Bacterial bioindicators enable biological status classification along the continental Danube river
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Bacterial bioindicators enable biological status classification along the continental Danube river. / Fontaine, Laurent; Pin, Lorenzo; Savio, Domenico; Friberg, Nikolai; Kirschner, Alexander K.T.; Farnleitner, Andreas H.; Eiler, Alexander.
In: Communications Biology , Vol. 6, No. 1, 862, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Bacterial bioindicators enable biological status classification along the continental Danube river
AU - Fontaine, Laurent
AU - Pin, Lorenzo
AU - Savio, Domenico
AU - Friberg, Nikolai
AU - Kirschner, Alexander K.T.
AU - Farnleitner, Andreas H.
AU - Eiler, Alexander
N1 - Publisher Copyright: © 2023, Springer Nature Limited.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1038/s42003-023-05237-8
DO - 10.1038/s42003-023-05237-8
M3 - Journal article
C2 - 37596339
AN - SCOPUS:85168352912
VL - 6
JO - Communications Biology
JF - Communications Biology
SN - 2399-3642
IS - 1
M1 - 862
ER -
ID: 365546918