Test of a conservation intervention highlights temporal variability in hybridization dynamics in <i>Catostomus</i> fishes
Bibliographic record
Abstract
Abstract Non-native species are a leading threat to fish biodiversity. They pose risks to native populations through human-mediated introductions resulting in hybridization events, which could result in demographic or genetic swamping. Catostomus fishes in the Upper Colorado River Basin are an example of this. Extensive hybridization occurs between non-native white suckers ( C. commersonii ) and native flannelmouth and bluehead suckers ( C. latipinnis and C. discobolus ). This system provides a suitable model for using genomic analyses to test the efficacy of an intervention to reduce the abundance of non-native species and production of hybrid offspring. This study implemented a Resistance Board Weir (RBW) as a fish barrier across Roubideau Creek, a tributary of the Gunnison River in Colorado (USA), to restrict non-native sucker participation in spawning events. Conducted over four years, the study gathered genomic data from larval fish samples, pre- and post-implementation of the RBW. We used genomic data to determine the efficacy of a RBW at limiting non-native and hybridized sucker larval production. We found no significant effect of the weir on the proportion of white sucker ancestry in larval fish across the four years of the study, which included three years of weir usage when access was successfully controlled for variable amounts of time. Overall, this work provides insight into the efficacy of a resistance board weir as a management tool for non-native suckers, and highlights interannual variability. This work contributes valuable information for policy and fisheries management in Colorado.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".