Ecological Roles and Shared Microbes Differentiate the Plastisphere from Natural Particle-Associated Microbiomes in Urban Rivers
Bibliographic record
Abstract
The "plastisphere," comprising microbes associated with microplastics (MPs), may have substantial ecological impacts on riverine ecosystems. However, little is known about how the microbiomes associated with anthropogenic MPs compare with those associated with natural particles (NPs) in urban rivers with varying MP pollution levels. We therefore conducted a comparative analysis of the metagenomes associated with MPs and NPs (100-5000 μm) and river water (RW) across 10 urban river systems. Although we found similarities in taxonomic and functional compositions between the microbiomes associated with MPs and NPs, the plastisphere exhibited distinct associations with specialized taxa and life-history strategies. These unique traits enhanced the potential of the plastisphere for complex carbohydrate and plastic degradation, nitrate and nitric oxide reduction, and antibiotic resistance and virulence compared with the NP or RW microbiomes. Furthermore, MPs supported the sharing of unique microbes with the surrounding RW; these shared microbes possessed enhanced horizontal gene transfer capabilities and potentially could disperse traits of the plastisphere into the broader RW microbiomes. This study highlights the distinct ecological roles and shared microbes of the plastisphere, indicating that MP pollution may substantially and uniquely impact the function and health of riverine ecosystems.
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 | 0.001 | 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".