| As an oceanic cephalopod with strong warm water,the sthenoteuthis oualaniensis has a short life cycle,strong reproduction ability,and can withstand high-intensity fishing pressure.it is one of the most potential species in the South China Sea.The fishery forecast is an important content of marine fishery research,it can not only shorten the time to find fishing grounds,but also provide scientific and technical guidance for the optimal management and production of fishery resources in the South China Sea.In recent years,with the increasing depletion of offshore fishery resources and the development of pelagic fishery,its development has become the focus of research work.This paper mainly studies the fishing ground distribution of Sthenoteuthis oualaniensis and the construction of fishing forecast model in open South China Sea.The main work is summarized as follows:1.Sea surface temperature,chlorophyll a concentration,net primary productivity,sea surface height anomaly,sea surface salinity and surface current velocity obtained by satellite remote sensing and fishing log production data,the generalized linear model and spatial autocorrelation GLM were selected to standardize the CPUE data of sthenoteuthis oualaniensis.According to the minimum Akaike Information Criterion,it is revealed that the standardized results of the S-GLM are better than the GLM,and the S-GLM based on exponential spatial autocorrelation is the best.The standardized CPUE is lower than or close to the nominal CPUE.2.The generalized additive model is used to analyze the relationship between marine environmental factors and distribution of fishing grounds.It is found that except for the chlorophyll aconcentration,other factors have significant effects on CPUE.Except for surface current velocity,other factors has a non-linear relationship with CPUE.The fishing grounds are mainly concentrated around 9.75～11°N,113.75～115.75°E and 7～9.25°N,111.75～114.75°E.impact factors sorted by importance are as follows: longitude and latitude,net primary productivity,sea surface temperature,sea surface height anomaly,surface current velocity,sea surface salinity.3.Based on standardized CPUE data,the Yield Density function is used to establish a single factor suitability index model for each season,and the corresponding suitability curve is drawn.The results of the study showed that the optimal sea surface temperature for the fishing grounds of sthenoteuthis oualaniensis officinalis is highest in summer and lowest in winter;sea surface height anomaly is highest in autumn and lowest in winter;net primary productivity is highest in winter and and it is basically the same in other seasons;The optimal sea surface salinity for the four seasons is basically the same;sea surface salinity is highest in winter and reaches a trough in autumn.4.Principal components analysis is used to determine the weight of each habitat factor,thereby constructing a habitat suitability indes model.It is found that the weight of sea surface salinity in spring and winter is the largest,and the weight of net primary productivity in summer and autumn is the largest.The model verification results show that the forecast accuracy rates of the operating fishing areas in the four seasons were all above60%,and the overall average relative error of the CPUE is 17.87%.5.The BP artificial neural network model is used to predict the central fishing ground of the sthenoteuthis oualaniensis.It is found that the optimal error back propagation artificial neural network models structure in spring,summer and autumn is the 8-6-1 model,and the optimal model structure in winter is the 8-7-1 model.Using 2019 data to verify the central fishing grounds,it is found that the average overall relative error of the CPUE is 2.655%.Compared with the HSI model,it is found that the BP neural network models is better than the HSI model. |
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