| Albacore(Thunnus alalunga)is a highly migratory oceanic fish,which is abundant in Vanuatu waters and has high economic value.It is one of the important fishing targets of tuna longline fishing vessels in China and has a good development prospect.In recent years,the marine climate has been changing,and the location of albacore fishing ground has changed accordingly.Therefore,the analysis of the distribution of albacore fishing ground,resource status and its relationship with spatial and environmental factors can provide a theoretical basis for the assessment of albacore fishery resources and the forecast of fishery situation.Based on the fishery data of albacore tuna caught by seven fishing vessels of CNFC overseas fisheries Co.,Ltd.in Vanuatu waters of the Western and Central Pacific Ocean from 2018 to 2020,combined with the data of Marine environmental factors such as sea surface temperature(SST),sea surface salinity(SSS)and chlorophyll A concentration(Chl-a).The generalized additive model was used to standardize the nominal CPUE of albacore and to explore the different effects of many factors on CPUE.The key environmental factors were selected by using the results of grey relational analysis,and the grey system theory model of albacore based on different environmental factors was established to explore the optimal resource abundance prediction model for albacore,so as to provide a more scientific and reasonable basis for the development and utilization of albacore in Vanuatu waters.The main conclusions are as follows:(1)Distribution characteristics of catch and CPUE of albacore.The yield of albacore near islands was less,and then increased first and then decreased with the increase of offshore distance.The spatial variation trends of catch and fishing effort were similar to some extent.From January to December in 2018 to 2020,the monthly variation trends of albacore catch and CPUE in Vanuatu waters were almost the same,showing a trend of first decreasing and then increasing,and then decreasing and increasing again.(2)Fluctuation characteristics of barycenter of albacore fishing ground.From the perspective of the interannual variation,the barycenter of fishing ground from 2018 to2020 is relatively concentrated around 171°E and 17°S.From January to December in2018,the barycenter of fishing ground showed a relatively concentrated distribution,mainly in the range of 169°E to 172°E,14°S to 18°S,and had a relatively obvious change law;From January to December in 2019,the barycenter of the fishing ground showed a change in the north-south direction,mainly distributed between 169°E to172°E and 14°S to 19°S;From January to December in 2020,the barycenter of fishing ground showed a large span between consecutive months in the east-west direction and the north-south direction,mainly distributed between 169°E to 172°E and 15°S to18°S,and showed a trend of first moving northward and then southward as in 2019.(3)Relationship between CPUE and environmental factors of albacore.The sea surface temperature in the fishing ground of albacore ranged from 26.40℃to 30.04℃,and the optimum sea surface temperature of fishing area ranged from 27.5℃to29.5℃.The sea surface salinity ranged from 34.35 to 35.05,and the optimum sea surface salinity ranged from 34.4 to 35.The mixed layer depth ranged from 25 to 100m,and the optimal mixed layer depth ranged from 38 to 75m.The sea surface temperature anomaly ranged from 0.38 to 1.48℃,and the optimal sea surface temperature anomaly ranged from 0.4 to 1.2℃.The chlorophyll A concentration ranged from 0.04 and 0.2mg/m~3,and the optimal chlorophyll A concentration ranged from 0.055 and 0.085mg/m~3.(4)Generalized additive model was used to standardize CPUE.The model results showed that the CPUE was affected by year,month,longitude,latitude,sea surface temperature(SST),mixed layer depth(MLD),chlorophyll A concentration(Chl-a),sea surface salinity(SSS)and sea surface temperature anomaly(SSTA).The effects on fishing ground from big to small are month,year,latitude,sea surface temperature,mixed layer depth,sea surface salinity,chlorophyll A concentration and longitude respectively.Among the five environmental factors,sea surface temperature anomaly has relatively weak influence,while sea surface temperature has relatively strong influence.(5)Grey system prediction model.The grey relational analysis method was used to judge the influence degree of each factor,and the combination of different environmental factors was selected to establish six kinds of GM(1,N)resource quantity prediction models.The results showed that the accuracy of albacore grey prediction model from high to low were respectively model 4(GM(1,7)model without SSTA),model 6(GM(1,7)model without Chl-a),model 1(GM(1,7)model with all factors including GM(1,8)model),model 5(GM(1,7)model without SSS),model 3(GM(1,7)model without MLD),model 2(GM(1,7)model without SST),and model 4 without SSTA had the best fitting effect,showing a better prediction effect for albacore abundance in Vanuatu waters. |
