<i>Brassicaceae</i> host plants mask the feedback from the previous year's soil history on bacterial communities, except when they experience drought
Bibliographic record
Abstract
Soil history operates through time to influence the structure and biodiversity of soil bacterial communities. Examining how different soil histories endure will help clarify the rules of bacterial community assembly. In this study, we established three different soil histories in field trials; the following year these plots were planted with five different Brassicaceae species. We hypothesized that the previously established soil histories would continue to structure the subsequent Brassicaceae bacterial root and rhizosphere communities. We used a MiSeq 16S rRNA metabarcoding strategy to determine the impact of different soil histories on the structure and biodiversity of the bacterial root and rhizosphere communities from the five different Brassicaceae host plants. We found that the Brassicaceae hosts were consistently significant factors in structuring the bacterial communities. Four host plants (Sinapis alba, Brassica napus, B. juncea, B. carinata) formed similar bacterial communities, regardless of different soil histories. Camelina sativa host plants structured phylogenetically distinct bacterial communities compared to the other hosts, particularly in their roots. Soil history established the previous year was only a significant factor for bacterial community structure when the feedback of the Brassicaceae host plants was weakened, potentially due to limited soil moisture during a dry year. Understanding how soil history is involved in the structure and biodiversity of bacterial communities through time is a limitation in microbial ecology and is required for employing microbiome technologies in improving agricultural systems.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".