Secondary insects and diseases contribute to sudden aspen decline in southwestern Colorado, USA
Bibliographic record
Abstract
Reports of drought-associated forest mortality have increased around the world, but the mechanisms of mortality are rarely direct in nature. Biotic agents may kill trees that could otherwise recover and can perpetuate and expand mortality after the stress is relieved. Sudden aspen decline (SAD) has caused rapid, widespread branch dieback and mortality of quaking aspen ( Populus tremuloides Michx.). We compared insects and diseases in 162 damaged and neighboring healthy plots to determine contributing factors and their ecological roles. Cytospora canker, bronze poplar borer, and aspen bark beetles were the most common agents in damaged plots and correlated with crown loss and other factors related to SAD. This was the first documented outbreak of Trypophloeus populi , an aspen bark beetle. As bark beetles and bronze poplar borer increased in damaged stands, they tended to attack trees with healthier crowns. Environmental stress may have directly affected the success of these agents by increasing host susceptibility followed by a density-dependent increase in the insects’ invasive ability. In contrast, Cytospora canker had an identical relationship to crown loss in healthy versus damaged plots, suggesting that it was not limited by inoculum but responded to host susceptibility. Most other pathogens and insects contributed little to SAD and appear to be primary or weakening agents. The biotic agents of mortality in a decline differ greatly from primary agents and play complex and varied roles in healthy versus declining stands.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".