Dataset Information

Bacterial bioindicators enable biological status classification along the continental Danube 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 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.

SUBMITTER: Fontaine L

PROVIDER: S-EPMC10439154 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bacterial bioindicators enable biological status classification along the continental Danube river.

Fontaine Laurent L Pin Lorenzo L Savio Domenico D Friberg Nikolai N Kirschner Alexander K T AKT Farnleitner Andreas H AH Eiler Alexander A

Communications biology 20230818 1

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. Bacte ...[more]

PMID: 37596339

Similar Datasets

Project description:The microbial faecal pollution of rivers has wide-ranging impacts on a variety of human activities that rely on appropriate river water quality. Thus, detailed knowledge of the extent and origin of microbial faecal pollution is crucial for watershed management activities to maintain safe water use. In this study, the microbial faecal pollution levels were monitored by standard faecal indicator bacteria (SFIB) along a 2580 km stretch of the Danube, the world's most international river, as well as the Danube's most important tributaries. To track the origin of faecal pollution, host-associated Bacteroidetes genetic faecal marker qPCR assays for different host groups were applied in concert with SFIB. The spatial resolution analysis was followed by a time resolution analysis of faecal pollution patterns over 1 year at three selected sites. In this way, a comprehensive faecal pollution map of the total length of the Danube was created, combining substantiated information on both the extent and origin of microbial faecal pollution. Within the environmental data matrix for the river, microbial faecal pollution constituted an independent component and did not cluster with any other measured environmental parameters. Generally, midstream samples representatively depicted the microbial pollution levels at the respective river sites. However, at a few, somewhat unexpected sites, high pollution levels occurred in the lateral zones of the river while the midstream zone had good water quality. Human faecal pollution was demonstrated as the primary pollution source along the whole river, while animal faecal pollution was of minor importance. This study demonstrates that the application of host-associated genetic microbial source tracking markers in concert with the traditional concept of microbial faecal pollution monitoring based on SFIB significantly enhances the knowledge of the extent and origin of microbial faecal pollution patterns in large rivers. It constitutes a powerful tool to guide target-oriented water quality management in large river basins.

Dataset Information

Bacterial bioindicators enable biological status classification along the continental Danube river.

Publications

Bacterial bioindicators enable biological status classification along the continental Danube river.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets