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

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

Ecological Modelling, 2020, vol. 428, issue C

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.

Keywords: Ulva prolifera; Growth model; Green tides; Biomass; Yellow Sea (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:428:y:2020:i:c:s0304380020301447

DOI: 10.1016/j.ecolmodel.2020.109072

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