High-velocity upward shifts in vegetation are ubiquitous in mountains of western North America
Bibliographic record
Abstract
The velocity of climate change and its subsequent impact on vegetation has been well characterized at high elevations and latitudes, including the Arctic. But whether species and ecosystems are keeping pace with the velocity of temperature change is not as well documented. Some evidence indicates that species are less able to keep pace with the velocity of climate change along elevational gradients than latitudinal ones. If substantiated this finding could warrant reconsideration of a current cornerstone of conservation planning. Here we use 27 years of high-resolution satellite data to quantify changes in vegetation cover across elevation within nine mountain ranges in western North America, spanning tropical Mexico to subarctic Canada and from coastal California to interior deserts. Across these ranges we show a uniform pattern at the highest elevations in each range, where increases in vegetation have occurred ubiquitously over the past three decades. At these highest elevations, the realized velocity of vegetation varies among mountain ranges from 19.8–112.8 m · decade -1 (mean = 67.3 m · decade -1 ). This is equivalent, with respect to gradients in temperature, to a 14.4–104.3 km · decade -1 poleward shift (mean = 56.1 km · decade -1 ). This realized velocity is 4.4 times larger than previously reported for plants, and is among the fastest rates predicted for the velocity of climate change. However, in three of the five mountain ranges with long-term climate data, realized velocities fail to keep pace with changes in temperature, a finding with important implications for conservation of biological diversity.
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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 | 0.000 | 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".