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A dynamic biomass model of emergent aquatic vegetation under different water levels and salinity

Author

Listed:
  • Li, Yang
  • Yuan, Lin
  • Cao, Hao-Bing
  • Tang, Chen-Dong
  • Wang, Xian-Ye
  • Tian, Bo
  • Dou, Shen-Tang
  • Zhang, Li-Quan
  • Shen, Jian

Abstract

Emergent aquatic vegetation (EAV) is an important part of wetland ecosystems that provide multiple ecological services. However, human activities and natural changes often influence wetland hydrological regimes such as water levels, salinity, and other factors, which greatly influence the survival and growth of wetland plants. Based on field measurements and control experiments, we developed an EAV model to simulate biomass dynamics under changing conditions of water levels and salinity. This model successfully reproduced the seasonal biomass variation of three typical emergent plants, Phragmites australis, Typha angustifolia and Scirpus mariqueter, and simulated the response of EVA biomass under multiple scenarios of water levels and salinity in the Chongming Dongtan Nature Reserve (CDNR), Shanghai, China. Results suggest that there is a negative correlation between salinity and biomass. An optimal range of water levels are suitable for EAV, and biomass will decrease when the water levels are below or above their optimal range. Applying this dynamic EAV model is a cost-effective approach to find a sustainable and nature-based solution to managing and predicting wetland vegetation changes. The model and approach used in this study may provide a sustainable and nature-based solution for management and protection of wetland ecosystems, and may be transferrable to other wetland systems as well.

Suggested Citation

  • Li, Yang & Yuan, Lin & Cao, Hao-Bing & Tang, Chen-Dong & Wang, Xian-Ye & Tian, Bo & Dou, Shen-Tang & Zhang, Li-Quan & Shen, Jian, 2021. "A dynamic biomass model of emergent aquatic vegetation under different water levels and salinity," Ecological Modelling, Elsevier, vol. 440(C).
    • Handle: RePEc:eee:ecomod:v:440:y:2021:i:c:s0304380020304622
    DOI: 10.1016/j.ecolmodel.2020.109398
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    References listed on IDEAS

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