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7643439 
Journal Article 
[Habitat suitability of macroinvertebrates: A case study in Qiaobian River, a tributary of Yangtze River, China] 
Su, Y; Li, W; Li, J; Sun, X; Hu, W 
2020 
1 
Shengtai Xuebao / Acta Ecologica Sinica
ISSN: 1000-0933
EISSN: 1872-2032 
40 
16 
5844-5854 
Chinese 
ZOOREC:ZOOR15611079684 
The health of the Yangtze River Basin is threatened by increasing hydropower development and human activities. How to properly diagnose river has become a global concern. Constructing high accuracy and strong applicability habitat suitability assessment model to evaluate river habitat health statue can provide theoretical basis for habitat quality assessment and habitat restoration in rivers. In this paper, the habitat suitability of macroinvertebrates in Qiaobian River, a tributary of the Yangtze, China was evaluated by investigating the distribution of macroinvertebrates, chemical and physical parameters during January and April, 2019. Base on macroinvertebrate habitat suitability, the key habitat factors were screened by Canonical correlation analysis (CCA) and Independent analysis (Pearson correlation analysis and Spearman correlation analysis). Suitability curves were determined by Generalized Additive Model ( GAM) and Polynomial Fitting Model (PFM). The results indicated that: (1) five factors, including CODMn, total nitrogen (TN), dissolved oxygen (DO), turbidity and water depth, had significant effects on Corbicula fluminea, the dominant species of Qiaobian River. The Corbicula fluminea was negatively correlated with CODMn, TN, turbidity and water depth and positively correlated with DO. (2) Most suitable CODMnof 1.228 mg/ L, TN of 0.269 mg/ L, DO of 11.170 mg/ L, water depth of 0.300m and turbidity of 1.130 NTU. (3) While both methods were applicable in the case of linear fitting, Generalized Additive Model is better than Polynomial Fitting Model in the case of nonlinear fitting, and the GAM can avoid the over-fitting phenomenon in the process of polynomial fitting when dealing with data sets with large discretization degree. The research shows that the GAM can more accurately and reasonably simulate the relationship between habitat factors and biological selection, which has important practical significance for river habitat quality evaluation and habitat restoration. © 2020 Science Press. All rights reserved.