Promoting soil microbial community development in early primary succession on waste rock by mulching with ramial chipped wood, in a boreal context
Bibliographic record
Abstract
The ecological restoration of mine waste rock corresponds to a context of primary succession, characterized by mineral substrates poor in organic matter and nutrients. The addition of organic matter, specifically in the form of ramial chipped wood (RCW) mulch, could facilitate the development of soil microbial communities crucial to ecosystem recovery on mineral substrates. This study examined the interaction between pioneer boreal tree species and soil microorganisms, exploring how a RCW mulch influences the development of microbial and plant communities in primary succession on different mineral substrates: waste rock and sand. The methodology of this research used an experimental design of four complete randomized blocks on an area composed of waste rock at the Lapa Mine, Quebec. Treatments ( n = 4) included two mineral substrates (scarified waste rock or sand) with or without the addition of RCW. The study focused on seedlings of two tree species: Pinus banksiana and Betula papyrifera . Microbial community development was analyzed by metabarcoding, focusing on the rhizosphere of tree seedlings and bulk soil, five years after tree seedling establishment. After five years, RCW mulch boosted bacterial species richness and diversity, particularly around Pinus banksiana and on waste rock, although its effect on fungal diversity was less marked. RCW also favored the development of bacterial and fungal functional groups useful for plant growth. Microbial diversity was more influenced by the physicochemical properties of mineral substrates than by tree species, indicating a preponderant influence of the mineral substrate physicochemical properties during the very early microbial succession. By promoting beneficial bacterial diversity in pioneer trees, RCW appears to be a promising strategy for supporting ecological restoration in disturbed boreal environments.
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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 | 0.000 | 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".