Multi-Level Responses of Benthic Macroinvertebrates to Experimental Microplastic Pollution in a Boreal Lake
Bibliographic record
Abstract
The effects of plastic pollution on freshwater benthic macroinvertebrates are not well understood. In the present study, we investigated the effects of microplastics on benthic macroinvertebrates under an environmentally relevant gradient of pollution. Twelve open-bottom limnocorrals were deployed in the littoral zone of a boreal lake at the International Institute for Sustainable Development – Experimental Lakes Area in Ontario, Canada. In June 2022, a mixture of polymers (polystyrene, polyethylene terephthalate, and polyethylene) was added to the limnocorrals to achieve target sediment concentrations of 251 to 2,511,886 microplastic particles per kg of dry weight sediment. Naturally pre-colonized leaf litterbags (5x3 mm mesh size) were added to the limnocorrals and sampled after eight weeks of microplastic exposure to characterize the benthic macroinvertebrate community. We quantified the relationship between sediment microplastic concentrations and benthic macroinvertebrate responses at the organismal (head size of Hyalella azteca), population (sex ratio of Hyalella azteca and relative abundance of nine taxa), and community (total abundance, diversity, structure) levels. We found evidence of moderate effects of microplastic on the sex ratio of Hyalella azteca (F1,10 =5.88, p = 0.036), and weak effects on total richness (lm, F1,10 = 3.827, p = 0.079) and community structure (PERMANOVA pseudo-F1,34 = 1.99, p = 0.083) of benthic macroinvertebrates. Moreover, we found no evidence of effects of microplastics on size of Hyalella azteca nor on total abundance or diversity of benthic macroinvertebrates. The results of this study suggest that under ecologically relevant conditions, exposure to microplastics may only have minor effects on benthic macroinvertebrates, at least on the short term.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".