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

Stock Assessment of Small Yellow Croaker (Larimichthys polyactis) Off the Coast of China Using Per-Recruit Analysis Based on Bayesian Inference

“…In response to the controversy, small yellow croakers were divided into five groups according to the intensive investigation regions (Zhu et al, 2020), in which Group 3 was included in Group 4 (Figure 1). Some researchers studied small yellow croaker in the region between 32.0 and 34.0 • N (e.g., Zhang et al, 2010a;Liu et al, 2013) according to the subpopulation divisions proposed by Lin et al (1965), whereas some investigated the population in the area between 31.0 and 34.0 • N (e.g., Lin et al, 2004;Yan et al, 2006).…”

“…Some researchers studied small yellow croaker in the region between 32.0 and 34.0 • N (e.g., Zhang et al, 2010a;Liu et al, 2013) according to the subpopulation divisions proposed by Lin et al (1965), whereas some investigated the population in the area between 31.0 and 34.0 • N (e.g., Lin et al, 2004;Yan et al, 2006). Thus, Groups 3 and 4 were regarded as two independent groups since it is unclear whether they belong to the same subpopulation (Zhu et al, 2020). Zhu et al (2020) used the Bayesian hierarchical models to estimate life history parameters and their uncertainty for each group based on 25 growth curves, 41 LWRs, and 16 natural mortality rates collected from the literature (Supplementary Table 1).…”

“…Thus, Groups 3 and 4 were regarded as two independent groups since it is unclear whether they belong to the same subpopulation (Zhu et al, 2020). Zhu et al (2020) used the Bayesian hierarchical models to estimate life history parameters and their uncertainty for each group based on 25 growth curves, 41 LWRs, and 16 natural mortality rates collected from the literature (Supplementary Table 1). The resulting estimates of each type of life history parameter were 30,0000 sets of MCMC samples for each of the five groups.…”

“…Recent advances in Bayesian estimation provide a new method to obtain random values for life history parameters. Bayesian analysis can incorporate multiple sources of information to reduce uncertainty in life history parameter estimates and obtain more realistic estimates of uncertainty (Pulkkinen et al, 2011;Froese et al, 2014;Romakkaniemi, 2015;Zhu et al, 2020). Possible parameter estimates derived from Bayesian inference are given as samples drawn from its posterior distribution using the Markov chain Monte Carlo (MCMC) technique.…”

“…In a previous study, the probability distributions of life history parameters were estimated using the Bayesian hierarchical approach for five groups of small yellow croakers (Zhu et al, 2020). Based on this work, a framework was presented that quantified uncertainty in per-recruit analysis for small yellow croaker and examined the status of each group of small yellow croakers using the YPR model and the SSBPR model by incorporating uncertainty in parameters.…”