Crop, genotype, and field environmental conditions shape bacterial and fungal seed epiphytic microbiomes
Bibliographic record
Abstract
Seeds are reproductive structures able to carry and transfer microorganisms that play an important role in plant fitness. Genetic and external factors are reported to be partly responsible for the plant microbiome assemblage, but their contribution in seeds is poorly understood. In this study, wheat, canola, and lentil seeds were analyzed to characterize diversity, structure, and persistence of seed-associated microbial communities. Five lines and 2 generations of each crop were subjected to high-throughput amplicon sequencing of the 16S rRNA and internal transcribed spacer (ITS) regions. Bacterial and fungal communities differed most by crop type (30% and 47% of the variance), while generation explained an additional 10% and 15% of the variance. The offspring (i.e., generation harvested in 2016 at the same location) exhibited a higher number of common amplicon sequence variants (ASVs) and less variability in microbial composition. Additionally, in every sample analyzed, a "core microbiome" was detected consisting of 5 bacterial and 12 fungal ASVs. Our results suggest that crop, genotype, and field environmental conditions contributed to the seed-associated microbial assemblage. These findings not only expand our understanding of the factors influencing the seed microbiome but may also help us to manipulate and exploit the microbiota naturally carried by seeds.
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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".