Synergistic effects of environmental factors on benthic diversity: Machine learning analysis

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-05-06 DOI:10.1016/j.watres.2025.123789

Yiyang Feng , Mengyu Yang , Hao Chen, Kun Zhang, Fuju Ran, Ziyan Chen, Haijun Yang

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Abstract

This study examines the water environmental factors of the Cangshan stream and benthic animal communities by using random forest, gradient boosting decision tree, and support vector machine models to analyze the complex response mechanisms of benthic animal diversity and community structure to environmental factors. Feature importance analysis, SHAP values, and 3D response surface analysis are applied to quantitatively assess the non-linear driving effects of environmental factors and their interactions. The findings suggest that total phosphorus and conductivity are central factors influencing benthic animal diversity, with moderate levels fostering community diversity, whereas high levels of total nitrogen and conductivity significantly reduce diversity. Benthic animals exhibit a non-linear response pattern to dissolved oxygen and temperature, with the interaction between dissolved oxygen and temperature highlighting the significant promotion of diversity under low-temperature, high-oxygen conditions, whereas high-temperature, low-oxygen conditions exert evident environmental stress on communities. The results of the multifactor synergistic effect analysis indicate that the moderate synergistic interaction between total phosphorus and conductivity significantly enhances diversity, whereas high total nitrogen levels weaken this positive effect. Model performance comparisons reveal that the RF outperforms the other models in terms of coefficient of determination, mean squared error, and mean absolute error, particularly in capturing complex non-linear relationships and factor interactions. Through machine learning, this study reveals the multidimensional driving mechanisms of environmental factors on benthic animal community characteristics, emphasizing the potential to capture non-linear relationships and multifactor interactions, thereby providing scientific evidence and innovative approaches for stream ecosystem conservation and management.

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环境因素对底栖生物多样性的协同效应：机器学习分析

本文采用随机森林、梯度增强决策树和支持向量机模型对苍山流域水环境因子和底栖动物群落进行了研究，分析了底栖动物多样性和群落结构对环境因子的复杂响应机制。应用特征重要性分析、SHAP值和三维响应面分析定量评估环境因素及其相互作用的非线性驱动效应。结果表明，总磷和电导率是影响底栖动物多样性的核心因素，中等水平的总磷和电导率有利于群落多样性，而高水平的总氮和电导率会显著降低群落多样性。底栖动物对溶解氧和温度的响应呈非线性模式，溶解氧和温度的交互作用突出了低温、高氧条件下群落多样性的显著促进，而高温、低氧条件对群落产生明显的环境胁迫。多因素协同效应分析结果表明，总磷和电导率之间的适度协同作用显著增强了多样性，而高总氮水平则削弱了这种正作用。模型性能比较表明，RF在决定系数、均方误差和平均绝对误差方面优于其他模型，特别是在捕获复杂的非线性关系和因素相互作用方面。本研究通过机器学习揭示了环境因素对底栖动物群落特征的多维驱动机制，强调了捕获非线性关系和多因素相互作用的潜力，从而为河流生态系统的保护和管理提供了科学依据和创新方法。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.