Parallel introgression and selection on introduced alleles in a native species
Bibliographic record
Abstract
As humans cause the redistribution of species ranges, hybridization between previously allopatric species is on the rise. Such hybridization can have complex effects on overall fitness of native species as new allelic combinations are tested. Widespread species introductions provide a unique opportunity to study selection on introgressed alleles in independent, replicated populations. We examined selection on alleles that repeatedly introgressed from introduced rainbow trout (Oncorhynchus mykiss) into native westslope cutthroat trout (Oncorhynchus clarkii lewisi) populations in western Canada. We found that the degree of introgression of individual single nucleotide polymorphisms from the invasive species into the native is correlated between independent watersheds. A number of rainbow trout alleles have repeatedly swept to high frequency in native populations, suggesting parallel adaptive advantages. Using simulations, we estimated large selection coefficients up to 0.05 favoring several rainbow trout alleles in the native background. Although previous studies have found reduced hybrid fitness and genome-wide resistance to introgression in westslope cutthroat trout, our results suggest that some introduced genomic regions are strongly favored by selection. Our study demonstrates the utility of replicated introductions as case studies for understanding parallel adaptation and the interactions between selection and introgression across the genome. We suggest that understanding this variation, including consideration of beneficial alleles, can inform management strategies for hybridizing species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".