Overstory tree mortality resulting from reintroducing fire to long-unburned longleaf pine forests: the importance of duff moisture
Bibliographic record
Abstract
In forests historically maintained by frequent fire, reintroducing fire after decades of exclusion often causes widespread overstory mortality. To better understand this phenomenon, we subjected 16 fire-excluded (ca. 40 years since fire) 10 ha longleaf pine ( Pinus palustris Mill.) stands to one of four replicated burning treatments based on volumetric duff moisture content (VDMC): wet (115% VDMC); moist (85% VDMC); dry (55% VDMC); and a no-burn control. During the first 2 years postfire, overstory pines in the dry burns suffered the greatest mortality (mean 20.5%); pine mortality in the wet and moist treatments did not differ from the control treatment. Duff reduction was greatest in the dry burns (mean 46.5%), with minimal reduction in the moist and wet burns (14.5% and 5%, respectively). Nested logistic regression using trees from all treatments revealed that the best predictors of individual pine mortality were duff consumption and crown scorch (P < 0.001; R 2 = 0.34). Crown scorch was significant only in dry burns, whereas duff consumption was significant across all treatments. Duff consumption was related to moisture content in lower duff (Oa; R 2 = 0.78, P < 0.001). Restoring fire to long-unburned forests will require development of burn prescriptions that include the effects of duff consumption, an often overlooked fire effect.
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 | 0.000 |
| 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".