Volume 41 Issue 3
Mar.  2022
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Jing Wang, Binduo Xu, Ying Xue, Chongliang Zhang, Mingkun Li, Yiping Ren. Performance evaluation of fixed-station sampling design for a fishery-independent survey with multiple objectives[J]. Acta Oceanologica Sinica, 2022, 41(3): 94-102. doi: 10.1007/s13131-021-1932-x
Citation: Jing Wang, Binduo Xu, Ying Xue, Chongliang Zhang, Mingkun Li, Yiping Ren. Performance evaluation of fixed-station sampling design for a fishery-independent survey with multiple objectives[J]. Acta Oceanologica Sinica, 2022, 41(3): 94-102. doi: 10.1007/s13131-021-1932-x

Performance evaluation of fixed-station sampling design for a fishery-independent survey with multiple objectives

doi: 10.1007/s13131-021-1932-x
Funds:  The Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) under contract No. 2018SDKJ0501-2; the National Key Research and Development Program of China under contract No. 2019YFD0901304.
More Information
  • Corresponding author: E-mail: bdxu@ouc.edu.cn
  • Received Date: 2021-01-25
  • Accepted Date: 2021-03-04
  • Available Online: 2021-11-10
  • Publish Date: 2022-03-01
  • Fixed-station sampling design was widely used in fishery-independent surveys because of its characteristics of convenient sampling station setting, but the non-probabilistic (fixed) nature made it more uncertainty of drawing inferences on population. The performance of fixed-station sampling design for multispecies survey has not been evaluated, and we are uncertain if the design could detect the temporal trends of different populations in multispecies fishery-independent survey. In this study, spatial distribution of abundance indices for three species with different spatial distribution patterns including small yellow croaker (Larimichthys polyactis), whitespotted conger (Conger myriaster) and Fang’s blenny (Enedrias fangi) were simulated using ordinary kriging interpolation as the “true” population distribution. The performance of fixed-station sampling design was compared with simple random sampling design by resampling the simulated “true” populations in this simulation study. The results showed that the fixed-station sampling design had the power to detect the seasonal trends of species abundance. The effectiveness of fixed-station sampling design were different in different species distribution patterns. When the species had even distribution, fixed-station sampling design could get high quality abundance data; when the distribution was uneven with heterogeneity or patchiness, fixed-station sampling design tended to underestimate or overestimate the abundance. Evidently, the estimates of abundance index based on the fixed-station sampling design must be calibrated cautiously while applying them for fisheries stock assessment and management. This study suggested that fixed-station sampling design could catch the temporal dynamics of population abundance, but the abundance estimates from the fixed-station sampling design could not be treated as the absolute estimates of populations.
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  • [1]
    Andrew N L, Mapstone B D. 1987. Sampling and the description of spatial pattern in marine ecology. Oceanography and Marine Biology, 25: 39−90.
    [2]
    Beare D J, Burns F, Greig A, et al. 2004. Long-term increases in prevalence of North Sea fishes having southern biogeographic affinities. Marine Ecology Progress Series, 284: 269–278. doi: 10.3354/meps284269
    [3]
    Bethlehem J. 2009. Estimators. In: Bethlehem J, ed. Applied Survey Methods: A Statistical Perspective. Hoboken: John Wiley & Sons, Inc., 15–42
    [4]
    Blanchard J, Maxwell D L, Jennings S. 2008. Power of monitoring surveys to detect abundance trends in depleted populations: the effects of density-dependent habitat use, patchiness, and climate change. ICES Journal of Marine Science, 65(1): 111–120. doi: 10.1093/icesjms/fsm182
    [5]
    Bonar S A, Contreras-Balderas S, Iles A C. 2009. An introduction to standardized sampling: chapter 1. In: Bonar S A, Hubert W A, Willis D W, eds. Standard Methods for Sampling North American Freshwater Fishes. Bethesda, Maryland, USA: American Fisheries Society, 1–12
    [6]
    Cao Jie, Chen Yong, Chang J H, et al. 2014. An evaluation of an inshore bottom trawl survey design for American lobster (Homarus americanus) using computer simulations. Journal of Northwest Atlantic Fishery Science, 46: 27–39. doi: 10.2960/J.v46.m696
    [7]
    Chen Y. 1996. A Monte Carlo study on impacts of the size of subsample catch on estimation of fish stock parameters. Fisheries Research, 26(3–4): 207–223
    [8]
    Dorner H, Graham N, Bianchi G, et al. 2015. From cooperative data collection to full collaboration and co-management: a synthesis of the 2014 ICES symposium on fishery-dependent information. ICES Journal of Marine Science, 72(4): 1133–1139. doi: 10.1093/icesjms/fsu222
    [9]
    Field A P. 2005. Is the meta-analysis of correlation coefficients accurate when population correlations vary?. Psychol Methods, 10(4): 444−67.
    [10]
    García S, Luengo J, Herrera F. 2000. Data Preprocessing in Data Mining. Cham: Springer, 39–57
    [11]
    Graham N, Grainger R, Karp W A, et al. 2011. An introduction to the proceedings and a synthesis of the 2010 ICES Symposium on Fishery-Dependent Information. ICES Journal of Marine Science, 68(8): 1593–1597. doi: 10.1093/icesjms/fsr136
    [12]
    Guan Lisha, Chen Yong, Wilson J A. 2017. Evaluating spatio-temporal variability in the habitat quality of Atlantic cod (Gadus morhua) in the Gulf of Maine. Fisheries Oceanography, 26(1): 83–96. doi: 10.1111/fog.12188
    [13]
    Guo Xupeng, Jin Xianshi, Dai Fangqun. 2006. Growth variations of small yellow croaker (Pseudosciaena polyactis Bleeker) in the Bohai Sea. Journal of Fishery Sciences of China, 13(2): 243–249
    [14]
    Hubbard W D, Miranda L E. 1986. Competence of non-random electrofishing sampling in assessment of structural indices. Proceedings of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies, 40: 79–84
    [15]
    Jiao Yan, Chen Yong, Schneider D, et al. 2004. A simulation study of impacts of error structure on modeling stock-recruitment data using generalized linear models. Canadian Journal of Fisheries and Aquatic Sciences, 61(1): 122–133. doi: 10.1139/f03-149
    [16]
    Kawazu M, Kameda T, Kurogi H, et al. 2015. Biological characteristics of Conger myriaster during the initial stage of spawning migration in the East China Sea. Fisheries Science, 81(4): 663–671. doi: 10.1007/s12562-015-0893-4
    [17]
    Kim J K, Kim Y H, Kim M J, et al. 2010. Genetic diversity, relationships and demographic history of the small yellow croaker, Larimichthys polyactis (Pisces: Sciaenidae) from Korea and China inferred from mitochondrial control region sequence data. Animal Cells and Systems, 14(1): 45–51. doi: 10.1080/19768351003764973
    [18]
    Kiraly I A, Coghlan Jr S M, Zydlewski J, et al. 2014. Comparison of two sampling designs for fish assemblage assessment in a large river. Transactions of the American Fisheries Society, 143(2): 508–518. doi: 10.1080/00028487.2013.864706
    [19]
    Li Bai, Cao Jie, Chang J H, et al. 2015. Evaluation of effectiveness of fixed-station sampling for monitoring American lobster settlement. North American Journal of Fisheries Management, 35(5): 942–957. doi: 10.1080/02755947.2015.1074961
    [20]
    Li Shiyan, Han Dongyan, Ma Qiuyun, et al. 2014. Feeding habits of Enedrias fangi in Jiaozhou Bay based on carbon and nitrogen stable isotope analysis. Journal of Fishery Sciences of China, 21(6): 1220–1226
    [21]
    Littell J S, McKenzie D, Kerns B K, et al. 2011. Managing uncertainty in climate-driven ecological models to inform adaptation to climate change. Ecosphere, 2(9): 1–19
    [22]
    Liu Yong, Chen Yong, Cheng Jiahua, et al. 2011. An adaptive sampling method based on optimized sampling design for fishery-independent surveys with comparisons with conventional designs. Fisheries Science, 77(4): 467–478. doi: 10.1007/s12562-011-0355-6
    [23]
    Mazzoni R, Iglesias-Rios R. 2002. Distribution pattern of two fish species in a coastal stream in southeast Brazil. Brazilian Journal of Biology, 62(1): 171–178. doi: 10.1590/S1519-69842002000100019
    [24]
    McClelland M A, Sass G G. 2012. Assessing fish collections from random and fixed site sampling methods on the Illinois River. Journal of Freshwater Ecology, 27(3): 325–333. doi: 10.1080/02705060.2012.658213
    [25]
    Nelson G A. 2014. Cluster sampling: a pervasive, yet little recognized survey design in fisheries research. Transactions of the American Fisheries Society, 143(4): 926–938. doi: 10.1080/00028487.2014.901252
    [26]
    Oliver M A, Webster R. 1990. Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information System, 4(3): 313–332. doi: 10.1080/02693799008941549
    [27]
    Paloheimo J E, Chen Y. 1996. Estimating fish mortalities and cohort sizes. Canadian Journal of Fisheries and Aquatic Sciences, 53(7): 1572–1579. doi: 10.1139/f96-077
    [28]
    Payne J L, Bush A M, Heim N A, et al. 2016. Ecological selectivity of the emerging mass extinction in the oceans. Science, 353(6305): 1284–1286. doi: 10.1126/science.aaf2416
    [29]
    Perry A L, Low P J, Ellis J R, et al. 2005. Climate change and distribution shifts in marine fishes. Science, 308(5730): 1912–1915. doi: 10.1126/science.1111322
    [30]
    Pikitch E K, Santora C, Babcock E A, et al. 2004. Ecosystem-based fisheries management. Science, 305(5682): 346–347. doi: 10.1126/science.1098222
    [31]
    Pokhrel R M, Kuwano J, Tachibana S. 2013. A kriging method of interpolation used to map liquefaction potential over alluvial ground. Engineering Geology, 152(1): 26–37. doi: 10.1016/j.enggeo.2012.10.003
    [32]
    Pooler P S, Smith D R. 2005. Optimal sampling design for estimating spatial distribution and abundance of a freshwater mussel population. Journal of the North American Benthological Society, 24(3): 525–537. doi: 10.1899/04-138.1
    [33]
    Questel J M, Clarke C, Hopcroft R R. 2013. Seasonal and interannual variation in the planktonic communities of the northeastern Chukchi Sea during the summer and early fall. Continental Shelf Research, 67: 23–41. doi: 10.1016/j.csr.2012.11.003
    [34]
    Quist M C, Gerow K G, Bower M R, et al. 2006. Random versus fixed-site sampling when monitoring relative abundance of fishes in headwater streams of the upper Colorado River basin. North American Journal of Fisheries Management, 26(4): 1011–1019. doi: 10.1577/M05-153.1
    [35]
    Schabenberger O, Gotway C A. 2005. Statistical Methods for Spatial Data Analysis: Texts in Statistical Science. Boca Raton: Chapman and Hall, 511–513
    [36]
    Simmonds E J, Döring R, Daniel P, et al. 2011. The role of fisheries data in the development evaluation and impact assessment in support of European fisheries plans. ICES Journal of Marine Science, 68(8): 1689–1698. doi: 10.1093/icesjms/fsr067
    [37]
    Skibo K M, Schwarz C J, Peterman R M. 2008. Evaluation of sampling designs for Red Sea Urchins Strongylocentrotus franciscanus in British Columbia. North American Journal of Fisheries Management, 28(1): 219–230. doi: 10.1577/M06-293.1
    [38]
    Sun Chunyang, Wang Yingbin. 2020. Impacts of the sampling design on the abundance index estimation of Portunus trituberculatus using bottom trawl. Acta Oceanologica Sinica, 39(6): 48–57. doi: 10.1007/s13131-020-1607-z
    [39]
    Tang Fenghua, Shen Xinqiang, Wang Yunlong. 2011. Dynamics of fisheries resources near Haizhou Bay waters. Fisheries Science, 30(6): 335–341
    [40]
    VanDerWal J, Murphy H T, Kutt A S, et al. 2013. Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nature Climate Change, 3(3): 239–243. doi: 10.1038/nclimate1688
    [41]
    Warren W G. 1994. The potential of sampling with partial replacement for fisheries surveys. ICES Journal of Marine Science, 51(3): 315–324. doi: 10.1006/jmsc.1994.1032
    [42]
    Xu Lili, Xue Ying, Jiao Yan, et al. 2017. Population structure and spatial distribution of Oratosquilla oratoria in Haizhou Bay and adjacent waters. Periodical of Ocean University of China, 47(4): 28–36
    [43]
    Yu Hao, Jiao Yan, Su Zhenming, et al. 2012. Performance comparison of traditional sampling designs and adaptive sampling designs for fishery-independent surveys: a simulation study. Fisheries Research, 113(1): 173–181. doi: 10.1016/j.fishres.2011.10.009
    [44]
    Zhang Chunguang. 2010. Fauna Sinica: Osteichthyes Anguilliformes Notacanthiformes. Beijing: Science Press, 199–203
    [45]
    Zhang Yiming, Neelakantan A, Park C, et al. 2019. Adaptive sampling with varying sampling cost for design space exploration. AIAA Journal, 57(3): 1032–1043. doi: 10.2514/1.J057470
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