The potential significance of ericoid mycorrhizal fungi in ombrotrophic peatland biogeochemistry
Bibliographic record
Abstract
Northern peatlands have sequestered a huge amount of carbon through exceptionally low microbial activity which is partly attributed to their nutrient-poor conditions. Evergreen shrubs, a dominant species in ombrotrophic bogs, adapt to this nutrient-poor condition by developing organic nutrient acquisition strategies, mediated by ericoid mycorrhizal association. However, the mycorrhizal symbionts together with nutrient cycling have been omitted in peatland models, precluding our ability to simulate the significance of nutrient limitation in peatlands following environmental changes. To address this issue, we further developed the well-established peatland model MWM by incorporating a mechanistic mycorrhizal fungi model and both nitrogen and phosphorus cycles. The new model was adopted to simulate the fertilization effect on peatlands and evaluated against measurements from the long-term fertilization experiments at Mer Bleue, a raised ombrotrophic bog located in southern Ontario, Canada. The model successfully reproduced the observed dramatic changes with fertilization in mycorrhizal performance, vegetation composition and carbon cycle. Greater availability of inorganic nutrients diminished the role of mycorrhizal fungi in plant nutrient uptake. More assimilated carbon was allocated to shrub growth, which then inhibited the growth of sphagnum moss and ultimately posed a threat to the carbon-sequestration capacity of peatlands. Therefore, mycorrhizal activities, which have been overlooked in past peatland studies, could play a significant role in understanding how peatlands respond to increased nutrient deposition in the future.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".