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Restoration of aquatic vegetation can mitigate the risk of eutrophication in large shallow lakes

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Abstract

The role of aquatic vegetation restoration in mitigating lake eutrophication is well-recognized, yet the underlying nutrient dynamics-vegetation response relationship remains poorly quantified, hindering a mechanistic understanding of grass-algae regime shifts. To address this, the Wetland Eco-dynamic Model for Submerged Plants (WET) was developed to mechanistically simulate ecological processes within the water–sediment-plant continuum by coupling hydrodynamics, nutrient cycling, and plant growth modules. Applied to Lake Taihu (2005–2019), the model effectively simulated submerged plant responses to hydrological and physicochemical parameters, with simulated vegetation cover validation achieving an R2 of 0.68. A multi-scale analysis further identified a significant positive correlation between N:P ratios and aquatic vegetation cover. The model was subsequently employed to simulate vegetation dynamics under 121 distinct nutrient regulation scenarios, revealing that aquatic vegetation cover exerts a stronger influence on total phosphorus than on total nitrogen, a relationship characterized by steady-state nonlinearity. The findings demonstrate that under stable meteorological, hydrological, and pollution load conditions, managing aquatic vegetation is an effective eutrophication mitigation strategy for large, shallow lakes. Specifically for the phosphorus-limited Lake Taihu, simulations indicate that increasing vegetation coverage to 27.7% would shift its trophic status from eutrophic to oligotrophic-mesotrophic. By systematically quantifying these nonlinear responses, this study provides operational thresholds and a scientific basis for lake ecological management, overcoming a key limitation of traditional qualitative descriptions.

Data availability

All data supporting the findings of this study are included in the manuscript. The datasets used and analysed during the current study available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China [grant number 52479078], the National Key R&D Program of China [grant number 2021YFC3201004], and the National Key R&D Program of China [grant number 2021YFC3201003].

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Authors and Affiliations

  1. State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China

    Xiang Li, Rui Xia, Zhongwen Yang, Yan Chen, Chao Yan, Junde Ming, Qiang Hu & Hao Liu

  2. National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China

    Rui Xia

  3. National Joint Research Center for Ecological Conservation and High Quality Development of the Yellow River Basin, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China

    Rui Xia

  4. Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China

    Yan Chen

  5. Northwest University College of Urban and Environmental Sciences, Northwest University, Xi’an, 710127, China

    Chao Yan & Hao Liu

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  1. Xiang Li
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  2. Rui Xia
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  3. Zhongwen Yang
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  4. Yan Chen
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  8. Hao Liu
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Contributions

Xiang LI: Resources, Data curation, Methodology, Formal analysis, Writing—original draft. Rui XIA: Resources, Methodology, Formal analysis, Writing—original draft. Zhongwen YANG: Resources, Data curation, Formal analysis. Yan CHEN: Data curation, Formal analysis. Chao YAN: Data curation, Formal analysis. Junde MING: Data curation, Formal analysis. Qiang HU: Data curation, Formal analysis. Hao LIU: Data curation, Formal analysis.

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Correspondence to
Rui Xia.

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Li, X., Xia, R., Yang, Z. et al. Restoration of aquatic vegetation can mitigate the risk of eutrophication in large shallow lakes.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-31045-7

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  • DOI: https://doi.org/10.1038/s41598-025-31045-7

Keywords

  • Algal bloom
  • Submerged plants
  • State transformation
  • Threshold mutation
  • GOTM-FABM-PCLake
  • Taihu Lake

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