Intermittent water stress favors microbial generalists that better help wheat under drought
Bibliographic record
Abstract
Abstract Microorganisms can improve plant resistance to drought through various mechanisms such as the production of plant hormones, osmolytes, antioxidants, and exopolysaccharides. It is, however, unclear how previous exposure to water stress affects the functional capacity of the soil microbial community to help plants resist drought. We compared two soils that had either a continuous or intermittent water stress history for almost forty years. We grew wheat in these soils and subjected it to a water stress, after which we collected the rhizosphere soil and shotgun sequenced its metagenome. Wheat growing in the soil with an intermittent water stress history maintained a higher fresh biomass when subjected to water stress. Genes related to resistance to drought were more abundant in the metagenome and more prevalent, diversified, and redundant in the metagenome assembled genomes of the soil with an intermittent water stress history as compared to the soil with a continuous water stress history. We suggest that an intermittent water stress history selects for generalists that are adapted to both low and replete water contents, and that these generalists harbor a larger repertoire of genes beneficial for life under water stress.
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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 | 0.001 |
| Research integrity | 0.001 | 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".