Differentially abundant bacteria drive the N <sub>2</sub> -fixation of a widespread moss in the forest-tundra transition zone
Bibliographic record
Abstract
ABSTRACT Bryophytes maintain symbiosis with epiphytic bacteria influencing the local nutrient budget. Moss bacterial communities are composed of a core microbiome and bacteria recruited from environmental sources. Notably, symbiotic N 2 -fixing bacteria contribute to the N budget in northern ecosystems through biological nitrogen fixation. This process may be affected by the abundance of diazotrophs and moss nutrient content. We used the abundant moss Racomitrium lanuginosum in a forest tundra and shrub tundra in Northern Quebec, Canada, to investigate the bacterial and diazotrophic communities associated with habitat type using amplicon sequencing of the bacterial 16S rRNA and nifH genes and test whether the moss core microbiome has recruitment from the soil bacteria community. The nifH amplicons and element analysis were used to test the effect of diazotrophic abundance and moss nutrient content on N 2 -fixation activity estimated by acetylene reduction assays. Moss microbial communities between tundra types hosted similar bacterial diversity but differentially abundant groups. The core microbiome of R. lanuginosum is composed of bacteria strongly associated with northern mosses with no significant recruitment from the soil. The relative abundances of dominant diazotrophs are significantly correlated with acetylene reduction rates. In contrast, the moss nutrient content did not significantly drive N 2 -fixation. The proteobacterial genera Azorhizobium and Rhodomicrobium represent newly reported bacteria associated with N 2 -fixation rates in the tundra. We identified critical bacterial groups related to moss-bacterial symbiosis and N 2 -fixation in the forest-tundra transition zone, a changing environment susceptible to climate warming.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".