Loading [MathJax]/jax/output/SVG/jax.js
Volume 19 Issue 3
Jun.  2023
Turn off MathJax
Article Contents
Abstract
References
CAI Yancong, SUN Mingshuai, XU Youwei, CHEN Zuozhi. Spatial heterogeneity of relationship between distribution of Uroteuthis chinensis and marine environment in offshore waters of northern South China Sea[J]. South China Fisheries Science, 2023, 19(3): 1-10. DOI: 10.12131/20220288
Citation: CAI Yancong, SUN Mingshuai, XU Youwei, CHEN Zuozhi. Spatial heterogeneity of relationship between distribution of Uroteuthis chinensis and marine environment in offshore waters of northern South China Sea[J]. South China Fisheries Science, 2023, 19(3): 1-10. DOI: 10.12131/20220288
shu

Spatial heterogeneity of relationship between distribution of Uroteuthis chinensis and marine environment in offshore waters of northern South China Sea

  • 1.

    South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences/Key Laboratory of Sustainable Utilization of Open-Sea Fishery, Ministry of Agriculture and Rural Affairs, Guangzhou 510300, China

  • 2.

    Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China

More Information
  • Received Date: November 10, 2022
  • Revised Date: January 17, 2023
  • Accepted Date: February 15, 2023
  • Available Online: February 18, 2023
    Graphical Abstract
    • Abstract
  • Cephalopods are one of the most potienial fishery species but are vulnerable to environment changes, and their complex interactions lead to the spatial heterogeneity in resource-environment relationship. Uroteuthis chinensis is an important economic species in the offshore waters of northern South China Sea, occuping a dominant position in the fishery community structure. Therefore, understanding the spatial characteristics of the resource-environment relationship is beneficial to its development, utilization and protection. Based on the fishery resources survey data in the offshore waters of northern South China Sea in summer of 2014, we established a geographically weighted regression (GWR) model to explore the spatial characteristics of the relationship between the resource distribution of U. chinensis and the marine environment in this area, and to reveal the main influencing factors. The results of model evaluation indexes show that the minimum Akaike information criterion (AIC) and adjusted R-Square (R2adj) for GWR model were 224.81 and 0.46, respectively, both of which were better than those of the traditional global linear regression model. Thus, the GWR model could more truly reflect the spatial heterogeneity on resource-environment relationship for U. chinensis. The impact of chlorophyll a on resources was a coexistence of positive and negative effects in the whole sea, but the other environment variables had consistent positive effects on resources. The stock distribution in the coastal waters of Guangdong was mainly affected by sea surface salinity, sea surface temperature and chlorophyll a, and the latter two were the primary influencing factors in western Guangdong and Pearl River Estuary-Eastern Guangdong, respectively, but the Beibu Gulf was only significantly affected by water depth. Under the impact of dominant environmental factors, U. chinensis stock denstiy showed obvious regional differentiation characteristics, especially those in the Pearl River Estuary-Eastern Guangdong significantly different from the other areas. In conclusion, GWR model provides an effective means to explore and understand the local characteristics of cephalopod resource-environment relationship.
    • FullText(HTML)
    • References (43)
  • [1]
    张壮丽, 叶孙忠, 洪明进, 等. 闽南—台湾浅滩渔场中国枪乌贼生物学特性研究[J]. 福建水产, 2008(1): 1-5.
    [2]
    郭金富, 陈丕茂. 南海头足类资源开发利用研究[J]. 热带海洋, 2000, 19(4): 51-58.
    [3]
    黄梓荣. 南海北部陆架区头足类的种类组成和资源密度分布[J]. 南方水产, 2008, 4(5): 1-7.
    [4]
    SIN Y W, YAU C, CHU K H. Morphological and genetic differentiation of two loliginid squids, Uroteuthis (Photololigo) chinensis and Uroteuthis (Photololigo) edulis (Cephalopoda: Loliginidae), in Asia[J]. J Exp Mar Biol Ecol, 2009, 369(1): 22-30. doi: 10.1016/j.jembe.2008.10.029
    [5]
    JIN Y, LIU B L, CHEN X J, et al. Morphological beak differences of loliginid squid, Uroteuthis chinensis and Uroteuthis edulis, in the northern South China Sea[J]. J Oceanol Limnol, 2018, 36(2): 559-571. doi: 10.1007/s00343-017-6285-0
    [6]
    韩青鹏, 陆化杰, 陈新军, 等. 南海北部海域中国枪乌贼角质颚的形态学分析[J]. 南方水产科学, 2017, 13(4): 122-130.
    [7]
    李建华, 张鑫浩, 金岳, 等. 基于耳石和角质颚微结构的中国枪乌贼年龄与生长比较[J]. 海洋渔业, 2018, 40(5): 513-521.
    [8]
    SUKRAMONGKOL N, TSUCHIYA K, SEGAWA S. Age and maturation of Loligo duvauceli and L. chinensis from Andaman Sea of Thailand[J]. Rev Fish Biol Fish, 2006, 17(2/3): 237-246.
    [9]
    JIN Y, LI N, CHEN X J, et al. Comparative age and growth of Uroteuthis chinensis and Uroteuthis edulis from China Seas based on statolith[J]. Aquac Fish, 2019, 4(4): 166-172. doi: 10.1016/j.aaf.2019.02.002
    [10]
    ISLAM R, HAJISAMAE S, PRADIT S, et al. Feeding habits of two sympatric loliginid squids, Uroteuthis (Photololigo) chinensis (Gray, 1849) and Uroteuthis (Photololigo) duvaucelii (d'Orbigny, 1835), in the lower part of the South China Sea[J]. Molluscan Res, 2018, 38(3): 155-162. doi: 10.1080/13235818.2017.1409066
    [11]
    欧瑞木. 中国枪乌贼性腺成熟度分期的初步研究[J]. 海洋科学, 1983(1): 44-46.
    [12]
    JIANG L H, KANG L, WU C W, et al. Complete mitochondrial genome of the Loligo chinensis[J]. Mitochondrial DNA B, 2017, 2(2): 504-505. doi: 10.1080/23802359.2017.1361347
    [13]
    JIANG L H, KANG L S, WU C W, et al. A comprehensive description and evolutionary analysis of 9 Loliginidae mitochondrial genomes[J]. Hydrobiologia, 2017, 808(1): 115-124.
    [14]
    欧瑞木. 南海北部鱿鱼资源周期波动机制的初步探讨[J]. 水产科技情报, 1978(9): 1-4, 32.
    [15]
    BRUNSDON C, FOTHERINGHAM A S, CHARLTON M E. Geographically weighted regression: a method for exploring spatial nonstationarity[J]. Geogr Anal, 1996, 28(4): 281-298.
    [16]
    FOTHERINGHAM A S, BRUNSDON C, CHARLTON M. Geographically weighted regression: the analysis of spatially varying relationships [M]. Chichester: Wiley, 2002: 1-64.
    [17]
    FOTHERINGHAM A S, CHARLTON M, BRUNSDON C. The geography of parameter space: an investigation of spatial non-stationarity[J]. Int J Geogr Inf Syst, 1996, 10(5): 605-627. doi: 10.1080/02693799608902100
    [18]
    LIU C D, WAN R, JIAO Y, et al. Exploring non-stationary and scale-dependent relationships between walleye (Sander vitreus) distribution and habitat variables in Lake Erie[J]. Mar Freshw Res, 2017, 68(2): 270-281. doi: 10.1071/MF15374
    [19]
    LIU C D, LIU J C, JIAO Y, et al. Exploring spatial nonstationary environmental effects on Yellow Perch distribution in Lake Erie[J]. PeerJ, 2019, 7: e7350. doi: 10.7717/peerj.7350
    [20]
    LIKONGWE P, KIHORO J, NGIGI M, et al. Modeling spatial non-stationarity of Chambo in South-East Arm of Lake Malawi[J]. Asian J Appl Sci Eng, 2015, 4(2): 81-90.
    [21]
    WINDLE M J S, ROSE G A, DEVILLERS R, et al. Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic[J]. ICES J Mar Sci, 2010, 67(1): 145-154. doi: 10.1093/icesjms/fsp224
    [22]
    WINDLE M J S, ROSE G A, DEVILLERS R, et al. Spatio-temporal variations in invertebrate-cod-environment relationships on the Newfoundland-Labrador Shelf, 1995-2009[J]. Mar Ecol Prog Ser, 2012, 469: 263-278. doi: 10.3354/meps10026
    [23]
    CULLEN D W, GUIDA V. Use of geographically weighted regression to investigate spatial non-stationary environmental effects on the distributions of black sea bass (Centropristis striata) and scup (Stenotomus chrysops) in the Mid-Atlantic Bight, USA[J]. Fish Res, 2021, 234: 105795. doi: 10.1016/j.fishres.2020.105795
    [24]
    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 Univ China, 2018, 17(3): 647-658. doi: 10.1007/s11802-018-3495-9
    [25]
    陈广威, 陈吕凤, 朱国平, 等. 南乔治亚岛冬季南极磷虾渔场时空分布及其驱动因子[J]. 生态学杂志, 2017, 36(10): 2803-2810.
    [26]
    陈吕凤, 朱国平. 基于地理权重回归模型的南设得兰群岛北部南极磷虾渔场空间分布影响分析[J]. 应用生态学报, 2018, 29(3): 938-944.
    [27]
    贾明秀, 黄六一, 褚建伟, 等. 基于GAM和GWR模型分析环境因子对南极磷虾资源分布的非线性和非静态性影响[J]. 中国海洋大学学报(自然科学版), 2019, 49(8): 19-26.
    [28]
    赵杨, 张学庆, 卞晓东. 基于地理加权回归的渤海沙氏下鱵鱼仔稚鱼栖息地指数[J]. 应用生态学报, 2018, 29(1): 293-299.
    [29]
    白思琦, 邹晓荣, 张鹏, 等. 环境因子对东南太平洋智利竹䇲鱼渔场时空分布异质性影响[J]. 南方水产科学, 2021, 17(1): 17-24.
    [30]
    WANG D, WAN R, LI Z, et al. The non-stationary environmental effects on spawning habitat of fish in estuaries: a case study of Coilia mystus in the Yangtze Estuary[J]. Front Mar Sci, 2021, 8: 766616. doi: 10.3389/fmars.2021.766616
    [31]
    蔡研聪, 徐姗楠, 陈作志, 等. 南海北部近海渔业资源群落结构及其多样性现状[J]. 南方水产科学, 2018, 14(2): 10-18. doi: 10.3969/j.issn.2095-0780.2018.02.002
    [32]
    曾雷, 陈国宝, 李纯厚, 等. 大亚湾湾口游泳生物群落季节异质特征与生态效应分析[J]. 南方水产科学, 2019, 15(3): 22-32. doi: 10.12131/20180246
    [33]
    NAKAYA T, CHARLTON M, BRUNSDON C, et al. GWR4.09 user manual: windows application for geographically weighted regression modelling [EB/OL]. (2016-03-24)[2023-01-11]. https://gwrtools.github.io/author/taylor-oshan.html.
    [34]
    陈强, 朱慧敏, 何溶, 等. 基于地理加权回归模型评估土地利用对地表水质的影响[J]. 环境科学学报, 2015, 35(5): 1571-1580.
    [35]
    YU H R, GONG H L, CHEN B B, et al. Analysis of the influence of groundwater on land subsidence in Beijing based on the geographical weighted regression (GWR) model[J]. Sci Total Environ, 2020, 738: 139405. doi: 10.1016/j.scitotenv.2020.139405
    [36]
    袁玉芸, 瓦哈甫·哈力克, 关靖云, 等. 基于GWR模型的于田绿洲土壤表层盐分空间分异及其影响因子[J]. 应用生态学报, 2016, 27(10): 3273-3282. doi: 10.13287/j.1001-9332.201610.022
    [37]
    MELA C F, KOPALLE P K. The impact of collinearity on regression analysis: the asymmetric effect of negative and positive correlations[J]. Appl Econ, 2002, 34(6): 667-677. doi: 10.1080/00036840110058482
    [38]
    KALA A K, TIWARI C, MIKLER A R, et al. A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters[J]. PeerJ, 2017, 5: e3070. doi: 10.7717/peerj.3070
    [39]
    CIANNELLI L, FAUCHALD P, CHAN K S, et al. Spatial fisheries ecology: recent progress and future prospects[J]. J Mar Syst, 2008, 71(3/4): 223-236.
    [40]
    LI Y, JIAO Y, BROWDER J A. Modeling spatially-varying ecological relationships using geographically weighted generalized linear model: a simulation study based on longline seabird bycatch[J]. Fish Res, 2016, 181: 14-24. doi: 10.1016/j.fishres.2016.03.024
    [41]
    BOOTH A J. Incorporating the spatial component of fisheries data into stock assessment models[J]. ICES J Mar Sci, 2000, 57(4): 858-865. doi: 10.1006/jmsc.2000.0816
    [42]
    BABCOCK E A, PIKITCH E K, MCALLISTER M K, et al. A perspective on the use of spatialized indicators for ecosystem-based fishery management through spatial zoning[J]. ICES J Mar Sci, 2005, 62(3): 469-476. doi: 10.1016/j.icesjms.2005.01.010
    [43]
    马彩华. 南海地理学与鱼类生物多样性及渔业区划关系的研究 [D]. 青岛: 中国海洋大学, 2004: 9.
    • Related Articles

  • Cited by

    Periodical cited type(4)

    1. 赖胜，杨慧林，陈美玲，龙馨怡，刘淑丽，简敏菲. 鄱阳湖南矶湿地苔草植被根际土壤微生物群落结构分析. 江西师范大学学报(自然科学版). 2023(01): 82-90 .
    2. 冯敬宾，任春华，江晓，严岩，董俊德，胡超群. 大亚湾夏季表层浮游细菌生物量分布与环境变量的关系. 海洋湖沼通报. 2019(02): 81-89 .
    3. 关统伟，向慧平，王鹏昊，邓奥宇，董丹，赵顺先，张习超. 基于高通量测序的郫县豆瓣不同发酵期细菌群落结构及其动态演替. 食品科学. 2018(04): 106-111 .
    4. 宋宇. 草莓不同生长时期土壤微生物群落结构变化. 贵州农业科学. 2018(08): 59-62 .

    Other cited types(3)

Catalog

    Get Citation
    PDF
    XML
    Article views (467) PDF downloads (68) Cited by(7)
    Related

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return