Soil amendments alter understory vegetation composition and functional diversity in poorly regenerated logging sites in Quebec, Canada
Bibliographic record
Abstract
Soil amendments are increasingly used in boreal forest plantations to enhance seedling growth, but their effects on other compartments such as understory vegetation remain poorly understood. This study evaluates the impact of amendments [biochar (2.6 Mg ha −1 ), wood ash (7 Mg ha −1 ), and manure (105 Mg ha −1 )], applied alone or in combination, on understory vegetation after two growing seasons. We measured diversity indices and evaluated understory vegetation community composition. The effects of amendments on plant functional traits were assessed at the species level. We also examined functional diversity and calculated the community-weighted mean to assess the impact of amendments on the functional composition of the plant community. Our results highlight that manure significantly increased Shannon index of diversity from 1.87 to 2.13, with more grasses and non-native legumes with an acquisitive strategy and competitive ability. Functional diversity was the highest for manure treatments (=19.90) and the lowest for treatments without manure (=16.80). In contrast, biochar and wood ash did not significantly alter plant diversity. Community composition was similar between the biochar and control treatments, while wood ash amendment, despite overlapping in plant composition with biochar, resulted in additional species. Biochar and wood ash treatments contained more ruderal and forest herbs and woody species typical of forest disturbances, with wood ash significantly increasing leaf nitrogen concentration by 9 % compared to treatments without wood ash. Together, these findings suggest that soil amendments alter diversity of understory vegetation and act as functional filters on plant communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".