Metabarcoding reveals unique microbial mat communities and evidence of biogeographic influence in low‐oxygen, high‐sulfur sinkholes and springs
Bibliographic record
Abstract
Abstract High‐sulfur, low‐oxygen environments formed by underwater sinkholes and springs create unique habitats populated by microbial mat communities. To explore the diversity and biogeography of these mats, samples were collected from three sites in Alpena, Michigan, one site in Monroe, Michigan, and one site in Palm Coast, Florida. Our study investigated previously undescribed eukaryotic diversity in these habitats and further explored their bacterial communities. Mat samples and water parameters were collected from sulfur spring sites during the spring, summer, and fall of 2022. Cyanobacteria and diatoms were cultured from mat subsamples to create a culture‐based DNA reference library. Remaining mat samples were used for metabarcoding of the 16S and rbcL regions to explore bacterial and diatom diversity, respectively. Analyses of water chemistry, alpha diversity, and beta diversity articulated a range of high‐sulfur, low‐oxygen habitats, each with distinct microbial communities. Conductivity, pH, dissolved oxygen, temperature, sulfate, and chloride had significant influences on community composition but did not describe the differences between communities well. Chloride concentration had the strongest correlation with microbial community structure. Mantel tests revealed that biogeography contributed to differences between communities as well. Our results provide novel information on microbial mat composition and present evidence that both local conditions and biogeography influence these unique communities.
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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 | 0.000 |
| 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".