Warming intensifies soil pathogen negative feedback on a temperate tree
Bibliographic record
Abstract
The soil pathogen-induced Janzen-Connell (JC) effect is considered as a primary mechanism regulating plant biodiversity worldwide. As predicted by the framework of the classic plant disease triangle, severity of plant diseases is often influenced by temperature, yet insufficient understanding of how increasing temperatures affect the JC effect contributes uncertainty in predictions about how global warming affects biodiversity. We conducted a 3-yr field warming experiment, combining open-top chambers with pesticide treatment, to test the effect of elevated temperatures on seedling mortality of a temperate tree species, Prunus padus, from a genus with known susceptibility to soil-borne pathogens. Elevated temperature significantly increased the mortality of P. padus seedlings in the immediate vicinity of parent trees, concurrent with increased relative abundance of pathogenic fungi identified to be virulent to Prunus species. Our study offers experimental evidence suggesting that global warming significantly intensifies the JC effect on a temperate tree species due to increased relative abundance of pathogenic fungi. This work advances our understanding about changes in the JC effect linked to ongoing global warming, which has important implications for predicting tree diversity in a warmer future.
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) | 0.002 | 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".