Soil drivers of fungal, bacterial and plant diversity in contaminated Southern Californian sites: Implications for dryland bioremediation
Bibliographic record
Abstract
This exploratory study surveyed seven contaminated brownfields and Superfund sites in Southern California to identify locally adapted species tolerant of mixed organic and metal contamination under arid and semi-arid conditions. Five novel native plants, including Brickellia californica , Baccharis salicifolia , Baccharis sarothroides , Eriogonum fasciculatum , and Heterotheca grandiflora were identified as hyperaccumulators of copper (Cu), alongside a non-native species from the Asteraceae family, Helminthotheca echioides . Additional metal-accumulating plants (including native plants) for lead (Pb), chromium (Cr), arsenic (As), and nickel (Ni) were identified, and warrant further evaluation for their phytoremediation potential. Dominant microbial communities included fungal taxa from the Ascomycota and bacterial taxa from the Proteobacteria , with bioremediation candidates detected across sites. Redundancy and principal component analyses revealed that site variability, water-extractable organic carbon, and soil texture significantly shaped microbial communities. These findings provide insight into plant and microbial candidates for bioremediation and the soil drivers influencing their success in Southern California. The results support experimental research into regionally adapted, climate-resilient bioremediation and ecological restoration strategies for contaminated arid and semi-arid environments. • Five new copper hyperaccumulators found on California brownfield sites. • Native plants identified with potential to accumulate Pb, Cr, As, and Ni. • Ascomycota fungi and Proteobacteria dominate contaminated site microbiomes. • WEOC and soil texture drive microbial structure on polluted dryland sites. • Drought-adapted species show promise for dryland bioremediation efforts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".