Bacterial bioindicators enable biological status classification along the continental Danube river

Area of Science:

• Environmental microbiology
• Aquatic ecology
• Bioindicator development

Background:

• Aquatic ecosystems rely on bacteria, yet effective biomonitoring tools are scarce due to limited data and predictive models.
• Predictable bacterial diversity patterns exist across aquatic environments, suggesting potential for bioindicator development.

Purpose of the Study:

• To identify reliable bacterial bioindicators for assessing the ecological status of aquatic systems.
• To develop predictive models for biological status classification using microbial community data.

Main Methods:

• Utilized metabarcoding, multivariate statistics, and machine learning to analyze bacterial communities.
• Investigated spatio-temporal dynamics and environmental gradients influencing bacterial beta-diversity.
• Employed network analysis on amplicon sequence variants to identify key indicator genera.

Main Results:

• Bacterial beta-diversity dynamics were strongly linked to environmental gradients, highlighting potential bioindicators.
• Spatio-temporal microbial data enabled accurate prediction of downstream biological status from upstream conditions.
• Genera Fluviicola, Acinetobacter, Flavobacterium, and Rhodoluna were identified as robust bioindicators.

Conclusions:

• A few bacterial amplicon sequence variants from globally distributed genera can effectively assess river system status.
• Informational redundancy among bacterial bioindicators allows for accurate status modeling with minimal taxa.
• This approach enhances biomonitoring capabilities for aquatic ecosystems.