Prescribed Fire Increases Seedling Recruitment in a Natural Pitch Pine (Pinus rigida) Population at its Northern Range Limit
Bibliographic record
Abstract
Disturbances, including wildfire, play an important role in forest maintenance, and have been modified over time. Determining the importance of historical disturbance can be complex, especially if disturbance regimes differ over a species' range. Pinus rigida (pitch pine) is associated with wildfire in the core of its range; however, the association becomes less certain toward its range margins, including at the northeast extent of its range in the Thousand Islands Ecosystem (TIE), Ontario, where the species is rare. To test for fire dependence of seedling recruitment in a natural pitch pine population at this range limit, we compared the efficiency of prescribed fire to mechanical and control treatments. We used a Before–After Control–Impact (BACI) design at two sites in the TIE, controlling for the effects of canopy cover, understory cover, and depth to mineral soil. Pitch pine seedlings were observed for the first time in decades in the TIE following treatment; only fire had a significant positive effect on recruitment. Our results suggest that prescribed fire is effective in increasing pitch pine seedling recruitment even in a marginal natural pitch pine population. We discuss what mechanisms might explain these results, as well as restoration considerations including the potential for modified mechanical disturbance treatments where prescribed fires might not be feasible.
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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".