Limits to post‐fire vegetation recovery under climate change
Bibliographic record
Abstract
Record-breaking fire seasons in many regions across the globe raise important questions about plant community responses to shifting fire regimes (i.e., changing fire frequency, severity and seasonality). Here, we examine the impacts of climate-driven shifts in fire regimes on vegetation communities, and likely responses to fire coinciding with severe drought, heatwaves and/or insect outbreaks. We present scenario-based conceptual models on how overlapping disturbance events and shifting fire regimes interact differently to limit post-fire resprouting and recruitment capacity. We demonstrate that, although many communities will remain resilient to changing fire regimes in the short-term, longer-term changes to vegetation structure, demography and species composition are likely, with a range of subsequent effects on ecosystem function. Resprouting species are likely to be most resilient to changing fire regimes. However, even these species are susceptible if exposed to repeated short-interval fire in combination with other stressors. Post-fire recruitment is highly vulnerable to increased fire frequency, particularly as climatic limitations on propagule availability intensify. Prediction of community responses to fire under climate change will be greatly improved by addressing knowledge gaps on how overlapping disturbances and climate change-induced shifts in fire regime affect post-fire resprouting, recruitment, growth rates, and species-level adaptation capacity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".