Active phytoextraction of toluene shifts the microbiome and enhances degradation capacity in hybrid poplar
Bibliographic record
Abstract
Hybrid poplars are widely recognized for their effectiveness in remediating subsurface aromatic hydrocarbon contaminants, including benzene, toluene, ethylbenzene, and xylene isomers (BTEX). While BTEX compounds are frequently found in the transpiration streams of poplars at contaminated sites, the microbial dynamics within these trees, particularly in response to hydrocarbon exposure, remain underexplored. This study utilized high-throughput amplicon sequencing to investigate the trunk microbiome in hybrid poplars at a field-scale toluene phytoremediation site. Across the plant growth season (spring to late summer), we observed a significant seasonal increase in bacterial diversity and richness, particularly in trees located in areas with the highest groundwater and in planta toluene concentrations. During late summer, the microbiomes of these trees were enriched with hydrocarbon-degrading taxa, including Acinetobacter , Pseudomonas , Burkholderia , Sandaracinobacter , and Allorhizobium–Rhizobium, and exhibited enhanced capacities for aerobic toluene degradation based on functional predictions. These findings reveal selective pressures exerted by hydrocarbons on endophytic microbial communities and underscore their role in mitigating volatile contaminant emissions. This study advances our understanding of microbial dynamics in phytoremediation systems and highlights the potential for leveraging endophytes to optimize contaminant degradation. • In planta toluene exposure drives seasonal shifts in Hybrid poplar trunks microbiome. • Microbial community shifts indicate strong hydrocarbon-driven selection pressures. • Enriched hydrocarbon-degrading taxa were found in high-toluene exposure areas. • Late summer microbiomes showed enhanced aerobic toluene degradation capacity. • Microbiome analysis enhances phytoremediation strategies at hydrocarbon-impacted sites.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".