Ecological outcomes of hybridization vary extensively in <i>Catostomus</i> fishes
Bibliographic record
Abstract
Hybridization outcomes vary geographically and can depend on the environment. Hybridization can also reshape biotic interactions, leading to ecological shifts. If hybrids function differently ecologically in ways that enhance or reduce fitness, and those ecological roles vary geographically, ecological factors might explain variation in hybridization outcomes. However, relatively few studies have focused on ecological traits of hybrids. We compared the feeding ecology of Catostomus fish species and hybrids by using stable isotopes (δ13C and δ15N) as a proxy for diet and habitat use, and compared two native species, an introduced species, and three interspecific hybrid crosses. We included hybrids and parental species from seven rivers where hybridization outcomes vary. Relative isotopic niches of native species varied geographically, but native species did not fully overlap in isotopic space in any river sampled, suggesting little overlap of resource use between historically sympatric species. The introduced species overlapped with one or both native species in every river, suggesting similar resource use and potential competition. Hybrids occupied intermediate, matching, or more transgressive isotopic niches, and varied within and among rivers. Ecological outcomes of hybridization varied across locations, implying that hybridization might have unpredictable, idiosyncratic ecological effects.
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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) | 0.001 | 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".