Population Reproductive Structure of Rainbow Trout Determined by Histology and Advancing Methods to Assign Sex and Assess Spawning Capability
Bibliographic record
Abstract
Abstract Rainbow TroutOncorhynchus mykisshave been intensively studied and gametogenesis has been described, but the use of reproductive indices in field studies has not been widely applied when assessing variability in growth or recruitment dynamics. We integrated descriptions for gametogenesis within the framework of standardized terminology for reproductive development in teleosts to develop sex‐specific criteria for assignment of reproductive phases. We used these descriptions and histological analysis of gonad tissue collected from Rainbow Trout in the Colorado River downstream from Glen Canyon Dam to quantify season‐, size‐, and sex‐specific variation in population reproductive structure. The accuracy of nonlethal methods (manual expression and ultrasonography) was evaluated for assigning sex by comparing estimates with those determined by histology. Rainbow Trout were sampled through an annual spawning cycle from October 2018 to April 2019. Spawning capable males were available across the entire period, with a higher proportion earlier in the season compared to later. Females were spawning capable in October, with a peak in February; by April, they were in the early developing phase, indicating that spawning had ended. Elevated levels of atresia (19% for fall spawners) and evidence for delayed maturation were identified, suggesting energetic limitations on the reproductive potential of the population. For both sexes, the probability of being spawning capable increased with fork length, with minimum sizes of ≥283 mm for females and ≥187 mm for males. Sex assignment using ultrasonography was more accurate (46%) than manual expression of gametes (9%), as only a small proportion of males and females expressed gametes. Probabilities of correct sex assignment using ultrasonography were strongly influenced by reproductive phase, with spawning capable fish (females: 100%; males: 77%) having significantly higher probabilities of correct sex assignment compared to immature fish. Furthermore, probabilities of correct sex assignment increased with fish size and were higher for females than for males. Results provide a framework for quantifying spawning capability and population reproductive structure in ongoing research to better understand the drivers of recruitment variability in aquatic ecosystems.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".