Liu Muyuan, Huang Yuzhou, Hu Jing, He Junyu, Xiao Xi
Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang, 316021, China.
Ocean Academy, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang, 316021, China.
Environ Sci Ecotechnol. 2022 Dec 30;14:100233. doi: 10.1016/j.ese.2022.100233. eCollection 2023 Apr.
The algal community structure is vital for aquatic management. However, the complicated environmental and biological processes make modeling challenging. To cope with this difficulty, we investigated using random forests (RF) to predict phytoplankton community shifting based on multi-source environmental factors (including physicochemical, hydrological, and meteorological variables). The RF models robustly predicted the algal communities composed by 13 major classes (Bray-Curtis dissimilarity = 9.2 ± 7.0%, validation NRMSE mostly <10%), with accurate simulations to the total biomass (validation R > 0.74) in Norway's largest lake, Lake Mjosa. The importance analysis showed that the hydro-meteorological variables (Standardized MSE and Node Purity mostly >0.5) were the most influential factors in regulating the phytoplankton. Furthermore, an in-depth ecological interpretation uncovered the interactive stress-response effect on the algal community learned by the RF models. The interpretation results disclosed that the environmental drivers (i.e., temperature, lake inflow, and nutrients) can jointly pose strong influence on the algal community shifts. This study highlighted the power of machine learning in predicting complex algal community structures and provided insights into the model interpretability.
藻类群落结构对水生生态管理至关重要。然而，复杂的环境和生物过程使得建模具有挑战性。为应对这一困难，我们研究了使用随机森林（RF）基于多源环境因素（包括物理化学、水文和气象变量）来预测浮游植物群落的变化。RF模型有力地预测了由13个主要类别组成的藻类群落（Bray-Curtis差异=9.2±7.0%，验证NRMSE大多<10%），并对挪威最大的湖泊米约萨湖的总生物量进行了准确模拟（验证R>0.74）。重要性分析表明，水文气象变量（标准化MSE和节点纯度大多>0.5）是调节浮游植物的最具影响力的因素。此外，深入的生态学解释揭示了RF模型所了解到的对藻类群落的交互胁迫响应效应。解释结果表明，环境驱动因素（即温度、湖泊入流和营养物质）可共同对藻类群落变化产生强烈影响。本研究突出了机器学习在预测复杂藻类群落结构方面的能力，并为模型可解释性提供了见解。
