Fuel loads and composition across a pine dominance gradient within fire-dependent pine–oak mixedwoods of the southeastern U.S. undergoing hardwood encroachment
Bibliographic record
Abstract
Fire exclusion and encroachment by shade-tolerant, often fire-sensitive, and/or opportunistic tree species threaten dominance of fire-dependent, open pine ( Pinus spp.) forests throughout the southeastern U.S., largely due to encroaching species’ traits that reduce flammability. To better understand fuels within fire-dependent pine ecosystems undergoing encroachment, we measured loads of varying fuel types (herbaceous, shrub, leaf litter, duff, and woody debris) in 96 plots with little to no recent management across a pine dominance gradient at five sites in east-central Alabama, USA in 2022. Across pine-, mixedwoods-, and hardwood-dominated plots, we found similar mean basal area (36.5–40.2 m 2 ha −1 ), canopy cover (>90%), and total fuel loads (20–22 Mg ha −1 ). Encroaching species had lower basal area and density in pine-dominated plots, but their mean leaf litter fuel loads were similar among plot types (0.9–1.5 Mg ha −1 ). However, encroaching species’ relative contribution to leaf litter fuelbeds declined with increasing pine basal area due to a significant linear increase in pine leaf litter fuel loads. Overall, leaf litter was the only fuel to co-vary with pine dominance, suggesting fuel homogenization with encroachment. Future studies experimentally manipulating leaf litter fuels could determine thresholds where encroaching species’ fire-suppressing leaf litter negates pine’s fire-promoting effects.
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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 | 0.001 | 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".