Network analysis of 16S rRNA sequences suggests microbial keystone taxa contribute to marine N2O cycling
Bibliographic record
Abstract
Abstract The mechanisms by which large-scale microbial community function emerges from complex ecological interactions between individual taxa and functional groups remain obscure. We leveraged network analyses of 16S rRNA amplicon sequences obtained over a seven-month timeseries in seasonally anoxic Saanich Inlet (Vancouver Island, Canada) to investigate relationships between microbial community structure and water column N2O cycling. Taxa separately broadly into three discrete subnetworks with contrasting environmental distributions. Oxycline subnetworks were structured around keystone aerobic heterotrophs that correlated with nitrification rates and N2O supersaturations, linking N2O production and accumulation to taxa involved in organic matter remineralization. Keystone taxa implicated in anaerobic carbon, nitrogen, and sulfur cycling in anoxic environments clustered together in a low-oxygen subnetwork that correlated positively with nitrification N2O yields and N2O production from denitrification. Close coupling between N2O producers and consumers in the anoxic basin is indicated by strong correlations between the low-oxygen subnetwork, PICRUSt2-predicted nitrous oxide reductase (nosZ) gene abundances, and N2O undersaturation. This study implicates keystone taxa affiliated with common ODZ groups as a potential control on water column N2O cycling and provides a theoretical basis for further investigations into marine microbial interaction networks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".