Factors influencing the establishment and growth of tree seedlings at Subarctic alpine treelines
Bibliographic record
Abstract
Abstract Significant treeline advance can only occur with successful establishment, growth, and survival of new seedlings. Several studies have examined microsite factors at single locations to explain the presence/absence of seedlings at treeline. We conducted a much larger observational study and included multiple factors to determine (1) which variable(s) was/were most important, (2) whether their importance differed between aspects, and (3) whether the same variables explained variation in seedling growth and damage. We analyzed five biophysical and six shrub variables along four forest–tundra ecotones in southwest Yukon at 640 points. The model that best explained seedling occurrence was similar between north‐ and south‐facing slopes. Of all variables, seedling occurrence was best explained by the proximity, height, and upslope orientation of shrubs (relative to the seedling). The data indicate an optimal range of shrub cover, which differed with aspect. On north‐facing slopes, seedlings occurred most often when shrub cover exceeded 13%, while on south‐facing slopes seedlings occurred most when shrub cover was between 9% and 72%. We also found that with the exception of shrub‐related factors, very few biophysical variables explained size and growth characteristics and damage of tree seedlings, suggesting that the relative strength and importance of variables change depending on the life stage and size of the tree seedling. Collectively, our results demonstrate a non‐random distribution of seedlings in the forest–tundra ecotone, suggesting that as shrub distributions change with climate change, colonization sites for seedlings will also be influenced.
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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 | 0.001 |
| 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".