The Willow Microbiome Is Influenced by Soil Petroleum-Hydrocarbon Concentration with Plant Compartment-Specific Effects
Bibliographic record
Abstract
The interaction between plants and microorganisms, which is the driving force behind the decontamination of petroleum hydrocarbon (PHC) contamination in phytoremediation technology, is poorly understood. Here, we aimed at characterizing the variations between plant compartments in the microbiome of two willow cultivars growing in contaminated soils. A field experiment was set-up at a former petrochemical plant in Canada and after two growing seasons, bulk soil, rhizosphere soil, roots, and stems samples of two willow cultivars (Salix purpurea cv. FishCreek, and Salix miyabeana cv. SX67) growing at three PHC contamination concentrations were taken. DNA was extracted and bacterial 16S rRNA gene and fungal internal transcribed spacer (ITS) regions were amplified and sequenced using an Ion Torrent Personal Genome Machine (PGM). Following multivariate statistical analyses, the level of PHC-contamination appeared as the primary factor influencing the willow microbiome with compartment-specific effects, with significant differences between the responses of bacterial, and fungal communities. Increasing PHC contamination levels resulted in shifts in the microbiome composition, favoring putative hydrocarbon degraders, and microorganisms previously reported as associated with plant health. These shifts were less drastic in the rhizosphere, root, and stem tissues as compared to bulk soil, probably because the willows provided a more controlled environment, and thus, protected microbial communities against increasing contamination levels. Insights from this study will help to devise optimal plant microbiomes for increasing the efficiency of phytoremediation technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".