Spore germination response to capsule size and smoke: co‐expression of bet‐hedging and best‐bet strategies in peatland mosses
Bibliographic record
Abstract
Smoke-mediated spore germination in mosses is a fire-adaptive evolutionary trait that might control plant composition after fire. How capsule size, either alone or in combination with smoke, affects spore germination in peatland mosses remains unknown. We selected three peatland mosses, Sphagnum fuscum, S. squarrosum, and Polytrichum strictum and measured volumes of 40 capsules per species, categorizing them into large- and small-capsule groups. We then assessed spore diameters within each capsule group and examined how capsule size affects spore germination following smoke-water treatment. We found a positive correlation between capsule and spore size only in S. squarrosum. Spore viability was consistent across capsules in all species. Large-capsule spores had higher germination than small-capsule spores in Sphagnum. However, germination was slower in spores from large than small capsules in Sphagnum species, suggesting a trade off between germination percentage and germination speed. Smoke water enhanced germination speed in large-capsule but not in small-capsule spores in all species. Smoke water released dormancy in large-capsule spores of S. squarrosum and S. fuscum by 100 % and 45 %, respectively, which was significantly higher than that in small-capsule spores (33 % and 4 %). There was no such capsule size-dependent difference in P. strictum. The study suggests that the variation inspore germinability among capsules and consistency in smoke-responsive germination of spores, regardless of capsule size, represent dual expressions of bet-hedging (spreading germination time) and best-bet (germinating in best time) strategies in Sphagnum, enabling them to maintain population persistence in peatlands subject to natural and anthropogenic disturbances.
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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".