DOI QR코드

DOI QR Code

Study of Biomass Estimation Methods for the Freshwater Cladoceran Species, Simocephalus serrulatus (Koch, 1841)

담수산 지각류 Simocephalus serrulatus (Koch, 1841) 생체량 산정 방법 연구

  • Hye-Ji Oh (Department of Environmental Science and Engineering, Kyung Hee University) ;
  • Geun-Hyeok Hong (Department of Environmental Science and Engineering, Kyung Hee University) ;
  • Yerim Choi (Department of Environmental Science and Engineering, Kyung Hee University) ;
  • Kwang-Hyeon Chang (Department of Environmental Science and Engineering, Kyung Hee University)
  • 오혜지 (경희대학교 환경학및환경공학과) ;
  • 홍근혁 (경희대학교 환경학및환경공학과) ;
  • 최예림 (경희대학교 환경학및환경공학과) ;
  • 장광현 (경희대학교 환경학및환경공학과)
  • Received : 2023.06.20
  • Accepted : 2023.06.28
  • Published : 2023.06.30

Abstract

The medium-large cladoceran species Simocephalus spp. predominantly occur in habitats with developed aquatic vegetation. Accordingly, due to Simocephalus' high contribution to zooplankton community biomass in the lake's littoral zone and wetland habitats, estimating their biomass is important to understand the matter cycling based on biological interactions within the aquatic food web. In this study, we reviewed the length-weight regression equations used previously to estimate Simocephalus biomass, directly measured S. serrulatus' body specification (length, width and area) and their biomass(dry weight) using instruments such as a microscopic digital camera and a microscale, and performed regression analysis between each other. When S. serrulatus biomass was estimated using the equation (Kawabata and Urabe, 1998) presented in 『Biomonitoring Survey and Assessment Manual』, Korea, errors between estimates and measures were relatively large compared to the S. serrulatus species-specific biomass estimate equation developed by Lemke and Benke (2003). In addition, both equations showed not only increasing trends in error (estimate-measure) with increasing S. serrulatus' body length, but also in error variance among similar-sized individuals. The results of regression analysis with dry weight by body specifications indicated that the most appropriate equation for estimating the biomass of S. serrulatus was derived from the width-dry weight exponential regression equation (R2=0.9555). The review and development study of such species-specific biomass estimation equations for zooplankton can be used as a tool to understand their role and function in aquatic ecosystem food webs.

Simocephalus spp.는 수생식물이 발달한 호소의 수변부 및 습지 서식처에서 우점 출현하는 중대형 지각류로, 해당 서식처의 동물플랑크톤 군집 생체량(biomass)에 기여하는 정도가 높아 먹이망 내 생물학적 상호작용을 기반으로 한 물질 순환을 이해하기 위해서는 Simocephalus종들의 생체량 산정이 중요하다. 본 연구에서는 S. serrulatus의 생체량 추정에 사용되어지고 있던 기존의 선행 산정식을 검토하고, 현미경 디지털 카메라와 미세 저울 같은 장비를 사용하여 개체의 다양한 체측값(체장, 너비 및 면적)과 생체량(건중량)을 직접 측정하여 상호 간 회귀 분석을 실시하였다. 국내 『생물측정망 조사 및 평가지침 - 보구간편』에서 제시하고 있는 Simocephalus spp.의 생체량 산정식(Kawabata and Urabe, 1998)을 사용하여 S. serrulatus 생체량을 추정했을 때, Lemke and Benke (2003)에 의해 개발된 S. serrulatus종 특이적 생체량 산정식 대비 추정치-실측치 간 오차가 상대적으로 크게 나타났으며, 두 산정식에서 모두 개체 체장 증가에 따른 오차 증가 및 유사 체장 개체 간 오차 편차 증가 경향이 보여졌다. 체측값별로 건중량과 회귀 분석을 실시한 결과, S. serrulatus 생체량 추정에 가장 적합한 산정식은 너비-건중량 지수 회귀식(R2=0.9555)으로 도출되었다. 이 같은 종 특이적 생체량 산정식의 검토 및 개발 연구는 수생태계 먹이망 내 동물플랑크톤 역할 및 기능 파악하는 데 도구(tool)로써 활용될 수 있다.

Keywords

Acknowledgement

이 논문은 2023년 정부(교육부)의 재원으로 한국연구재단 기초연구사업의 지원을 받아 수행된 연구임(No. 2021R1A6A3A13039786).

References

  1. Alcaraz, M., E. Saiz, A. Calbet, I. Trepat and E. Broglio. 2003. Estimating zooplankton biomass through image analysis. Marine Biology 143: 307-315. https://doi.org/10.1007/s00227-003-1094-8
  2. Beaver, J.R., C.E. Tausz, K.M. Black and B.A. Bolam. 2020. Cladoceran body size distributions along temperature and trophic gradients in the conterminous USA. Journal of Plankton Research 42: 613-629. https://doi.org/10.1093/plankt/fbaa053
  3. Brucet, S., D. Boix, X.D. Quintana, E. Jensen, L.W. Nathansen, C. Trochine, M. Meerhoff, S. Gascon and E. Jeppesena. 2020. Factors influencing zooplankton size structure at contrasting temperatures in coastal shallow lakes: Implications for effects of climate change. Limnology and Oceanography 55: 1697-1711. https://doi.org/10.4319/lo.2010.55.4.1697
  4. Burgess, S., E.W. Jackson, L. Schwarzman, N. Gezon and J.T. Lehman. 2015. Improved estimates of calanoid copepod biomass in the St. Lawrence Great Lakes. Journal of Great Lakes Research 41: 484-491. https://doi.org/10.1016/j.jglr.2015.02.007
  5. Burks, R.L., E. Jeppesen and D.M. Lodge. 2001. Littoral zone structures as Daphnia refugia against fish predators. Limnology and Oceanography 46: 230-237. https://doi.org/10.4319/lo.2001.46.2.0230
  6. Chang, K.H., D.I. Seo, S.M. Go, M. Sakamoto, G.S. Nam, J.Y. Choi, M.S. Kim, K.S. Jeong, G.H. La and H.Y. Kim. 2016. Feeding behavior of crustaceans (Cladocera, Copepoda and Ostracoda): food selection measured by stable isotope analysis using R package SIAR in mesocosm experiment. Korean Journal of Ecology and Environment 49: 279-288. https://doi.org/10.11614/KSL.2016.49.4.279
  7. Choi, J.Y., S.K. Kim, G.H. La, K.S. Jeong, H.W. Kim, T.K. Kim and G.J. Joo. 2012. Microcrustacean community dynamics in Upo Wetlands: impact of rainfall and physico-chemical factor on microcrustacean community. Korean Journal of Limnology 45: 340-346.
  8. Choi, J.Y., G.H. La, S.K. Kim, K.S. Jeong and G.J. Joo. 2013. Zooplankton community distribution in aquatic plant zone: influence of epiphytic rotifers and cladcoerans in accordance with aquatic plants cover and types. Korean Journal of Ecology and Environment 46: 86-93. https://doi.org/10.11614/KSL.2013.46.1.086
  9. Cremona, F., K. Blank and J. Haberman. 2021. Effects of environmental stressors and their interactions on zooplankton biomass and abundance in a large eutrophic lake. Hydrobiologia 848: 4401-4418. https://doi.org/10.1007/s10750-021-04653-3
  10. Dumont, H.J., I. Van de Velde and S. Dumont. 1975. The dry weight estimate of biomass in a selection of Cladocera, Copepoda and Rotifera from the plankton, periphyton and benthos of continental waters. Oecologia 19: 75-97. https://doi.org/10.1007/BF00377592
  11. Faerovig, P.J., T. Andersen and D.O. Hessen. 2002. Image analysis of Daphnia populations: Non-destructive determination of demography and biomass in cultures. Freshwater Biology 47: 1956-1962. https://doi.org/10.1046/j.1365-2427.2002.00946.x
  12. Kane, D.D., S.I. Gordon, M. Munawar, M.N. Charlton and D.A. Culver. 2009. The planktonic index of biotic integrity (P-IBI): An approach for assessing lake ecosystem health. Ecological Indicator 9: 1234-1247. https://doi.org/10.1016/j.ecolind.2009.03.014
  13. Kawabata, K. and J. Urabe. 1998. Length-weight relationships of eight freshwater planktonic crustacean species in Japan. Freshwater Biology 39: 199-205. https://doi.org/10.1046/j.1365-2427.1998.00267.x
  14. Kim, S.K. and J.Y. Choi. 2022. Selective consumption of pelagic cladocerans by bluegill sunfish (Lepomis macrochirus Rafinesque) contributes to dominance of epiphytic cladocerans. Water 14: 3781.
  15. Ku, D., Y.J. Chae, Y. Choi, C.W. Ji, Y.S. Park, I.S. Kwak, Y.J. Kim, K.H. Chang and H.J. Oh. 2022. Optimal method for biomass estimation in a cladoceran Species, Daphnia magna (Straus, 1820): Evaluating length-weight regression equations and deriving estimation equations using body length, width and lateral area. Sustainability 14(15): 9216.
  16. Lemke, A.M. and A.C. Benke. 2003. Growth and reproduction of three cladoceran species from a small wetland in the south-eastern USA. Freshwater Biology 48(4): 589-603. https://doi.org/10.1046/j.1365-2427.2003.01034.x
  17. Lombardo, A., A. Franco, A. Pivato and A. Barauss. 2015. Food web modeling of a river ecosystem for risk assessment of down-the-drain chemicals: A case study with AQUATOX. Science of The Total Environment 508: 214-227. https://doi.org/10.1016/j.scitotenv.2014.11.038
  18. Long, S.X., P.B. Hamilton, Y. Yang, S. Wang, C. Chen and R. Tao. 2018. Differential bioaccumulation of mercury by zooplankton taxa in a mercury-contaminated reservoir Guizhou China. Environmental Pollution 239: 147-160. https://doi.org/10.1016/j.envpol.2018.04.008
  19. Maia-Barbosa, P.M. and R.L. Bozelli. 2005. Length-weight relationships for five cladoceran species in an Amazonian Lake. Brazilian Archives of Biology and Technology 48: 303-308. https://doi.org/10.1590/S1516-89132005000200018
  20. Mitchell, B.D. and W.D. Williams. 1982. Population dynamics and production of Daphnia carinata (King) and Simocephalus exspinosus(Koch) in waste stabilization ponds. Australian Journal of Marine and Freshwater Research 33: 837-64. https://doi.org/10.1071/MF9820837
  21. NIBR, National Institute of Biological Resources. 2019. National species list of Korea. II. Vertebrates, Invertebrates, Protozoans. National Institute of Biological Resources, Yangpyeong, Korea: Designzip. 908pp.
  22. NIER, National Institute of Environmental Research. 2016. Cladocera : a practical guide to common freshwater zooplankton. Han-River Environment Research Center, Yangpyeong, Korea: 42-48.
  23. NIER, National Institute of Environmental Research. 2017. Biomonitoring Survey and Assessment Manual. National Institute of Environmental Research. National Institute of Environmental Research, Incheon, Korea.
  24. Oh, H.J., Y. Choi, H. Kim, G.H. Hong, Y.S. Park, Y.J. Kim and K.H. Chang. 2022. Validation of suitable zooplankton enumeration method for species diversity study using rarefaction curve and extrapolation. Korean Journal of Limnology 55(4): 274-284. https://doi.org/10.11614/KSL.2022.55.4.274
  25. Park, R.A., J.S. Clough and M.C. Wellman. 2008. AQUATOX: Modeling environmental fate and ecological effects in aquatic ecosystems. Ecological Modelling 213: 1-15. https://doi.org/10.1016/j.ecolmodel.2008.01.015
  26. Stamou, G., M. Katsiapi, M. Moustaka-Gouni and E. Michaloudi, E. 2019. Grazing potential - A functional plankton food web metric for ecological water quality assessment in Mediterranean lakes. Water 11: 1274.
  27. Stamou, G., A.D. Mazaris, M. Moustaka-Gouni, M. Spoljar, I. Ternjej, T. Drazina, Z. Dorak and E. Michaloudi. 2022. Introducing a zooplanktonic index for assessing water quality of natural lakes in the Mediterranean region. Ecological Informatics 69: 101616.
  28. USEPA, US Environmental Protection Agency. 2003. Standard Operating Procedure for Zooplankton Analysis-LG403. United States Environmental Protection Agency, Chicago, USA.
  29. USEPA, US Environmental Protection Agency. 2014. Modeling environmental fate and ecological effects in aquatic ecosystems; Volume 1, User's manual, EPA-820-R-14-005. United States Environmental Protection Agency Office of water, Washington D.C., USA.
  30. Vakkilainen, K., T. Kairesalo, J. Hietala, D.M. Balayla, E. Becares, W.J. Van de Bund, E. Van Donk, M. Fernandez-Alaez, M. Gyllstrom, L.-A. Hansson, M.R. Miracle, B. Moss, S. Romo, J. Rueda and D. Stephen. 2004. Response of zooplankton to nutrient enrichment and fish in shallow lakes: A pan-European mesocosm experiment. Freshwater Biology 49: 1619-1632. https://doi.org/10.1111/j.1365-2427.2004.01300.x
  31. Yao, N., B. Feng, M. Zhang, L. He, H. Zhang and Z. Liu. 2021. Impact of industrial production, dam construction, and agriculture on the Z-IBI in river ecosystems: A case study of the wanan river basin in China. Water 13(2): 123.
  32. Zhan, H., Z. Duan, Z. Wang, M. Zhong, W. Tian, H. Wang and H. Huang. 2019. Freshwater Lake ecosystem health assessment and its response to pollution stresses based on planktonic index of biotic integrity. Environmental Science Pollution Research 26: 35240-35252. https://doi.org/10.1007/s11356-019-06655-0
  33. Zhang, L., J. Cui, T. Song and Y. Liu. 2018. Application of an AQUATOX model for direct toxic effects and indirect ecological effects assessment of Polycyclic aromatic hydrocarbons(PAHs) in a plateau eutrophication lake, China. Ecological Modelling 388: 31-44. https://doi.org/10.1016/j.ecolmodel.2018.09.019