Hybridization and mitochondrial genome introgression between<i>Rana chensinensis</i>and<i>R. kukunoris</i>
Bibliographic record
Abstract
Mitochondrial genome (mito-genome) introgression among metazoans is commonplace, and several biological processes may promote such introgression. We examined two proposed processes for the mito-genome introgression between Rana chensinensis and R. kukunoris: natural hybridization and sex-biased dispersal. We sampled 477 individuals from 28 sites in the potential hybrid zone in the western Tsinling Mountains. Mitochondrial gene (cyt-b) trees were used to examine the introgression events. Microsatellite DNA loci, cyt-b and morphological data were used to identify hybrids and to examine the extent of natural hybridization. We detected rampant bidirectional introgressions, both ancient and recent, between the two species. Furthermore, we found a wide hybrid zone, and frequent and asymmetric hybridization. The hybrid zone cline analysis revealed a clear mitochondrial-nuclear discordance; while most nuclear markers displayed similar and steep clines, cyt-b had a displaced cline centre and a more gradual and wider cline. We also detected strong and asymmetric historical maternal gene flow across the hybrid zone. This widespread hybridization and detected low mito-nuclear conflicts may, at least partially, explain the high frequency of introgression. Lastly, microsatellite data and population genetic methods were used to assess sex-biased dispersal. A weak pattern of female-biased dispersal was detected in both species, suggesting it may not play an important role in the observed introgression. Our data are consistent with the hybridization hypothesis, but support for the sex-biased dispersal hypothesis is weak. We further suggest that selective advantages of the R. kukunoris-type mito-genome in thermal adaptation may also contribute to the introgression between the two 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".