Rapid genetic adaptation to a novel ecosystem despite a massive bottleneck
Bibliographic record
Abstract
Introduced and invasive species make excellent natural experiments for investigating rapid evolution. Here, we describe the effects of genetic drift and rapid genetic adaptation in pink salmon (Oncorhynchus gorbuscha) that were accidentally introduced to the Great Lakes via a single introduction event 31-generations ago. Using whole-genome resequencing for 134 fish spanning five sample groups across the native and introduced range, we estimate that the progenitor population’s effective population size was 146,886 at the time of introduction, whereas the founding population’s effective population size was just 72—a 2040-fold decrease. As expected with a severe bottleneck, we show reductions in genome-wide measures of genetic diversity, specifically a 37.7% reduction in the number of SNPs and an 8.2% reduction in observed heterozygosity. Despite this decline in genetic diversity, we provide evidence for putative selection at 47 loci across multiple chromosomes in the introduced populations, including missense variants in genes associated with circadian rhythm, immunological response, and maturation, which match expected or known phenotypic changes in the Great Lakes. For one of these genes, we use a species-specific agent-based model to rule out genetic drift and conclude that a strong response to selection occurred in a period gene (per2) that plays a predominant role in determining an organism’s daily clock, matching large day length differences experienced by introduced salmon during important phenological periods. Together, these results inform how populations might evolve rapidly to new environments, even with a small pool of standing genetic variation.
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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 | 0.001 |
| 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".