Trait phenology and fire seasonality co‐drive seasonal variation in fire effects on tree crowns
Bibliographic record
Abstract
The plume of hot gases rising above a wildfire can heat and kill the buds in tree crowns. This can reduce leaf area and rates of photosynthesis, growth, and reproduction, and may ultimately lead to mortality. These effects vary seasonally, but the mechanisms governing this seasonality are not well understood. A trait-based physical model combining buoyant plume and energy budget theories shows the seasonality of bud necrosis height may originate from temporal variation in climate, fire behaviour, and/or bud functional traits. To assess the relative importance of these drivers, we parameterized the model with time-series data for air temperature, fireline intensity, and bud traits from Pinus contorta, Picea glauca, and Populus tremuloides. Air temperature, fireline intensity, and bud traits all varied significantly through time, causing significant seasonal variation in predicted necrosis height. Bud traits and fireline intensity explained almost all the variation in necrosis height, with air temperature explaining relatively minor amounts of variation. The seasonality of fire effects on tree crowns appears to originate from seasonal variation in functional traits and fire behaviour. Our approach and results provide needed insight into the physical mechanisms linking environmental variation to plant performance via functional traits.
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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 | 0.001 |
| 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 | 0.001 | 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".