Geographic patterns of upward shifts in treeline vegetation across western North America, 1984–2017
Bibliographic record
Abstract
Abstract. Previous research has shown that (1) treelines are shifting upward in elevation on high mountain peaks worldwide, and (2) the rate of the upward shift appears to have increased markedly in recent decades, at least in a few cases that have been studied in detail. Because treeline elevational shift is a process manifested over broad scales of space and time, a particular challenge has been that of obtaining a broad enough view of patterns of treeline shift to permit inferences about geographic and environmental patterns. What is more, intensive studies of treelines have been concentrated in north temperate regions such that little information is available about treeline shift patterns at lower latitudes. We attempted to address this challenge by analyzing long time series of vegetation indices derived from Landsat imagery obtained and prepared via Google Earth Engine from the 1980s to the present. We sampled vegetation indices at points spaced every 100 m along 100 km transects radiating out in eight directions from 115 high peaks across western North America (Canada to Central America), which means that we are sampling approximately every second or third pixel in the corresponding Landsat images. Considerable data preparation was necessary, including ending transects <2 km into closed forest, identifying current treelines via reference to Google Earth imagery, and consideration only of up to <1 km above the treeline. Patterns that emerged were – as is well known – that treelines are generally higher at lower latitudes but – previously unknown – that the magnitude of treeline shifts is nonrandomly distributed with respect to latitude, longitude, and their interaction. This analysis, via a broad-scale view of treeline shifts over almost 40 years and a geographic span of more than 40° of latitude, demonstrates that climate change effects and consequent treeline shifts are most dramatic in tropical regions where few or no detailed treeline studies have been or are being conducted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".