Timing of fire relative to seed development may enable non-serotinous species to recolonize from the aerial seed banks of fire-killed trees
Bibliographic record
Abstract
Abstract. The existence of non-serotinous, non-sprouting species in fire regimes where serotiny confers an adaptive advantage is puzzling, particularly when these species recruit poorly from soil seed banks or from burn edges. In this paper, white spruce (Picea glauca (Moench) Voss) was used to show how the timing of fire relative to seed development may permit non-serotinous species to recolonize burned areas from the aerial seed banks of fire-killed trees. To estimate survival of seeds within closed cones during crown fires, cone heating was simulated using a one-dimensional conduction model implemented in a three-dimensional computational fluid dynamics fire behavior model. To quantify the area burned when germinable seed would be contained within closed cones during a mast year, empirical fire occurrence and seed development (germinability and cone opening) data were compared for multiple locations across the white spruce range. Approximately 12% of cones contained viable seed following crown fire simulations (0.072 m s−1 mean spread rate; 9147 kW m−1 mean intensity), and roughly half of the historical area burned resulted from fires that occurred when closed cones would contain germinable seed. Together, these results suggest that non-serotinous species may recolonize burned areas from in situ aerial seed banks, and that this may be an important cause of their existence in fire regimes to which they otherwise seem poorly suited.
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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) | 0.000 | 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".