Boreal moss-microbe interactions are revealed through metagenome assembly of novel bacterial species
Bibliographic record
Abstract
Moss-microbe interactions contribute to ecosystem processes in boreal forests. Yet, how host-specific characteristics and the environment drive the composition and metabolic potential of moss microbiomes is still poorly understood. In this study, we use shotgun metagenomics to identify the taxonomy and metabolic potential of the bacteria of four moss species of the boreal forests of Northern Québec, Canada. To characterize moss bacterial community composition and diversity, we assembled the genomes of 110 potentially novel bacterial species. Our results highlight that moss genus, species, gametophyte section, and to a lesser extent soil pH and soil temperature, drive moss-associated bacterial community composition and diversity. In the brown gametophyte section, two Stigonema spp. showed partial pathway completeness for photosynthesis and nitrogen fixation, while all brown-associated Hyphomicrobiales had complete assimilatory nitrate reduction pathways and many nearly complete carbon fixation pathways. Several brown-associated species showed partial to complete pathways for coenzyme M and F420 biosynthesis, important for methane metabolism. In addition, green-associated Hyphomicrobiales (Methylobacteria spp.) displayed potential for the anoxygenic photosystem II pathway. Overall, our findings demonstrate how host-specific characteristics and environmental factors shape the composition and metabolic potential of moss bacteria, highlighting their roles in carbon fixation, nitrogen cycling, and methane metabolism in boreal forests.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".