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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".