A glyphosate-based herbicide selects for genetic changes while retaining within-species diversity in a freshwater bacterioplankton community
Bibliographic record
Abstract
Bacterial populations evolve rapidly in the lab when faced with experimentally-applied selective pressures. Yet how bacteria evolve in nature, in more complex multi-species communities, is both challenging to study and essential to our understanding of ecosystem responses to rapid anthropogenic change. It has been theorized that selection purges within-species diversity in genome-wide selective sweeps, but the prevalence of such sweeps in response to known selective pressures in nature remains unclear. To track bacterial evolution in a semi-natural context, we applied Roundup, a glyphosate-based herbicide (GBH) as a selective pressure to 1000 L ponds containing bacterioplankton communities from a pristine lake. Using metagenomic analyses, we found that GBH treatment substantially affected community diversity, reducing species richness twofold, but did not consistently purge within-species genetic diversity over the four weeks of the experiment. We identified several functional categories of genes targeted by GBH selection across 11 different species of bacteria. There was no evidence for selection on the enzyme targeted by glyphosate, which interferes with amino acid synthesis; however genes involved more broadly in amino acid transport and metabolism were more likely to experience changes in allele frequency, particularly in inferred GBH-sensitive species. Together, these results show how environmental change can rapidly affect bacterial community structure while leaving within-species diversity largely intact. Even without evident genome-wide selective sweeps, we identify consistent genetic targets of selection, pointing to alternative mechanisms of GBH resistance in nature, and suggesting a role for soft or gene-specific selective sweeps in adaptation.
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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 | 0.000 | 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".