Temporal changes in water temperature and salinity drive the formation of a reversible plastic-specific microbial community
Bibliographic record
Abstract
Plastic is a ubiquitous pollutant in the marine environment. Here, we investigated how temporal changes in environmental factors affect the microbial communities formed on plastic (polyethylene terephthalate; PET) versus a ceramic substrate. In situ mesocosms (N = 90 replicates) were deployed at the sediment-water interface of a coastal lagoon and sampled every 4 weeks for 424 days. Sequencing data (16S rRNA) was parsed based on variation in temperature with the exposure starting in fall 2016 and remaining in situ through the next four seasons (winter, spring, summer and fall 2017). PET biofilms were distinct during the summer when salinity and temperature were highest. In particular, a significant shift in the relative abundance of Ignavibacteriales and Cytophagales was observed during the summer, but PET and ceramic communities were again indistinguishable the following fall. Water temperature, salinity and pH were significant drivers of PET biofilm diversity as well as the relative abundance of plastic-discriminant taxa. This study illustrates the temporal and successional dynamics of PET biofilms and clearly demonstrates that increased water temperature, salinity, pH and exposure length play a role in the formation of a plastic-specific microbial community, but this specificity can be lost with a change in environmental conditions.
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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 | 0.000 | 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".