lead to overfitting and
 non-identifiability issues. To address these challenges, we developed a
 suite of state-space length-based age-structured models and applied them
 to the Korean chub mackerel (Scomber japonicus) stock. Our research
 demonstrated that incorporating temporal variations in fishery-dependent
 processes can rectify model mis-specification but may compromise
 robustness, which can be diagnosed through a series of model checking
 processes. To tackle non-identifiability, we used a non- degenerate
 estimator, implementing a gamma distribution as a penalty for the standard
 deviation parameters of observation errors. This penalty function enabled
 the simultaneous estimation of both process and observation error
 variances with minimal bias, a notably challenging task in SSMs. These
 results highlight the importance of model checking and the effectiveness
 of the penalized approach in estimating SSMs. Additionally, we discussed
 novel assessment outcomes for the mackerel stock.
description: Certain datasets, such as CPUE (Catch Per Unit
 Effort) and length composition data, were digitized from figures published
 in earlier studies openly accessible online (Kim et al., 2018; Jung, 2019;
 Gim, 2019) using WebPlotDigitizer (Rohatgi,
 2022). The accuracy of the digitization was verified by
 comparing the digitized data with the original figures.
 The data for the length composition were rounded to
 integers and the CPUE (Catch Per Unit Effort) to two decimal places in the
 actual analysis. However, we presented the data in its raw form for
 transparency regarding digitization. 
 Figures used for digitization were sourced from the following
 publications: Kim, K., Hyun,
 S.-Y., & Seo, Y. I. (2018). (Korean) Inference of age compositions
 in a sample of fish from fish length data. <em>The Korean Journal of
 Fisheries and Aquatic Sciences</em>, 51(1), 79–90. DOI Gim, J. (2019). A length-based model for Korean chub mackerel (<em>Scomber japonicus</em>) stock. (Master's thesis, Pukyong National University). Jung, Y. (2019). A Bayesian state-space production model for Korean chub mackerel (<em>Scomber japonicus</em>) stock. (Master's thesis, Pukyong National University).
description: # **Enhancing data-limited assessments with random effects: A case study
 on Korea chub mackerel (*Scomber japonicus*)** *Kyuhan Kim, Nokuthaba
 Sibanda, Richard Arnold, and Teresa A'mar* ## How to run the code 1.
 Navigate to the `Analysis` folder. 2. Execute the scripts by entering
 `makefile` in the terminal, ensuring that the working directory is set to
 the appropriate subfolders in this order: `Modelfitting`, `Diagnostics`,
 and `EstimCheck`. The execution order should be: i. `Modelfitting` ii.
 `Diagnostics` iii. `EstimCheck` ## Important things to note when running
 the code * Certain datasets, such as CPUE (Catch Per Unit Effort) and
 length composition data, were digitized from figures published in earlier
 studies openly accessible online (Kim et al., 2018; Jung, 2019; Gim, 2019)
 using WebPlotDigitizer ([Rohatgi,
 2022](https://automeris.io/WebPlotDigitizer/)). The accuracy of the
 digitization was verified by comparing the digitized data with the
 original figures. * The data for the length composition were rounded to
 integers and the CPUE (Catch Per Unit Effort) to two decimal places in the
 actual analysis. However, we presented the data in its raw form for
 transparency regarding digitization. * Results from simulation runs, such
 as those from parametric bootstrap, are not stored in this repository due
 to their substantial file size. However, the repository does include the
 code necessary to conduct these simulations. Please note that running
 these simulations on a local machine without parallelization may be
 time-consuming. Should you require the simulation results, they can be
 requested directly from the author of this repository. ## References
 Figures used for digitization were sourced from the following
 publications: * Kim, K., Hyun, S.-Y., & Seo, Y. I. (2018). (Korean)
 Inference of age compositions in a sample of fish from fish length data.
 *The Korean Journal of Fisheries and Aquatic Sciences*, 51(1), 79–90.
 [DOI](https://doi.org/10.5657/KFAS.2018.0079) * Gim, J. (2019). A
 length-based model for Korean chub mackerel (*Scomber japonicus*) stock.
 (Master's thesis, Pukyong National University). * Jung, Y. (2019). A
 Bayesian state-space production model for Korean chub mackerel (*Scomber
 japonicus*) stock. (Master's thesis, Pukyong National University). I
 confirm that the code file included in this submission contains all
 necessary information to support the research findings and is compatible
 with the CC0 license waiver.