Adaptation to complex environments reveals pervasive trade-offs and genetic targets with pleiotropic effects
Bibliographic record
Abstract
Abstract Much of our knowledge on the dynamics of adaptation comes from experimental evolution studies that expose populations to a single selective pressure. However, populations in nature rarely adapt to a single stress at a time. Instead, various biotic and abiotic factors come together to produce complex selective environments. Here, we used experimental evolution to describe adaptive dynamics in the presence of simultaneous stressors, and to quantify the evolution of trade-offs between stressors in a complex environment. We adapted populations of the yeast Saccharomyces cerevisiae to a full-factorial combination of four stressors over the course of 15 serial transfers. Rapid fitness increases were accompanied by the accumulation of mutations in genes and pathways related to specific stressors. Trade-offs evolved rapidly, with the order and mode of trade-off evolution varying between environments due to the inherent physiological and genetic basis of resistance to each stressor. Compensatory evolution for maladaptation as a result of initial trade-offs was typically mediated by fine-tuning of genes associated with the initially adapted environmental component, rather than those associated with the maladapted trait. As environmental complexity increased, mutations had increasingly broader effects across multiple biological processes. Although shared mutations at the individual SNP level were rare, recurrent mutations affecting the same genes and putative biological processes were abundant across environmental complexity. Our results suggest that adaptation in complex environments follows genetic trajectories shaped by pleiotropy and trade-offs, but these trajectories may impose lasting constraints on future 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".