Study On Fishery Forecast Research Of Cololabis Saira Northen Pacific Ocean Based On Habitat Model

The high seas fishing ground of Northen Pacific Ocean is the important fishing ground of China Mainland, Northen Pacific Ocean contains a wealth of fishery resources. China’s saury fishing industry has been rapid developmentduo to the convenient location,with number of fishing fleets and catches substantially increased.But sometimes the number of fishing vessels, human resources and the production of saury can not form a proportional relationship.The quality and distribution of saury fish are related to the change of marine environmental factors.It is very important to study the effect of habitat on the formation and distribution of saury fishing ground.The fishing ground of saury fishery in the Northwest Pacific Oceans is in the outside of the 200 nautical mile exclusive economic zone （EEZ）of Russia and Japan. Specifically,the longitude and latitude scope are 37-49°N ,144-168 °E.The Habitat suitability index （HSI） model is used to study the fishery forecast of saury, which can help the production of saury inNorthen Pacific Ocean.According to the production data of China Mainland from 2003 to 2015 in the high seas of Northen Pacific Ocean,Data including latitude and longitude,date, daily yield ,nets, length overall, main engine power, total power of fish lamp set.In this paper, firstly, we standardized the nominal CPUE （Catch Per Unit of effort） using generalized linear model （GLM） and generalized additive model （GAM）. We further analyzed the spatial and temporal variations of the abundance of Cololabis saira using standardized CPUE. According to the fishery production data is divided into three different space series （0.5°×0.5°、0.5°×1°、1°×1） , Three HSI models were established to analyze the different spatial scale variation coefficients （CV） values of the different HSI models. As the latitude is determined, the CV value increases as the longitude increases. When the longitude is constant, the CV value increases as the attitude increases. The space series is 0.5°×0.5° and the CV value is the smallest, that spatial scale is the most suitable for the research and analysis of the saury fishery forecast.According to production data of Cololabis saira and ocean environmental data from May to December of 2014 in the high sea of North Pacifc Ocean, we analyzed the relationships between spatial and temporal distribution of C.saira fishing ground and sea surface temperature （SST）, chlorophyll-a concentration（SSC） and sea surface salinity using software of fisheries geographic information system and mathematical statistics methods. The results indicated that: In the sea survey area （37°N-49°N,144°E-168°E）, August to November are the best fishing period of C.saira production, which means the yield of these months is highest; The ranges of SST, SSS and SSC in Csaira fishing ground from May to December are 10-18℃, -25～35cm、0.4-1.2 mg/m3 and respectively, and the suitable operation ranges are 13-17℃, -5～20cm and 0.4-0.8 mg/m3; K-S results showed that ranges of SST、SSH and SSCin fishing ground is suitable for operation, so we can use SST, SSH and SSC as indices of fishing ground selecting of C.saira.Evaluate and analyze the impact of weightings for different environmental factors of sea surface temperature, sea surface height and sea surface chlorophyll-a in HSI modeling.Weighting for habitat variables used in a multi-factors HSI modeling reflect different influences of the variables on distribution ofsaury fishery in the Northt Pacific Ocean.In this paper, the SST, SSH,SSC three habitat factors set some different weight series to establish the HSI model.The formula is : WSST+ WSSH+ WSSC=1.The range of SI is 0.1-0.8 and the spacing is 0.01. Calculating the possibility of all weight series .To Calculate the CPUE percentages under all different weight series and compared to identify the most suitable HSI model based on the residual standard error （RSR）.Used to analyze and compare the different weights under the HSI model differences.The model that yielded the minimum RSR value was chosen as the best model.And the results showed that the most suitable model was established by SST, SSH and SSCwith the weights of 0.41, 0.35 and 0.25respectively.The HSI model is validated using fishery data of 2015. we applied biomass combining environmental data from remote sensing as adaptive index and established the habitat suitability index model （HSI） based on sea surface temperature （SST）,sea surface height （SSH） , concentration of sea surface Chlorophyll a （SSC） by using arithmetic mean value method, geometric mean value method ,maximum value method and minimum value method.A total of 181 operating areas （0.5°×0₅ °）,The results of the four HSI models according to t test（P<0.05） are less than 0.05.By calculating the correct rate the arithmetic average method whose correct rateexceed 85% is the best model.The HSI values of each month 2015 were calculated using the arithmetic mean method.Using Marine Explorer4.0 to make CPUE and HSI for the base map overlay.CPUE high sea areas are basically distributed in the HSI high seas in those graphics.Monthly CPUE values are positively correlated with HSI values.The relationship between CPUE and HSI is obtained by equation fitting CPUE=9.73HSI-1.85（P=0.0002,R2=0.9614）,By comparing the predicted value with the actual value, we can verify the fitting degree of the equation.The study suggests that the habitat index model can better predict the catfish resource catches.