IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v397y2019icp25-35.html
   My bibliography  Save this article

Modelling fish communities in relation to water quality in the impounded lakes of China’s South-to-North Water Diversion Project

Author

Listed:
  • Guo, Chuanbo
  • Chen, Yushun
  • Liu, Han
  • Lu, Yin
  • Qu, Xiao
  • Yuan, Hui
  • Lek, Sovan
  • Xie, Songguang

Abstract

Large scale inter-basin water diversion projects would have a set of ecological and environmental impacts on aquatic ecosystems. However, knowledge regarding water transfer induced water quality changes in determining fish communities in the impounding ecosystems remain largely unknown. In the current study, we filled this research gap by using a set of machine learning algorithms to ensemble modelling fish community indices with water quality indicators in the impounded lakes along the eastern route of China’s South-to-North Water Diversion Project (SNWDP). Overall, our results realed that water quality changes can be good predictors for the variation of fish community structures in these lakes. Although different model techniques highlighted different variable importance of water quality in determining fish communities, there is generally a consensus that the hydrological related water quality indicators like: water clarity (e.g., total suspended solids) and nutrient loading (e.g., phosphate) contributed the most to the changes of fish communities. Our results also indicated that water diversions could bring knock-on effects on fish communities. Thus, more attention should be paid to long-term ecological effects from future water diversions.

Suggested Citation

  • Guo, Chuanbo & Chen, Yushun & Liu, Han & Lu, Yin & Qu, Xiao & Yuan, Hui & Lek, Sovan & Xie, Songguang, 2019. "Modelling fish communities in relation to water quality in the impounded lakes of China’s South-to-North Water Diversion Project," Ecological Modelling, Elsevier, vol. 397(C), pages 25-35.
    • Handle: RePEc:eee:ecomod:v:397:y:2019:i:c:p:25-35
    DOI: 10.1016/j.ecolmodel.2019.01.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380019300298
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2019.01.014?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. Guo, Chuanbo & Lek, Sovan & Ye, Shaowen & Li, Wei & Liu, Jiashou & Li, Zhongjie, 2015. "Uncertainty in ensemble modelling of large-scale species distribution: Effects from species characteristics and model techniques," Ecological Modelling, Elsevier, vol. 306(C), pages 67-75.
    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. Marchetto, Elisa & Da Re, Daniele & Tordoni, Enrico & Bazzichetto, Manuele & Zannini, Piero & Celebrin, Simone & Chieffallo, Ludovico & Malavasi, Marco & Rocchini, Duccio, 2023. "Testing the effect of sample prevalence and sampling methods on probability- and favourability-based SDMs," Ecological Modelling, Elsevier, vol. 477(C).
    2. Beaumont, Linda J. & Graham, Erin & Duursma, Daisy Englert & Wilson, Peter D. & Cabrelli, Abigail & Baumgartner, John B. & Hallgren, Willow & Esperón-Rodríguez, Manuel & Nipperess, David A. & Warren, , 2016. "Which species distribution models are more (or less) likely to project broad-scale, climate-induced shifts in species ranges?," Ecological Modelling, Elsevier, vol. 342(C), pages 135-146.
    3. Ahmadi, Kourosh & Mahmoodi, Shirin & Pal, Subodh Chandra & Saha, Asish & Chowdhuri, Indrajit & Nguyen, Trinh Trong & Jarvie, Scott & Szostak, Marta & Socha, Jaroslaw & Thai, Van Nam, 2023. "Improving species distribution models for dominant trees in climate data-poor forests using high-resolution remote sensing," Ecological Modelling, Elsevier, vol. 475(C).
    4. Zhonghyun Kim & Taeyong Shim & Seo Jin Ki & Dongil Seo & Kwang-Guk An & Jinho Jung, 2021. "Evaluation of Classification Algorithms to Predict Largemouth Bass ( Micropterus salmoides ) Occurrence," Sustainability, MDPI, vol. 13(17), pages 1-11, August.

    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:397:y:2019:i:c:p:25-35. 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.