Spatio‐temporal dynamics of alternative male phenotypes in coho salmon populations in response to ocean environment
Bibliographic record
Abstract
1. The coexistence of alternative reproductive phenotypes will probably be shaped by spatial and temporal variability in the environment. However, the effects of such variability on coexistence and the scale at which it operates are seldom understood. 2. To quantify such effects, we examined spatial and temporal dynamics in the abundance and frequency of alternative phenotypes of male coho salmon, Oncorhynchus kisutch Walbaum, which mature as either large fighters (age-3 'hooknoses') or small sneakers (age-2 'jacks'). Using over 20 years of data on coded-wire tagged fish released from nine Oregon hatcheries, we tested for the effects of ocean environment independent of those due to freshwater rearing. 3. Annual fluctuations of the abundance of jack and hooknose males within populations were correlated strongly by brood year (cohort) but not by return year (breeding group). This occurred independently of significant effects of release practice (i.e. the number of fish released, body size at release and date of release), indicating that a synchronized fluctuation in mortality during the first year at sea was the predominant cause. As a result, the annual frequency of the alternative phenotypes at breeding varied considerably within populations. 4. Spatial patterns in the annual fluctuations of the two phenotypes were similar (i.e. synchronous among populations), except that jacks showed local spatial structure (decreased synchrony with distance) not evident among hooknoses. This suggests that oceanic processes affecting the two phenotypes operate at different spatial scales. Despite effects on salmon abundance, the ocean environment had little influence through its effects on salmon growth on the relative frequencies of the alternative phenotypes within and among populations. 5. The results provide insight into the evolutionary dynamics of alternative phenotypes, including an intragenerational time lag that increases annual variability in phenotype frequencies at breeding (return years) and the significance of local freshwater processes, rather than oceanic processes, on phenotype expression. Freshwater processes, such as juvenile growth, timing of migration and breeding competition, operating at evolutionary and intragenerational time-scales, are probably the predominant forces affecting phenotype frequency.
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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 | 0.001 | 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) | 0.001 | 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".