Bacterial bioindicators enable biological status classification along the continental Danube river

Laurent Fontaine, Lorenzo Pin, Domenico Savio, Nikolai Friberg, Alexander K T Kirschner, Andreas H Farnleitner, Alexander Eiler

Research output: Journal article (peer-reviewed)Journal article

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 show metabarcoding in combination with multivariate statistics and machine learning allows to identify bacterial bioindicators for existing biological status classification systems. Bacterial beta-diversity dynamics follow environmental gradients and the observed associations highlight potential bioindicators for ecological outcomes. Spatio-temporal links spanning the microbial communities along the river allow accurate prediction of downstream biological status from upstream information. Network analysis on amplicon sequence veariants identify as good indicators genera Fluviicola, Acinetobacter, Flavobacterium, and Rhodoluna, and reveal informational redundancy among taxa, which coincides with taxonomic relatedness. The redundancy among bacterial bioindicators reveals mutually exclusive taxa, which allow accurate biological status modeling using as few as 2-3 amplicon sequence variants. As such our models show that using a few bacterial amplicon sequence variants from globally distributed genera allows for biological status assessment along river systems.

Original languageEnglish
Article number862
Pages (from-to)862
JournalCommunications Biology
Volume6
Issue number1
DOIs
Publication statusPublished - Dec 2023

Keywords

  • Environmental Biomarkers
  • Flavobacterium
  • Machine Learning
  • Microbiota/genetics
  • Rivers

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