Length-based stock assessment methods are widely applied in data-limited fisheries, yet the effects of how length-frequency data are temporally grouped prior to analysis remain poorly examined. Temporal grouping is routinely used to increase sample size and approximate equilibrium conditions, but it may also alter the size structure presented to assessment models and bias inference. In this study, we evaluate how alternative temporal grouping schemes influence stock status inference within a single length-based framework, using the length-based spawning potential ratio (LBSPR) model as a diagnostic tool. Using a 30-year length-frequency dataset from a tropical purse seine fishery in the Northeast Atlantic as an illustrative case, we applied LBSPR under four practice-relevant temporal grouping schemes: full-period pooling, a broad regime-based scheme, decadal blocks, and five-year blocks. Life history parameters and model settings were held constant across schemes to isolate the effect of temporal grouping. A sensitivity analysis of biological parameters was conducted for the finest temporal scheme to contextualise robustness. Results show that temporal grouping alone can substantially alter the inferred status of the illustrative case. The fully pooled scheme produced an apparently favourable status signal, whereas finer temporal groupings revealed extended periods of inferred reproductive depletion, followed by a more recent recovery. Sensitivity analyses indicate that, while biological parameter uncertainty influences the magnitude of estimates, it does not overturn the dominant effect of temporal grouping on inferred status patterns. This study demonstrates that temporal grouping is not a neutral preprocessing step but a structural decision with the potential to conceal or reveal exploitation signals in length-based assessments. We argue that temporal grouping should be treated as an explicit sensitivity dimension in data-limited assessment workflows. By shifting attention from stock-specific outcomes to data-structuring choices, this work provides practical guidance for improving transparency and robustness in length-based stock status inference.

This study presents a comprehensive data-limited stock assessment of bonga shad (Ethmalosa fimbriata) and lesser African threadfin (Galeoides decadactylus) in the Central Gulf of Guinea using complementary catch- and abundance-based approaches, including Abundance-based Maximum Sustainable Yield (AMSY), Catch-based Maximum Sustainable Yield ++ (CMSY++), and the Bayesian State-space Schaefer Model (BSM). These models were applied because they are suitable for evaluating stock status in data-limited fisheries using catch and abundance information. While AMSY primarily uses abundance information, CMSY++ integrates catch and productivity priors, whereas BSM incorporates state-space error structures to account for observation uncertainty. Catch time series (1990–2021) were extracted from Food and Agricultural Organization (FAO) FishstatJ accessed in 2023, with catch values for 2022–2023 cautiously extrapolated from recent trends due to the temporary absence of updated official statistics. Standardized and scaled relative abundance indices from Cameroonian and Nigerian EEZ were used to support model estimation and assess the stock status. For Ethmalosa fimbriata, the results from CMSY++ and BSM yielded an MSY estimate of 126 × 10³ t and 95.5 × 10³ t, respectively, while for G. decadactylus, MSY from CMSY++ and BSM were 9.1 × 10³ and 13.4 × 10³, respectively. Stock status indicators suggested the stock was fully exploited based on both AMSY (F/F_MSY = 0.83) and CMSY++ (F/F_MSY = 1.03) and overfished based on BSM (F/F_MSY = 1.77). For G. decadactylus, the analysis based on AMSY suggested an overfished stock state (F/F_MSY = 1.2), while under CMSY++ and BSM, the stock is fully exploited. The log scale CPUE was symmetrical within the expected bounds, and the posterior parameter distributions were constrained, indicating that the model passed the convergence test and had robust parameter estimates. The study recommends maintaining catches within MSY-based reference points as the total allowable catch (TAC) and emphasizes the need for improved data continuity, regional collaboration, and precautionary management for long-term sustainability of fisheries resources in the Central Gulf of Guinea.