Double whammy: high-severity fire and drought in ponderosa pine forests of the Southwest
Bibliographic record
Abstract
We examine regeneration dynamics across landscapes under extreme climate conditions and a human-altered fire regime in ponderosa pine (Pinus ponderosa Douglas ex Lawson & C. Lawson) forests of the American Southwest. Our research asks how well these forests recover when unprecedented conditions of a high-severity fire regime combine with historical drought conditions. Tree recruitment is documented at five sites in New Mexico after high-severity fires that burned forests in the drought that prevailed from ∼1945 to 1958. We develop a water-balance type model to evaluate how altered microclimate conditions in the years after a fire and during a drought may inhibit ponderosa pine regeneration in comparison with drought conditions alone. We empirically identify two pathways of forest recovery following high-severity fires during drought: recovery to nonforest types, either dense shrubfields or shrubs in grasslands (four sites) or recovery to hyperdense forest (one site). Model simulations predict fewer favorable opportunities for germination, fewer periods favorable for seedling establishment, shortening of favorable establishment periods, and more adverse conditions because of later spring and earlier fall hard freezes. Our research suggests that a specific climate window critical to the capacity of southwestern ponderosa pine trees to regenerate is narrowed by a synchronous occurrence of high-severity fire and drought.
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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".