Abstract
Small yellow croaker, Larimichthys polyactis, is a commercially important species in China. The uncertainty in its main life history parameters has not been addressed. Evaluation of the uncertainty in these parameters is urgently needed for quant if ic a t i on of the uncertainty level in stock assessment. Small yellow croaker off the coast of China was divided into five groups according to the intensive survey regions. The posterior probability distributions of the parameters on the growth, length-weight relationship, length-fecundity relationship (LFR) and the natural mortality rate were estimated for each group using the Bayesian hierarchical model (BHM). The posteriors of these parameters were also calculated just at species level using the common Bayesian model (CBM). The estimate of a particular life history parameter and its possible range were obtained for each group of small yellow croaker, which can be used to quantify the uncertainty level in stock assessment. But The LFR parameter, especially af, is still uncertain for all groups. More studies are also needed to reduce the uncertainty in natural mortality rate for small yellow croaker in Bohai Sea and North Yellow Sea. BHM can improve the estimate of the parameter. But the precision of the estimate is influenced by both the dataset size and the dataset dispersion of a group. At species level the parameter was estimated more precisely by CBM than by BHM due to the simple model structure and the large dataset size.
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Acknowledgements
This study was funded by the Natural Science Foundation of Shandong Province, China (No. ZR2010DQ019) and the China Postdoctoral Science Foundation (No. 2017M622180). The authors are very grateful to Dr. Henni Pulkkinen, Natural Resources Institute Finland (Luke), who gave the advice on dealing with the missing data.
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Zhu, L., Liang, Z., Ge, C. et al. An Application of the Bayesian Hierarchical Approach to Refining the Information on Main Life History Parameters for Small Yellow Croaker, Larimichthys polyactis, off the Coast of China. Ocean Sci. J. 55, 143–155 (2020). https://doi.org/10.1007/s12601-020-0010-1
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DOI: https://doi.org/10.1007/s12601-020-0010-1