Environmental impact mechanism of skipjack tuna fishery in Western and Central Pacific Ocean based on Multi-scale Geographical Weighted Regression Model (MGWR)
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摘要: 鲣 (Katsuwonus pelamis) 是中西太平洋金枪鱼围网捕捞的重要资源，其资源分布受环境影响明显。为探索环境对鲣渔获率影响的空间异质性特征，利用中国大陆2005—2019年中西太平洋金枪鱼围网综合的1°×1°渔业及海洋环境数据，对标准化后的环境因子及渔获率选用多尺度地理加权回归 (Multi-scale Geographically Weighted Regression, MGWR)方法进行研究。结果表明：1) 与传统广义加性模型 (Generalized Additive Model, GAM) 相比，考虑环境影响空间异质性问题的地理加权回归模型 (Geographically Weighted Regression, GWR) 和MGWR拟合优度有明显提升，校正后R²分别为0.273、0.846、0.871，而且拟合结果空间分布形态更符合真实情况。2) 发现各环境因子对鲣资源分布存在显著的空间非平稳性影响。各海洋环境因子对鲣渔获率分布影响的空间异质性程度(各环境变量变异系数大小)依次为水下55 m东西向海流速度 (Sea Water X Velocity at 55 m depth, U55) >海表面温度 (Sea Surface Temperature, SST) >净初级生产力(Net Primary Productivity, NPP) >100 m盐度(Sea Water Salinity at 100 m depth, S100)> 55 m南北向海流速度(Sea Water Y Velocity at 55 m depth, V55)。3)发现各环境因子的影响存在明显尺度效应差异，NPP作用尺度为44，其次为S100和U55为48，SST的作用尺度为54，V55为全局尺度。4)总体上，S100、NPP、SST、V55和U55对鲣渔获率正向影响比例依次为73.5%、64.8%、66.8%、80.8%和32.3%；其中S100、NPP和SST对鲣渔获率空间分布的影响是相似的，具体表现为东西向差异，170°E以西主要为正向影响，170°E以东为负向影响；U55是负向影响为主的因子。
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关键词:
- 鲣 /
- 多尺度地理加权回归模型 /
- 空间异质性 /
- 中西太平洋
Abstract: Katsuwonus pelamis is an important resource for tuna purse seine fishing in the Western and Central Pacific, and its resource distribution is significantly affected by the environment. In order to explore the characteristics of spatial heterogeneity of environmental impact on tuna catch rate, we used the 1°×1° fishery and marine environmental data of the Western and central Pacific Ocean tuna purse-seine from 2005 to 2019 in mainland China, and investigated the standardized environmental factors and catch rates by using Multi-scale Geographically Weighted Regression (MGWR) method. The results show that: 1) Compared with the traditional Generalized Additive Model (GAM), the Geographically Weighted Regression (GWR) and MGWR with spatial heterogeneity of environmental impacts had improved the fit performance significantly. 2) Significant spatial non-stationarity was found for each environmental factor on the distribution of tuna resources. The degree of spatial heterogeneity (The magnitude of the coefficient of variation for each environmental variable) of each environmental factor on the distribution of tuna catch rate followed a descending order of Sea Water X Velocity at 55 m depth (U55) > Sea Surface Temperature (SST) > Net Primary Productivity (NPP) >Sea Water Salinity at 100 m depth (S100) > Sea Water Y Velocity at 55 m depth (V55). 3) The effects of the environmental factors were found to have significant scale effects. 4) Overall, the positive effects of S100, NPP, SST, V55 and U55 on the catch rate of tuna were 73.5%, 64.8%, 66.8%, 80.8% and 32.3% in order of magnitude; among them, S100, NPP and SST on the spatial distribution of bonito catch rate were similar, specifically in terms of east-west differences, with positive effects mainly west of 170°E and negative effects east of 170°E; U55 was the factor with predominantly negative effects. -
图 1 MGWR模型各环境因子局部回归系数和显著性的空间分布
Figure 1. Spatial distribution of local regression coefficients and significance of each environmental factor in MGWR model
图 2 不同模型渔获潜能标准化预测及2005—2019年平均CPUE空间分布
Figure 2. Standardized catch potential prediction and average CPUE spatial distribution of different models from 2005 to 2019
表 1 解释变量间方差膨胀因子
Table 1. Variance inflation factor among explanatory variables
解释变量
Explanatory variable单位
Unit方差膨胀因子
VIF海表面温度
Sea Surface Temperature, SSTK 1.558 100 m水深盐度
Sea Water Salinity at 100 m depth, S100‰ 1.090 净初级生产力
Net Primary Productivity, NPPmol·(m2·s)−1 1.858 55 m东西向海水流速
Mean zonal (E-W) current velocity at
55 m depth, U55m·s−1 1.007 55 m南北向海水流速
Mean meridional (N-S) current velocity at
55 m depth, V55m·s−1 1.796 
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表 2 GAM、GWR和MGWR不同回归模型性能评价对比
Table 2. Comparison of statistical parameters of different linear regression models (GAM, GWR and MGWR)
参数
Parameter广义加性
模型
GAM地理加权
回归
GWR多尺度地理
加权回归
MGWR残差平方和 RSS 254 400.4 148.607 129.136 赤池信息准则 AICc 1 477.598 1 572.437 1 287.873 拟合优度 R² 0.369 0.879 0.895 校正后的拟合优度
Adjusted R²0.273 0.846 0.871
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表 3 MGWR与GWR带宽对比结果
Table 3. Bandwidth comparison between classical MGWR and GWR models
变量
Variable多尺度地理
加权回归
MGWR占比
Proportion/
%地理加权
回归
GWR占比
Proportion/
%常数项Intercept 48 3.91 65 5.29 海表面温度SST 54 4.40 65 5.29 100米水深盐度
S10048 3.91 65 5.29 55米东西向海水
流速U5548 3.91 65 5.29 55米南北向海水
流速V551 227 99.84 65 5.29 净初级生产力NPP 44 3.51 65 5.29
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表 4 基于MGWR的各环境因子的局部系数统计描述
Table 4. Statistical description of MGWR local coefficient
变量
Variable均值
Mean标准差
SD最小值
Min.最大值
Max.正值比例
Positive ratio/%负值比例
Negative ratio/%显著性检验
P常数项 Intercept 0.036 0.350 −0.552 0.939 38.975 61.025 1 海表面温度 SST 0.162 0.331 −0.555 1.551 66.802 33.198 <0.05 100米水深盐度 S100 0.574 0.952 −0.359 4.649 73.556 26.444 <0.05 东西向海水流速 U55 0.180 0.504 −2.215 0.997 32.303 67.697 <0.05 南北向海水流速 V55 0.069 0.109 −0.142 0.553 80.797 19.203 <0.05 净初级生产力 NPP 0.172 0.351 −0.787 1.806 64.849 35.151 <0.05
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[1] 陈新军, 郑波. 中西太平洋金枪鱼围网渔业鲣鱼资源的时空分布[J]. 海洋学研究, 2007(02): 13-22. doi: 10.3969/j.issn.1001-909X.2007.02.002 [2] WILLIAMS P, RUAIA T. Overview of tuna fisheries in the western and central Pacific Ocean, including economic conditions –2019[C]//16th WCPFC Scientific Committee Meeting (SC16). Pohnpei State, Federated States of Micronesia: WCPFC, 2020: 7. [3] DUERI S, BOPP L, MAURY O. Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution[J]. Global Change Biol, 2014, 20(3): 742-753. doi: 10.1111/gcb.12460 [4] 杨晓明, 戴小杰, 田思泉, 等. 中西太平洋金枪鱼围网渔场变动的预报分析[J]. 中国水产科学, 2016, 23(06): 1417-1425. [5] 王学昉, 许柳雄, 周成, 等. 中西太平洋金枪鱼围网鲣鱼自由鱼群捕获成功率与温跃层特性的关系[J]. 上海海洋大学学报, 2013, 22(5): 763-769. [6] 魏广恩. 北太平洋柔鱼渔场的时空分析与资源丰度的预测[D]. 上海: 上海海洋大学, 2018: 77 [7] 陈洋洋, 陈新军, 郭立新, 等. 基于BP神经网络的中西太平洋鲣鱼渔场预报模型构建与比较[J]. 广东海洋大学学报, 2017, 37(6): 65-73. [8] ROBINSON M, SEI-ICHI S. Ensemble modelling of skipjack tuna (Katsuwonus pelamis) habitats in the Western North Pacific using satellite remotely sensed data: a comparative analysis using Machine-Learning Models[J]. Remote Sens-Basel, 2020, 12(16): 2591. doi: 10.3390/rs12162591 [9] VENABLES W N, DICHMONT C M. GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research[J]. Fish Res, 2004, 70(2/3): 319-337. [10] 杨彩莉, 杨晓明, 朱江峰. 不同类型厄尔尼诺事件中环境因子对中西太平洋金枪鱼围网鲣分布响应[J]. 南方水产科学, 2021, 17(3): 8-18. [11] DUQUE-LAZO J, GILS H V, GROEN T A, et al. Transfer ability of species distribution models: the case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia[J]. Ecol Model, 2016, 320: 62-70. doi: 10.1016/j.ecolmodel.2015.09.019 [12] 姜珊, 杨晓明, 朱江峰. 中西太平洋金枪鱼围网鲣渔获率与海洋环境关系的时空异质性[J]. 中国水产科学, 2022, 29(5): 744-754. [13] FENG Y J, LIU Y, CHEN X J. Modeling monthly spatial distribution of Ommastrephes bartramii CPUE in the northwest Pacific and its spatially nonstationary relationships with the marine environment[J]. J Ocean U China, 2018, 17(3): 647-658. doi: 10.1007/s11802-018-3495-9 [14] 沈体雁, 于瀚辰, 周麟, 等. 北京市二手住宅价格影响机制——基于多尺度地理加权回归模型(MGWR)的研究[J]. 经济地理, 2020, 40(3): 75-83. [15] FOTHERINGHAM A S, YANG W, KANG W. Multiscale geographically weighted regression (MGWR)[J]. Ann Am Assoc Geogr, 2017, 107(6): 1247-1265. [16] YU H, FOTHERINGHAM A S , LI Z, et al. On the measurement of bias in geographically weighted regression models[J]. Spat Stat-Neth, 2020: 100453. [17] 李斌, 邢汉发, 刘烨菲. 基于MGWR模型的城市景观与热环境关系分析[J]. 西安理工大学学报, 2021, 37(4): 517-525. [18] 周天军, 邹立维, 陈晓龙. 第六次国际耦合模式比较计划(CMIP6)评述[J]. 气候变化研究进展, 2019, 15(5): 445-456. [19] BUI D T, LOFMAN O, REVHAUG I, et al. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression[J]. Nat Hazards, 2011, 59(3): 1413-1444. doi: 10.1007/s11069-011-9844-2 [20] HASTIE T J, TIBSHIRANI R J. Generalized Additive Models[M]. London: Chapman and Hall, 1990: 17. [21] TSENG C T, SUN C L, YEH S Z, et al. Spatio-temporal distributions of tuna species and potential habitats in the Western and Central Pacific Ocean derived from multisatellite data[J]. Int J Remote Sens, 2010, 31(17/18): 4543-4558. [22] SONG X L, MI N, et al. Spatial non-stationary characteristics between grass yield and its influencing factors in the Ningxia temperate grasslands based on a mixed geographically weighted regression model[J]. J Geogr Sci, 2022, 32(6): 1076-1102. doi: 10.1007/s11442-022-1986-5 [23] 祝新明, 宋小宁, 冷佩, 等. 多尺度地理加权回归的地表温度降尺度研究[J]. 遥感学报, 2021, 25(8): 1749-1766. [24] JONES S, SILAS E G. Synopsis of biological data on skipjack Katsuwonus pelamis (Linnaeus) 1758 (Indian Ocean): FAO Fisheries Biology Synopsis No. 64 Species Synopsis No. 21[R]. Rome: FAO, 1963: 663-694. [25] DRUON J N, CHASSOT E, MURUA H, et al. Skipjack tuna availability for purse seine fisheries is driven by suitable feeding habitat dynamics in the Atlantic and Indian Oceans[J]. Front Mar Sci, 2017, 4: 315. doi: 10.3389/fmars.2017.00315 [26] 周甦芳, 沈建华, 樊伟. ENSO现象对中西太平洋鲣鱼围网渔场的影响分析[J]. 海洋渔业, 2004(3): 167-172. [27] LEHODEY P M, BERTIGNAC M, HAMPTON J, et al. El Niña Southern Oscillation and tuna in the western Pacific[J]. Nature 1997, 389: 715-718. [28] MEEHL G A. Characteristics of surface current flow inferred from a Global Ocean Current Data Set[J]. J Phys Oceanogr, 1982, 12(6): 538-555. doi: 10.1175/1520-0485(1982)012<0538:COSCFI>2.0.CO;2 [29] 郭爱, 余为, 陈新军, 等. 中国近海鲐鱼资源时空分布与海洋净初级生产力的关系研究[J]. 海洋学报, 2018, 40(8): 42-52. [30] 黄易德. 中西太平洋正鲣资源时空分布特性之研究[D]. 基隆: 台湾海洋大学, 2002: 83. [31] SUGIMOTO T, KIMURAA S, TADOKOROB K. Impact of El Nino eventsand climate regime shift on living resources in the western North Pacific[J]. PROG OCEANOGR, 2001, 49: 113-127. doi: 10.1016/S0079-6611(01)00018-0 [32] 杨晓明, 戴小杰, 田思泉, 等. 中西太平洋鲣鱼围网渔业资源的热点分析和空间异质性[J]. 生态学报, 2014, 34(13): 3771-3778. [33] 杨晓明, 戴小杰, 王学昉, 等. 基于点格局的中西太平洋金枪鱼围网中鲣的空间格局特征[J]. 中国水产科学, 2017, 24(3): 633-639. [34] 杨胜龙, 周甦芳, 周为峰, 等. 基于Argo数据的中西太平洋鲣渔获量与水温、表层盐度关系的初步研究[J]. 大连水产学院学报, 2010, 25(1): 34-40. [35] 叶泰豪, 冯波, 颜云榕, 等. 中西太平洋鲣渔场与温盐垂直结构关系的研究[J]. 海洋湖沼通报, 2012(1): 49-55. [36] PICAUT J, IOUALALEN M, MENKES C, et al. Mechanism of the zonal displacements of the Pacific warm pool: implications for ENSO[J]. Science, 1996, 274(5292): 1486-1489. doi: 10.1126/science.274.5292.1486 [37] ZHANG F, ZHANG R H. Interannually varying salinity effects on ENSO in the tropical Pacific: a diagnostic analysis from Argo[J]. Ocean Dyn, 2015, 65(5): 691-705. doi: 10.1007/s10236-015-0829-7 [38] 唐未, 王学昉, 吴峰, 等. 基于最大熵模型模拟西印度洋剑鱼栖息地的时空分布[J]. 海洋学报, 2022, 44(10): 100-108. [39] 张小龙, 付东洋, 刘大召, 等. 基于EOF分析中西太平洋金枪鱼围网渔场的海洋环境[J]. 海洋学研究, 2019, 37(2): 81-94. [40] 唐浩, 许柳雄, 陈新军, 等. 基于GAM模型研究时空及环境因子对中西太平洋鲣鱼渔场的影响[J]. 海洋环境科学, 2013, 32(4): 518-522. -
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