Plant Identity Shaped Rhizospheric Microbial Communities More Strongly Than Bacterial Bioaugmentation in Petroleum Hydrocarbon-Polluted Sediments
Bibliographic record
Abstract
Manipulating the plant-root microbiota has the potential to reduce plant stress and promote their growth and production in harsh conditions. Community composition and activity of plant-roots microbiota can be either beneficial or deleterious to plant health. Shifting this equilibrium could then strongly affect plant productivity in anthropized areas. In this study, we tested whether repeated bioaugmentation with Proteobacteria influenced plant productivity and the microbial communities associated with the rhizosphere of four plant species growing in sediments contaminated with petroleum hydrocarbons. A mesocosm experiment was performed in randomized block design with two factors: 1) presence or absence of four plants species collected from a sedimentation basin of a former petrochemical plant, and 2) bioaugmentation or not with a bacterial consortium composed of ten isolates of Proteobacteria. Plants were grown in a greenhouse over four months. MiSeq amplicon sequencing, targeting the bacterial 16S rRNA gene and the fungal ITS, was used to assess microbial community structures of sediments from planted or unplanted microcosms. Our results showed that while bioaugmentation caused a significant shift in microbial communities, presence of plant and their species identity had a stronger influence on the structure of the microbiome in petroleum hydrocarbons contaminated sediments. The outcome of this study provides knowledge on the diversity and behavior of rhizosphere microbes associated with indigenous plants following repeated bioaugmentation, underlining the importance of plant selection in order to facilitate their efficient management, in order to accelerate processes of land reclamation.
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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) | 0.000 | 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".