Interactions of sapsuckers and<i>Cytospora</i>canker can facilitate decline of riparian willows
Bibliographic record
Abstract
Drought has caused large-scale plant mortality in ecosystems around the globe. Most diebacks have affected upland forest species. In the past two decades, a large-scale decline of riparian willows (Salix L.) has occurred in Rocky Mountain National Park, Colorado. We examined whether climatic or biotic factors drive and maintain the willow community decline. We compared annual growth and dieback of willows inside and outside of 14-year-old ungulate exclosures and measured groundwater depth and predawn xylem pressures of stems as indicators of drought stress. We also performed an aerial photo analysis to determine the temporal dynamics of the decline. Aerial photo analysis indicated willow decline occurred between 2001 and 2005 and was best explained by an increase in moose population and a decrease in peak stream flows. A new mechanism for willow stem dieback was identified, initiated by red-naped sapsucker wounding willow bark. Wounds became infected with fungus that girdled the stem. DNA analyses confirmed Valsa sordida (Cytospora chrysosperma) as the lethal fungus. Captured sapsuckers had V. sordida spores on feet and beaks identifying them as one possible vector of spread. Predawn xylem pressure potentials remained high through the growing season on all study willows regardless of depth to ground water. Our results indicate that additional mechanisms may be involved in tall willow decline.
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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".