We present the first quantitative review of the stock status relative to the stock biomass (B) and the exploitation rate (U) that achieved the maximum sustainable yield (MSY) (BMSY and UMSY, respectively) for 37 Japanese stocks contributing 61% of the total marine capture production in Japan. BMSY and UMSY were estimated by assuming three types of stock-recruitment (S-R) relationships and an age-structured population model or by applying a surplus production model. The estimated stock status shows that approximately half of the stocks were overfishing (U/UMSY > 1), and approximately half of the stocks were overfished (B/BMSY < 0.5) during 2011–2013. Over the past 15 years, U decreased and B slightly increased on average. The rate of decrease in the U of the stocks managed by the total allowable catch (TAC) was significantly greater than that of the other stocks, providing evidence of the effectiveness of TAC management in Japan. The above statuses and trends were insensitive to the assumption of the S-R relationship. The characteristics of Japanese stocks composed mainly of resources with relatively high natural mortality, i.e. productivity, suggest that Japanese fisheries have great potential of exhibiting a quick recovery and increasing their yield by adjusting the fishing intensity to an appropriate level.

Summary1. Binary data are popular in ecological and environmental studies; however, due to various uncertainties and complexities present in data sets, the standard generalized linear model with a binomial error distribution often demonstrates insufficient predictive performance when analysing binary and proportional data. 2. To address this difficulty, we propose an asymmetric logistic regression model that uses a new parameter to account for data complexity. We observe that this parameter controls the model's asymmetry and is important for adjusting the weights associated with observed data in order to improve model fitting. This model includes the ordinary logistic regression model as a special case. It is easily implemented using a slight modification of GLM or GLMER in statistical software R. 3. Simulation studies suggest that our new approach outperforms a traditional approach in terms of both predictive accuracy and variable selection. In a case study involving fisheries data, we found that the annual catch amount had a greater impact on stock status prediction, and improved predictive capability was supported with a smaller AIC compared to a generalized linear model. 4. In summary, our method can enhance the applicability of a generalized linear model to various ecological problems using a slight modification, and significantly improves model fitting and model selection.

The abundance and recruitment of anchovy Engraulis spp. and sardine Sardinops spp. alternate in a synchronized way across the Pacific. Convergent cross mapping (CCM) indicated that climate change drives the alternation of the two species in the California Current System. However, climate indices patterns in the western North Pacific contrast with those in the eastern North Pacific, despite synchronous species alternations occurring. Therefore, it is of great interest to clarify whether climate change, or any other factors, affects the population dynamics of Japanese anchovy and Japanese sardine in the western North Pacific. Using CCM, we tested whether climate change and interspecific interactions affect the population dynamics of these two species. We found that climate change affected recruitment, and we clarified the spatiotemporal pattern of this effect. This result supports the existing hypotheses that population dynamics are regulated by climate change in the western North Pacific. The present study also detected interspecific interactions between sardine and anchovy, which might promote the alternation of the two species, and has not been reported in other regions.

This paper proposes a new and flexible statistical method for marginal increment analysis that directly accounts for periodicity in circular data using a circular-linear regression model with random effects. The method is applied to vertebral marginal increment data for Alaska skate Bathyraja parmifera. The best fit model selected using the AIC indicates that growth bands are formed annually. Simulation, where the underlying characteristics of the data are known, shows that the method performs satisfactorily when uncertainty is not extremely high.

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