Warm, dry conditions inhibit aspen growth, but tree growth and size predict mortality risk in the southwestern United States
Bibliographic record
Abstract
Widespread, rapid aspen (Populus tremuloides Michx.) mortality since the beginning of the 21st century, sometimes called sudden aspen decline (SAD), has been documented in many locations across North America, but it has been particularly pronounced in the southwestern United States. We investigated the relationship among aspen growth, mortality, and climate across three forest types in northern Arizona using cross-dated tree-ring samples from 126 live and 132 dead aspens. Aspen growth was negatively correlated with warm temperatures and positively associated with higher precipitation. Using survival analysis techniques to investigate the links between aspen mortality, tree traits, and climatic conditions, we found that tree traits played a larger role in mortality risk than climate factors. Trees with larger diameters, older trees, and trees with faster growth rates over the past 50 years had a reduced risk of mortality. Management actions aimed at maintaining the most vigorous, fastest growing aspen in the region could help mitigate the impacts of a warmer, drier future.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".