Four decades of soil water stress history together with host genotype constrain the response of the wheat microbiome to soil moisture
Bibliographic record
Abstract
There is little understanding about how soil water stress history and host genotype influence the response of wheat-associated microbiome under short-term decreases in soil moisture. To address this, we investigated how plant breeding history (four wheat genotypes; two with recognized drought resistance and two without) and soil water stress history (same wheat field soil from Saskatchewan with contrasting long-term irrigation) independently or interactively influenced the response of the rhizosphere, root and leaf bacterial and fungal microbiota to short-term decreases in soil water content (SWC). We used amplicon sequencing (16S rRNA gene for bacteria and ITS region for fungi) to characterize the wheat microbiome. Fungal and bacterial communities responses to short-term decreases in SWC were mainly constrained by soil water stress history, with some smaller, but significant influence of plant genotype. One exception was the leaf-associated fungal communities, for which the largest constraint was genotype, resulting in a clear differentiation of the communities based on the genotype's sensitivity to water stress. Our results clearly indicate that soil legacy does not only affect the response to water stress of the microbes inhabiting the soil, but also of the microorganisms more closely associated with the plant tissues, and even of the plant itself.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".