Understanding and predicting the ecological status of urbanized rivers is crucial for their restoration and management. However, the complex and nonlinear nature of ecological responses poses a challenge to the development of predictive models. Here, the study investigated and predicted the status of eukaryotic plankton communities in urbanized rivers by coupling environmental DNA metabarcoding, the alternative stable states theory, and supervised machine learning (SML) models. The results revealed two distinct states of eukaryotic plankton communities under similar environmental conditions: one state was characterized by the enrichment of a diverse phytoplankton population and the high relative abundance of protozoa, whereas the alternative state was characterized by abundant phytoplankton and fungi with an associated risk of algal blooms. Turbidity was identified as a key driver based on the SML model and Mantel test. Potential analysis demonstrated that the response pattern of eukaryotic plankton communities to turbidity was thresholds with hysteresis (Threshold1 = 17 NTU, Threshold2 = 24 NTU). A reduction in turbidity induced a regime shift in the eukaryotic plankton community toward an alternative state associated with a risk of algal blooms. In the prediction of ecological status, both SML models showed excellent performance (R2 > 0.80, RMSE < 0.1, Kappa > 0.70). Additionally, SHapley Additive exPlanations analysis identified turbidity, chlorophyll-a, chemical oxygen demand (COD), ammonia nitrogen and green algae's amplicon sequence variants as crucial features for prediction, with turbidity and COD showing a synergistic effect on ecological status. A framework was further proposed to enhance the understanding and prediction of ecological status in urbanized rivers. The obtained results of this study demonstrated the feasibility of using SML models to predict and explain the ecological status of urbanized rivers with alternative stable states. This provides valuable insights for the application of SML models in the restoration and management of urbanized rivers.

中文翻译：

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用于理解和预测城市化河流中双稳态真核浮游生物群落状况的监督机器学习


了解和预测城市化河流的生态状况对于其恢复和管理至关重要。然而，生态响应的复杂性和非线性性质对预测模型的开发构成了挑战。在这里，该研究通过耦合环境 DNA 元条形码、替代稳定态理论和监督机器学习 （SML） 模型，调查和预测了城市化河流中真核浮游生物群落的状况。结果揭示了在相似环境条件下真核浮游生物群落的两种不同状态：一种状态的特点是浮游植物种群多样化和原生动物的高相对丰度，而另一种状态的特点是丰富的浮游植物和真菌，具有藻类大量繁殖的相关风险。根据 SML 模型和 Mantel 检验，浊度被确定为关键驱动因素。电位分析表明，真核浮游生物群落对浑浊度的响应模式是滞后阈值 （阈值 1 = 17 NTU，阈值 2 = 24 NTU）。浑浊度的降低导致真核浮游生物群落向与藻华风险相关的替代状态转变。在生态状况预测中，两种 SML 模型均表现出优异的性能 （R2 > 0.80， RMSE < 0.1， Kappa > 0.70）。此外，SHapley 加法解释分析确定浊度、叶绿素-a、化学需氧量 （COD）、氨氮和绿藻的扩增子序列变体是预测的关键特征，浊度和 COD 对生态状况显示出协同作用。 进一步提出了一个框架，以加强对城市化河流生态状况的理解和预测。本研究获得的结果证明了使用 SML 模型预测和解释具有替代稳定状态的城市化河流生态状况的可行性。这为 SML 模型在城市化河流恢复和管理中的应用提供了有价值的见解。