中国水产科学
中國水產科學
중국수산과학
Journal of Fishery Sciences of China
2014年
4期
832-851
,共20页
杨嘉樑%黄洪亮%宋利明%饶欣%吴越%齐广瑞
楊嘉樑%黃洪亮%宋利明%饒訢%吳越%齊廣瑞
양가량%황홍량%송리명%요흔%오월%제엄서
长鳍金枪鱼%栖息环境综合指数%分位数回归%库克群岛%捕捞效率
長鰭金鎗魚%棲息環境綜閤指數%分位數迴歸%庫剋群島%捕撈效率
장기금창어%서식배경종합지수%분위수회귀%고극군도%포로효솔
Thunnus alalunga%integrated habitat index%quantile regression%Cook Islands%fishing efficiency%CPUE
根据2012年9-11月在库克群岛(the Cook Islands)海域利用金枪鱼延绳钓调查所获得的共计43个站点的长鳍金枪鱼(Thunnus alalunga)渔获率数据,以及测得的温度、盐度、叶绿素浓度、水平海流及垂直海流数据等环境因子数据,采用分位数回归方法分析了各水层(40~280 m,每40 m为一层)及整个水体中各个环境因子与长鳍金枪鱼渔获率的关系,并利用43个站点内随机选择的验证站点对不同水层的研究结果进行了验证。研究结果表明:(1)长鳍金枪鱼在各水层及整个水体的单位捕捞努力量渔获量(CPUE)分布呈偏正态分布;(2)调查期间建模站点和验证站点内的预测CPUE与名义CPUE间均无显著性差异;(3)栖息地综合指数(IHI)模型的预测能力较好,且在水深40~80 m、160~200 m及整个水体范围内能有效预测长鳍金枪鱼的分布情况;(4)不同水层影响长鳍金枪鱼分布的因素不同,如在较浅水层(40~80 m)长鳍金枪鱼的渔获率与水色的的关联较大,在80~120 m 水层则主要受水温的影响、在混合水层所在的120~160 m水层则主要受海流的影响,在较深的水层(160~240 m)则主要受饵料分布及水温的影响;(5)长鳍金枪鱼偏好觅食的水层应为160~240 m 水层;(6)长鳍金枪鱼 IHI 指数分布较高的两个海域分别为13°S-15°S,162°W-167°W与11°S-12°S,161°W-167°W。建议在上述两个海域作业时,应使钓具沉降到160~240 m水层,从而在避免兼捕其他水层渔获的同时,提高长鳍金枪鱼的捕捞效率。
根據2012年9-11月在庫剋群島(the Cook Islands)海域利用金鎗魚延繩釣調查所穫得的共計43箇站點的長鰭金鎗魚(Thunnus alalunga)漁穫率數據,以及測得的溫度、鹽度、葉綠素濃度、水平海流及垂直海流數據等環境因子數據,採用分位數迴歸方法分析瞭各水層(40~280 m,每40 m為一層)及整箇水體中各箇環境因子與長鰭金鎗魚漁穫率的關繫,併利用43箇站點內隨機選擇的驗證站點對不同水層的研究結果進行瞭驗證。研究結果錶明:(1)長鰭金鎗魚在各水層及整箇水體的單位捕撈努力量漁穫量(CPUE)分佈呈偏正態分佈;(2)調查期間建模站點和驗證站點內的預測CPUE與名義CPUE間均無顯著性差異;(3)棲息地綜閤指數(IHI)模型的預測能力較好,且在水深40~80 m、160~200 m及整箇水體範圍內能有效預測長鰭金鎗魚的分佈情況;(4)不同水層影響長鰭金鎗魚分佈的因素不同,如在較淺水層(40~80 m)長鰭金鎗魚的漁穫率與水色的的關聯較大,在80~120 m 水層則主要受水溫的影響、在混閤水層所在的120~160 m水層則主要受海流的影響,在較深的水層(160~240 m)則主要受餌料分佈及水溫的影響;(5)長鰭金鎗魚偏好覓食的水層應為160~240 m 水層;(6)長鰭金鎗魚 IHI 指數分佈較高的兩箇海域分彆為13°S-15°S,162°W-167°W與11°S-12°S,161°W-167°W。建議在上述兩箇海域作業時,應使釣具沉降到160~240 m水層,從而在避免兼捕其他水層漁穫的同時,提高長鰭金鎗魚的捕撈效率。
근거2012년9-11월재고극군도(the Cook Islands)해역이용금창어연승조조사소획득적공계43개참점적장기금창어(Thunnus alalunga)어획솔수거,이급측득적온도、염도、협록소농도、수평해류급수직해류수거등배경인자수거,채용분위수회귀방법분석료각수층(40~280 m,매40 m위일층)급정개수체중각개배경인자여장기금창어어획솔적관계,병이용43개참점내수궤선택적험증참점대불동수층적연구결과진행료험증。연구결과표명:(1)장기금창어재각수층급정개수체적단위포로노역량어획량(CPUE)분포정편정태분포;(2)조사기간건모참점화험증참점내적예측CPUE여명의CPUE간균무현저성차이;(3)서식지종합지수(IHI)모형적예측능력교호,차재수심40~80 m、160~200 m급정개수체범위내능유효예측장기금창어적분포정황;(4)불동수층영향장기금창어분포적인소불동,여재교천수층(40~80 m)장기금창어적어획솔여수색적적관련교대,재80~120 m 수층칙주요수수온적영향、재혼합수층소재적120~160 m수층칙주요수해류적영향,재교심적수층(160~240 m)칙주요수이료분포급수온적영향;(5)장기금창어편호멱식적수층응위160~240 m 수층;(6)장기금창어 IHI 지수분포교고적량개해역분별위13°S-15°S,162°W-167°W여11°S-12°S,161°W-167°W。건의재상술량개해역작업시,응사조구침강도160~240 m수층,종이재피면겸포기타수층어획적동시,제고장기금창어적포로효솔。
We developed an“Integrated Habitat Index (IHI)”model based on the quantile regression method using sur-vey data collected at 43 sites in waters near the Cook Islands from September, 2012 through November, 2012. The model variables included vertical profile data for temperature, salinity, chlorophyll-a, horizontal current, vertical current and catch per unit effort (CPUE) of albacore tuna(Thunnus alalunga), and the interactions among these variables. Mod-els were developed for five 40 m water strata between 40 m and 240 m and the entire water column to predict the spatial distribution of albacore tuna. The environmental variables measured at modeling sites were used as inputs to the IHI models to predict the IHI value of the 5 strata and the entire water column. We tested for a significant difference be-tween the observed CPUE and predicted CPUE within the 5 water strata and the entire water column using a Wilcoxon test. The Spearman correlation coefficients were assumed to indicate the predictive power of the IHI model. The trend line of the arithmetic average about the predicted IHI for the 5 strata was compared with the CPUEs at the specific depth stratum. The environmental variables at validation sites were used to validate the model’s predictive power. These data were input into the CPUE models to predict the CPUE of the 5 water strata and the entire water column. We used a Wilcoxon test to compare between the predicted and observed CPUEs within the 5 water strata and the entire water column to validate the IHI results. The CPUE for albacore tuna in the 5 strata and the entire water column exhibited a skewed normal distribution with a longer left tail. There was no significant difference between the nominal CPUEs and predictive CPUEs of albacore in the 5 water strata and the entire water column at the modelling sites or the validation sites. The IHI models had good predictive power, and were able to accurately predict the distribution of albacore tuna in the 40-80 m and 160-200 m strata and in the entire water column. The key environmental parameters in the IHI models differed among the depth strata. In the shallow water (40-80 m), there was a close relationship between the CPUE and water color. Conversely, water temperature, sea currents, and bait and water temperature were the most significant vari-ables in the 80-120 m, 120-160 m, and 160-240 m strata, respectively. Albacore tuna prefer to feed at around 160-240 m, and generally between 160 and 200 m. The CPUE was 20.31 ind/(1000 hooks) in this water strata. The IHIs for al-bacore tuna in the area between 13°S-15°S, 162°W-167°W and 11°S-12°S, 161°W-167°W were relatively high. When fishing in these areas, our results suggest that fishing gear should be deployed at a depth of 160-240 m to increase CPUE and reduce by-catch.