Background
Effective fisheries management depends on robust stock assessments, which traditionally require extensive time-series data on catch, effort, size, and age structure. However, many ecologically and commercially important fish stocks—particularly hard to age tropical species—are data-limited. These constraints often restrict available information to sporadic length frequency samples and basic life history traits, impeding the application of conventional assessment models and delaying fisheries sustainability advice.
Aim
Length-Based Indicators (LBIs) provide a practical approach for assessing data-limited fish stocks by evaluating the proportion of small or immature versus large, mature individuals in annual length composition data. Despite their usefulness, LBIs lack clearly defined biological reference points, such as those equivalent to Maximum Sustainable Yield (MSY), which limits their effectiveness in assessing the risk of overfishing or setting informed harvest limits. SMILE-RP (Simulation Model for Length-based Empirical Indicators and Reference Points) has been developed to address this challenge.
Methods
The SMILE-RP is a deterministic population dynamics modelling framework designed to estimate stock-specific LBI reference points using available life history parameters, maturity schedules, and gear selectivity profiles. The model generates biologically meaningful thresholds that can act as proxies for MSY-based reference points. The performance of LBIs was evaluated using a virtual fishery, by comparing stock status classifications based on SMILE-RP-derived reference points to those using conventional or provisional benchmarks. Classification accuracy was assessed using sensitivity (true positive rate) and specificity (true negative rate) metrics.
Results
Results show reference points derived from SMILE-RP improves classification accuracy of the stock status, reducing both false positives and false negatives. Additionally, the model was applied to a real-world fish stock, producing results that closely align with those from a traditional stock assessment.
Conclusion
SMILE-RP provides a transparent, flexible, and biologically grounded tool for deriving actionable reference points where data are scarce. SMILE-RP supports more informed, precautionary, and effective management of fish stocks, particularly in situations where conventional, data-intensive assessment methods are challenging to implement.