Post-fire early successional vegetation buffers surface microclimate and increases survival of planted conifer seedlings in the southwestern United States
Bibliographic record
Abstract
Climate change and fire exclusion have increased the flammability of western United States forests, leading to forest cover loss when wildfires occur under severe weather conditions. Increasingly large high-severity burn patches limit natural regeneration because of dispersal distance, increasing the chance of conversion to non-forest. Post-fire planting can overcome dispersal limitations, yet warmer and drier post-fire conditions can still reduce survival. We examined how two shrub species with different structures affect below-shrub microclimate and survival rates of planted tree seedlings (Pinus ponderosa Lawson & C. Lawson, Pinus edulis Engelm., Pinus strobiformis Engelm., Pseudotsuga menziesii (Mirb.) Franco) following a high-severity fire in northern New Mexico. We expected that Gambel oak (Querus gambelii Nutt.), with its denser canopy, would buffer below-shrub climate causing higher survival rates of planted seedlings more than the lower canopy density New Mexico locust (Robinia neomexicana A. Gray). Seedlings planted under Gambel oak had survival rates 10% to 35% greater than those planted under New Mexico locust. Higher light availability beneath New Mexico locust corresponded to higher temperatures, lower humidity, and higher vapor pressure deficit, impacting the mortality of planted tree seedlings. These results indicate that by waiting for post-fire shrub establishment, selective use of shrubs can buffer microclimate and increase post-fire planting success in the southwestern United States.
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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".