Complex hybridization between deeply diverged fish species in a disturbed ecosystem
Bibliographic record
Abstract
Abstract Over the past two decades researchers have documented the extent of natural hybridization between closely related species using genomic tools. Many species across the tree of life show evidence of past hybridization with their evolutionary relatives. In some cases, this hybridization is complex – involving gene flow between more than two species. While hybridization is common over evolutionary timescales, some researchers have proposed that it may be even more common in contemporary populations where anthropogenic disturbance has modified myriad aspects of the environments in which organisms live and reproduce. Here, we develop a flexible tool for local ancestry inference in hybrids derived from three source populations and describe a complex, recent hybridization event between distantly related swordtail fish lineages ( Xiphophorus ) and its potential links to anthropogenic disturbance. Impact Summary As sequencing tools have advanced, we have found that barriers between animal species are more porous than once thought. Researchers have found evidence for hybridization between species throughout many branches of the tree of life. In some cases, these hybridization events can involve more than two species. Here, we develop a flexible and user-friendly tool that can be used to identify three-way hybrids and report the discovery of hybrids with ancestry from three swordtail ( Xiphophorus ) species from an anthropogenically impacted site on the Río Calnali in Hidalgo, Mexico. Researchers have studied hybrids between two Xiphophorus species along this river for decades, but this is the first documented case of hybridization involving three species. We explore hypotheses for what drove this hybridization event, including anthropogenic pollutants and reduced water quality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".