Hybridization decreases native cutthroat trout reproductive fitness
Bibliographic record
Abstract
Examining natural selection in wild populations is challenging, but crucial to understanding many ecological and evolutionary processes. Additionally, in hybridizing populations, natural selection may be an important determinant of the eventual outcome of hybridization. We characterized several components of relative fitness in hybridizing populations of Yellowstone cutthroat trout and rainbow trout in an effort to better understand the prolonged persistence of both parental species despite predictions of extirpation. Thousands of genomic loci enabled precise quantification of hybrid status in adult and subsequent juvenile generations; a subset of those data also identified parent-offspring relationships. We used linear models and simulations to assess the effects of ancestry on reproductive output and mate choice decisions. We found a relatively low number of late-stage (F3+) hybrids and an excess of F2 juveniles relative to the adult generation in one location, which suggests the presence of hybrid breakdown decreasing the fitness of F2+ hybrids later in life. Assessments of reproductive output showed that Yellowstone cutthroat trout are more likely to successfully reproduce and produce slightly more offspring than their rainbow trout and hybrid counterparts. Mate choice appeared to be largely random, though we did find statistical support for slight female preference for males of similar ancestry. Together, these results show that native Yellowstone cutthroat trout are able to outperform rainbow trout in terms of reproduction and suggest that management action to exclude rainbow trout from spawning locations may bolster the now-rare Yellowstone cutthroat trout.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".