Bacterial bioindicators for biological status classification along a continental river

Abstract Despite the importance of bacteria in aquatic ecosystems and their predictable diversity patterns across space and time, biomonitoring tools for status assessment relying on these organisms are widely lacking. This is partly due to insufficient data and models to identify reliable microbial predictors. Here, we used metabarcoding in combination with multivariate statistics and machine learning to identify bacterial bioindicators for existing biological status classification systems based on macroinvertebrates as well as for prediction of chlorophyll a concentration along the Danube River. Bacterial beta-diversity dynamics followed environmental gradients and the observed associations highlighted potential bioindicators at different taxonomic levels. Spatio-temporal links spanning the microbial communities along the river allowed accurate prediction of downstream biological status from upstream information. Network analysis on Amplicon Sequence Variants (ASV) identified as good indicators revealed informational redundancy among taxa, which coincided with taxonomic relatedness. The redundancy among bacterial bioindicators revealed mutually exclusive taxa, which allow accurate biological status modeling using only a few ASVs holding sufficient shares of non-redundant information. Our study shows bacterial indicators are robust for biological status classification and therefore should be considered for integration into bioassessment schemes such as ecological status classification in the EU Water Framework Directive.