Landscape-Scale Variability in the Composition, Growth and Pattern of Alpine Treeline Vegetation
Bibliographic record
Abstract
The productivity and distribution of northern and alpine plant species are predicted to increase and advance upslope and northwards in response to climate warming, particularly across the treeline ecotone. However, responses to warming in the past century have been highly variable, especially in regions with complex topography. In order to improve predictions of future change, I used field surveys, dendrochronology, and remote sensing to characterize variability in plant community composition, woody plant growth, and tree spatial patterns across treelines in a range of topographic settings. I then attempted to explain this variability using measured edaphic, climatic, and topographic variables. I found substantial differences in community composition, woody plant growth, and treeline ecotone abruptness between north and south-facing slopes. Shallow active layers and cold soils on north-facing slopes resulted in low shrub cover and slow rates of woody plant growth. The comparatively high cover of tall deciduous shrubs on south-facing slopes, in turn, restricted tree seedling establishment and resulted in relatively abrupt treeline ecotones. Trends in tree growth and the degree of clustering between tree stems varied between mountain ranges with different slope angles. Rapid spring runoff in steep valleys likely caused a spring soil moisture deficit that curbed tree growth over the past several decades, while high exposure to damaging winter winds in shallow valleys resulted in clustered patterns of tree stems. My findings suggest that changes in treeline vegetation over the next century will depend not just on rising air temperatures, but also on edaphic variables, wind exposure, snowpack dynamics, and tree-shrub interactions. Given that much of the landscape-scale variability in the composition, growth, and pattern of treeline vegetation can be attributed to slope aspect and angle, I recommend that these factors be included in predictive models of future vegetation 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".