*2.3. Use of Biotic Indices*

To assess the status of stream health and integrity in the Seomjin River, we calculated the values of biotic indices from the collected benthic macroinvertebrate data. Bearing in mind that there is no clear-cut distinction of ecosystem assessment in accordance with biotic indices, we considered both globally popular and regionally specific indices. In this respect, five biotic indices were selected to evaluate the abundance, diversity, dominance, evenness, and richness: McNaughton's dominance index (DI, [26]), Shannon–Weaver index (H', [27]), Richness index (RI, [28]), Evenness index (EI, [29]), and Benthic Macroinvertebrates index (BMI; [30]). In particular, BMI is a modified version (i.e., conceptually the same) of the saprobic index of Zelinka and Marvan [31], which ranges from 0 to 100. The BMI has been used for the bio-assessment of benthic macroinvertebrates in Korea [32]. The governing equation is expressed as:

$$BMI = \left(4 - \frac{\sum\_{i=1}^{n} s\_i h\_i g\_i}{\sum\_{i=1}^{n} h\_i g\_i}\right) \times 25\tag{1}$$

where *si* denotes the saprobic value of the species *i*, *hi* denotes the relative abundance ranking of the species *i*, and *gi* denotes the weight value of the species *i* of the total number of species *n*. There is a subtle difference between the saprobic index and BMI. While the saprobic index takes the absolute biomass of the species for *hi*, BMI uses the relative ranking of species abundance. Kong et al. [30] reported that BMI was a more capable means of assessing stream health and integrity when utilizing the information of relative abundance.

### *2.4. Multivariate Analysis for Data Ordination*

We used canonical correspondence analysis (CCA) in order to relate the benthic macroinvertebrate communities to the surrounding environmental variables. The environmental variables included the land-use percent coverage of cropland, urban land, forest, and wetland, in addition to the ambient water quality parameters TN, TP, NH3, Chl-a, and BOD. The benthic macroinvertebrate data included 22 genus groups, except for the family group of Chironomidae. Prior to the CCA application, we calculated the length of the gradient based on detrended correspondence analysis in order to examine the adequacy of CCA application [33]. As the length was 4.16 (greater than 4), CCA was used for data ordination [34,35]. All the variables were modified by log transformation to stabilize the data close to normal distribution [36]. For statistical analysis, we used SPSS ver. 18 software (IBM, New York, NY, USA) and the open-source software PAST3 (SOFTPEDIA, Romania).
