Drivers of understory vegetation 18 years after novel experimental partial-harvest treatments in Canadian boreal forests
Bibliographic record
Abstract
Partial harvesting has been proposed as a silvicultural strategy to promote forest sustainability in boreal regions. To succeed, this approach requires repeated assessments of post-harvest recovery by forest communities. To maintain stand-level biodiversity, a clear, long-term understanding of understory responses to harvesting is essential. We evaluated the understory diversity and composition in eastern Canadian boreal forests 18 years after experimental partial harvests. Six study blocks of mature even-aged black spruce (half young and half old) were subjected to three shelterwood harvest treatments: seed-tree harvest, clearcut, and untreated control. Path analyses were used to assess the indirect impacts of harvesting on understory diversity via the soil substrate, light conditions, and levels of living and dead wood. Analyses were run on the understory community greater than one year before and for 18 years after harvesting. Understory diversity responded poorly for one-year post-harvest but peaked 10 years later. Species richness increased by 2–3 times, depending on the harvesting intensity, before gradually resembling unharvested stands as the canopy regenerated. Species richness and diversity recovered after two decades, regardless of harvesting intensity, although species composition took longer to return to its pre-harvest state. Live and dead trees were the primary drivers of understory change during the first decade, while prior understory composition became more influential 10–18 years post-harvest. Partial harvesting can promote gradual canopy recovery, conserve habitats, and support the recovery of understory vegetation. These results underscore the dual roles of environmental factors and pre-existing understory composition in fostering stand recovery.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".