IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v428y2020ics0304380020301447.html
   My bibliography  Save this article

A dynamic growth model of Ulva prolifera: Application in quantifying the biomass of green tides in the Yellow Sea, China

Author

Listed:
  • Sun, Ke
  • Ren, Jeffrey S.
  • Bai, Tao
  • Zhang, Jihong
  • Liu, Qing
  • Wu, Wenguang
  • Zhao, Yunxia
  • Liu, Yi

Abstract

Large-scale green tides caused by Ulva prolifera have been recurrent in the Yellow Sea of China since 2007. Efficient control of the intensity of green tides requires an understanding of the causes of macroalgae growth. In this study, a dynamic growth model was established to predict the growth of U. prolifera in response to variations in environmental factors. The model was parameterised and validated using data from both laboratory and field experiments. When applied to U. prolifera in the Yellow Sea, the model could generally reproduce the field observations of green tides in 2012. Scenario simulations were performed to analyse the effects of initial biomass, temperature and nutrients on the dynamics of green tide. The results suggest that temperature was not a limiting factor, but the optimisation of temperature would slightly increase the intensity of green tide. The scale of green tide was collectively determined by the initial biomass and nutrient availability. Dissolved inorganic nitrogen was the most critical nutrient controlling the magnitude and time of green tide, and dissolved organic nitrogen could also contribute to some extent. The development of green tide was not limited by dissolved inorganic phosphorus or dissolved organic phosphorus. These results further improve the current understanding of the mechanisms of green tides in the Yellow Sea and help control green tide disasters. The model could be applicable to other locations and coupled with hydrodynamic models to study green tides at a fine spatiotemporal scale.

Suggested Citation

  • Sun, Ke & Ren, Jeffrey S. & Bai, Tao & Zhang, Jihong & Liu, Qing & Wu, Wenguang & Zhao, Yunxia & Liu, Yi, 2020. "A dynamic growth model of Ulva prolifera: Application in quantifying the biomass of green tides in the Yellow Sea, China," Ecological Modelling, Elsevier, vol. 428(C).
  • Handle: RePEc:eee:ecomod:v:428:y:2020:i:c:s0304380020301447
    DOI: 10.1016/j.ecolmodel.2020.109072
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380020301447
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2020.109072?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aveytua-Alcázar, Leslie & Camacho-Ibar, Victor F. & Souza, Alejandro J. & Allen, J.I. & Torres, Ricardo, 2008. "Modelling Zostera marina and Ulva spp. in a coastal lagoon," Ecological Modelling, Elsevier, vol. 218(3), pages 354-366.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel Garcia-Vicuña & Laida Esparza & Fermin Mallor, 2022. "Hospital preparedness during epidemics using simulation: the case of COVID-19," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 213-249, March.

    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. Brush, Mark J. & Nixon, Scott W., 2010. "Modeling the role of macroalgae in a shallow sub-estuary of Narragansett Bay, RI (USA)," Ecological Modelling, Elsevier, vol. 221(7), pages 1065-1079.
    2. Aveytua-Alcazar, L. & Melaku Canu, D. & Camacho-Ibar, V.F. & Solidoro, C., 2020. "Changes in upwelling regimes in a Mediterranean-type lagoon: A model application," Ecological Modelling, Elsevier, vol. 418(C).
    3. Azevedo, Ana & Lillebø, Ana Isabel & Lencart e Silva, João & Dias, João Miguel, 2017. "Intertidal seagrass models: Insights towards the development and implementation of a desiccation module," Ecological Modelling, Elsevier, vol. 354(C), pages 20-25.

    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:eee:ecomod:v:428:y:2020:i:c:s0304380020301447. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.