海州湾4种鱼类生长特征的空间异质性

王琨; 张崇良; 王晶; 任一平

doi:10.3969/j.issn.0253-4193.2019.12.006

海州湾4种鱼类生长特征的空间异质性

doi: 10.3969/j.issn.0253-4193.2019.12.006

王琨^1,,
张崇良^1, ,,
王晶¹,
任一平^{1, 2}

1.
中国海洋大学水产学院，山东青岛 266003
2.
青岛海洋科学与技术试点国家实验室海洋渔业科学与食物产出过程功能实验室，山东青岛 266071

基金项目: 国家重点研发计划项目（2018YFD0900904）。

详细信息

作者简介:
王琨（1995—），男，内蒙古自治区乌海市人，研究方向为渔业资源评估。E-mail：wangkun_ouc@qq.com

通讯作者:
张崇良，副教授，研究方向为渔业资源。E-mail：Zhangclg@ouc.edu.cn

中图分类号: S931.1
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- 文章访问数: 247
- HTML全文浏览量: 12
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- 被引次数: 0
出版历程
- 收稿日期: 2018-12-26
- 修回日期: 2019-07-22
- 网络出版日期: 2021-04-21
- 刊出日期: 2019-12-25

Spatial heterogeneity of growth traits of four fish species in the Haizhou Bay

Wang Kun^1
,,
Zhang Chongliang^{1
, ,},
Wang Jing¹,
Ren Yiping^{1, 2}

1.
Fisheries College, Ocean University of China, Qingdao 266003, China
2.
Laboratory for Marine Fisheries Science and Food Production Processes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266071, China

摘要

摘要: 传统的渔业资源评估均假设鱼类的生长参数是匀质的，然而近年来越来越多的研究表明海洋鱼类生长存在空间异质性。为探究海州湾鱼类生长参数的空间异质性现象，本研究分析了2013–2018年海州湾及其邻近海域方氏云鳚(Pholis fangi)、尖海龙(Syngnatus acus)、小黄鱼(Larimichthys polyactis)和赤鼻棱鳀(Thryssa kammalensis)的空间分布，使用电子体长频率分析方法结合Bootstrap重抽样方法估算了这4种鱼类的生长参数及其在深、浅水区域中的差异。结果显示，这4种鱼类生长参数均表现出一定的空间异质性，其中尖海龙和小黄鱼生长参数的空间异质性表现较为明显。这种差异可能是由于空间上的理化条件、群落结构以及物种本身洄游分布的差异而产生的。
- 海州湾 /
- 鱼类 /
- 生长参数 /
- 空间异质性 /
- 电子体长频率分析
Abstract: Growth parameters of fish are commonly assumed homogeneous in traditional fish stock assessment. However, increasing studies in recent years have shown that the growth of marine fish is characterized by spatial heterogeneity. To evaluate the spatial heterogeneity of growth traits of the fishes in the Haizhou Bay and its adjacent waters, this study analyzed the spatial distribution of 4 fish species and estimated their von Bertalanffy growth function parameters using otter trawl data collected from 2013 to 2018. We fitted the growth equations for Pholis fangi, Syngnatus acus, Larimichthys polyactis and Thryssa kammalensis using Electronic Length Frequency Analysis method in combination with the Bootstrap and compared the differences in growth parameters between deep- and shallow-water regions. The results show that growth parameters of the fish species exhibit certain levels of spatial heterogeneity, in particular Syngnatus acus and Larimichthys polyactis show substantial spatial heterogeneity. These differences may be attributed to the variations in spatial physical and chemical conditions, community structure and migration of the species.
- Haizhou Bay /
- fish /
- growth parameter /
- spatial heterogeneity /
- electronic length frequency analysis

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图 1 海州湾及邻近海域渔业资源底拖网调查站位

Fig. 1 Stations for bottom trawl survey in the Haizhou Bay and adjacent waters

下载: 全尺寸图片幻灯片

图 2 方氏云鳚、尖海龙、赤鼻棱鳀和小黄鱼在海州湾海域的生物量分布

a.方氏云鳚；b.尖海龙；c.赤鼻棱鳀；d.小黄鱼

Fig. 2 Biomass distribution of Pholis fangi, Syngnatus acus, Thryssa kammalensis and Larimichthys polyactis in the Haizhou Bay

a. Pholis fangi; b. Syngnatus acus; c. Thryssa kammalensis; d. Larimichthys polyactis

下载: 全尺寸图片幻灯片

图 3 研究的4个鱼种在两区域中的体长分布频率

Fig. 3 Body length frequency distribution of four fish species in different regions

下载: 全尺寸图片幻灯片

图 4 不同区域中生长参数分布的差异

L_∞表示生物体的极限体长；K表示生长曲线的平均曲率；灰色阴影区域表示L_∞与K的95%联合置信区间

Fig. 4 The differences in distribution of growth parameters in different areas

The shaded gray area represents the 95% simultaneous confidence intervals between L_∞ and K

下载: 全尺寸图片幻灯片

图 5 研究的4种鱼类von Bertalanffy生长方程曲线的分布

每张图片由1 000条生长曲线组成，虚线（CI=95%）表示95% Bootstrap置信区间所对应的生长曲线, 粗实线（Max.Dens.）表示概率密度最大的参数值所对应的生长曲线

Fig. 5 The distribution of von Bertalanffy growth function curves of four fish species

Each panel consists of 1 000 growth curves. The dashed line (CI=95%) represents the growth curve corresponding to the 95% Bootstrap confidence interval. The heavy line represents the growth curve corresponding to the parameter values with the maximum probability density

下载: 全尺寸图片幻灯片

图 6 海州湾及其邻近海域渔业生物群落结构聚类分析结果空间示意图

Fig. 6 Spatial diagram of clustering of fish community structure in the Haizhou Bay and its adjacent waters

下载: 全尺寸图片幻灯片

表 1 各物种在不同区域中生长参数的估计结果

Tab. 1 Estimation of growth parameters by species and areas

物种	区域	L_∞/mm	K/a⁻¹
方氏云鳚	1	182.92±4.33	0.48±0.09
方氏云鳚	2	185.56±3.48	0.50±0.05
尖海龙	1	213.28±8.69	0.74±0.07
尖海龙	2	203.34±6.67	0.44±0.06
赤鼻棱鳀	1	223.05±6.85	0.26±0.09
赤鼻棱鳀	2	125.52±4.49	0.50±0.06
小黄鱼	1	183.79±0.29	0.29±0.06
小黄鱼	2	215.00±0.21	0.21±0.03

下载: 导出CSV

参考文献(49)

[1]	von Bertalanffy L. A quantitative theory of organic growth (inquiries on growth laws. Ⅱ)[J]. Human Biology, 1938, 10(2): 181−213.
[2]	詹秉义. 渔业资源评估[M]. 北京: 中国农业出版社, 1995: 25-31. Zhan Bingyi. Fish Stock Assessment[M]. Beijing: China Agriculture Press, 1995: 25–31.
[3]	Beverton R J H, Holt S J. On the Dynamics of Exploited Fish Populations[M]. Netherlands: Springer Science & Business Media, 1957: 35–37.
[4]	Du Pontavice H, Randon M, Lehuta S, et al. Investigating spatial heterogeneity of von Bertalanffy growth parameters to inform the stock structuration of common sole, Solea solea, in the Eastern English Channel[J]. Fisheries Research, 2018, 207: 28−36. doi: 10.1016/j.fishres.2018.05.009
[5]	Midway S R, Wagner T, Arnott S A, et al. Spatial and temporal variability in growth of southern Flounder (Paralichthys lethostigma)[J]. Fisheries Research, 2015, 167: 323−332. doi: 10.1016/j.fishres.2015.03.009
[6]	Williams A J, Farley J H, Hoyle S D, et al. Spatial and sex-specific variation in growth of albacore tuna (Thunnus alalunga) across the South Pacific Ocean[J]. PLoS One, 2012, 7(6): e39318. doi: 10.1371/journal.pone.0039318
[7]	Randon M, Réveillac E, Rivot E, et al. Could we consider a single stock when spatial sub-units present lasting patterns in growth and asynchrony in cohort densities? A flatfish case study[J]. Journal of Sea Research, 2018, 142: 91−100. doi: 10.1016/j.seares.2018.09.012
[8]	Punt A E. The performance of a size-structured stock assessment method in the face of spatial heterogeneity in growth[J]. Fisheries Research, 2003, 65(1/3): 391−409.
[9]	Lorenzen K. Toward a new paradigm for growth modeling in fisheries stock assessments: embracing plasticity and its consequences[J]. Fisheries Research, 2016, 180: 4−22. doi: 10.1016/j.fishres.2016.01.006
[10]	刘勇, 程家骅. 小黄鱼Larimichthys polyactis体长−体重关系幂指数与产卵群体空间分布相关性研究[J]. 海洋学报, 2014, 36(6): 124−130. doi: 10.3969/j.issn.0253-4193.2014.06.015 Liu Yong, Cheng Jiahua. Study on the correlation between spatial distributions of the spawning groups and the power b in length-weight relation function of small yellow croaker (Larimichthys polyactis)[J]. Haiyang Xuebao, 2014, 36(6): 124−130. doi: 10.3969/j.issn.0253-4193.2014.06.015
[11]	栾静, 徐宾铎, 薛莹, 等. 海州湾方氏云鳚体长与体重分布特征及其关系[J]. 中国水产科学, 2017, 24(6): 1323−1331. Luan Jing, Xu Binduo, Xue Ying, et al. Size distribution and length-weight relationships in Pholis fangi in Haizhou Bay[J]. Journal of Fishery Sciences of China, 2017, 24(6): 1323−1331.
[12]	Ma Qiuyun, Jiao Yan, Ren Yiping. Linear mixed-effects models to describe length-weight relationships for yellow croaker (Larimichthys polyactis) along the north coast of China[J]. PLoS One, 2017, 12(2): e0171811. doi: 10.1371/journal.pone.0171811
[13]	Ying Yiping, Chen Yong, Lin Longshan, et al. Risks of ignoring fish population spatial structure in fisheries management[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2011, 68(12): 2101−2120. doi: 10.1139/f2011-116
[14]	Stephenson R L. Stock complexity in fisheries management: a perspective of emerging issues related to population sub-units[J]. Fisheries Research, 1999, 43(1/3): 247−249.
[15]	Xu Binduo, Ren Yiping, Chen Yong, et al. Optimization of stratification scheme for a fishery-independent survey with multiple objectives[J]. Acta Oceanologica Sinica, 2015, 34(12): 154−169. doi: 10.1007/s13131-015-0739-z
[16]	中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 12763.6–2007, 海洋调查规范第6部分: 海洋生物调查[S]. 北京: 中国标准出版社, 2008. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, National Standardization Management Committee of China. GB/T 12763.6–2007, Specifications for oceanographic survey—Part 6: Marine biological survey[S]. Beijing: Standard Press of China, 2008.
[17]	Pauly D, David N. ELEFAN Ⅰ, a BASIC program for the objective extraction of growth parameters from length-frequency data[J]. Meeresforschung, 1981, 28: 205−211.
[18]	Taylor M H, Mildenberger T K. Extending electronic length frequency analysis in R[J]. Fisheries Management and Ecology, 2017, 24(4): 330−338. doi: 10.1111/fme.12232
[19]	Efron B, Tibshirani R J. An Introduction to the Bootstrap[M]. Boca Raton, FL: Chapman and Hall/CRC, 1993.
[20]	Magnusson A, Punt A E, Hilborn R. Measuring uncertainty in fisheries stock assessment: the delta method, bootstrap, and MCMC[J]. Fish and Fisheries, 2013, 14(3): 325−342. doi: 10.1111/j.1467-2979.2012.00473.x
[21]	Powers S, Jones P B. Two basic programs to compute Hotelling's T-square statistic[J]. Educational and Psychological Measurement, 1986, 46(3): 663−665. doi: 10.1177/0013164486463022
[22]	Mildenberger T K, Taylor M H, Wolff M. TropFishR: an R package for fisheries analysis with length-frequency data[J]. Methods in Ecology and Evolution, 2017, 8(11): 1520−1527. doi: 10.1111/2041-210X.12791
[23]	林美华. 黄海海底地貌分区及地貌类型[J]. 海洋科学, 1989(6): 7−15. Lin Meihua. The submarine geomorphological zones and geomorphological types in the Huanghai Sea[J]. Marine Sciences, 1989(6): 7−15.
[24]	中国海湾志编撰委员会. 中国海湾志: 第四分册—山东半岛南部和江苏省海湾[M]. 北京: 海洋出版社, 1993. China gulf Chronicles compilation committee. Compilation Committee of Chinese Bay: Ⅳ[M]. Beijing: China Ocean Press, 1993.
[25]	于非, 张志欣, 刁新源, 等. 黄海冷水团演变过程及其与邻近水团关系的分析[J]. 海洋学报, 2006, 28(5): 26−34. doi: 10.3321/j.issn:0253-4193.2006.05.003 Yu Fei, Zhang Zhixin, Diao Xinyuan, et al. Analysis of evolution of the Huanghai Sea Cold Water Mass and its relationship with adjacent water masses[J]. Haiyang Xuebao, 2006, 28(5): 26−34. doi: 10.3321/j.issn:0253-4193.2006.05.003
[26]	Hilborn R, Walters C J. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty[M]. New York: Springer, 1992.
[27]	Quinn T J, Deriso R B. Quantitative Fish Dynamics[M]. New York: Oxford University Press, 1999.
[28]	Pikitch E K, Santora C, Babcock E A, et al. Ecosystem-based fishery management[J]. Science, 2004, 305(5682): 346−347. doi: 10.1126/science.1098222
[29]	Fujita T, Inada T, Ishito Y. Depth-gradient structure of the demersal fish community on the continental shelf and upper slope off Sendai Bay, Japan[J]. Marine Ecology Progress Series, 1995, 118: 13−23. doi: 10.3354/meps118013
[30]	Connolly S R, Roughgarden J. A latitudinal gradient in northeast Pacific intertidal community structure: evidence for an oceanographically based synthesis of marine community theory[J]. The American Naturalist, 1998, 151(4): 311−326. doi: 10.1086/286121
[31]	Moranta J, Stefanescu C, Massutí E, et al. Fish community structure and depth-related trends on the continental slope of the Balearic Islands (Algerian Basin, western Mediterranean)[J]. Marine Ecology Progress Series, 1998, 171: 247−259. doi: 10.3354/meps171247
[32]	李敏, 李增光, 徐宾铎, 等. 时空和环境因子对海州湾方氏云鳚资源丰度分布的影响[J]. 中国水产科学, 2015, 22(4): 812−819. Li Min, Li Zengguang, Xu Binduo, et al. Effects of spatiotemporal and environmental factors on the distribution and abundance of Pholis fangi in Haizhou Bay using a generalized additive model[J]. Journal of Fishery Sciences of China, 2015, 22(4): 812−819.
[33]	Small L F, Menzies D W. Patterns of primary productivity and biomass in a coastal upwelling region[J]. Deep Sea Research Part A. Oceanographic Research Papers, 1981, 28(2): 123−149. doi: 10.1016/0198-0149(81)90086-8
[34]	Ryther J H. Photosynthesis and fish production in the sea[J]. Science, 1969, 166(3901): 72−76. doi: 10.1126/science.166.3901.72
[35]	石琼, 范明君, 张勇. 中国经济鱼类志[M]. 武汉: 华中科技大学出版社, 2015. Shi Qiong, Fan Mingjun, Zhang Yong. Economic Ichthyography of China[M]. Wuhan: Huazhong University of Science and Technology Press, 2015.
[36]	王小荟. 海州湾主要鱼种的空间分布及其与环境因子的关系[D]. 青岛: 中国海洋大学, 2013. Wang Xiaohui. Spatial distribution of dominant fish species in Haizhou Bay and their relationships with environmental factors[D]. Qingdao: Ocean University of China, 2013.
[37]	徐兆礼, 陈佳杰. 小黄鱼洄游路线分析[J]. 中国水产科学, 2009, 16(6): 931−940. doi: 10.3321/j.issn:1005-8737.2009.06.014 Xu Zhaoli, Chen Jiajie. Analysis on migratory routine of Larimichthys polyactis[J]. Journal of Fishery Sciences of China, 2009, 16(6): 931−940. doi: 10.3321/j.issn:1005-8737.2009.06.014
[38]	Morais P, Daverat F. An Introduction to Fish Migration[M]. Boca Raton: Chemical Rubber Company Press, 2016.
[39]	Secor D H. Migration Ecology of Marine Fishes[M]. Baltimore: Johns Hopkins University Press, 2015.
[40]	Thresher R E, Koslow J A, Morison A K, et al. Depth-mediated reversal of the effects of climate change on long-term growth rates of exploited marine fish[J]. Proceedings of the National Academy of Sciences of the United States of America, 2007, 104(18): 7461−7465. doi: 10.1073/pnas.0610546104
[41]	Denit K, Sponaugle S. Growth variation, settlement, and spawning of gray snapper across a latitudinal gradient[J]. Transactions of the American Fisheries Society, 2004, 133(6): 1339−1355. doi: 10.1577/T03-156.1
[42]	Björnsson B, Steinarsson A, Oddgeirsson M. Optimal temperature for growth and feed conversion of immature cod (Gadus morhua L.)[J]. ICES Journal of Marine Science, 2001, 58(1): 29−38. doi: 10.1006/jmsc.2000.0986
[43]	Sinclair A F, Swain D P, Hanson J M. Disentangling the effects of size-selective mortality, density, and temperature on length-at-age[J]. Canadian Journal of Fisheries and Aquatic Sciences, 2002, 59(2): 372−382. doi: 10.1139/f02-014
[44]	Swain D P, Sinclair A F, Hanson J M. Evolutionary response to size-selective mortality in an exploited fish population[J]. Proceedings of the Royal Society B: Biological Sciences, 2007, 274(1613): 1015−1022. doi: 10.1098/rspb.2006.0275
[45]	Aikio S, Herczeg G, Kuparinen A, et al. Optimal growth strategies under divergent predation pressure[J]. Journal of Fish Biology, 2013, 82(1): 318−331. doi: 10.1111/jfb.12006
[46]	Schwamborn R, Mildenberger T K, Taylor M H. Assessing sources of uncertainty in length-based estimates of body growth in populations of fishes and macroinvertebrates with bootstrapped ELEFAN[J]. Ecological Modelling, 2019, 393: 37−51. doi: 10.1016/j.ecolmodel.2018.12.001
[47]	Stokes T K, Law R. Fishing as an evolutionary force[J]. Marine Ecology Progress Series, 2000, 208: 307−309.
[48]	Ernande B, Dieckmann U, Heino M. Adaptive changes in harvested populations: plasticity and evolution of age and size at maturation[J]. Proceedings of the Royal Society B: Biological Sciences, 2004, 271(1537): 415−423. doi: 10.1098/rspb.2003.2519
[49]	Law R. Fishing, selection, and phenotypic evolution[J]. ICES Journal of Marine Science, 2000, 57(3): 659−668. doi: 10.1006/jmsc.2000.0731