Impact of Microplastic on Freshwater Sediment Biogeochemistry and Microbial Communities Is Polymer Specific
Bibliographic record
Abstract
Plastic debris is a growing threat in freshwater ecosystems and transport models predict that many plastics will sink to the benthos. Among the most common plastics found in the Laurentian Great Lakes sediments are polyethylene terephthalate (especially fibers; PET), polyvinylchloride (particles; PVC), and styrene-butadiene rubber resulting from tire wear (“crumb rubber”; SBR). These materials vary substantially in physical and chemical properties, and their impacts on benthic biogeochemistry and microbial community structure and function are largely unknown. We used a microcosm approach to evaluate the impact of these three plastics on benthic-pelagic coupling, sediment properties, and sediment microbial community structure and function using sediments from Irondequoit Bay, a major embayment of Lake Ontario in Rochester, New York, USA. Benthic metabolism and nitrogen and phosphorous cycling were all uniquely impacted by the different polymers. PET fibers and PVC particles demonstrated the most unique effects, with decreased ecosystem metabolism in sediments containing PET and greater nutrient uptake in sediments with PVC. Microbial diversity was reduced in all treatments containing plastic, but SBR had the most substantial impact on microbial community function, increasing the relative importance of metabolic pathways such as hydrocarbon degradation and sulfur metabolism. Our results suggest that individual polymers have unique impacts on the benthos, with divergent implications for ecosystem function. This provides deeper insight into the myriad ways plastic pollution may impact aquatic ecosystems and will help to inform risk assessment and policy interventions by highlighting which materials pose the greatest risk.
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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 | 0.001 | 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".