Taxon-Function Decoupling as an Adaptive Signature of Lake Microbial Metacommunities Under a Chronic Polymetallic Pollution Gradient
Bibliographic record
Abstract
Adaptation of microbial communities to anthropogenic stressors can lead to reductions in microbial diversity and disequilibrium of ecosystem services. Such adaptation can change the molecular signatures of communities with differences in taxonomic and functional composition. Understanding the relationship between taxonomic and functional variation remains a critical issue in microbial ecology. Here, we assessed the taxonomic and functional diversity of a lake metacommunity system along a polymetallic pollution gradient caused by 60 years of chronic exposure to acid mine drainage (AMD). Our results highlight three adaptive signatures. First, a signature of taxon - function decoupling was detected in the microbial communities of moderately and highly polluted lakes. Second, parallel shifts in taxonomic composition occurred between polluted and unpolluted lakes. Third, variation in the abundance of functional modules suggested a gradual deterioration of ecosystem services (i.e. photosynthesis) and secondary metabolism in highly polluted lakes. Overall, changes in the abundance of taxa, function, and more importantly the polymetallic resistance genes such as copA, copB, czcA, cadR,cCusA, were correlated with trace metal content (mainly Cadmium) and acidity. Our findings highlight the impact of polymetallic pollution gradient at the lowest trophic levels.
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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 | 0.001 |
| 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".