Biotic and abiotic drivers of tree seedling recruitment across an alpine treeline ecotone
Bibliographic record
Abstract
Treeline responses to climate change ultimately depend on successful seedling recruitment, which requires dispersal of viable seeds and establishment of individual propagules in novel environments. In this study, we evaluated the effects of several abiotic and biotic drivers of early tree seedling recruitment across an alpine treeline ecotone. In two consecutive years, we sowed seeds of low- and high-elevation provenances of Larix decidua (European larch) and Picea abies (Norway spruce) below, at, and above the current treeline into intact vegetation and into open microsites with artificially removed surface vegetation, as well as into plots protected from seed predators and herbivores. Seedling emergence and early establishment in treatment and in control plots were monitored over two years. Tree seedling emergence occurred at and several hundred metres above the current treeline when viable seeds and suitable microsites for germination were available. However, dense vegetation cover at lower elevations and winter mortality at higher elevations particularly limited early recruitment. Post-dispersal predation, species, and provenance also affected emergence and early establishment. This study demonstrates the importance of understanding multiple abiotic and biotic drivers of early seedling recruitment that should be incorporated into predictions of treeline dynamics under climate change.
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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".