Influences of fine-scale disturbance on germinant success in a treeline ecotone
Bibliographic record
Abstract
Fine-scale disturbance can increase seed access to suitable substrates, facilitating germinant emergence and survival, which are necessary elements for treeline advance. We conducted an experiment to test this hypothesis in a white spruce (Picea glauca) treeline ecotone in southwest Yukon, Canada. Sixty seed germination quadrats were established at two elevations (treeline and alpine tundra) and subjected to three levels of simulated disturbance. We sowed 125 seeds in half of the quadrats (30) and measured their emergence and survival over 3 years. Soil temperature, moisture, and organic depth were recorded in all treatments. Treeline quadrats had significantly greater seedling emergence and survival than alpine tundra quadrats. Mean soil temperature, moisture, and organic layer depth were all greater in treeline quadrats. Partially scarified quadrats had the highest germinant emergence compared to unscarified and completely scarified quadrats. Completely scarified quadrats had the highest temperature range and the lowest soil moisture. The results indicate that moderate levels of disturbance can positively influence seedling emergence, while more severe disturbance can lead to high temperature ranges and moisture loss that negate the benefits of lower interspecific competition. Collectively, our findings suggest that fine-scale disturbance can play a significant role in influencing seedling presence in treeline ecotones.
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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".