Experimental assessment of tree canopy and leaf litter controls on the microbiome and nitrogen fixation rates of two boreal mosses
Bibliographic record
Abstract
Summary Nitrogen (N 2 )‐fixing moss microbial communities play key roles in nitrogen cycling of boreal forests. Forest type and leaf litter inputs regulate moss abundance, but how they control moss microbiomes and N 2 ‐fixation remains understudied. We examined the impacts of forest type and broadleaf litter on microbial community composition and N 2 ‐fixation rates of Hylocomium splendens and Pleurozium schreberi . We conducted a moss transplant and leaf litter manipulation experiment at three sites with paired paper birch ( Betula neoalaskana ) and black spruce ( Picea mariana ) stands in Alaska. We characterized bacterial communities using marker gene sequencing, determined N 2 ‐fixation rates using stable isotopes ( 15 N 2 ) and measured environmental covariates. Mosses native to and transplanted into spruce stands supported generally higher N 2 ‐fixation and distinct microbial communities compared to similar treatments in birch stands. High leaf litter inputs shifted microbial community composition for both moss species and reduced N 2 ‐fixation rates for H. splendens , which had the highest rates. N 2 ‐fixation was positively associated with several bacterial taxa, including cyanobacteria. The moss microbiome and environmental conditions controlled N 2 ‐fixation at the stand and transplant scales. Predicted shifts from spruce‐ to deciduous‐dominated stands will interact with the relative abundances of mosses supporting different microbiomes and N 2 ‐fixation rates, which could affect stand‐level N inputs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".