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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.
Afiliación
  • Fontaine L; Section for Aquatic Biology and Toxicology, Centre for Biogeochemistry in the Anthropocene, Department of Biosciences, University of Oslo, Blindernv. 31, 0371, Oslo, Norway.
  • Pin L; Section for Aquatic Biology and Toxicology, Centre for Biogeochemistry in the Anthropocene, Department of Biosciences, University of Oslo, Blindernv. 31, 0371, Oslo, Norway.
  • Savio D; Norsk Institutt for Vannforskning (NIVA) Gaustadalléen 21, 0349, Oslo, Norway.
  • Friberg N; Division Water Quality and Health, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria.
  • Kirschner AKT; Interuniversity Cooperation Centre for Water and Health, Vienna, Austria.
  • Farnleitner AH; Research Group for Microbiology and Molecular Diagnostics 166/5/3, Institute of Chemical, Environmental and Bioscience Engineering, TU Wien, Vienna, Austria.
  • Eiler A; Norsk Institutt for Vannforskning (NIVA) Gaustadalléen 21, 0349, Oslo, Norway.
Commun Biol ; 6(1): 862, 2023 08 18.
Article en En | MEDLINE | ID: mdl-37596339
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biomarcadores Ambientales / Microbiota Tipo de estudio: Prognostic_studies Idioma: En Revista: Commun Biol Año: 2023 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biomarcadores Ambientales / Microbiota Tipo de estudio: Prognostic_studies Idioma: En Revista: Commun Biol Año: 2023 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido