Mycorrhizal Communities and Their Effect on Tree Growth at a Post-Mining Site
Bibliographic record
Abstract
Abstract Purpose Primary succession of vegetation in post-mining areas offers an opportunity to study how plant species and individuals interact in space and notably how biotic interactions such as mycorrhizal symbiosis contribute to the revegetalization of degraded environments. Our study aimed to characterize the taxonomical and spatial structure of mycorrhizal communities and determine how mycorrhizae affect seedling growth at a post-mining site. Methods Mycorrhizal fungal communities were identified from fine roots of tree seedlings in a mine tailings site in Quebec, Canada. We used next-generation DNA sequencing to determine mycorrhizal richness and abundance, and analyzed patterns of species sharing based on host plant species or distance. The influence of neighbourhood competition, mycorrhizal communities and soil nutrients on seedling growth were characterized. Results Fungal DNA was amplified from 40% of plant samples, and 474 fungal operational taxonomic units (OTUs) were identified. Ectomycorrhizae were shared among all host species with no significant host specificity but were segregated at the scale of individual plants. The site was characterized by extremely low soil nitrogen and phosphorus concentrations and high arsenic levels. Growth of most host plants was not affected by neighbourhood competition, soil nitrogen, shoot biomass, mycorrhizal richness, mycorrhizal abundance, or sharing of mycorrhizae. Conclusion We found that plant-plant interactions, mycorrhizal networks and soil nutrients were not important factors determining plant growth at this site due to strong nutrient limitations and high As contamination. Considering the low specificity and low access to mycorrhizae in degraded environments, revegetalization projects could introduce mycorrhizae to boost seedling growth.
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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 | 0.001 |
| 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 | 0.000 |
| 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".