Probability distribution characteristics of stock density in offshore of northern South China Sea.

doi:10.13287/j.1001-9332.201907.015

Abstract

Abstract: Based on catch data from the bottom trawl survey by eight cruises in offshore of northern South China Sea during 2014-2017, we analyzed the stock density distribution and explored its probability distribution with statistical method, which was further used to estimate the mean stock density in this region. The results showed that the coefficient of variation (CV) for stock density ranged from 0.67 to 1.03 for all the periods, indicating a highly uneven spatial distribution of stock density. The frequency distribution of fishery resource density was characterized by obvious right-skewed, which was dominated by stock density of 0-1000 kg·km^-2. The results of one sample Kolmogorov-Smirnov test indicated that three probability distribution patterns were suitable for stock density in this region, including Lognormal, Gamma and Weibull distributions. In terms of the mean stock density estimation, the values from Lognormal showed no statistically significant difference from those from others, but the opposite result was obtained between Gamma and Weibull distributions. Compared with 1960s-1970s, the appropriate probability distribution pattern of stock density has changed from single to multiple types. Variation of the proportion of low catch resulted from the changes in the structure of fishery resources, fishing effort and climate change might cause the alte-ration of probability distribution.

CAI Yan-cong, HUANG Zi-rong, XU You-wei, SUN Ming-shuai, XU Shan-nan, ZHANG Kui, CHEN Zuo-zhi. Probability distribution characteristics of stock density in offshore of northern South China Sea.[J]. Chinese Journal of Applied Ecology, 2019, 30(7): 2426-2436.

References

[1] Xu Z-B (徐质斌), Li X-L (李相林). Various causes of China’s fishery resource decline and their combined solution. Fishery Modernization (渔业现代化), 2008, 35(3): 47-51 (in Chinese)
[2] Xu H-D (许罕多). The growth of the production of China’s marine fishing industry under the condition of resource recession: An empirical analysis based on the 1956-2011 fisheries data. Journal of Shandong Univer-sity (Philosophy and Social Sciences) (山东大学学报: 哲学社会科学版), 2013(5): 86-93 (in Chinese)
[3] Chen Z-Z (陈作志), Qiu Y-S (邱永松). Status and sustainable utilization of fishery resources of South China Sea. Journal of Hubei Agricultural College (湖北农学院学报), 2002, 22(6): 507-510 (in Chinese)
[4] Chen Z-Z (陈作志), Qiu Y-S (邱永松). Esitimation of growth and mortality parameters of Parargyrops edita Tanaka in Beibu Bay. Journal of Fisheries of China (水产学报), 2003, 27(3): 251-257 (in Chinese)
[5] Cai Y-C (蔡研聪), Chen Z-Z (陈作志), Xu S-N (徐姗楠). Tempo-spatial distribution of Evynnis cardinalis in Beibu Gulf. South China Fisheries Science (南方水产科学), 2017, 13(4): 1-10 (in Chinese)
[6] Zhang K (张魁), Chen Z-Z (陈作志), Qiu Y-S (邱永松). Decadal changes in growth, mortality and maturity parameters of Evynnis cardinalis in Beibu Gulf. South China Fisheries Science (南方水产科学), 2016, 12(6): 9-16 (in Chinese)
[7] Hou G (侯刚), Zhu L-X (朱立新), Lu H-S (卢伙胜). Growth, mortality and population composition of Crimson Sea Bream, Paragyrops edita Tanaka in Beibu Gulf. Journal of Guangdong Ocean University (广东海洋大学学报), 2008, 28(3): 50-55 (in Chinese)
[8] Wang X-H (王雪辉), Qiu Y-S (邱永松), Du F-Y (杜飞雁). Study on the growth, mortality and optimum catchable size of Nemipterus virgatus in the northern South China Sea. Journal of Ocean University of China (中国海洋大学学报), 2004, 34(2): 224-230 (in Chinese)
[9] Wang X-H (王雪辉), Qiu Y-S (邱永松), Du F-Y (杜飞雁). Fisheries biology of Nemipterus bathybius and its optimum catchable size in northern South China Sea. Chinese Journal of Applied Ecology (应用生态学报), 2005, 16(12): 2428-2434 (in Chinese)
[10] Chen Z-Z (陈作志), Kong X-L (孔啸兰), Xu S-N (徐姗楠), et al. Dynamic changes of population para-meters of Nemipterus bathybius in the Beibu Gulf. Journal of Fisheries of China (水产学报), 2012, 36(4): 584-591 (in Chinese)
[11] Wang Y-Z (王跃中), Sun D-R (孙典荣), Chen Z-Z (陈作志), et al. The influence of climatic environmental factors and fishing pressure on changes of hairtail catches in the northern South China Sea. Acta Ecologica Sinica (生态学报), 2012, 32(24): 7948-7957 (in Chinese)
[12] Zhu J-F (朱江峰), Qiu Y-S (邱永松). Growth and mortality of hairtails and their dynamic pool models in the northern South China Sea. Acta Oceanologica Sinica (海洋学报), 2005, 27(6): 93-99 (in Chinese)
[13] Sun D-R (孙典荣), Qiu Y-S (邱永松). Esitimation of growth and mortality parameter of Priacanthus macracanthus in Beibu Bay. Journal of Fujian Fisheries (福建水产), 2003(1): 7-12 (in Chinese)
[14] Zhang K (张魁), Chen Z-Z (陈作志), Wang Y-Z (王跃中), et al. Population structure of Priacanthus macracanthus in the Beibu Gulf, and parameters for its growth, mortality and maturity. Journal of Tropical Oceanography (热带海洋学报), 2016, 35(5): 20-28 (in Chinese)
[15] Jia Z-Y (贾振宇), Zhang J-H (张俊华), Ding S-Y (丁圣彦), et al. Spatial variation of soil phosphorus in flooded area of the Yellow River based on GIS and geo-statistical methods: A case study in Zhoukou City, Henan, China. Chinese Journal of Applied Ecology (应用生态学报), 2016, 27(4): 1211-1220 (in Chinese)
[16] Xia P-L (夏鹏亮), Wang R (王瑞), Tan J (谭军). Geostatistical analysis on distribution pattern of the tobacco budworm larva in Enshi, Hubei, China. Chinese Journal of Applied Ecology (应用生态学报), 2014, 25(3): 825-830 (in Chinese)
[17] Tong W-J (童武君), Zhang S-Y (章守宇). Preliminary study on distribution of fishery resources in LvHua-SanHeng sea area based on Kriging. Journal of Biology (生物学杂志), 2011, 28(6): 62-67 (in Chinese)
[18] Yang M-X (杨铭霞), Chen X-J (陈新军), Feng Y-J (冯永玖), et al. Analysis of fishing ground for Ommastrephes bartramii in the Northwest Pacific based on different statistical interpolation methods. South China Fisheries Science (南方水产科学), 2015, 11(2): 79-84 (in Chinese)
[19] Aidoo EN, Mueller U, Goovaerts P, et al. Evaluation of geostatistical estimators and their applicability to characterise the spatial patterns of recreational fishing catch rates. Fisheries Research, 2015, 168: 20-32
[20] Mueller U, Kangas M, Sporer E, et al. Variability in the spatial and temporal distribution of the saucer scallop, Amusium balloti, in Shark Bay-management implications. Marine and Freshwater Research, 2012, 63: 1152-1164
[21] Monestiez P, Dubroca L, Bonnin E, et al. Geostatistical modelling of spatial distribution of Balaenoptera physalus in the Northwestern Mediterranean Sea from sparse count data and heterogeneous observation efforts. Ecological Modelling, 2006, 193: 615-628
[22] Chen K-Y (陈科屹), Zhang H-R (张会儒), Lei X-D (雷相东). Geostatistical analysis on the spatial pattern of Quercus mongolica population in different communities. Chinese Journal of Applied Ecology (应用生态学报), 2018, 29(5): 1542-1550 (in Chinese)
[23] Guan W-J (官文江), Tian S-Q (田思泉), Zhu J-F (朱江峰), et al. A review of fisheries stock assessment models. Journal of Fishery Sciences of China (中国水产科学), 2013, 20(5): 1112-1120 (in Chinese)
[24] Webley JAC, Mayer DG, Taylor SM. Investigating confidence intervals generated by zero-inflated count models: Implications for fisheries management. Fisheries Research, 2011, 110: 177-182
[25] Li J-Q (李九奇), Ye C-C (叶昌臣), Wang W-B (王文波), et al. A stock assessment of small yellow croaker by Bayes-based Pella-Tomlinson model in the East China Sea. Journal of Shanghai Ocean University (上海海洋大学学报), 2011, 20(6): 873-882 (in Chinese)
[26] Taylor CC. Nature of Variability in Trawl Catches. Fishery Bulletin, 1953, 54: 145-166
[27] Stefánsson G. Analysis of groundfish survey abundance data: Combining the GLM and delta approaches. ICES Journal of Marine Science, 1996, 53: 577-588
[28] Smith SJ. A comparison of estimators of location for skewed populations, with application to groundfish trawl surveys// Doubleday WG, Rivard D, eds. Bottom Trawl Surveys. Ottawa: Canadian Special Publication of Fisheries and Aquatic Science, 1981: 154-163
[29] Finney DJ. On the distribution of a variate whose loga-rithm is normally distributed. Journal of the Royal Statistical Society, 1941, 7: 155-161
[30] McConnaughey RA, Conquest LL. Trawl survey estimation using a comparative approach based on lognormal theory. Fishery Bulletin, 1993, 91: 107-118
[31] Pennington M. Efficient estimators of abundance, for fish and plankton surveys. Biometrics, 1983, 39: 281-286
[32] Pennington M. Estimating the mean and variance from highly skewed marine data. Fishery Bulletin, 1996, 94: 498-505
[33] Smith SJ. Evaluating the efficiency of the Δ-distribution mean estimator. Biometrics, 1988, 44: 485-493
[34] Li F (李凡), Li X-S (李显森), Zhao X-Y (赵宪勇). Bottom trawl survey data analysis based on Delta-distribution model and its application in the estimation of small yellow croak and silver pomfret in Yellow Sea. Journal of Fisheries of China (水产学报), 2008, 32(1): 145-151 (in Chinese)
[35] Yuan X-W (袁兴伟), Jiang Y-Z (姜亚洲), Cheng J-H (程家骅). Estimating the average stock density with dominating large catches based on Δ-distribution model. Progress in Fishery Sciences (渔业科学进展), 2011, 32(1): 1-7 (in Chinese)
[36] Yuan X-W (袁兴伟), Liu Z-L (刘尊雷), Yang S-L (杨胜龙), et al. Comparative study on estimating stock density of Larimichthys polyactis in the East China Sea based on lognormal theory. Marine Fisheries (海洋渔业), 2012, 34(1): 15-22 (in Chinese)
[37] Chen Z-X (陈正兴), Li H-Q (李辉权). The pattern of probability distribution on the density of demersal fishes stock in the northern continental shelf of the South China Sea. Journal of Fisheries of China (水产学报), 1987, 11(2): 159-166 (in Chinese)
[38] Bell JD, Craik GJS, Pollard DA, et al. Estimating length frequency distributions of large reef fish underwater. Coral Reefs, 1985, 4: 41-44
[39] Chen D-L (陈敦隆), Meng T-X (孟田湘), Zhou B-B (周彬彬). Studies on the spatial distribution pattern of an important food organism-Pacific euphausiid, Euphausia pacifica Hansen, in the Yellow Sea by means of spatial probability distribution pattern of biological population. Transactions of Oceanology and Limnology (海洋湖沼通报), 1981(1): 1-10 (in Chinese)
[40] Cai Y-C (蔡研聪), Xu S-N (徐姗楠), Chen Z-Z (陈作志), et al. Current status of community structure and diversity of fishery resources in offshore northern South China Sea. South China Fisheries Science (南方水产科学), 2018, 14(2): 10-18 (in Chinese)
[41] Bureau of Fisheries and Fishery Administration of Ministry of Agriculture (农业部渔业渔政管理局). Statistics of China Fisheries (1950-2015). Beijing: China Agriculture Press, 2016 (in Chinese)
[42] Zheng T (郑彤), Tang Y (唐议). Analysis of current status of Chinese marine fishing fleet of South China Sea area. Journal of Shanghai Ocean University (上海海洋大学学报), 2016, 25(4): 620-627 (in Chinese)
[43] Han Y (韩杨), Zhang Y-Z (张溢卓), Sun H-W (孙慧武). Development trends of marine capture fishe-ries in South China Sea. Agricultural Outlook (农业展望), 2015, 11(11): 51-55 (in Chinese)
[44] Alabia ID, García Molinos J, Saitoh S-I, et al. Distribution shifts of marine taxa in the Pacific Arctic under contemporary climate changes. Diversity and Distributions, 2018, 24: 1583-1597
[45] Champion C, Hobday AJ, Tracey SR, et al. Rapid shifts in distribution and high-latitude persistence of ocea-nographic habitat revealed using citizen science data from a climate change hotspot. Global Change Biology, 2018, 24: 5440-5453
[46] Sumaila UR, Cheung WWL, Lam VWY, et al. Climate change impacts on the biophysics and economics of world fisheries. Nature Climate Change, 2011, 1: 449-456
[47] Cheng Z-M (程泽梅), Tang C-L (汤超莲), Cai B (蔡兵), et al. Influencing factors of SST variation along the South China Coast during 1960-2013. Tropical Geography (热带地理), 2016, 36(6): 906-914 (in Chinese)
[48] Monllor-Hurtado A, Pennino MG, Sanchez-Lizaso JL. Shift in tuna catches due to ocean warming. PLoS One, 2017, 12(6): e0178196
[49] Cheung WWL, Oyinlola MA. Vulnerability of flatfish and their fisheries to climate change. Journal of Sea Research, 2018, 140: 1-10