Ocean and Polar Research

  • 제39권1호
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  • Pages.13-21
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  • 2017
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  • 1598-141X(pISSN)
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  • 2234-7313(eISSN)
한국해양과학기술원 (Korea Institute of Ocean Science & Technology)
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Optimal Monitoring Frequency Estimation Using Confidence Intervals for the Temporal Model of a Zooplankton Species Number Based on Operational Taxonomic Units at the Tongyoung Marine Science Station

  • Cho, Hong-Yeon (Ocean Data Science Section, Large Facility Operations and Support Department, KIOST) ;
  • Kim, Sung (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST) ;
  • Lee, Youn-Ho (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST) ;
  • Jung, Gila (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST) ;
  • Kim, Choong-Gon (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST) ;
  • Jeong, Dageum (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST) ;
  • Lee, Yucheol (Biological Sciences, College of Natural Sciences, Inha University) ;
  • Kang, Mee-Hye (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST) ;
  • Kim, Hana (Department of Taxonomy and Systematics, National Marine Biodiversity Institute of Korea) ;
  • Choi, Hae-Young (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST) ;
  • Oh, Jina (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST) ;
  • Myong, Jung-Goo (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST) ;
  • Choi, Hee-Jung (Marine Ecosystem and Biological Research Center, Marine Life and Ecosystem Division, KIOST)
  • 투고 : 2016.12.19
  • 심사 : 2017.03.16
  • 발행 : 2017.03.30
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초록

Temporal changes in the number of zooplankton species are important information for understanding basic characteristics and species diversity in marine ecosystems. The aim of the present study was to estimate the optimal monitoring frequency (OMF) to guarantee and predict the minimum number of species occurrences for studies concerning marine ecosystems. The OMF is estimated using the temporal number of zooplankton species through bi-weekly monitoring of zooplankton species data according to operational taxonomic units in the Tongyoung coastal sea. The optimal model comprises two terms, a constant (optimal mean) and a cosine function with a one-year period. The confidence interval (CI) range of the model with monitoring frequency was estimated using a bootstrap method. The CI range was used as a reference to estimate the optimal monitoring frequency. In general, the minimum monitoring frequency (numbers per year) directly depends on the target (acceptable) estimation error. When the acceptable error (range of the CI) increases, the monitoring frequency decreases because the large acceptable error signals a rough estimation. If the acceptable error (unit: number value) of the number of the zooplankton species is set to 3, the minimum monitoring frequency (times per year) is 24. The residual distribution of the model followed a normal distribution. This model can be applied for the estimation of the minimal monitoring frequency that satisfies the target error bounds, as this model provides an estimation of the error of the zooplankton species numbers with monitoring frequencies.

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참고문헌

  1. Akaike H (1974) A new look at the statistical model identification. IEEE T Automat Contr AC-19(6):716-723
  2. Allan JD (1976) Life history patterns in zooplankton. Am Nat 110(971):165-180 https://doi.org/10.1086/283056
  3. Anttila S, Ketola M, Vakkilainen K, Kairesalo T (2012) Assessing temporal representativeness of water quality monitoring data. J Environ Monit 14:589-595 https://doi.org/10.1039/C2EM10768F
  4. Blaxter M, Mann J, Chapman T, Thomas F, Whitton C, Floyd R, Abebe E (2005) Defining operational taxonomic units using DNA barcode data. Philos Transactions R Soc B: Biol Sci 360(1462):1935-1943 https://doi.org/10.1098/rstb.2005.1725
  5. Bucklin A, Nishida S, Schnack-Schiel S, Wiebe PH, Lindsay D, Machida RJ, Copley NJ (2010) A census of zooplankton of the global ocean. In: McIntyre A (ed) Life in the world's oceans: diversity, distribution, and abundance. Wiley-Blackwell Pub, Chichester, pp 247-265
  6. Carew ME, Pettigrove VJ, Metzeling L, Hoffmann AA (2013) Environmental monitoring using next generation sequencing: rapid identification of macroinvertebrate bioindicator species. Front Zool 10(1):45 https://doi.org/10.1186/1742-9994-10-45
  7. Cho H, Jeong WM, Jun KC (2013) Relationship analysis on the monitoring period and parameter estimation error of the coastal wave climate data. J Kor Soc Coast Ocean Eng 25(1):1-6 https://doi.org/10.9765/KSCOE.2013.25.1.1
  8. Conover RJ, Huntley M (1991) Copepods in ice-covered seas-distribution, adaptations to seasonally limited food, metabolism, growth patterns and life cycle strategies in polar seas. J Mar Syst 2:1-41 https://doi.org/10.1016/0924-7963(91)90011-I
  9. Dixon W, Chiswell B (1996) Review of aquatic monitoring program design. Water Res 30(9):1935-1948 https://doi.org/10.1016/0043-1354(96)00087-5
  10. Dowd M, Martin JL, Legresley MM, Hanke A, Page FH (2004) A statistical method for the robust detection of interannual changes in plankton abundance: analysis of monitoring data from the Bay of Fundy, Canada. J Plankton Res 26(5):509-523 https://doi.org/10.1093/plankt/fbh051
  11. Efron B (1979) Bootstrap methods: another look at the Jackknife. Ann Stat 7(1):1-26 https://doi.org/10.1214/aos/1176344552
  12. Hwang MO, Shin K, Baek SH, Lee W-J, Kim S, Jang M-C (2011) Annual variations in community structure of mesozooplankton by short-term sampling in Jangmok Harbor of Jinhae Bay. Ocean and Polar Res 33:235-253 https://doi.org/10.4217/OPR.2011.33.3.235
  13. Irigoien X, Huisman J, Harris RP (2004) Global biodiversity patterns of marine phytoplankton and zooplankton. Nature 429:863-867 https://doi.org/10.1038/nature02593
  14. Johnson JB, Omland KS (2004) Model selection in ecology and evolution. Trends Ecol Evol 19(2):101-108 https://doi.org/10.1016/j.tree.2003.10.013
  15. Juergen G, Uwe L (2015) Nortest: tests for normality. R package version 1.0.3. http://CRAN.R-project.org/package=nortest Accessed 3 Sep 2016
  16. KIOST (2016) Development of in-situ diagnostic techniques on the dynamics of marine pelagic ecosystem structure. Korea Institute of Ocean Science and Technology, BSPE 99311-10858-3, 185 p
  17. Lee CR, Park C, Yang SR, Sin YS (2006) Spatio-temporal variation of mesozooplankton in Asan Bay. The Sea 11:1-10
  18. Lee JK, Park C, Lee DB, Lee SW (2012) Variations in plankton assemblage in a semi-closed Chunsu Bay, Korea. The Sea, J Kor Soc Oceanogr 17:95-111
  19. Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, Boehm JT, Machida RJ (2013) A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool 10(1):34 https://doi.org/10.1186/1742-9994-10-34
  20. Li W, Fu L, Niu B, Wu S, Wooley J (2012) Ultrafast clustering algorithms for metagenomic sequence analysis. Brief Bioinform 13(6):656-668 https://doi.org/10.1093/bib/bbs035
  21. Link JS, Brodziak JKT, Edwards SF, Overholtz WJ, Mountain D, Jossi JW, Smith TD, Fogarty M (2002) Marine ecosystem assessment in a fisheries management context. Can J Fish Aquat Sci 59:1429-1440 https://doi.org/10.1139/f02-115
  22. Lo WT, Chung CL, Shih CT (2004) Seasonal distribution of copepods in Tapong Bay, southwestern Taiwan. Zool Stud 43:464-474
  23. Machida RJ, Hashiguchi Y, Nishida M, Nishida S (2009) Zooplankton diversity analysis through single-gene sequencing of a community sample. BMC Genomics 10(1):438 https://doi.org/10.1186/1471-2164-10-438
  24. Martinez WL, Martinez AR (2005) Exploratory data analysis with MATLAB. Chapman Hall/CRC, Boca Raton, 405 p
  25. Millard SP, Lettenmaier DP (1986) Optimal design of biological sampling programs using the analysis of variance. Estuar Coast Shelf Sci 22(5):637-656 https://doi.org/10.1016/0272-7714(86)90018-1
  26. Moon SY, Oh H-J, Soh HY (2010) Seasonal variation of zooplankton communities in the southern coastal waters of Korea. Ocean and Polar Res 32:411-426 https://doi.org/10.4217/OPR.2010.32.4.411
  27. Nishida S, Nishikawa J (2011) Biodiversity of marine zooplankton in Southeast Asia (Project-3: plankton group). In: Nishida S, Fortes MD, Miyazaki N (eds) Coastal marine science in Southeast Asia-synthesis report of the Core University Program of the Japan Society for the Promotion of Science, 2001-2010 Coastal Marine Science. Terrapub, Tokyo, pp 59-71
  28. Nogueira MG (2001) Zooplankton composition, dominance and abundance as indicators of environmental compartmentalization in Jurumirim Reservoir (Paranapanema River), Sao Paulo, Brazil. Hydrobiologia 455:1-18 https://doi.org/10.1023/A:1011946708757
  29. Rubinstein RY, Kroese DP (2008) Simulation and the Monte Carlo method. Wiley, Hoboken, 372 p
  30. Tittensor DP, Mora C, Jetz W, Lotze HK, Ricard D, Berghe EV, Worm B (2010) Global patterns and predictors of marine biodiversity across taxa. Nature 466:1098-1101 https://doi.org/10.1038/nature09329
  31. U.S. Environmental Protection Agency (2010). Sampling and consideration of variability (temporal and spatial) for monitoring of recreational waters, EPA-823-R-10-005. https://www.epa.gov/sites/production/files/2015-11/documents/sampling-consideration-recreational-waters.pdf Accessed 6 Jun 2016
  32. Wand MP, Jones MC (1995). Kernel smoothing. Chapman & Hall/CRC, Boca Raton, 224 p
  33. Zhang Z, Schwartz S, Wagner L, Miller W (2000) A greedy algorithm for aligning DNA sequences. J Comput Biol 7(1/2):203-214 https://doi.org/10.1089/10665270050081478