Predicting Aquatic Habitat Degradation Through Multivariate Relationships Between Water Chemistry and Biological Indicators

Aquatic habitat deterioration often results from the simultaneous occurrence of nutrient enrichment, organic contamination, changes in oxygen conditions, salinization, siltation, and thermal alteration; however, these factors rarely act independently. In the classical approach to water monitoring, water quality and biological status are considered separately, and therefore, predicting ecological degradation is impossible until the problem reaches its peak. In this study, a multivariate approach is suggested, which connects the physico-chemical parameters of water with biological metrics, such as macroinvertebrate richness, diversity, and the proportion of sensitive taxa, to estimate the level of aquatic habitat degradation. Analysis of a representative data set was performed to showcase the process. It was found that dissolved oxygen, total phosphorus, biochemical oxygen demand, nitrate, and conductivity were the variables with the highest correlation with habitat condition. At the same time, the multivariate model discriminated between low, moderate, and high degrees of degradation better than each chemical variable alone. The findings imply that biological indicators consistently respond to chemical stress, and that such models can be used in early warning systems and priority setting.