Anthropogenic Stressors Shape Genetic Structure: Insights from a Model Freshwater Population along a Land Use Gradient
Bibliographic record
Abstract
Environmental pollution including mutagens from wastewater effluents and discontinuity by man-made barriers are considered typical anthropogenic pressures on microevolutionary processes that are responsible for the loss of biodiversity in aquatic ecosystems. Here, we tested for the effects of wastewater treatment plants (WWTPs), weirs and other stressors on the invertebrate species Gammarus pulex at the population genetic level combining evolutionary ecotoxicology, body burden analysis and testing for exposure to mutagens. Exposure to chemical pollution alone and in combination with the presence of weirs resulted in a depression of allelic richness in native G. pulex populations. Our results suggest that the input of a mutagenic effluent from a WWTP resulted in a strong increase in private alleles over the affected populations. In addition, the presence of weirs along the river disrupted the migration across the river and thus the gene flow between G. pulex upstream and downstream. This study provides strong evidence that the assessment of genetic variation including private alleles together with the contamination of mutagenic and nonmutagenic chemical pollution offers new insights into the regulation of genetic population structure and highlights the relevance of emerging anthropogenic pressures at the genetic level.
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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 | 0.001 |
| 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 | 0.000 | 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".