| Neon flying squid?Ommastrephes bartramii?is an oceanic cephalopod economic fish species widely living in the Northwest Pacific Ocean,and is an important target for fishing and development in China.Fishery forecasting research of neon flying squid is an important basis for ensuring the scientific production of ocean squid fishing in China.In traditional statistical mathematical statistics methods,the number of samples required is large and obeys the typical distribution,which also increases the difficulty of fishery forecasting,and the gray system theory can effectively solve this problem.As a discipline of uncertain system theory,its advantages are that it allows a small number of samples and obeys arbitrary distribution.This has great application and development prospects for fishery sciences that lack data.Therefore,this study used the fishing data of neon flying squid from 1995 to 2017,combined with marine remote sensing environmental factors?such as sea surface temperature,chlorophyll concentration,Pacific Decadal Oscillation index,etc.?,used the methods of gray correlation,gray clustering,and gray prediction in the gray system theory to study the following items:?1?the clustering characteristics of the abundance of neon flying squid in different years and months;?2?the division of the main fishing seasons and catastrophic years;?3?the relationship between abundance changes of neon flying squid and oceanic environment factors deriving from remote sensing;?4?how to forecast the main fishing season,the catastrophe year,and the resource abundance of neon flying squid.This paper aims to provide technical support for the scientific sustainable development and scientific management of fishery production enterprises in North Pacific Ocean.The results of this study could be summarized as follows:?1?The clustering characteristics of the abundance of neon flying squid in different years and months.The grey relational clustering method was used to cluster the resource abundance?CPUE?of the squid from May to December between 1998and 2017,and the effects of different intensity El Ni?o and La Ni?a events and environmental factors on the squid CPUE were analyzed.The results show that the longitude groups with longitude CPUE clustering are more distinct than the annual latitude groups,and the monthly longitude groups are more seasonal than the monthly latitude groups.Group 2 with two strong La Ni?a event years has the highest CPUE,and the Sea surface temperature anomaly of the spawning and fattening farms?SGSSTA and FGSSTA?is also the largest,but the concentration of the Chl a concentration is the lowest;the CPUE of the group 4 containing the three weak La Ni?a events is slightly lower than the annual average CPUE.The SSTA is larger,the concentration of the concentration of the Chl a concentration is close to 0;the SSTA of the spawning field and the fattening field of the group 3 is the smallest,but the concentration of the Chl a concentration is the highest.From May to December,the average SST was high in summer and low in winter.SGSST and FGSST increased first and then decreased.SST is highest in August and September,the lowest in November and December.The concentration of Chl a was the opposite,the highest in May-July and the lowest in August and September The study suggests that abnormal climate events of different intensities are important factors affecting the abundance of squid resources:strong La Nina events will increase the abundance of squid resources,and weak La Nina events will slightly reduce the abundance of squid resources,while above medium intensity The El Ni?o event has greatly reduced the abundance of squid resources.?2?Fishing seasons characteristics of Ommastrephes bartrami and prediction of the main fishing season.The results show that the earliest fishing season of neon flying squid is May 12 and lasts until the end of the year;the primary period in fishing season is from August to November each year,and the first main fishing season basically occurs in August.The average relative error of the grey wave forecasting GM?1,1?model group is 6.83%,the average relative error of the date series forecast during the main fishing season is 8.19%,and the average relative error of the validation data is 15.82%.This model can be used to predict the main fishing season of neon flying squid.?3?Grey catastrophe year prediction for the abundance of Ommastrephes bartramii.The gray catastrophic prediction method was used to establish the GM?1,1?model for the upper and lower catastrophic years and to forecast the future catastrophic years.The results show that the average relative error of the lower limit catastrophic prediction model established based on GLM-model-standardized CPUE is 15.32%,the average relative error of the upper catastrophic prediction model is8.19%,and the accuracy tests for both models attain the level I accuracy.The study forecasts that the next upper catastrophic year may occur in 2021(CPUE?2.39t·v-1·y-1),and the lower catastrophe year occurred in 2027(CPUE?2.13 t·v-1·y-1).The study also suggests that the Pacific Decadal Oscillation?PDO?and El Ni?o-La Nina events are important factors driving large fluctuations in the squid abundance.?4?Forecasting model for the abundance of Ommastrephes bartrami.We used the GM?1,1?model to analyze the resource abundance?CPUE?of different time lengths,and selected the CPUE sequence with the smallest relative error and the smallest variance as the parent sequence.And this study used grey correlation analysis analyze the connection between the parent sequence and environmental factors,including Pacific Decadal Oscillation Index?PDO?,average sea surface temperature at spawning ground?SGSST?,average sea surface temperature at fattening ground?FGSST?,average chlorophyll concentration?SGC?at spawning ground,the average chlorophyll concentration in the fattening farm?FGC?,and based on the evaluation results,6 different orders of gray prediction models[GM?0,N?model and GM?1,N?model]were established.The model with the smallest error was selected as the best model for predicting the abundance of Ommastrephes bartrami resources.The research results showed that the 8-year CPUE sequence modeling is the best,and its average relative error is the smallest,which is 6.28%.And the prediction accuracy of the GM?0,N?models is generally higher than that of the GM?1,N?models.The GM?0,5?model including SGSST in February,FGSST in October,FGC in August,and PDO in October have the best model efforts,the relative error of fitting is 3.87%,and the relative error of prediction is 1.18%,which can be used as the optimal model for predicting the abundance of Ommastrephes bartramii in the North Pacific Ocean.In summary,we analyze the relationship between resource abundance changes and marine remote sensing environmental factors by clustering the abundance of neon flying squid resources in the North Pacific,and apply grey system theory to forecast the main fishing seasons,catastrophe years,and abundance of neon flying squid.This result provided a more reliable technical support for the fishery forecast of neon flying squid?Ommastrephes bartramii?. |
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