Based on the samples collected in Chinese scientific surveys off the Costa Rica Dome from July to August in 2009, statolith microstructure of the jumbo flying squid, Dosidicus gigas, was analysed for studying their age, growth and population structure. Three typical growth zones (postnuclear, dark and peripheral zones), abnormal increments (checks) and aberrant microstructure (additional centre and additional rings) were found in the statoliths examined. Squid sizes ranged from 205 to 429 mm dorsal mantle length (ML) with ages no more than 10 months for females and 8 months for males. Back-calculated hatching dates were from November 2008 to April 2009. Growth in ML was best described by a linear function for both the sexes, while growth in body weight was best quantified by an exponential function for females and a power curve for males. The maximum absolute daily growth rates and instantaneous growth rate in ML were reached during 181–210 and 151–180 days for females and males, respectively. The Costa Rica Dome and its adjacent waters were considered as a potential spawning ground because of presence of not only high proportion of mature squid but also rhynchoteuthion paralarvae, and high primary productivity from the strong upwelling. This study suggests that D. gigas have complicated intra-specific population structure and large spatial variability in the key life history parameters.

Generalized linear Bayesian (GLBM) non-hierarchical and hierarchical models were developed for standardization of catch per unit effort (CPUE). The GLBM containing the covariates of month, latitude, sea surface temperature (SST), sea surface salinity (SSS) and sea level height (SLH) had the best fit for the Chinese squid-jigging fishery of Ommastrephes bartramii in the northwest Pacific Ocean based on deviance information criteria. This best-fitting model tends to be more ecologically sound than other CPUE standardization models, such as generalized linear models and generalized additive models. GLBM was also used to deal with the problems of estimating stock abundance index (i.e. standardized CPUE) resulting from increased spatial heterogeneity of spatial dynamics of fishing efforts in the squid fishery by predicting the standardized CPUE for unfished areas. The standardized CPUE based on data including predicted CPUE of unfished areas was lower than the derived CPUE based on data with observed CPUE alone, in particular during the fishing peak of August to October. This study indicates that it is more appropriate to use the standardized CPUE derived from data including both predicted CPUE of unfished areas and observed CPUE of fished area as a stock abundance index. We suggest that the proposed method be used in CPUE standardization to account for impacts of large spatial heterogeneity of fishing efforts in fisheries