Warming affects foliar fungal diseases more than precipitation in a Tibetan alpine meadow
Bibliographic record
Abstract
The effects of global change on semi-natural and agro-ecosystem functioning have been studied extensively. However, less well understood is how global change will influence fungal diseases, especially in a natural ecosystem. We use data from a 6-yr factorial experiment with warming (simulated using infrared heaters) and altered precipitation treatments in a natural Tibetan alpine meadow ecosystem, from which we tested global change effects on foliar fungal diseases at the population and community levels, and evaluated the importance of direct effects of the treatments and community-mediated (indirect) effects (through changes in plant community composition and competence) of global change on community pathogen load. At the population level, we found warming significantly increased fungal diseases for nine plant species. At the community level, we found that warming significantly increased pathogen load of entire host communities, whereas no significant effect of altered precipitation on community pathogen load was detected. We concluded that warming influences fungal disease prevalence more than precipitation does in a Tibetan alpine meadow. Moreover, our study provides new experimental evidence that increases in disease burden on some plant species and for entire host communities is primarily the direct effects of warming, rather than community-mediated (indirect) effects.
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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 | 0.000 | 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) | 0.000 | 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".