Predictable Patterns of Disruptive Selection in Stickleback in Postglacial Lakes
Bibliographic record
Abstract
Disruptive selection is often assumed to be relatively rare, because it is dynamically unstable and hence should be transient. However, frequency-dependent interactions such as intraspecific competition may stabilize fitness minima and make disruptive selection more common. Such selection helps explain the maintenance of genetic variation and may even contribute to sympatric speciation. There is thus great interest in determining when and where disruptive selection is most likely. Here, we show that there is a general trend toward weak disruptive selection on trophic morphology in three-spine stickleback (Gasterosteus aculeatus) in 14 lakes on Vancouver Island. Selection is inferred from the observation that, within a lake, fish with intermediate gill raker morphology exhibited slower growth than phenotypically extreme individuals. Such selection has previously been shown to arise from intraspecific competition for alternate resources. However, not all environments are equally conducive to disruptive selection, which was strongest in intermediate-sized lakes where both littoral and pelagic prey are roughly balanced. Also, consistent with theory, we find that sexual dimorphism in trophic traits tends to mitigate disruptive selection. These results suggest that it may be possible to anticipate the kinds of environments and populations most likely to experience disruptive selection.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".