Eastern white pine and red pine forest regeneration following secondary disturbance
Bibliographic record
Abstract
Eastern white ( Pinus strobus Lin.) and red pine ( Pinus resinosa Ait.) forests of North America were historically shaped by low to moderate intensity surface fires that created favorable pine regeneration microhabitats. Following European colonization, pine forest area and volume were reduced due to fire suppression, intensive logging, and changing climatic conditions. Ecosystem-based forest management aims to apply sustainable practices inspired by natural disturbances. This study evaluates the ability of shelterwood harvest to emulate surface fires to stimulate pine regeneration. We sampled plots in Témiscamingue (Quebec) and Nipissing (Ontario) that experienced surface fire, shelterwood harvest, or no recent disturbance. We compared disturbance impacts on pine regeneration density and environmental factors influencing regeneration, including canopy and understory vegetation cover, germination substrate cover, and soil characteristics. Fire significantly enhanced pine regeneration compared to shelterwood harvest. Average canopy opening was similar, but shelterwood harvest created canopy heterogeneity. Post-fire white pine regeneration density responded to a threshold of canopy opening heterogeneously reached in shelterwood harvest. In contrast to shelterwood harvest, fire created burnt duff, an effective germination substrate, significantly decreased organic forest floor thickness, and increased pH and nitrogen availability. Similar trends in the effects of both disturbances on canopy and understory vegetation did not result in similar pine regeneration densities, but variations in germination substrates and soil chemistry explained the differences observed. Our findings emphasize the importance of the chemical effects of fire. We recommend combining shelterwood harvest with site preparation techniques that emulate surface fire effects, such as prescribed burning or soil amendments. • Shelterwood harvest alone cannot ensure an abundant regeneration of white and red pines like surface fire. • The chemical effect of surface fire at the seed microhabitat scale is key for abundant regeneration of white and red pines. • Burnt duff is the preferable germination substrate for white and red pine seedlings. • Lower soil acidity and carbon-nitrogen ratio after fire favored seedling density more than nitrogen stocks after harvest. • Surface fire improves red pine regeneration, but this species has different additional needs than white pine.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".