Microbial succession during the degradation of bioplastic in coastal marine sediment favors sulfate reducing microorganisms
Bibliographic record
Abstract
Marine environments are sinks for many contaminants, including petroleum-based plastic waste. Bioplastics, or biodegradable plastics derived from renewable resources, are considered promising alternatives as numerous studies have demonstrated their degradation in marine environments. However, their rates of degradation vary and microbial consortia responsible for its degradation are not well characterized. Previous research by our group has shown that polyhydroxyalkanoate (PHA) stimulates sulfate reducing microorganisms (SRM), enriches sulfate reduction gene pools, and accumulates antibiotic and metal resistance genes. Here, we quantify the degradation rate of PHA pellets in marine sediment and present the long-term temporal changes in PHA-associated SRM communities over 424 days. For comparative purposes, polyethylene terephthalate (PET) and ceramic served as biofilm controls and the free-living microorganisms in the overlying water column served as a non-biofilm control. PHA experienced a 51% mass loss after 424 days and a generalized additive mixed model predicted that 100% mass loss would require 909 days. Throughout the course of the 424-day exposure, PHA was colonized by a distinct microbial community while PET and ceramic were colonized by similarly structured communities. SRM comprised a larger proportion of the overall community (25 – 40%) in PHA-associated biofilms as compared to PET and ceramic controls across all timepoints. Further, the diversity of SRM was greater within PHA biofilms than PET and ceramic biofilms. This study shows that PHA degrades relatively slowly and promotes a long-term shift in microbial community structure toward sulfate reduction, demonstrating the ability of this manufactured polymer to alter its environment via the disruption of biogeochemical cycling, indicating that PHA rises to the level of pollutant in benthic marine systems.
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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 | 0.000 |
| 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".