MICROBIAL DIVERSITY AND COMMUNITY COMPOSITION IN THE OPEN OCEAN AND MARINE SEDIMENT
Bibliographic record
Abstract
Over the last fifteen years, there has been a large increase in the literature on microbial community composition in marine sediment (Inagaki et al., 2006, 2015; Biddle et al., 2012; Briggs et al., 2012; Breucker et al., 2013; Lloyd, 2014; Teske et al., 2014; Nunoura et al., 2016; Walsh et al., 2016; Petro et al., 2017; Harrison et al., 2018; Hoshino et al., 2020), and seawater (Quaiser et al., 2011; Hamdan et al., 2013; Walsh et al., 2016; Medina-Silva et al., 2018; Mestre et al., 2018; Quero et al., 2019). As molecular study of these biomes progresses, and the tools available for detailed analyses expand, it has become important to evaluate those tools for their effectiveness and limitations. By combining environmental microbial investigations with evaluation of some of the most common genetic protocols, I have characterized microbial diversity and community composition in (i) Pacific, Atlantic, and Arctic seawater and (ii) Pacific and Atlantic sediment, and I have identified the common results obtainable using (i) two different 16S ribosomal RNA gene (rRNA) hypervariable regions of interest, and (ii) the two amplicon analysis pipelines most commonly used to determine microbial diversity and community composition. My first manuscript, “Influence of 16S rRNA Hypervariable Region on Estimates of Bacterial Diversity and Community Composition in Seawater and Marine Sediment”, looks at the bacterial diversity and community composition of deep-ocean sediment and overlying seawater from one site in the Central North Atlantic and one site in the Equatorial Pacific. In each case, we amplified both the V4 and V6 hypervariable regions of the 16S rRNA gene of each sample and clustered the sequences into operational taxonomic units (OTUs) of 97% similarity. In doing so, we determined that while OTU-level diversity metrics and community composition are quite different between the two tags, (i) vertical patterns of relative diversity are broadly the same, (ii) community composition is very similar for both tags at the class level, and (iii) while the open ocean communities are very similar between the Pacific and Atlantic oceans, the sediment communities of each ocean differ greatly. My second manuscript, “Patterns of Relative Bacterial Richness and Community Composition in Seawater and Marine Sediment are Robust for both Operational Taxonomic Units and Amplicon Sequence Variants”, examines how the choice of bioinformatic analysis pipeline affects characterization of taxonomic richness and community composition in seawater (from 12 sites in the North Atlantic Ocean and Canadian Arctic) and sediment
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".