Predicting bend-induced heterogeneity in sediment microbial communities by integrating bacteria-based index of biotic integrity and supervised learning algorithms.

Prioritizing the relationship between heterogeneity of sediment habitats and river bends is critical when planning and reconstructing urban rivers. However, the exact relationship between ecological heterogeneity and river bends remains ambiguous. Therefore, this research proposed a new approach to quantify and predict bend-induced ecological heterogeneity, incorporating the bacteria-based index of biotic integrity (Ba-IBI), path model, and random forest regression model. The developed Ba-IBI quantified heterogeneity in sediment microbial communities, ranging from low (1.40) to high (3.97). A path model was developed and validated in order to further investigate the relative contributions of environmental factors to the Ba-IBI. The established path model, which was considered acceptable with a CMIN/df = 1.949 < 4, suggested that primary environmental factors affecting the sediment bacterial communities were flow velocity and ammonium concentration in sediment. To further characterize the relationship between environmental factors and the Ba-IBI, a function was constructed using the random forest regression model that predicts the responses of sediment bacterial communities to environmental factors with R = 0.6126. The proposed approach and prediction tools will provide knowledge to improve natural channel design and post-project evaluations in river restoration projects.