A single episode of sexual reproduction can prevent population extinction under multiple stressors
Bibliographic record
Abstract
Abstract Recent studies have shown that organisms can adapt to changing environments through rapid contemporary evolution. Although intraspecific genetic variation is necessary for rapid evolution to occur, little is known how genetic variation has been produced and maintained before rapid evolution. Here we show that a single episode of sexual reproduction can produce a large amount of intraspecific trait variation that allows population growth in degraded environments by laboratory experiments of a green alga, Closterium peracerosum–strigosum–littorale complex. We observed population dynamics of the alga under multiple stressors and confirmed that high salinity and low pH decreased population growth rates. By comparing parental and their hybrid F 1 populations, we observed larger variation in population growth rates of F 1 populations (i.e., transgressive segregation) when pH was low. Interestingly, even when parental populations had negative growth rates, some F 1 populations showed positive growth rates in severe environmental conditions due to the large variation in population growth. By utilizing the recently obtained genomic information of the alga, we found greater enrichment of genes with copy number variations in terms related to pH stress than those to salt stress in a gene ontology (GO) enrichment analysis. Our results suggest that recombination and variation in the number of gene copies might produce large genetic variation in the F 1 generation. This will be an important step toward a better understanding of evolutionary rescue, where rapid evolution prevents population extinction in changing environments.
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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 | 0.001 |
| 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".