Abstract

The health of medium and small river ecosystems is threatened by increasing hydropower development and human activities. How to properly diagnose rivers has become a global concern. As a well-accepted theory, the aquatic organism density can be an indicator of river health. A new river health assessment method based on macroinvertebrates habitat suitability curves (M-HSC) was proposed. In this study, the health of Qiaobian River (QBR), a tributary of Yangtze River, China was evaluated by investigating the distribution of macroinvertebrates, chemical and physical parameters during winter 2018 (low flow season) and summer 2019 (high flow season). Based on habitat suitability of dominant macroinvertebrates, the key habitat factors were screened by canonical correspondence analysis (CCA) and Pearson correlation analysis. Suitability curves were determined by Generalized Additive Model (GAM). Ecosystem health comprehensive index method was used to evaluate the health status. The results show most suitable conditions for Corbicula fluminea containing chemical oxygen demand (CODMn) of 1.48 mg L−1, total nitrogen (TN) of 0.27 mg L−1, dissolved oxygen (DO) of 11.17 mg L−1, pH of 8.42, turbidity of 1.76 NTU, and water depth (Dep) of 0.35 m. The health status of QBR is spatially heterogeneous with the apparently better upstream than the downstream. In general, 25, 12.5, 12.5% of the samples were classified as nature, health and sub-health status, respectively and the rest 50% were lower than sub-health. The results are consistent with the environmental quality standards for surface water in China (GB3838-2002), suggesting the applicability of macroinvertebrates habitat suitability for evaluating river health. By minimizing the temporal and spatial limitations of comprehensive evaluation method and indicator species method, this study, for the first time, used macroinvertebrates habitat suitability curves to assess the health of medium and small rivers. The study will provide new insights for future river health assessments.

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