0
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Developing prokaryotic water quality indicators

      , , ,

      ARPHA Conference Abstracts

      Pensoft Publishers

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Despite the importance of prokaryotes in aquatic ecosystem and their predictable diversity patterns across space and time, biomonitoring tools relying on prokaryotes are widely lacking. Using metabarcoding, as well as other molecular methods, we were able to identify multiple prokaryotic descriptors and illustrate their reliability and advantages in aquatic environmental assessment. Multivariate statistical and machine learning methods combined with variation coefficient and overall prevalence of taxonomic groups were used to detect possible biological indicators among prokaryotes for various anthropogenic pressures, i.e. acidification, eutrophication and faecal contamination in aquatic environments. In addition, text mining approaches provide powerful alternatives for sequence based status classification and source tracking of contaminants. While these individual sequencing based indicator approaches seem to be powerful, alpha and beta diversity indices provide so far minor precision in ecological status classification. Reasons are the often non-linear association between prokaryotic alpha and beta-diversity with environmental gradients as indicated by first modeling attempts. Still, our results suggest that the limitations in reliably describing reference communities and developing general and robust classification systems for water quality assessment based on prokaryotic sequencing data can be overcome by extensive training data.

          Related collections

          Author and article information

          Contributors
          Journal
          ARPHA Conference Abstracts
          ACA
          Pensoft Publishers
          2603-3925
          March 04 2021
          March 04 2021
          : 4
          Article
          10.3897/aca.4.e65409
          © 2021

          Comments

          Comment on this article