High egg retention in <scp>Chinook Salmon <i>Oncorhynchus tshawytscha</i></scp> carcasses sampled downstream of a migratory barrier
Bibliographic record
Abstract
Barriers in rivers have the potential to severely decrease functional connectivity between habitats. Failure to pass barriers and reach natal spawning habitat may compromise individual reproductive success, particularly for semelparous, philopatric species that rely on free-flowing rivers to reach natal habitat during their once-in-a-lifetime spawning migrations. To investigate the consequences of in-river barriers on fish spawning success, we quantified egg retention and spawning effort (caudal fin wear) in female Chinook Salmon Oncorhynchus tshawytscha carcasses collected downstream of the Whitehorse Hydro Plant on the upper Yukon River and at a nearby free-flowing tributary (Teslin River) from 2018 to 2020 (~2900 km migrations). Previous studies have demonstrated that a large proportion of fish attempting to reach spawning locations upstream of the hydro plant fail to pass the associated fishway. We estimated nearly all female salmon failing to pass the hydro plant attempted spawning in non-natal habitat downstream, but that these females retained ~34% of their total fecundity compared to ~6% in females from the free-flowing river. Females downstream of the hydro plant also had lower wear on their caudal fin, a characteristic that was correlated with increased egg deposition. Egg retention did not vary across years with different run sizes, and we propose that egg retention downstream of the hydro plant was not driven by density-dependent mechanisms. Findings from this work indicate that female Chinook Salmon can still deposit eggs following failed fish passage and failure to reach natal spawning sites, though egg retention rates are considerably higher and uncertainties remain about reproductive success. We encourage researchers to incorporate carcass surveys into fish passage evaluations for semelparous species to fully account for consequences of failed passage.
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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 | 0.000 |
| 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 | 0.000 |
| 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".