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To estimate a historical trajectory of black marlin stock abundance in the Indian Ocean, we standardized the CPUE of black marlin caught by Japanese longliners for 1979-2019. We defined the same area of analysis based on the spatial distribution of the mean body weight as Ijima (2018), and divided the time-period into two periods, 1979-1993 and 1994-2019. In this analysis, we applied Bayesian hierarchical spatial models. Since the catch data is countable and characterize by many zeros, we used zero-inflated Poisson generalized linear mixed model (ZIP-GLMM). All analyses were performed using R, specifically the R-INLA package. The INLA procedure, in accordance with the Bayesian approach, calculates the marginal posterior distribution of all random effects and then estimates parameters involved in the model. We applied a half Cauchy distribution as a prior for the random effect. Best model was selected from multiple models using Widely Applicable Bayesian Information Criterion (WAIC) for the defined area in each period. Gradual annual decline trend with interannual variation were generally observed for the standardized CPUEs during 1979-1993, while stable annual trends were observed for the standardized CPUEs during 1994-2019. The 95% credible intervals were wider due to the inclusion of spatial effect as compared to the previous non-spatial model for 1994-2017 (Ijima 2018), while the point estimates of the standardized CPUE trends were similar. To reduce the uncertainty in the estimation of the standardized CPUEs, selecting an appropriate catchability (q), applying the state space model and/or latent variable model will be essential to improve the stock assessment of this species.
