Ecological Indicators (Sep 2022)
Evaluating the necessity of geographical locality for patterning biological integrity and its responses to multiple stressors in river systems
Abstract
Water and sediment quality influence biotic communities, and relationships between the chemical composition and community structure provide important information about the regulation of river systems. We evaluated the effectiveness of clustering based on stress-response relationships and relationships with geographical location for the appropriate classification of rivers and streams. To address this issues, we applied clustering results using a self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) as random effects in linear mixed-effect models (LMMs). The dataset was composed of 16 water quality variables and 11 sediment quality variables with indices of benthic diatoms (TDI), macroinvertebrates (BMI), and fishes (FAI) surveyed at 84 stations along rivers of the Republic of Korea over 2 years. The clustering results of SOM were spatially sporadic based on the relationships between the variables, while those of Geo-SOM were based on these relationships as well as the geographical characteristics. The inclusion of random effects of SOM and Geo-SOM clustering improved the performance of all LMMs; however, the LMMs with random effects of Geo-SOM-based clustering were best for all three indices. Based on the variable importance from the Geo-SOM training and LMMs, the most influential water quality variables were phosphate and chlorophyll a, and the most influential sediment quality variables were sedimentary phosphate and Cr. The correlation coefficients for heavy metals from the Geo-SOM training and contribution to selected LMMs were generally lower than those of nutrient-associated variables, indicating that the rivers and streams of Korea were more influenced by organic pollution than by heavy metal pollution. Cluster-specific conditions were significantly affected by land-use patterns, showing negative relationships with the percentage of rice paddy area and positive relationships with the percentage of forest. This approach can be applied to the ecological classification of river basins and provides fundamental information for the implementation of sustainable management.
