Does wood mulch trigger microbially mediated positive plant-soil feedback in degraded boreal forest sites? A post hoc study
Bibliographic record
Abstract
Introduction: Reforestation of degraded lands in the boreal forest is challenging and depends on the direction and strength of the plant-soil feedback (PSF). Methods: Using a gradient in tree productivity (null, low and high) from a long-term, spatially replicated reforestation experiment of borrow pits in the boreal forest, we investigated the interplay between microbial communities and soil and tree nutrient stocks and concentrations in relation to a positive PSF induced by wood mulch amendment. Results: Three levels of mulch amendment underlie the observed gradient in tree productivity, and plots that had been amended with a continuous layer of mulch 17 years earlier showed a positive PSF with trees up to 6 m tall, a closed canopy, and a developing humus layer. The average taxonomic and functional composition of the bacterial and fungal communities differed markedly betweenlow- and high-productivity plots. Trees in high-productivity plots recruited a specialized soil microbiome that was more efficient at nutrient mobilization and acquisition. These plots showed increases in carbon (C), calcium (Ca), nitrogen (N), potassium (K), and phosphorus (P) stocks and as well as bacterial and fungal biomass. The soil microbiome was dominated by taxa from the fungal genus Cortinarius and the bacterial family Chitinophagaceae, and a complex microbial network with higher connectivity and more keystone species supported tree productivity in reforested plots compared to unproductive plots. Discussion: Therefore, mulching of plots resulted in a microbially mediated PSF that enhances mineral weathering and non-symbiotic N fixation, and in turn helps transform unproductive plots into productive plots to ensure rapid restoration of the forest ecosystem in a harsh boreal environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".