Warming shifts soil microbial communities and tropical tree seedling mortality
Bibliographic record
Abstract
Plant-soil feedback (PSF), regulated by both mycorrhizae and soil-borne pathogens, is a primary mechanism maintaining high tree species diversity in the tropics. But how warming actually affects PSF is not well understood. We conducted a field warming experiment to test PSF on seedling mortality of two tree species: a rhizobia-associated tree (Ormosia semicastrata, Fabaceae) suffering from host-specific soil-borne pathogens and an ectomycorrhizal fungi-associated tree (Cyclobalanopsis patelliormis, Fagaceae) with low susceptibility to soil-borne pathogens. Soil fungi from the warming versus control seedling plots were identified by molecular sequencing. Results showed that the elevated temperature lowered seedling mortality of O. semicastrata, but had no effect on C. patelliormis seedlings. This indicates that warming weakened the negative PSF on O. semicastrata, presumably due to the observed decrease of the relative abundance of plant-pathogenic fungi and increase of ectomycorrhizal fungi but did not affect the PSF on C. patelliormis. The differential warming effects on seedling mortality of species with different microbial associations affords an example showcasing how the change in soil-borne microbes in response to global warming would, in turn, through PSF, alter tropical tree species composition and diversity. This study helps shed mechanistic light on the debate of biodiversity change as driven by climate change.
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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".