Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context
Bibliographic record
Abstract
Hybridization can profoundly affect the genomic composition and phenotypes of closely related species, and provides an opportunity to identify mechanisms that maintain reproductive isolation between species. Recent evidence suggests that hybridization outcomes within a species pair can vary across locations. However, we still do not know how variable outcomes of hybridization are across geographic replicates, and what mechanisms drive that variation. In this study, we described hybridization outcomes across 27 locations in the North Fork Shoshone River basin (Wyoming, USA) where native Yellowstone cutthroat trout and introduced rainbow trout co-occur. We used genomic data and hierarchical Bayesian models to precisely identify ancestry of hybrid individuals. Hybridization outcomes varied across locations. In some locations, only rainbow trout and advanced backcrossed hybrids towards rainbow trout were present, while trout in other locations had a broader range of ancestry, including both parental species and first-generation hybrids. Later-generation intermediate hybrids were rare relative to backcrossed hybrids and rainbow trout individuals. Using an individual-based simulation, we found that outcomes of hybridization in the North Fork Shoshone River basin deviate substantially from what we would expect under null expectations of random mating and no selection against hybrids. Since this deviation implies that some mechanisms of reproductive isolation function to maintain parental taxa and a diversity of hybrid types, we then modelled hybridization outcomes as a function of environmental variables and stocking history that are likely to affect prezygotic barriers to hybridization. Variables associated with history of fish stocking were the strongest predictors of hybridization outcomes, followed by environmental variables that might affect overlap in spawning time and location.
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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 | 0.000 |
| 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".