Synergistic effects of environmental factors on benthic diversity: Machine learning analysis.
Journal:
Water research
Published Date:
Aug 15, 2025
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.
