Long-term pine regeneration, shrub layer dynamics, and understory community composition responses to repeated prescribed fire in <i>Pinus resinosa</i> forests
Bibliographic record
Abstract
Prescribed fire is increasingly viewed as a valuable tool for reversing ecological consequences of fire suppression within fire-adapted forests. While the use of burning treatments in northern temperate conifer forests has received considerable attention, the long-term (>10 year) effects on understory composition and dynamics have not been quantified. We describe the persistence of prescribed fire effects on the woody and herbaceous understory in a mature red pine (Pinus resinosa Ait.) forest in northern Minnesota, USA, over a ∼50-year period, as well as the relative roles of fire season and frequency in affecting vegetation responses. Burning treatments were applied from 1960 to 1970 on 0.4 ha experimental units and crossed fire season and frequency in a randomized block design. Burning altered shrub layer dynamics and composition in both the short and long terms and was influenced by both fire season and frequency, with frequent summer season burns having the largest impact, including greatest control of hazel (Corylus spp.). The application of fire facilitated regeneration of pine; however, recruitment into the overstory was limited. Additionally, community composition of the herbaceous understory diverged 40+ years following burning. This study highlights the importance of continued burning in affecting vegetation responses and the potential of fire as a long-lasting vegetation management tool in these forests.
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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) | 0.000 | 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".