Predicting macroinvertebrate average score per taxon (ASPT) at water quality monitoring sites in Japanese rivers

Abstract

Freshwater ecosystems provide essential services for human well-being but are impacted by multiple anthropogenic stressors. Biomonitoring with bioindicators such as river macroinvertebrates is fundamental for assessing the status of freshwater systems. In Japan, water quality and biomonitoring surveys are conducted separately, leading to a lack of nationwide information on the biological status of water quality monitoring (WQM) sites. In this study, we examined the co-occurrence of 983 biomonitoring sites with WQM sites to obtain a set of 237 “aligned” sites. Then, we developed a multiple linear regression model to estimate the average score per taxon (ASPT) from river macroinvertebrate data surveyed at these sites. The best model (i.e., with the smallest corrected Akaike information criterion) included eight predictors: elevation, catchment area, biological oxygen demand, suspended solids, minimum pH, the proportions of paddy fields and urban areas in the catchment, and the proportion of urban areas within a 3-km radius. The best multiple linear regression model could predict ASPT with reasonable accuracy, i.e., with an error of ±1 for 96% of the aligned data (R2 = 0.69; root mean squared error = 0.47) and 84% of the external validation dataset (R2 = 0.55; root mean squared error = 0.75). Using the best multiple linear regression model, we estimated ASPT values at 2925 WQM sites in rivers nationwide. Although caution should be exercised because of uncertainties in the estimation, the WQM sites were categorized into four levels of river environment quality by estimated ASPT values: “very good” (29% of WQM sites), “good” (50%), “fairly good” (14%), and “not good” (8%). Furthermore, we observed statistically significant correlations (p < 0.05; 0.4 ≤ r ≤ 0.7) between ASPT and all eight macroinvertebrate metrics examined, such as mayfly (Ephemeroptera) and stonefly (Plecoptera) richness, providing valuable information on the ecological implications of changes in ASPT. Our study provides a valuable statistical model for estimating ASPT and contributes to further understanding of the biological status of rivers across Japan.