Data from: Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States
Bibliographic record
Abstract
The combination of increasing fire-caused tree mortality and warmer, drier post-fire conditions is making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a novel dataset of post-fire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Post-fire regeneration is sensitive to high-severity fire, which limits seed availability, and post-fire climate, which influences seedling establishment and survival. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected impacts of climate change for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in post-fire regeneration. Across 40–42% of the study area, we project post-fire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031–2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981–2000 to 26–31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support post-fire conifer regeneration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".