Mechanical site preparation and nurse plant facilitation for the restoration of subarctic forest ecosystems
Bibliographic record
Abstract
Sustainable forest management implies successful regeneration following disturbances. Tree regeneration in subarctic ecosystems can, however, be constrained by limitations to seedling establishment related to cold soils, slow decomposition rates, and competition by ericaceous species. We established a field trial at the northern limit of commercial forests in Québec, Canada, to evaluate to what extent mechanical site preparation (MSP) and planting of a nurse N2-fixing species could promote conifer establishment on a site burned in 2007. The experiment comprised four treatments applied in 2010: standard MSP (disc trenching), standard MSP plus planting of Alnus crispa, intensive MSP, with larger furrows than standard MSP, and a control. Main plots were divided and planted in 2011 with Picea mariana (Mill.) Britton, Stearns & Poggenb. or Pinus banksiana Lamb. We monitored seedling survival, growth, nutrition, and microsite over a 3-year period. Results revealed interactions between treatments and planted species. Mechanical site preparation resulted in higher conifer growth relative to the control conditions, and planting Alnus resulted in growth gains similar to those obtained from intensive MSP. We measured competitive interactions between Alnus and the conifers that might eventually cancel out the initial benefits derived from facilitation by planting the nurse species. Longer term monitoring of interspecific interactions is needed to unravel the mechanisms responsible for the facilitative effect and identify the best management practices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".