Data from: Sexual dimorphism modifies habitat‐associated divergence: evidence from beach and creek breeding sockeye salmon
Bibliographic record
Abstract
Studies of parallel or convergent evolution (the repeated, independent evolution of similar traits in similar habitats) rarely explicitly quantify the extent of parallelism (i.e., variation in the direction and/or magnitude of divergence) between the sexes; instead they often investigate both sexes together or exclude one sex. However, differences in male and female patterns of divergence could contribute to overall variation in the extent of parallelism among ecotype pairs, especially in sexually dimorphic traits. Failing to properly attribute such variation could lead to underestimates of the importance of environmental variation in shaping phenotypes. We investigate the extent of parallelism in the body shape of male and female beach and creek spawning sockeye salmon (Oncorhynchus nerka) from two lake systems in western Alaska that were colonized independently after the last ice age. Although both sexes showed some degree of parallelism, patterns of beach‐creek body shape divergence vary between the sexes and between lake systems. Phenotypic change vector analyses revealed highly parallel aspects of divergence between males from different lake systems (males from beaches had deeper bodies than males from creeks) but weaker parallelism in females, and high parallelism between the sexes in one lake system but not the other. Body shape also had population‐specific components, which were mostly, but not entirely, explained by environmental variation in the form of creek depth. Our results highlight the importance of explicitly considering the extent of parallelism between the sexes, and environmental variation among sites within habitat types.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".