The resistance of the wheat microbial community to water stress is more influenced by plant compartment than reduced water availability
Bibliographic record
Abstract
Drought is a serious menace to agriculture across the world. However, it is still not clear how this will affect crop-associated microbial communities. Here, we experimentally manipulated precipitation in the field for two years and compared the bacterial communities associated with leaves, roots, and rhizosphere soils of two different wheat genotypes. The bacterial 16S rRNA gene was amplified and sequenced, while 542 microorganisms were isolated and screened for their tolerance to osmotic stress. The bacterial community was not significantly affected by the precipitation manipulation treatments but differed drastically from one plant compartment to the other. Forty-four isolates, mostly bacteria, showed high levels of resistance to osmotic stress by growing in liquid medium supplemented with 30% polyethylene glycol. The Actinobacteria were overrepresented among these isolates, and in contrast to our expectation, precipitation treatments did not influence the odds of isolating osmotic stress-resistant bacteria. However, the odds were significantly higher in the leaves as compared to the roots, the rhizosphere, or the seeds. Our results suggest that isolation efforts for wheat-compatible water stress resistant bacteria should be targeted at the leaf endosphere and that short-term experimental manipulation of precipitation does not result in a more resistant community.
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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) | 0.000 | 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 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".