Germination ecology of the endangered species Asterolasia buxifolia (Rutaceae): smoke response depends on season and light
Bibliographic record
Abstract
In fire-prone regions, many plant species rely on persistent seed banks for post-fire recovery. Understanding dormancy and germination cues is, therefore, important to predict population response. However, the germination ecology of species with physiologically dormant seeds in fire-prone regions is complex. We used the endangered species Asterolasia buxifolia, from riparian habitat in fire-prone south-eastern Australia, to investigate physiologically dormant seeds and their response to fire. We assessed whether fire cues alone promoted germination, or whether seasonal factors and light also played a role. Additionally, we tested the resilience of seeds to heat-shock temperatures produced in soil during fire, so as to identify potential factors that restrict such species to fire refugia. Seeds germinated only at winter seasonal temperatures, and had an obligate smoke and light requirement. Heat-shock treatments above 80°C slowed the germination rate. Smoke-related germination and the tolerance of A. buxifolia seeds to high fire-related temperatures demonstrated that recruitment dynamics can be driven by fire; however, germination is restricted to winter temperatures. This highlights the potential that changes to fire season may have on population persistence. The slow germination rate caused by heat, and a light requirement, may contribute to restricting this species to riparian habitat.
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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".