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AME prepress abstract   -  DOI: https://doi.org/10.3354/ame02006

Determining growth rates of heterotrophic bacteria from 16S rRNA gene sequence-based analyses of dilution experiments

Michael R. Landry*, Alexandra L. Freibott, Ariel Rabines, Andrew E. Allen

*Corresponding author:

ABSTRACT: Vital rates, including growth responses to environmental variability, are poorly characterized for the diverse taxa of heterotrophic bacterioplankton (HBact) in marine ecosystems. Here we evaluate the potential for combining molecular analyses with dilution experiments to assess taxon-specific growth (cell division) and net growth rates of HBact in natural waters. Two-treatment dilution experiments were conducted with in situ incubations under three environmental conditions in the California Current Ecosystem, at Offshore and Inshore sites during a warm upwelling-suppressed year (2014) and for normal inshore Upwelling, representing a 33-fold primary production range. Relative sequence reads from 16S rRNA metabarcoding were normalized to total HBact counts from flow cytometry for community abundance and rate calculations. Composition varied from dominance of Alphaproteobacteria (56%) in oligotrophic Offshore (SAR11) and mesotrophic Inshore waters (SAR11 and Rhodobacteria) to Bacteriodes/Flavobacteria dominance (64%) and mixed sub-taxon importance (Polaribacter, Rhodobacteria, Formosa) during Upwelling. Net growth rates in bottles, validated by comparison to ambient community net growth following a satellite-tracked drifter, varied from near steady state for Offshore and Inshore conditions to dynamic community changes during Upwelling. Mean growth rates doubled (0.33 to 0.62 d-1) over the productivity range, and taxon estimates varied from -0.17 d-1 (Formosa, Offshore) to 1.53 d-1 (SAR11, Upwelling). Increasing growth of Flavobacteria and Rhodobacteria paralleled their abundance and dominance increases with productivity. SAR11 growth remained higher than average with increasing production, despite declining abundances. We highlight possible PCR or 16S rRNA gene copy biases of growth rate estimates as research needs for further applications of this approach.