Flow cytometric monitoring of bacterioplankton phenotypic diversity predicts high population-specific feeding rates by invasive dreissenid mussels

Research output: Contribution to journalArticle

Authors

  • Ruben Props
  • Marian L. Schmidt
  • Jasmine Heyse
  • Henry A. Vanderploeg
  • Nico Boon
  • Vincent J. Denef

Institutes & Expert groups

  • University of Michigan
  • NOAA Great Lakes Environmental Research Laboratory
  • UGent - Universiteit Gent
  • EC - JRC - European Commission - Joint Research Centre

Documents & links

DOI

Abstract

Species invasion is an important disturbance to ecosystems worldwide, yet knowledge about the impacts of invasive species on bacterial communities remains sparse. Using a novel approach, we simultaneously detected phenotypic and derived taxonomic change in a natural bacterioplankton community when subjected to feeding pressure by quagga mussels, a widespread aquatic invasive species. We detected a significant decrease in diversity within 1 h of feeding and a total diversity loss of 11.6 � 4.1% after 3 h. This loss of microbial diversity was caused by the selective removal of high nucleic acid populations (29 � 5% after 3 h). We were able to track the community diversity at high temporal resolution by calculating phenotypic diversity estimates from flow cytometry (FCM) data of minute amounts of sample. Through parallel FCM and 16S rRNA gene amplicon sequencing analysis of environments spanning a broad diversity range, we showed that the two approaches resulted in highly correlated diversity measures and captured the same seasonal and lake-specific patterns in community composition. Based on our results, we predict that selective feeding by invasive dreissenid mussels directly impacts the microbial component of the carbon cycle, as it may drive bacterioplankton communities toward less diverse and potentially less productive states.

Details

Original languageEnglish
Pages (from-to)521-534
Number of pages14
JournalEnvironmental Microbiology
Volume20
Issue number2
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • Flow cytometric monitoring, phenotypic diversity

ID: 3751087