Successional change, restoration success, and resilience in boreal mixedwood vegetation communities over three decades
Bibliographic record
Abstract
Long-term studies of vegetation succession can inform restoration of degraded forests. We examined resilience of a boreal mixedwood vegetation community, asking whether treatments employed to restore wood production in a degraded ecosystem could also restore diversity and composition of vegetation communities. The Inga Lake trial, established in 1987 in northeastern British Columbia, used mechanical, fire, and chemical and manual treatments, encompassing a gradient of restoration effort, and tree planting to restore a shrubland to white spruce (Picea glauca (Moench) Voss) forest. We monitored vascular plant, bryophyte, and macrolichen composition five times over 31 years on five to seven treatments replicated five times. We used mixed-effects models and nonmetric multidimensional scaling to compare diversity and composition among treatments and with mature reference forests. Low- to high-effort restoration created a gradient from broadleaf- to spruce-dominated overstories. Diversity increased with restoration effort. Four of 253 taxa occurred in mature forests only. There was no evidence that lower versus higher effort treatments followed divergent successional pathways toward broadleaved versus spruce reference communities. Our results suggest that these mixedwood vegetation communities lie within a broad domain of successional attraction that confers high ecological resilience to disturbance. Gap cuttings to stimulate understory re-initiation and provide woody debris are recommended to complete the restoration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".