Tree establishment and growth drive treeline advance and change treeline form on Pikes Peak (Colorado) in response to recent anthropogenic warming
Bibliographic record
Abstract
Treeline advance is a well-established response of treeline ecotones to climate change. However, the degree to which tree growth and establishment drives treeline movement is widely debated. We used geographic information system (GIS) analysis of aerial photographs and dendrochronological analysis of tree growth and age structure to examine treeline dynamics on Pikes Peak (Colorado). The rate of treeline advance at the site (1938–2017) was 0.235 m·year–1, and it accelerated through time. Several sites have transformed from abrupt to diffuse topology. Regional temperatures significantly increased after the 1890s, particularly in the last half-century. Tree growth was inhibited by late spring snow in the 1935–1985 window and enhanced by growing season temperature in the 1965–2009 window. Tree establishment above treeline appears to have transformed treeline topology and set up the potential for further treeline advance. We conclude that if current climatic trends and system relationships continue, treeline should continue to advance because (i) there are large numbers of seedlings and saplings above the treeline due to continuous significant seedling recruitment and (ii) growth of trees in the treeline ecotone is positively related to growing season temperatures, which are increasing. Other limits to the system, such as drought or topographic barriers, may arise in the future.
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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".