Assessing the sensitivity of data-limited methods (DLMs) to the estimation of life-history parameters from length–frequency data

Data-limited methods (DLMs) in stock assessment may provide potential critical information for data-limited stock management. However, the sensitivity of those methods to life-history parameters is largely unknown, resulting in extra uncertainty and consequent risks. In the present study, we designed six parallel workflows (WFs) to incorporate classic and state-of-the-art methods of estimating life-history parameters and examined their influences on the assessment of small yellow croaker (Larimichthys polyactis) in Haizhou Bay, China. The sensitivity was evaluated with three objectives: (i) the evaluation of stock status with the spawning potential ratio following different assumptions; (ii) the length-based harvest control rules derived from three management procedures; and (iii) the management performance of these harvest control rules with simulation of management strategy evaluation. The results showed considerable sensitivity regarding the three objectives to the estimations with different WFs, indicating the previous practice of credulously accepting empirical values and indiscriminately selecting references are inadvisable. We also identified the most appropriate WFs used for different purposes with limited data, aiming to provide more reliable inputs for effective fisheries management.