Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling

Abstract

Short timescale observations are valuable for understanding microbial ecological processes. We assessed dynamics in relative abundance and potential activities by sequencing the small sub-unit ribosomal RNA gene (rRNA gene) and rRNA molecules (rRNA) of Bacteria, Archaea, and Eukaryota once to twice daily between March 2014 and May 2014 from the surface ocean off Catalina Island, California. Typically Ostreococcus, Braarudosphaera, Teleaulax, and Synechococcus dominated phytoplankton sequences (including chloroplasts) while SAR11, Sulfitobacter, and Fluviicola dominated non-phytoplankton Bacteria and Archaea. We observed short-lived increases of diatoms, mostly Pseudo-nitzschia and Chaetoceros, with quickly responding Bacteria and Archaea including Flavobacteriaceae (Polaribacter & Formosa), Roseovarius, and Euryarchaeota (MGII), notably the exact amplicon sequence variants we observed responding similarly to another diatom bloom nearby, 3 years prior. We observed correlations representing known interactions among abundant phytoplankton rRNA sequences, demonstrating the biogeochemical and ecological relevance of such interactions: (1) The kleptochloroplastidic ciliate Mesodinium 18S rRNA gene sequences and a single Teleaulax taxon (via 16S rRNA gene sequences) were correlated (Spearman r = 0.83) yet uncorrelated to a Teleaulax 18S rRNA gene OTU, or any other taxon (consistent with a kleptochloroplastidic or karyokleptic relationship) and (2) the photosynthetic prymnesiophyte Braarudosphaera bigelowii and two strains of diazotrophic cyanobacterium UCYN-A were correlated and each taxon was also correlated to other taxa, including B. bigelowii to a verrucomicrobium and a dictyochophyte phytoplankter (all r > 0.8). We also report strong correlations (r > 0.7) between various ciliates, bacteria, and phytoplankton, suggesting interactions via currently unknown mechanisms. These data reiterate the utility of high-frequency time series to show rapid microbial reactions to stimuli, and provide new information about in situ dynamics of previously recognized and hypothesized interactions.

Benchmarking microbial growth rate predictions from metagenomes

“…Despite the relatively low observed growth rates (0.19-0.44 per day), all three MGII MAGs had predicted maximum growth rates based on CUB near or over 2 per day. This suggests that MGII may be quite active in certain environments, as we previously reported for SPOT where MGII 16S rRNA sequences comprised over 40% of the microbial community (higher than SAR11) on one post-spring-bloom day [56]. Further, MGII have also been found to be very abundant in Monterey Bay, CA [57] and the Northern Gulf of Mexico "Dead Zone", where they were found to be over 10% of the total microbial community [45].…”

ILTER – The International Long-Term Ecological Research Network as a Platform for Global Coastal and Ocean Observation

“…Finally, at the site level, emerging molecular methods will bring major insights by identifying important microbial actors, determining their interactions, and providing deeper insights into biogeochemical processes at ILTER sites. One example of this technology is the MBARI Environmental Sample Processor (Scholin et al, 2009), which has been used to autonomously collect and preserve microbial samples (Ottesen et al, 2011), revealing complex shifts in microbial communities over very short time scales (Needham et al, 2018). See McQuillan and Robidart (2017) for a comprehensive review of recent advances.…”

Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations

“…More recently, Needham and Fuhrman looked at the succession of phytoplankton, archaea and bacteria, but only during 6 months [30]. Another study in the same ecosystem, relying on automated sampling, showed daily and highly dynamic population variations in the three domains of life, and extensively described the biological interactions that took place during the sampling period [56]. A study looking at bacterioplankton diversity and phytoplankton microscopy counts, has shown that despite interannual variations in phytoplankton blooms, bacterioplankton microdiversity patterns seem stable in both bloom and non-bloom conditions [57].…”