Analysis Of Fishing Techniques And Environment Factors Affecting The Catch Rate Of Bigeye Tuna In The Eastern Central Pacific Ocean

The Pacific Ocean is rich in tuna resources and it is also one of the most important fishing areas for our ocean tuna longline fleet.In recent years,due to the fierce competition in ocean fishing among various countries,the decrease of resources,the increasingly strict regulations of tuna management organizations on fishing,the increasingly stringent access fishing conditions,the aging of vessels,and the rise in oil prices have become a series of problems that domestic and foreign enterprises have to face.It is of great significance to understand the relationship between the distribution of the Pacific bigeye tuna(Thunnus obesus)fishery and the Marine environmental factors,so as to accurately predict the orientation of the fishery and adjust the operation mode according to different seasons and months to maximize the company’s benefits.In this paper,based on the fishery data produced in the Eastern Central Pacific Ocean and the biological data collected by Shanghai Heidi pelagic fishery co.ltd.(Hai Shang”38”、Hai Shang”88”、Hai Shang”98”)and combined with the statistics of Sea Surface Temperature Anomalies(SSTA),Chlorophyll a Concentration(Chl-a),Sea Surface Temperature(SST)and Sea Surface Salinity(SSS)obtained by satellite remote sensing,the biology of Bigeye tuna was statistically analyzed by Generalized Additive Model(GAM).Next,GIS software was used to understand the changes of bigeye tuna resources and spatial changes,and a generalized additive model was constructed to analyze the factors affecting the Catch Per Unit Effort(CPUE)of bigeye tuna and the changes of fisheries in the Eastern Central Pacific Ocean.Finally,the Grey System Theory is used to verify the influence degree of environmental factors and to find out the best yield prediction model for bigeye tuna which to provide reference for future actual production.The main research results are as follows:(1)The statistical analysis of the catch composition of various Hai Shang “88” shows that the highest water layer of bigeye tuna is 310-380 m from 14 th August to 6th December in 2017,corresponding to 6.682 tail/thousand hooks.The highest value of each hook position is No.10 ordinary material card,corresponding value is 15.42 tail / 1000 hooks,No.10 sea lamp is 11.74 tail / 1000 hooks,No.4 and No.7 fluorescent hook position corresponding value is 4.19 tail / 1000 hooks,4.95 tail / 1000 hooks.From 14 th August to 6th December in 2017,the highest water layer of bigeye tuna was 310-380 m,corresponding to 4.47 tails / 1000 hooks,while the lowest water layer was 100-170 m,corresponding to 1.428 tails / 1000 hooks.The highest value of each hook position is No.6 ordinary material card,corresponding value is 7.01 tail / 1000 hooks,No.6 sea lamp is 3.5 tail / 1000 hooks,No.4 and No.7 fluorescent hook position corresponding value is 3.7 tail / 1000 hooks and 6.75 tail / 1000 hooks.The use of fluorescent squid at the hook position of 7-10 at the hook depth of 310 m to 380 m can significantly attract the hook rate of bigeye tuna.To some extent,sea lantern attracts bigeye tuna and improves the hook rate at the same water level.Therefore,sea lantern can be used to attract target species and other economic species in the fishery.(2)The Eastern Central Pacific Ocean bigeye tuna fishing area is mainly distributed in the waters of 2°N-5°S,161°W-174°W and 5°S-12°S,147°W-157°W.From January to June of each year,fisheries are mainly distributed in 1°S-10°N,172°W-180°W waters.From the movement direction of the fishery from June to December,it can be seen that the fishery is mainly distributed in the south of 5°S and east of 155°W in 2015 and 2016.In 2017 and 2018,the fishery moves to the northwest,mainly distributed in the sea area of 5°S-0,160°W-170°W.In 2019,the fishery moves to the southeast,concentrated in the sea area of 5°S-9°S,150°W-155°W.In July of each year,the central fishery is mainly distributed in the sea area of 9°S～2°N,141°W～172°W;In August of each year,the central fishery is mainly distributed in the sea area of 11°S～2°N,142°W～166°W;In September of each year,the central fishery is mainly distributed in the sea area of 1°S～11°S,144°W～166°W,and cannot be accurately determined in other months.From February to April of each year,the number of fishing operations is less,and the low CPUE value is the low season.CPUE value tends to be flat from may to June.From August to January of the following year,the intensive CPUE value is high,which is the peak season.(3)The most suitable sea surface temperature range of Eastern Central Pacific Ocean bigeye tuna is 27.5 ℃ ～ 28.5 ℃,the most suitable salinity range is 35 ～ 35.5 ℃,and the most suitable chlorophyll a concentration range is 0.12 ～ 0.16 mg/m3.In July of each year,the sea surface temperature ranged from 27.86℃ to 29.16℃,the salinity ranged from 34.82～35.53,and the concentration of chlorophyll a ranged from 0.12～0.14 mg/m3.In August of each year,the sea surface temperature ranged from 27.98℃ to 28.58℃,the salinity ranged from 34.93～35.96,and the concentration of chlorophyll a ranged from 0.12～0.17 mg/m3.In September of each year,the sea surface temperature ranged from 27.23℃ to 28.66℃,the salinity ranged from 35.36～35.51‰,and the concentration of chlorophyll a ranged from 0.11～0.16 mg/m3,which cannot be accurately determined in other months.During El Ni?o,the CPUE value is generally higher than the CPUE value in the same month of the adjacent year in the operating fishery.However,in the months which happened la Nina,the CPUE value is lower than that in the previous month.In the next month,if the same month happens la Nina,the CPUE value will be higher.(4)The GAM model was used to analyze the impact on the catch rate of bigeye tuna in the Middle East and Pacific.Among the space-time factors,the year factor had the most significant effect on CPUE,followed by longitude,latitude,and month.Among the environmental factors,the most significant effect on CPUE is SSTA,followed by SST,SSS,and then Chl-a.The correlation between environmental factors and CPUE was SSTA > SST > SSS > Chl-a.At the same time,GM(1,n)prediction model was established in 5 combination models respectively.According to the average absolute error and average relative error,the influence of environmental factors on CPUE was judged and the conclusion of SSTA > SST > SSS > Chl-a was obtained,which was consistent with the correlation degree value and the interpretation rate of GAM.In addition,model 1: GM(1,8)model containing all environmental factors was adopted to predict fishery resource abundance,and the effect factors included Chl-a,SSTA in Nino 3.4 area,longitude center of gravity,latitude center of gravity,SSS,ship speed and SST,with this model owning the best prediction effect.