EVOLUTIONARY RESCUE OF SEXUAL AND ASEXUAL POPULATIONS IN A DETERIORATING ENVIRONMENT
Bibliographic record
Abstract
The environmental change experienced by many contemporary populations of organisms poses a serious risk to their survival. From the theory of evolutionary rescue, we predict that the combination of sex and genetic diversity should increase the probability of survival by increasing variation and thereby the probability of generating a type that can tolerate the stressful environment. We tested this prediction by comparing experimental populations of Chlamydomonas reinhardtii that differ in sexuality and in the initial amount of genetic diversity. The lines were serially propagated in an environment where the level of stress caused by salt increased over time from fresh water to the limits of marine conditions. In the long term, the combination of high diversity and obligate sexuality was most effective in supporting evolutionary rescue. Most of the adaptation to high-salt environments in the obligate sexual-high diversity lines had occurred by midway through the experiment, indicating that positive genetic correlations of adaptation to lethal stress with adaptation to sublethal stress greatly increased the probability of evolutionary rescue. The evolutionary rescue events observed in this study provide evidence that major shifts in ways of life can arise within short time frames through the action of natural selection in sexual populations.
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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 | 0.001 | 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 | 0.001 |
| 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".