Chub mackerel (Scomber japonicus) is a pelagic fish widely distributed in temperate and subtropical zones throughout the Indian and Pacific Oceans and is commercially exploited, particularly in the North Pacific. Although highly targeted in this region, little is known about their life history aspects. The objectives of this study are to evaluate the growth heterogeneities and ageing analysis of this species. We describe the length-at-age, weight-at-length, relative condition factor relationships, spatiotemporal heterogeneity and compare estimated growth parameter values to those reported from other regions. This study used data obtained from Chinese fishing vessels collected from 2016–2020 in the northwest Pacific Ocean. Length–weight data from 2686 specimens (40–294 mm, fork length; 0.8–311.8 g body weight) were analyzed, and the length–weight relationship was W = (1.41 × 10−6) × FL3.37. Seven linear mixed-effects models (LMEM) were used to analyze the heterogeneity of length-weight relationships of Chub mackerel. The length–weight relationships for Chub mackerel were best described by a model with random effects with both year and season (spring, summer, autumn) with the scalar parameter a. Age estimates were obtained from 175 specimens, and the length-at-estimated ages relationship was described using three non-linear candidate growth models. The von Bertalanffy growth model fit the data best for Chub mackerel in the northwest Pacific Ocean. Comparing the results to that of previous studies, we observed that individual Chub mackerel exhibited a slower growth rate than that observed in previous studies. In addition, relative condition factors varied among years, seasons, and regions. Information presented in this study provides an effective scientific basis for stock assessment and fishery management of Chub mackerel in the northwest Pacific Ocean.

Chub mackerel (Scomber japonicus) is a major targeted species in the Northwest Pacific Ocean, fished by China, Japan, and Russia, and predominantly captured with purse seine fishing gear. A formal stock assessment of Chub mackerel in the region has yet to be implemented by the managing authority, that is, the North Pacific Fisheries Commission (NPFC). This study aims to provide a wider choice of potential models for the stock assessment of Chub mackerel in the Northwest Pacific using available data provided by members of the NPFC. The five models tested in the present study are CMSY, BSM, SPiCT, JABBA, and JABBA-Select. Furthermore, the influence of different data types and input parameters on the performance of the different models used was evaluated. These effects for each model are catch time series for CMSY, catch time series and prior of the relative biomass for BSM, prior information for SPiCT, and selectivity coefficients for JABBA-Select. Catch and CPUE (catch per unit effort) data used are derived from NPFC, while some life history information is referred from other references. The results indicate that Chub mackerel stock might be slightly overfished, as indicated by CMSY (B2020/BMSY = 0.98, F2020/FMSY = 1.12), BSM (B2020/BMSY = 0.97, F2020/FMSY = 1.21), and the base case run for the JABBA-Select (SB2020/SBMSY = 0.99, H2020/HMSY = 0.99) models. The results of the models SPiCT (B2020/BMSY = 2.30, F2020/FMSY = 0.31) and JABBA (B2020/BMSY = 1.40, F2020/FMSY = 0.62) showed that the state of this stock may be healthy. Changes in the catch time series did not affect CMSY results but did affect BSM. The present study confirms that prior information for BSM and SPiCT models is very important in order to obtain reliable results on the stock status. The results of JABBA-Select showed that different selectivity coefficients can affect the stock status of a species, as observed in the present study. Based on the optimistic stock status indicated by the best model, JABBA, a higher catch is allowable, but further projection is required for specific catch limit setting. Results suggested that, as a precautionary measure, management would be directed towards maintaining or slightly reducing the fishing effort for the sustainable harvest of this fish stock, while laying more emphasis on accurately estimating prior input parameters for use in assessment models.