Bacterial Assembly in the Switchgrass Rhizosphere Is Shaped by Phylogeny, Host Genotype, and Growing Site
Bibliographic record
Abstract
If microbial traits are phylogenetically conserved, then variation in traits of plant hosts may influence rhizosphere microbiomes at higher taxonomic levels. To test this hypothesis and genotype-by-environment-by-microbiome (G × E × M) interactions in switchgrass ( Panicum virgatum), we assessed rhizosphere bacterial composition using 128 host genotypes grown at three distinct field sites. First, we found that growing site was a substantial driver of bacterial composition and that specific bacterial taxa correlated with differences in disease incidence and yield across environments. Second, broad-sense heritability analyses revealed that host genetic effects on rhizosphere composition were strongest at the genus level, suggesting a conserved genetic basis for shaping beneficial or pathogenic taxa. Third, we identified shared host genetic variants associated with bacterial abundance and plant metabolism, indicating possible linkages between key microbial traits and agronomic performance. These findings underscore the importance of incorporating rhizosphere microbiomes into switchgrass breeding efforts and call for further investigation of G × E × M interactions to pinpoint microbial interventions that enhance yield and disease resistance. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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".