Effective fisheries management relies on robust stock assessments, yet conventional approaches typically require long-term datasets on catch, effort, and population structure. For many ecologically and commercially important species—particularly in tropical regions where ageing is difficult—such data are often sparse or unavailable. As a result, assessments must rely on limited information, such as intermittent length-frequency samples and basic life-history parameters, constraining the application of traditional models and delaying management advice.
Length-Based Indicators (LBIs) offer a practical alternative for data-limited fisheries by quantifying the proportion of small (immature) versus large (mature) individuals in catch length compositions. While widely applied, LBIs lack well-defined biological reference points comparable to those linked to Maximum Sustainable Yield (MSY), limiting their ability to inform risk-based management and harvest strategies.
To address this gap, we developed SMILE-RP (Simulation Model for Length-based Empirical Indicators and Reference Points), a deterministic population dynamics framework that derives stock-specific LBI reference points from life-history traits, maturity schedules, and gear selectivity. The model produces biologically grounded thresholds that can serve as proxies for MSY-based benchmarks.
This study present a structured workflow for generating and evaluating LBI reference points, alongside a suite of performance metrics to identify indicators that are robust to uncertainty in fishery dynamics. Results demonstrate that LBIs based on the proportion of large and mature individuals reliably track spawning stock biomass and provide a meaningful signal of stock status. These findings support the use of LBIs, coupled with model-derived reference points, as a scalable and defensible approach for managing data-limited fisheries.