The combined effects of ocean warming and microplastic pollution on marine phytoplankton community dynamics
Bibliographic record
Abstract
Microplastics (MPs) and ocean warming present a dual threat to marine phytoplankton, with significant but not fully understood effects. This study assessed how projected MP pollution and rising water temperatures influence phytoplankton biomass, abundance, and diversity. While MPs at future concentrations did not impact biomass or abundance at current temperatures, under projected warming conditions, biomass decreased by 41 % and diversity by 38.8 % in MP-exposed samples. This suggests that MP toxicity, aggregation, and reduced light penetration, intensified by warming, can inhibit phytoplankton growth. Diatoms, crucial for global primary productivity, were especially affected, with declines in their abundance and diversity potentially reducing carbon sequestration by up to 10.45 billion tons annually. Community composition shifted towards fewer genera, implying lower biodiversity and resilience, which could disrupt marine food webs and affect human populations. Seagrass wetlands, a Nature-based Solution, might mitigate some impacts by trapping MPs and limiting their effects on phytoplankton. These results highlight the urgent need for further research to address and mitigate the combined impacts of MPs and warming on marine ecosystems, due to potential broad ecological and socioeconomic repercussions.
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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 | 0.001 |
| 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".