Ectomycorrhizal fungi contribute to soil organic matter cycling in sub-boreal forests
Bibliographic record
Abstract
Soils of northern temperate and boreal forests represent a large terrestrial carbon (C) sink. The fate of this C under elevated atmospheric CO2 and climate change is still uncertain. A fundamental knowledge gap is the extent to which ectomycorrhizal fungi (EMF) and saprotrophic fungi contribute to C cycling in the systems by soil organic matter (SOM) decomposition. In this study, we used a novel approach to generate and compare enzymatically active EMF hyphae-dominated and saprotrophic hyphae-enriched communities under field conditions. Fermentation-humus (FH)-filled mesh bags, surrounded by a sand barrier, effectively trapped EMF hyphae with a community structure comparable to that found in the surrounding FH layer, at both trophic and taxonomic levels. In contrast, over half the sequences from mesh bags with no sand barrier were identified as belonging to saprotrophic fungi. The EMF hyphae-dominated systems exhibited levels of hydrolytic and oxidative enzyme activities that were comparable to or higher than saprotroph-enriched systems. The enzymes assayed included those associated with both labile and recalcitrant SOM degradation. Our study shows that EMF hyphae are likely important contributors to current SOM turnover in sub-boreal systems. Our results also suggest that any increased EMF biomass that might result from higher below-ground C allocation by trees would not suppress C fluxes from sub-boreal soils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".