Distribution Patterns of the Freshwater Oligochaete Limnodrilus hoffmeisteri Influenced by Environmental Factors in Streams on a Korean Nationwide Scale

• Published in: Water (Basel) Vol. 9; no. 12; p. 921
• Main Authors: Kang, Hyejin, Bae, Mi-Jung, Lee, Dae-Seong, Hwang, Soon-Jin, Moon, Jeong-Suk, Park, Young-Seuk
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 27.11.2017
• Subjects: Altitude; Digital maps; distribution patterns of species; Environmental effects; environmental factor; Environmental factors; Environmental research; Geographic information systems; Geography; Hydrologic data; Hydrology; Influence; Limnodrilus hoffmeisteri; Machine learning; machine learning model; Multidimensional scaling; multiple scale; multivariate analyses; Oligochaeta; Ordination; Physicochemical properties; Sampling; Scaling; Streams; Turbidity; Velocity; Water depth; Water velocity
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w9120921

Aquatic oligochaetes are very common in streams, and are used as biological assessment indicators as well as in the biological management of organic-enriched systems. In this study, we analyzed the effects of environmental factors influencing the distribution of aquatic oligochaetes Limnodrilus hoffmeisteri in streams. We used 13 environmental factors in three categories (i.e., geography, hydrology, and physicochemistry). Data on the distribution of oligochaetes and environmental factors were obtained from 1159 sampling sites throughout Korea on a nationwide scale. Hierarchical cluster analysis (HCA) and nonmetric multidimensional scaling (NMDS) were performed to analyze the relationships between the occurrence of aquatic oligochaetes and environmental factors. A random forest model was used to evaluate the relative importance of the environmental factors affecting the distribution of oligochaetes. HCA classified sampling sites into four groups according to differences in environmental factors, and NMDS ordination reflected the differences of environmental factors, in particular, water depth, velocity, and altitude, among the four groups defined in the HCA. Furthermore, using a random forest model, turbidity and water velocity were evaluated as highly important factors influencing the distribution of L. hoffmeisteri.