The Institute of Ecological, Earth and Environmental Sciences, Baylor University, Waco, Texas, USA.
Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, Texas, USA.
Environ Microbiol. 2022 Nov;24(11):5174-5187. doi: 10.1111/1462-2920.16188. Epub 2022 Sep 16.
Models are widely used tools in aquatic science to understand the mechanism of phytoplankton growth and anticipate the occurrence of harmful algal blooms. However, model parameterization remains challenging and issues that may introduce prediction uncertainty exist. Many models use the Monod equation to predict cyanobacteria growth rate based on ambient nutrient concentrations. The half-saturation concentrations in the Monod equation varies greatly among different studies and depends on environmental conditions. In this study, we estimated the growth rate due to nutrient limitations for two cyanobacteria species (Microcystis aeruginosa and Dolichospermum flos-aquae) using a modified Monod model which allows the half-saturation concentration to vary according to initial nitrogen (N) conditions. The model is calibrated against observations from laboratory experiment where cyanobacteria growth and ambient nutrient concentrations were measured simultaneously, which is rarely done in the literature. Our results show this modified model produce better predictions on growth rate and biomass, indicating many commonly used mechanistic models may need improvement regarding phytoplankton growth representation. Furthermore, our study quantifies the flexibility in cyanobacteria growth parameter across a wide range of environmental N in eutrophic lakes thus provides important information for large-scale modelling applications.
模型是水生科学中广泛使用的工具,用于了解浮游植物生长的机制,并预测有害藻类大量繁殖的发生。然而,模型参数化仍然具有挑战性,并且存在可能引入预测不确定性的问题。许多模型使用 Monod 方程根据环境营养浓度预测蓝藻的生长速率。Monod 方程中的半饱和浓度在不同的研究中差异很大,并且取决于环境条件。在这项研究中,我们使用一种改良的 Monod 模型来估计两种蓝藻(铜绿微囊藻和水华鱼腥藻)由于营养限制而导致的生长速率,该模型允许半饱和浓度根据初始氮 (N) 条件而变化。该模型是根据实验室实验中的观测值进行校准的,该实验同时测量了蓝藻的生长和环境营养浓度,这在文献中很少见。我们的结果表明,该改良模型在生长速率和生物量的预测上表现更好,这表明许多常用的机制模型可能需要改进,以更好地表示浮游植物的生长。此外,我们的研究量化了富营养湖泊中蓝藻生长参数在广泛的环境 N 范围内的灵活性,因此为大规模模型应用提供了重要信息。