Are microbial communities different in prairie wetlands ponds with elevated methylmercury concentrations?
Bibliographic record
Abstract
Methylmercury (MeHg) is a neurotoxin that poses a significant threat to aquatic ecosystems, wildlife, and human populations through seafood consumption. Microbial communities in wetlands play a crucial role in the transformation of mercury, involving the HgcAB gene for mercury (Hg) methylation and the mer operon for Hg demethylation. However, the control mechanisms governing microbial activities and Hg transformations in these ecosystems remain poorly understood. We investigated eight prairie wetland ponds with MeHg concentrations ranging from 0.085 to 3.14 ng/L and identified the microbial communities and the physicochemical factors influencing their composition. Water and sediment samples were collected from each pond and analyzed for total Hg, MeHg, chlorophyll a, dissolved organic carbon (DOC), and sulfate (SO4) concentrations, along with various water quality parameters (temperature, pH, dissolved oxygen percentage, and oxidation-reduction potential). We employed 16S rRNA gene sequencing to characterize the sediment microbial communities and used generalized linear latent variable models, principal component analysis and principal coordinate analysis to explore the associations between taxon abundances and environmental covariates. Distinct patterns in the microbial communities of the prairie wetland ponds were observed with varying MeHg concentrations. We found that microbial communities were influenced by various environmental factors such that communities in ponds with elevated DOC, SO4, and MeHg concentrations were different, taxonomically and functionally than those in ponds with lower concentrations. Our study demonstrates that some ponds exhibited similar microbial communities while others displayed differences, depending on a complex interaction between environmental factors and microbial structure. We show that community structure differs in ponds with different concentrations of MeHg, DOC and SO4, highlighting their associations with the surrounding environmental conditions. Understanding these dynamics is important for formulating effective strategies for managing MeHg contamination in these ecologically significant wetland ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".