海洋科学
海洋科學
해양과학
Marine Sciences
2015年
10期
45-51
,共7页
东太平洋%大眼金枪鱼(Thunnus obesus)%栖息地指数%渔场预测
東太平洋%大眼金鎗魚(Thunnus obesus)%棲息地指數%漁場預測
동태평양%대안금창어(Thunnus obesus)%서식지지수%어장예측
Eastern Pacific%Thunnus obesus%habitat suitability index%fishing ground forecasting
开展东太平洋大眼金枪鱼(Thunnus obesus)渔场预报技术研究，建立基于多环境因子渔场预报模型，将对该资源的高效开发和利用具有重要的意义。作者根据2009~2011年东太平洋海域(20°N~35°S、85°W~155°W)延绳钓生产统计数据，结合海洋遥感获得的表温(SST)和海面高度(SSH)的数据，运用一元非线性回归方法，以渔获量为适应性指数，按季度分别建立了基于SST和SSH的大眼金枪鱼栖息地适应性指数，采用算术平均法获得基于 SST 和 SSH 环境因子的栖息地指数综合模型，并用2012年各月实际作业渔场进行验证。研究结果显示，大眼金枪鱼渔场多分布在SST为24~29℃、SSH为0.4~0.8 m的海域。指数模型较好地拟合了因子适应性曲线(P<0.05)。基于栖息地指数的2012年大眼金枪鱼中心渔场预报准确性平均达63%，可为金枪鱼延绳钓渔船寻找中心渔场提供指导。
開展東太平洋大眼金鎗魚(Thunnus obesus)漁場預報技術研究，建立基于多環境因子漁場預報模型，將對該資源的高效開髮和利用具有重要的意義。作者根據2009~2011年東太平洋海域(20°N~35°S、85°W~155°W)延繩釣生產統計數據，結閤海洋遙感穫得的錶溫(SST)和海麵高度(SSH)的數據，運用一元非線性迴歸方法，以漁穫量為適應性指數，按季度分彆建立瞭基于SST和SSH的大眼金鎗魚棲息地適應性指數，採用算術平均法穫得基于 SST 和 SSH 環境因子的棲息地指數綜閤模型，併用2012年各月實際作業漁場進行驗證。研究結果顯示，大眼金鎗魚漁場多分佈在SST為24~29℃、SSH為0.4~0.8 m的海域。指數模型較好地擬閤瞭因子適應性麯線(P<0.05)。基于棲息地指數的2012年大眼金鎗魚中心漁場預報準確性平均達63%，可為金鎗魚延繩釣漁船尋找中心漁場提供指導。
개전동태평양대안금창어(Thunnus obesus)어장예보기술연구，건립기우다배경인자어장예보모형，장대해자원적고효개발화이용구유중요적의의。작자근거2009~2011년동태평양해역(20°N~35°S、85°W~155°W)연승조생산통계수거，결합해양요감획득적표온(SST)화해면고도(SSH)적수거，운용일원비선성회귀방법，이어획량위괄응성지수，안계도분별건립료기우SST화SSH적대안금창어서식지괄응성지수，채용산술평균법획득기우 SST 화 SSH 배경인자적서식지지수종합모형，병용2012년각월실제작업어장진행험증。연구결과현시，대안금창어어장다분포재SST위24~29℃、SSH위0.4~0.8 m적해역。지수모형교호지의합료인자괄응성곡선(P<0.05)。기우서식지지수적2012년대안금창어중심어장예보준학성평균체63%，가위금창어연승조어선심조중심어장제공지도。
The technology research of fishing ground prediction for bigeye tuna in the eastern Pacific and the estab-lishment based on fishing ground prediction model of multiple environmental factors are of great importance for the efficient development and utilization of its resources. Bigeye tuna,Thunnus obesus, is one of the important tunas in the Eastern Pacific Ocean, and also one of the main fishing targets for Chinese tuna longline fishery. In this paper, based on the catch data from longline fishery in the areas (20°N~30°S and 85°~155°W) of Eastern Pacific Ocean during 2009~2011 and the environmental data from remote sensing including sea surface temperature (SST) and sea surface height (SSH), the catch is considered as the suitability index, and the suitability curves based on SST and SSH for one quarter were established by using a non-linear regression. The habitat suitability index model was set up by using arithmetic mean model (AMM), and was validated by using the actual catch data in 2012. The results showed that the fishing ground of bigeye tuna is located in the waters with 24~29℃ SST and 0.4~0.8 m SSH. The SI curve of each factor by using nonlinear regression is significant (P<0.05). Forecast accuracy of fisheries center is 63%, which is a high forecast accuracy. This forecasting model will play a guide role for fishing fleets in the tuna longline fishery.
