Seasonal and Spatial Variability of Biological N<sub>2</sub> Fixation in a Cool Temperate Bog
Bibliographic record
Abstract
Abstract Northern peatlands are globally important carbon (C) and nitrogen (N) sinks due to slow decomposition rates resulting in long‐term organic matter accumulation. Despite their large N storage, peatlands depend on sources of bio‐available N to sustain their biomass production. Di‐nitrogen (N2) fixation represents an important biological N source in ombrotrophic bogs, but its environmental controls are still poorly understood. We examined seasonal and spatial variability of Sphagnum‐associated N2 fixation across a hydrological transect (hummock‐hollow‐beaver pond edge) in a temperate ombrotrophic bog. We measured N2 fixation in live Sphagnum plants by acetylene reduction assay calibrated with a 15N2 tracer method, bi‐weekly, from May to November over two growing seasons. We found that N2 fixation increased with soil temperature at 5 cm in the living Sphagnum mat explaining the seasonal variability in N2 fixation. Peak N2 fixation rates occur in mid‐August, when N2 fixation rates are about 10 times larger than during the shoulder seasons (May and November). Spatially, N2 fixation was larger in wetter Sphagnum with larger gravimetric water content in Sphagnum. This relationship was most pronounced in the peak growing season when N2 fixation rates were the highest. Finally, we estimated that the Mer Bleue bog receives around 0.3 g N m−2 annually through Sphagnum‐associated N2 fixation, which accounts for about a fourth of the N accumulated annually into Sphagnum. Future contributions from Sphagnum‐associated N2 fixation to N budgets in peatlands will depend on temperature and moisture changes which have contrasting effects on N2 fixation rates.
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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 | 0.000 |
| 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".