Seed banks of an Arizona Pinus ponderosa landscape: responses to environmental gradients and fire cues
Bibliographic record
Abstract
We measured soil seed banks in 102 plots within a 110 000 ha Arizona Pinus ponderosa landscape, determined seed-bank responses to fire cues and tree canopy types (open or densely treed patches), compared seed-bank composition among ecosystem types, and assessed the utility of seed banks for ecological restoration. Liquid smoke was associated with increased community-level emergence from seed banks in greenhouse experiments, whereas heating to 100 °C had minimal effect and charred P. ponderosa wood decreased emergence. We detected 103 species in seed-bank samples and 280 species in aboveground vegetation. Erigeron divergens was the commonest seed-bank species; with the exception of Gnaphalium exilifolium , species detected in seed banks also occurred above ground. Although a dry, sandy-textured black-cinder ecosystem exhibited the greatest seed density, seed-bank composition was more ecosystem-specific than was seed density. Native graminoids (e.g., Carex geophila and Muhlenbergia montana ) were common in seed banks, whereas perennial forbs were sparse, particularly under dense tree canopies. Our results suggest that (i) smoke may increase emergence from seed banks in these forests, (ii) seed banks can assist establishment of major graminoids but not forbs during ecological restoration, and (iii) seed-bank composition is partly ecosystem-specific across the landscape.
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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".