基于权重分析和GAM模型的秋刀鱼舷提网作业性能影响因素
CSTR:
作者:
中图分类号:

S972.13

基金项目:

蓝色粮仓国家重点研发计划(2019YFD0901503)


Factors influencing the stick-held net status of Pacific saury (Cololabis saira) fishery based on weighted analysis and GAM
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [34]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    根据2016年7—10月和2017年6—10月蓬莱京鲁渔业有限公司“鲁蓬远渔019”在西北太平洋进行的海上秋刀鱼舷提网网具性能测试试验中收集的作业参数、网具深度以及不同水层水流速度等信息,结合提升回归树模型(boosting regression tree, BRT)权重分析结果,基于广义加性模型(generalized additive model, GAM)探讨各因素对舷提网网具作业性能的影响,分析影响因子与舷提网主要性能参数(最大沉降深度和提升速度)的关系。结果表明:影响网具最大沉降深度的因素中,权重在前4位的为30 m水层流速(20.15%)、60 m水层流速(18.92%)、下纲松放长度(16.85%)和10 m水层流速(15.52%);影响提升速度的前4位影响因子为绞网速度(23.17%)、30 m水层流速(20.05%)、10 m水层流速(18.27%)以及60 m水层流速(16.26%)。30 m水层流速、60 m水层流速以及下纲松放长度对网衣最大沉降深度的影响显著,网衣最大沉降深度与各水层流速呈负相关关系,与下纲松放长度呈正相关关系;绞网速度和水层流速(10、30和60 m)均显著影响提升速度,提升速度与绞网速度呈正相关关系,与水层流速呈负相关关系,绞网速度是影响网具提升速度最重要的因素,其次是30 m水层流速、10 m水层流速和60 m水层流速。

    Abstract:

    Based on the data collected by saury stick-held operation in the northwest Pacific from July to October in 2016 and from June to October in 2017, we analyzed the factors(fishing operations and marine environment) influencing the stick-held net status of Pacific saury(Cololabis saira) fishery based on boosting regression tree weighted analysis and generalized additive model. The results showed that: Among the factors influencing the maximum net sinking depth, the top four factors with weight are current speed of 30 m layer(20.15%), current speed of 60 m layer(18.92%), length of lead line loose(16.85%) and current speed of 10 m layer(15.52%); The top four influencing factors of lifting speed were hauling speed(23.17%), current speed of 30 m layer(20.05%), current speed of 10 m layer(18.27%) and current speed of 60 m layer(16.26%). Current speed of 30 m and 60 m layers and length of lead line loose had significant effects on the maximum net sinking depth;The maximum net sinking depth was positively correlated with the length of lead line loose, there was a negative correlation between the maximum net sinking depth and current speed of 10 m, 30 m and 60 m layers; hauling speed and current speed of 10 m, 30 m and 60 m layers had significant effects on lifting speed; the lifting speed was positively correlated with the hauling speed; there was a negative correlation between the lifting speed and current water speed. Hauling speed was the most important factor affecting the lifting speed, followed by current speed of 30 m layer, current speed of 10 m layer and current speed of 60 m layer.

    参考文献
    [1] CHANG Y J, LAN K W, WALSH W A, et al. Modelling the impacts of environmental variation on habitat suitability for Pacific saury in the northwestern Pacific Ocean[J]. Fisheries Oceanography, 2019, 28(3):291-304.
    [2] WATANABE Y, LO N C H. Larval production and mortality of Pacific saury,Cololabis saira, in the northwestern Pacific Ocean[J]. Fishery Bulletin, 1989, 87(3):601-613.
    [3] 花传祥,高玉珍,朱清澄,等.基于耳石微结构的西北太平洋秋刀鱼(Cololabis saira)年龄与生长研究[J].海洋学报, 2017, 39(10):46-53. HUA C X, GAO Y Z, ZHU Q C, et al. Age and growth of Pacific saury (Cololabis saira) in the northwest Pacific Ocean based on statolith microstruture[J]. Acta Ocean-ologica Sinica, 2017, 39(10):46-53.
    [4] 石永闯,朱清澄,黄硕琳,等.基于贝叶斯Schaefer模型的西北太平洋秋刀鱼资源评估和管理[J].渔业科学进展, 2019, 40(5):1-10. SHI Y C, ZHU Q C, HUANG S L, et al. Stock assessment of Pacific suary (Cololabis saira) in the northwest Pacific using a Bayesian Schaefer model[J]. Progress in Fishery Sciences, 2019, 40(5):1-10.
    [5] 王明彦,张勋,徐宝生.秋刀鱼Cololabis saira(Brevoort)舷提网渔业的现状及发展趋势[J].现代渔业信息, 2003, 18(4):3-7. WANG M Y, ZHANG X, XU B S. The statusand development trend of stick-heldnet fishery for Cololabis saira(Brevoort)[J]. Modern Fisheries Information, 2003, 18(4):3-7.
    [6] HUBBS C L, WISNER R L. Revision of the sauries (Pisces,Scomberesocidae) with descriptions of two new genera and one new species[J]. Fish Bulletin, 1980, 77(3):521-566.
    [7] 朱清澄,张衍栋,夏辉,等.秋刀鱼集鱼灯箱内不同灯位的照度实验比较研究[J].上海海洋大学学报, 2013, 22(5):778-783. ZHU Q C, ZHANG Y D, XIA H, et al. Comparative study of different saury aggregation light experiment[J]. Journal of ShanghaiOcean University, 2013, 22(5):778-783.
    [8] 石永闯,朱清澄,花传祥,等.基于海上实测的秋刀鱼舷提网沉降和提升性能研究[J].海洋通报, 2018, 37(4):459-467. SHI Y C, ZHU Q C, HUA C X, et al. Sinking and rising performance of saury stick-held basedon field measurements[J]. Marine Science Bulletin, 2018, 37(4):459-467.
    [9] YANG C X, LOU X B, MATSUI T, et al. Evaluating the technical efficiencies of fishing vessels to achieve effective management of overexploited fisheries[J]. Mitigation and Adaptation Strategies for Global Change, 2017, 22(8):1149-1162.
    [10] HUANG W B, HUANG Y C. Maturity characteristics of Pacific saury during fishing season in the northwest Pacific[J]. Journal of Marine Science and Technology, 2015, 23(5):819-826.
    [11] 郁岳峰,张勋,黄洪亮,等.秋刀鱼舷提网集鱼方法的研究[J].浙江海洋学院学报(自然科学版), 2006, 25(2):154-156. YU Y F, ZHANG X, HUANG H L, et al. Study on attracting fish method of stick-held net for Cololabis saira[J]. Journal of Zhejiang Ocean University (Natural Science), 2006, 25(2):154-156.
    [12] 张勋,郁岳峰,黄洪亮,等.秋刀鱼舷提网渔具设计的研究[J].浙江海洋学院学报(自然科学版), 2006, 25(1):40-45. ZHANG X, YU Y F, HUANG H L, et al. The study on designing method of stick-held net for Cololabis saira[J]. Journal of Zhejiang Ocean University (Natural Science), 2006, 25(1):40-45.
    [13] 石永闯,朱清澄,张衍栋,等.基于模型试验的秋刀鱼舷提网纲索张力性能研究[J].中国水产科学, 2016, 23(3):704-712. SHI Y C, ZHU Q C,ZHANG Y D, et al. Factors influencing the rope tension of saury stick-held lift nets[J]. Journal of Fishery Sciences of China, 2016, 23(3):704-712.
    [14] 石永闯,朱清澄,花传祥,等.秋刀鱼舷提网渔具性能模型试验与海上实测结果的比较评估[J].海洋学报, 2019, 41(2):123-133. SHI Y C, ZHU Q C, HUA C X, et al. Evaluation of saury stick-held net performance between model test and on-sea measurements[J]. Acta Oceanologica Sinica, 2019, 41(2):123-133.
    [15] DE'ATH G. Boosted trees for ecological modeling and prediction[J]. Ecology, 2007, 88(1):243-251.
    [16] BERG D. Bankruptcy prediction by generalized additive models[J]. Applied Stochastic Models in Business and Industry, 2007, 23(2):129-143.
    [17] 刘志强,许柳雄,唐浩,等.拖网作业参数对南极磷虾捕捞效率的影响[J].中国水产科学, 2019, 26(6):1205-1212. LIU Z Q, XU L X, TANG H, et al. Effects of trawling operation parameters on the fishing efficiency of Antarctic krill[J]. Journal of Fishery Sciences of China, 2019, 26(6):1205-1212.
    [18] BREIMAN L, FRIEDMAN J H, OLSHEN R A, et al. Classification and regression trees[M]. Boca Raton:Routledge, 1984:1-368.
    [19] HASTIE T, TIBSHIRANI R, FRIEDMAN J. The elements of statistical learning:data mining, inference, and prediction[M]. New York:Springer-Verlag, 2001:299-345.
    [20] TIAN Y J, UENO Y, SUDA M, et al. Decadal variability in the abundance of Pacific saury and its response to climatic/oceanic regime shifts in the northwestern subtropical Pacific during the last half century[J]. Journal of Marine Systems, 2004, 52(1/4):235-257.
    [21] ELITH J, LEATHWICK J R, HASTIE T. A working guide to boosted regression trees[J]. Journal of Animal Ecology, 2008, 77(4):802-813.
    [22] FRIEDMAN J, HASTIE T, TIBSHIRANI R. Additive logistic regression:a statistical view of boosting (with discussion and a rejoinder by the authors)[J]. The Annals of Statistics, 2000, 28(2):337-407.
    [23] 官文江,陈新军,高峰,等.GLM模型和回归树模型在CPUE标准化中的比较分析[J].上海海洋大学学报, 2014, 23(1):123-130. GUAN W J, CHEN X J, GAO F, et al. Comparisons of regression treeand GLM performance in CPUE standardization[J]. Journal of Shanghai Ocean University, 2014, 23(1):123-130.
    [24] 陈明鑫,许柳雄,唐浩,等.基于多元变量的南极磷虾拖网作业状态影响因素分析[J].上海海洋大学学报, 2021, 30(1):144-154. CHEN M X, XU L X, TANG H, et al. Factors influencing the trawling status of Antarctic krill fishery based on multivariate analysis[J]. Journal of Shanghai Ocean University, 2021, 30(1):144-154.
    [25] FROESCHKE B F, TISSOT P, STUNZ G W, et al. Spatiotemporal predictive models for juvenile southern flounder in Texas estuaries[J]. North American Journal of Fisheries Management, 2013, 33(4):817-828.
    [26] LEWIN W C, MEHNER T, RITTERBUSCH D, et al. The influence of anthropogenic shoreline changes on the littoral abundance of fish species in German lowland lakes varying in depth as determined by boosted regression trees[J].Hydrobiologia, 2014, 724(1):293-306.
    [27] 唐浩,许柳雄,王学昉,等.基于网具模型试验的金枪鱼围网性能分析[J].中国水产科学, 2015, 22(3):563-573. TANG H, XU L X, WANG X F, et al. Performance analysis of a tuna purse seine model[J]. Journal of Fishery Sciences of China, 2015, 22(3):563-573.
    [28] 徐国强,许柳雄,周成,等.基于海上实测的金枪鱼围网下纲沉降及提升性能[J].海洋渔业, 2015, 37(2):171-178. XU G Q, XU L X, ZHOU C, et al. Sinking and rising performance of tuna purse seine lead line based on field measurements[J]. Marine Fisheries, 2015, 37(2):171-178.
    [29] 刘树椿.深水围网沉降性能的测试及渔法研究[J].水产学报, 1988, 12(2):95-104. LIU S C. An experimental research on sinking per formance of purse seine in deeper waters of the East China Sea[J]. Journal of Fisheries of China, 1988, 12(2):95-104.
    [30] 周成,许柳雄,张新峰,等.金枪鱼围网沉降性能影响因子的多元回归分析[J].中国水产科学, 2013, 20(3):672-681. ZHOU C, XU L X, ZHANG X F, et al. Multiple regression analysis of the factors affecting the sinking per-formance of large-scale tuna purse seine[J]. Journal of Fishery Sciences of China, 2013, 20(3):672-681.
    [31] SUYAMA S, OSHIMA K, NAKAGAMI M, et al. Seasonal changes in otolith and somatic growth in age-0 Pacific saury Cololabis saira[J]. Fisheries Science, 2011, 77(2):223-233.
    [32] IITAKA Y. Model experiments on the sardine purse seine operatingin Hyuganada-6[J]. Bull JpnSocSci Fish, 1958, 24(6/7):407-410.
    [33] 徐国强,许柳雄,周成,等.金枪鱼围网下纲提升特性的研究[J].南方水产科学, 2015, 11(3):22-28. XU G Q, XU L X, ZHOU C, et al. Measurement and analysis of rising characteristics oftuna purse seine leadline[J]. South China Fisheries Science, 2015, 11(3):22-28.
    [34] KIM Y H. Geometry of the model purse seine in relation to enclosed volume during hauling operation[J]. Fisheries and Aquatic Science, 2000, 3(2):156-162.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

刘洋,石永闯,花传祥,朱清澄,王晓栋,孟令文.基于权重分析和GAM模型的秋刀鱼舷提网作业性能影响因素[J].上海海洋大学学报,2022,31(2):502-511.
LIU Yang, SHI Yongchuang, HUA Chuanxiang, ZHU Qingcheng, WANG Xiaodong, MENG Lingwen. Factors influencing the stick-held net status of Pacific saury (Cololabis saira) fishery based on weighted analysis and GAM[J]. Journal of Shanghai Ocean University,2022,31(2):502-511.

复制
分享
文章指标
  • 点击次数:3343
  • 下载次数: 1817
  • HTML阅读次数: 262
  • 引用次数: 0
历史
  • 收稿日期:2021-04-25
  • 最后修改日期:2021-06-09
  • 录用日期:2021-07-22
  • 在线发布日期: 2022-03-29
文章二维码