Bacterial assembly in the switchgrass rhizosphere is shaped by phylogeny, host genotype, and growing site
Bibliographic record
Abstract
Summary Since microbial traits are conserved at different taxonomic levels, plant hosts may influence microbiome composition differently at different levels to broadly promote or resist microbiota with traits that impact host fitness. We tested this hypothesis by assessing signals of host genetic influence on bacterial composition in the switchgrass rhizosphere using 128 genotypes in dissimilar growing sites. We employed three common gardens, combined with host genetic mapping, 16S rRNA gene sequence analysis, hierarchical modeling, tests of phylogenetic conservation of host influence, and genome-wide association analyses to determine the contributions of host genetics in shaping rhizosphere bacterial composition at different taxonomic levels. Modeling bacterial assembly showed that growing site was a strong factor shaping bacterial composition in the rhizosphere, though host genetic influence played a significant role. The heritability of bacterial abundance was strongest at the genus level. Phylogenetic signal for heritability was detected within the bacterial phylogeny but conserved clades differed between common gardens. We identified shared host genetic variants associated with bacterial abundance and host traits related to plant metabolism. Our results suggest further investigation is required regarding the genotype-by-environment-by-microbiome relationship to elucidate the factors shaping rhizosphere microbiome composition and the agroecological dynamics shaping plant phenotype.
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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".