本文選題：吉爾伯特群島 + 鰹魚��； 參考：《上海海洋大學(xué)》2016年碩士論文

【摘要】：鰹魚(Katsuwonus pelamis)是金槍魚圍網(wǎng)的最重要捕撈魚種,根據(jù)統(tǒng)計(jì)數(shù)據(jù)顯示,在2012~2014年期間中西太平洋金槍魚圍網(wǎng)捕撈的漁獲組成中鰹魚占78%。金槍魚圍網(wǎng)作為鰹魚捕撈效率最高的作業(yè)方式,開始于上世紀(jì)50~60年代,中國大陸自2001年涉足中西太平洋海域圍網(wǎng)捕撈,并且發(fā)展迅速,取得了不錯(cuò)的經(jīng)濟(jì)效益。近年來鰹魚的產(chǎn)量持續(xù)增加,但受入漁費(fèi)用的不斷提高,魚價(jià)持續(xù)低迷的影響,相關(guān)漁業(yè)企業(yè)的經(jīng)濟(jì)效益并未隨產(chǎn)量的增加的有所增長。所以,對于高生產(chǎn)成本的鰹魚圍網(wǎng)捕撈來說,準(zhǔn)確的把握中心漁場,合理的購買和使用作業(yè)天數(shù)將有效的降低生產(chǎn)成本。根據(jù)數(shù)據(jù)統(tǒng)計(jì),2012~2015年期間上海開創(chuàng)遠(yuǎn)洋漁業(yè)有限公司圍網(wǎng)船隊(duì)的鰹魚產(chǎn)量有38%來自于吉爾伯特群島(Gilbert Islands)海域。所以開展對該海域漁場分布的研究可以為漁業(yè)企業(yè)購買該海域的入漁天數(shù)提供參考,有助于生產(chǎn)船舶把握中心漁場。本文基于上海開創(chuàng)遠(yuǎn)洋漁業(yè)有限公司圍網(wǎng)船隊(duì)在2012~2015年間的生產(chǎn)數(shù)據(jù)結(jié)合海表面溫度(Sea Surface Temperature,SST),葉綠素a濃度(Sea Surface Chlorophyll-a,Chl-a),以及海表面鹽度(Sea Surface Salinity,SSS)三個(gè)環(huán)境因子數(shù)據(jù),對吉爾伯特群島海域漁場的時(shí)空分布及其與海洋表層環(huán)境因子間的關(guān)系做研究。主要結(jié)論如下:(1)基于生產(chǎn)數(shù)據(jù)和衛(wèi)星遙感數(shù)據(jù)的年份、月份、經(jīng)度、緯度、SST、Chl-a、SSS 7個(gè)因子,利用廣義可加模型(Generalized Additive Models,GAM)分析各因子對單位捕撈努力量漁獲量(Catch Per Unit Effort,CPUE)的相關(guān)性,結(jié)果表明GAM模型對CPUE的解釋率為7.82%,其中SST對CPUE的解釋貢獻(xiàn)率最大。影響因子按重要性排序依次為:SSTChl-a年份緯度經(jīng)度月份SSS,根據(jù)AIC準(zhǔn)則確認(rèn)最優(yōu)模型為:Ln(CPUE+c)~s(year,k=4)+s(month)+s(latitude)+s(sst)+ε(2)2012~2015年各月的產(chǎn)量變化大致呈現(xiàn)出以10~12個(gè)月為一個(gè)變化周期的規(guī)律;而從各對應(yīng)月份平均來看,5月份的平均產(chǎn)量最高,11月份的平均CPUE最高;(3)除1月份外,鰹魚產(chǎn)量重心集中在1.5°S~1.5°N,171°E~174°E范圍內(nèi)。產(chǎn)量重心在經(jīng)度方向上來回移動(dòng)未見有明顯的規(guī)律;在緯度上,上半年隨月份的推移大致呈從北到南移動(dòng),而下半年則來回在小范圍內(nèi)移動(dòng)。(4)除一月份的CPUE重心緯度要比產(chǎn)量重心在緯度上偏南1.5°外,其他CPUE重心隨月份的變動(dòng),大致和產(chǎn)量重心的變動(dòng)一致,相比較而言CPUE重心和產(chǎn)量重心在經(jīng)度上的偏差比緯度上的要小。(5)鰹魚漁場的分布并未趨向于一個(gè)固定的SST范圍,而是隨著整個(gè)海域SST的變化而變化。SST在29.4~30.6℃范圍內(nèi)鰹魚的產(chǎn)量占總產(chǎn)量的99.3%,SST在30.1℃時(shí)平均CPUE最大。(6)鰹魚產(chǎn)量主要分布在Chl-a為0.01-0.22mg/m3和SSS在33.6-35.4的范圍內(nèi),但是Chl-a和SSS對CPUE的影響不明顯。
[Abstract]:Skipjack Katsuwonus pelamis is the most important species of tuna purse seine. According to statistical data, the skipjack accounted for 78 percent of the total catch of tuna purse seine fishing in the Central and Western Pacific during the period from 2012 to 2014. Tuna purse seine, as the most efficient fishing method for skipjack, began in the 1950s and 1960s. The Chinese mainland has been engaged in purse seine fishing in the Central and Western Pacific Ocean since 2001, and has developed rapidly and achieved good economic benefits. In recent years, the production of skipjack has been increasing, but the economic benefits of the related fishery enterprises have not increased with the increase of the production. Therefore, for high-cost bonito seine fishing, accurately grasp the central fishing ground, reasonable purchase and use of operational days will effectively reduce production costs. According to the statistics, 38% of the production of skipjack fish in the seine fleet of Shanghai pioneering Ocean Fisheries Co., Ltd. from 2012 to 2015 came from the Gilbert Islandssea area of the Gilbert Islands. Therefore, the study on the distribution of fishing grounds in the sea area can provide a reference for the fishing enterprises to purchase the fishing days in the sea area, and help the production of vessels to grasp the central fishing ground. Based on the production data of the seine fleet of Shanghai pioneering Ocean Fisheries Co., Ltd. In the period from 2012 to 2015, the data of three environmental factors, Sea Surface temperature, Sea Surface Chlorophyll-a Chl-a, and Sea Surface SalinitySs, are combined in this paper. The temporal and spatial distribution of fishing grounds in Gilbert Islands and its relationship with marine surface environmental factors were studied. The main conclusions are as follows: (1) based on the year, month, longitude, latitude and latitude of the production data and the satellite remote sensing data, the correlation of each factor to catch Per Unit for fishing effort per unit fishing effort (catch for CPUE) is analyzed by using generalized Additive models (GAM). The results show that the interpretation rate of CPUE by GAM model is 7.82, and the contribution of SST to CPUE is the greatest. The order of influence factors is: SSTChl-a year latitude longitude month SSSs, according to the AIC criterion, the optimal model is: 1 / LnCU / CPUE / C ~ (1 / 9) / s ~ (+) / s ~ (+) S / S ~ (st) 蔚 ~ (2 +) = 10 ~ 12 months for each month from 2012 to 2015. The average yield of bonito fish in May was the highest and the average CPUE in November was the highest. (3) except in January, the center of gravity of bonito fish production was in the range of 1.5 擄Schion (1.5 擄N) and 171 擄E (174 擄E). In latitude, the first half of the year moved from north to south with the passage of the month. In the second half of the year, the center of gravity of CPUE moved back and forth in a small range. Except that the barycenter latitude of CPUE in January was 1.5 擄to the south of the barycenter of production, the change of CPUE barycenter with month was roughly consistent with the change of barycenter of production. By comparison, the longitude deviation between the CPUE barycenter and the yield barycenter is smaller than the latitude.) the distribution of bonito fishing grounds does not tend to a fixed SST range. However, with the change of SST in the whole sea area, the yield of skipjack fish in the range of 29.4 鈩，

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