IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i15p9216-d873331.html
   My bibliography  Save this article

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

Author

Listed:
  • Doyeong Ku

    (Department of Environmental Science and Engineering, Kyung Hee University, Yongin 17104, Korea)

  • Yeon-Ji Chae

    (Department of Environmental Science and Engineering, Kyung Hee University, Yongin 17104, Korea)

  • Yerim Choi

    (Department of Environmental Science and Engineering, Kyung Hee University, Yongin 17104, Korea)

  • Chang Woo Ji

    (Fisheries Science Institute, Chonnam National University, Yeosu 59626, Korea)

  • Young-Seuk Park

    (Department of Biology, Kyung Hee University, Seoul 02447, Korea)

  • Ihn-Sil Kwak

    (Department of Ocean Integrated Science, Chonnam National University, Yeosu 59626, Korea)

  • Yong-Jae Kim

    (Department of Life Science, Daejin University, Pocheon 11159, Korea)

  • Kwang-Hyeon Chang

    (Department of Environmental Science and Engineering, Kyung Hee University, Yongin 17104, Korea)

  • Hye-Ji Oh

    (Department of Environmental Science and Engineering, Kyung Hee University, Yongin 17104, Korea)

Abstract

Assessing the biomass of zooplankton compensates for the difference between number of individuals and the accumulated body weight of the community, which helps assess aquatic ecosystem food web functions. Daphnia are crustaceans that play an intermediate role in biological interactions within food webs. The morphology and body specification of Daphnia differ during growth; hence, it is essential to apply species-specific equations to estimate biomass. We evaluated the length–weight regression equations used previously to estimate Daphnia magna biomass and conducted regression analyses using various body specifications and biomass measurements taken directly using devices such as a microbalance and microscopic camera. Biomass estimated using an equation from the Environmental Protection Agency was significantly different from the direct measurement: average biomass was lower, indicating that the equation possibly underestimated actual biomass. The biomass of D. magna had a higher multiple R 2 value when length was compared with width and area, and a linear regression equation was the most suitable equation for biomass estimation. Because body specifications and biomass are affected by various environmental factors, the development of accurate species-specific biomass estimation equations will contribute to obtaining fundamental data with which the biological responses of zooplankton to aquatic ecosystem changes can be assessed.

Suggested Citation

  • Doyeong Ku & Yeon-Ji Chae & Yerim Choi & Chang Woo Ji & Young-Seuk Park & Ihn-Sil Kwak & Yong-Jae Kim & Kwang-Hyeon Chang & Hye-Ji 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 an," Sustainability, MDPI, vol. 14(15), pages 1-10, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9216-:d:873331
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/15/9216/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/15/9216/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xabier Irigoien & Jef Huisman & Roger P. Harris, 2004. "Global biodiversity patterns of marine phytoplankton and zooplankton," Nature, Nature, vol. 429(6994), pages 863-867, June.
    2. Park, Richard A. & Clough, Jonathan S. & Wellman, Marjorie Coombs, 2008. "AQUATOX: Modeling environmental fate and ecological effects in aquatic ecosystems," Ecological Modelling, Elsevier, vol. 213(1), pages 1-15.
    3. Zhang, Lulu & Cui, Jiansheng & Song, Tiance & Liu, Yong, 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, Elsevier, vol. 388(C), pages 31-44.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Grechi, Laura & Franco, Antonio & Palmeri, Luca & Pivato, Alberto & Barausse, Alberto, 2016. "An ecosystem model of the lower Po river for use in ecological risk assessment of xenobiotics," Ecological Modelling, Elsevier, vol. 332(C), pages 42-58.
    2. Hu, Wen & Li, Chun-hua & Ye, Chun & Wang, Ji & Wei, Wei-wei & Deng, Yong, 2019. "Research progress on ecological models in the field of water eutrophication: CiteSpace analysis based on data from the ISI web of science database," Ecological Modelling, Elsevier, vol. 410(C), pages 1-1.
    3. Liqiang Yang & Xiaotong He & Shaoguo Ru & Yongyu Zhang, 2024. "Herbicide leakage into seawater impacts primary productivity and zooplankton globally," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    4. Niu, Zhiguang & Gou, Qianqian & Wang, Xiujun & Zhang, Ying, 2016. "Simulation of a water ecosystem in a landscape lake in Tianjin with AQUATOX: Sensitivity, calibration, validation and ecosystem prognosis," Ecological Modelling, Elsevier, vol. 335(C), pages 54-63.
    5. Jean-Éric Tremblay & Dominique Robert & Diana Varela & Connie Lovejoy & Gérald Darnis & R. Nelson & Akash Sastri, 2012. "Current state and trends in Canadian Arctic marine ecosystems: I. Primary production," Climatic Change, Springer, vol. 115(1), pages 161-178, November.
    6. Bai, Jing & Zhao, Jian & Zhang, Zhenyu & Tian, Ziqiang, 2022. "Assessment and a review of research on surface water quality modeling," Ecological Modelling, Elsevier, vol. 466(C).
    7. Osakpolor, Stephen E. & Kattwinkel, Mira & Schirmel, Jens & Feckler, Alexander & Manfrin, Alessandro & Schäfer, Ralf B., 2021. "Mini-review of process-based food web models and their application in aquatic-terrestrial meta-ecosystems," Ecological Modelling, Elsevier, vol. 458(C).
    8. Yan, Jinxia & Liu, Jingling & You, Xiaoguang & Shi, Xuan & Zhang, Lulu, 2018. "Simulating the gross primary production and ecosystem respiration of estuarine ecosystem in North China with AQUATOX," Ecological Modelling, Elsevier, vol. 373(C), pages 1-12.
    9. Masuda, Yoshio & Yamanaka, Yasuhiro & Hirata, Takafumi & Nakano, Hideyuki & Kohyama, Takashi S., 2020. "Inhibition of competitive exclusion due to phytoplankton dispersion: a contribution for solving Hutchinson's paradox," Ecological Modelling, Elsevier, vol. 430(C).
    10. Taner, Mehmet Ümit & Carleton, James N. & Wellman, Marjorie, 2011. "Integrated model projections of climate change impacts on a North American lake," Ecological Modelling, Elsevier, vol. 222(18), pages 3380-3393.
    11. Nagisetty, Raja M. & Flynn, Kyle F. & Uecker, Dylan, 2019. "Dissolved oxygen modeling of effluent-dominated macrophyte-rich Silver Bow Creek," Ecological Modelling, Elsevier, vol. 393(C), pages 85-97.
    12. Zeng, Yong & Yang, Wei & Zhao, Yanwei, 2022. "Ecological impact of polycyclic aromatic hydrocarbons on Baiyangdian Lake based on an ecosystem model," Ecological Modelling, Elsevier, vol. 472(C).
    13. Ciric, C. & Ciffroy, P. & Charles, S., 2012. "Use of sensitivity analysis to identify influential and non-influential parameters within an aquatic ecosystem model," Ecological Modelling, Elsevier, vol. 246(C), pages 119-130.
    14. Ludovisi, Alessandro & Roselli, Leonilde & Basset, Alberto, 2012. "Testing the effectiveness of exergy-based tools on a seasonal succession in a coastal lagoon by using a size distribution approach," Ecological Modelling, Elsevier, vol. 245(C), pages 125-135.
    15. Gentile, U. & Marrone, S. & Nardone, R. & Bellini, E., 2020. "Computer-aided security assessment of water networks monitoring platforms," International Journal of Critical Infrastructure Protection, Elsevier, vol. 31(C).
    16. Goebel, N.L. & Edwards, C.A. & Zehr, J.P. & Follows, M.J. & Morgan, S.G., 2013. "Modeled phytoplankton diversity and productivity in the California Current System," Ecological Modelling, Elsevier, vol. 264(C), pages 37-47.
    17. Joydev Chattopadhyay & Ezio Venturino & Samrat Chatterjee, 2013. "Aggregation of toxin-producing phytoplankton acts as a defence mechanism – a model-based study," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(2), pages 159-174, April.
    18. Blancher, Eldon C. & Park, Richard A. & Clough, Jonathan S. & Milroy, Scott P. & Graham, W. Monty & Rakocinski, Chet F. & Hendon, J. Read & Wiggert, Jerry D. & Leaf, Robert, 2017. "Establishing nearshore marine secondary productivity baseline estimates for multiple habitats in coastal Mississippi and Alabama using AQUATOX 3.1 NME for use in the Deepwater Horizon natural resource," Ecological Modelling, Elsevier, vol. 359(C), pages 49-68.
    19. Tsakalakis, Ioannis & Pahlow, Markus & Oschlies, Andreas & Blasius, Bernd & Ryabov, Alexey B., 2018. "Diel light cycle as a key factor for modelling phytoplankton biogeography and diversity," Ecological Modelling, Elsevier, vol. 384(C), pages 241-248.
    20. Flynn, Kyle F. & Chapra, Steven C. & Suplee, Michael W., 2013. "Modeling the lateral variation of bottom-attached algae in rivers," Ecological Modelling, Elsevier, vol. 267(C), pages 11-25.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9216-:d:873331. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.