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Yang S.-L.,Ministry of Agriculture Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization | Yang S.-L.,CAS East China Sea Fisheries Research Institute | Jin S.-F.,CAS Institute of Atmospheric Physics | Hua C.-J.,Ministry of Agriculture Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization | Dai Y.,Ministry of Agriculture Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization
Chinese Journal of Applied Ecology | Year: 2015

In order to analyze the correlation between spatial-temporal distribution of the bigeye tuna (Thunnus obesus) and subsurface factors, the study explored the isothermal distribution of subsurface temperatures in the bigeye tuna fishing grounds in the tropical Atlantic Ocean, and built up the spatial overlay chart of the isothermal lines of 9, 12, 13 and 15℃ and monthly CPUE (catch per unit effort) from bigeye tuna long-lines. The results showed that the bigeye tuna mainly distributed in the water layer (150-450 m) below the lower boundary depth of thermocline. At the isothermal line of 12℃, the bigeye tuna mainly lived in the water layer of 190-260 m, while few individuals were found at water depth more than 400 m. As to the 13℃ isothermal line, high CPUE often appeared at water depth less than 250 m, mainly between 150-230 m, while no CPUE appeared at water depth more than 300 m. The optimum range of subsurface factors calculated by frequency analysis and empirical cumulative distribution function (ECDF) exhibited that the optimum depth range of 12℃ isothermal depth was 190-260 m and the 13℃ isothermal depth was 160-240 m,while the optimum depth difference range of 12℃ isothermal depth was -10 to 100 m and the 13℃ isothermal depth was -40 to 60 m. The study explored the optimum range of subsurface factors (water temperature and depth) that drive horizontal and vertical distribution of bigeye tuna. The preliminary result would help to discover the central fishing ground, instruct fishing depth, and provide theoretical and practical references for the longline production and resource management of bigeye tuna in the Atlantic Ocean. ©, 2015, Editorial Board of Chinese Journal of Applied Ecology. All right reserved. Source


Liu Z.-L.,CAS East China Sea Fisheries Research Institute | Liu Z.-L.,Ministry of Agriculture Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization | Yuan X.-W.,CAS East China Sea Fisheries Research Institute | Yuan X.-W.,Ministry of Agriculture Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization | And 8 more authors.
Chinese Journal of Applied Ecology | Year: 2015

Multiple hypotheses are available to explain recruitment rate. Model selection methods can be used to identify the best model that supports a particular hypothesis. However, using a single model for estimating recruitment success is often inadequate for overexploited population because of high model uncertainty. In this study, stock-recruitment data of small yellow croaker in the East China Sea collected from fishery dependent and independent surveys between 1992 and 2012 were used to examine density-dependent effects on recruitment success. Model selection methods based on frequentist (AIC, maximum adjusted R2 and P-values) and Bayesian (Bayesian model averaging, BMA) methods were applied to identify the relationship between recruitment and environment conditions. Interannual variability of the East China Sea environment was indicated by sea surface temperature (SST), meridional wind stress (MWS), zonal wind stress (ZWS), sea surface pressure (SPP) and runoff of Changjiang River (RCR). Mean absolute error, mean squared predictive error and continuous ranked probability score were calculated to evaluate the predictive performance of recruitment success. The results showed that models structures were not consistent based on three kinds of model selection methods, predictive variables of models were spawning abundance and MWS by AIC, spawning abundance by P-values, spawning abundance, MWS and RCR by maximum adjusted R2. The recruitment success decreased linearly with stock abundance (P<0.01), suggesting overcompensation effect in the recruitment success might be due to cannibalism or food competition. Meridional wind intensity showed marginally significant and positive effects on the recruitment success (P = 0.06), while runoff of Changjiang River showed a marginally negative effect (P=0.07). Based on mean absolute error and continuous ranked probability score, predictive error associated with models obtained from BMA was the smallest amongst different approaches, while that from models selected based on the P-value of the independent variables was the highest. However, mean squared predictive error from models selected based on the maximum adjusted R2 was highest. We found that BMA method could improve the prediction of recruitment success, derive more accurate prediction interval and quantitatively evaluate model uncertainty. ©, 2015, Editorial Board of Chinese Journal of Applied Ecology. All right reserved. Source

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