Long-term effects of fire and fire-return interval on population structure and growth of longleaf pine (Pinus palustris)
Bibliographic record
Abstract
We investigated the effect of fire and fire frequency on stand structure and longleaf pine ( Pinus palustris P. Mill.) growth and population demography in an experimental research area in a southwest Florida sandhill community. Data were collected from replicated plots that had prescribed fire-return intervals of 1, 2, 5, or 7 years or were left unburned. Experimental treatment burns have been ongoing since 1976. Plots were sampled to estimate species distribution, stand structure, and longleaf pine density in four developmental stage classes: grass, bolting, small tree, and large tree. Tree-ring growth measurements in combination with burn history were used to evaluate the effects of fire and fire-return interval on basal area increment growth. Fire-return interval impacted stand structure and longleaf pine population structure. Our results suggest that recruitment from the bolting stage to later stages may become adversely affected with very frequent fires (e.g., every 1 or 2 years). Although adult tree productivity was negatively impacted during fire years, tree growth during years between fire events was resilient such that growth did not differ significantly among fire-return intervals. Our study shows that the longleaf pine population as a whole is strongly regulated by fire and fire-return interval plays a key role in structuring this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".