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监督机器学习,以了解和预测城市化河流中可分化的真核浮游生物群体的状态

Jiahui Shang¹, Yi Li¹, Wenlong Zhang¹

¹Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, Hohai University, Nanjing 210098, PR China.

|September 13, 2024

概括

城市河流有两个不同的生态状态,受度的影响. 监督机器学习模型准确地预测河流的健康状况,帮助恢复工作.

科学领域:

生态生态学生态生态学
环境科学环境科学
计算生物学计算生物学

背景情况:

预测城市河流的生态状况对于管理和恢复至关重要.
河流中的生态反应是复杂的,非线性,具有挑战性的预测建模.

研究的目的:

调查和预测城市河流中的真核浮游生物群落状况.
应用替代稳态理论和监督机器学习 (SML) 模型.
确定关键的环境驱动因素,并制定一个预测框架.

主要方法:

环境DNA (eDNA) 对社区分析进行metabarcoding.
替代稳定状态理论,以了解社区动态.
监督机器学习 (SML) 模型用于预测和驾驶员识别.

主要成果:

确定了两个不同的真核浮游生物社区状态:一个多样化,一个带有藻类繁殖风险.
度是一个关键的驱动因素,表现出与hysteresis的值效应.
在预测生态状况方面,SML模型表现出高准确度 (R2>0.80).
度,叶绿素a,COD,氨和绿藻ASVs是重要的预测特征.

结论:

SML模型可用于预测和解释具有替代稳定状态的城市河流中的生态状况.
度和化学氧气需求 (COD) 对生态状态有协同作用.
该研究为加强河流生态状况预测和管理提供了一个框架.

关键词:

替代稳定状态理论替代稳定状态理论生态状态生态状态欧核生物浮游生物群体社区.监督的机器学习模型城市化的河流.

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