Natural selection drives patterns of lake–stream divergence in stickleback foraging morphology
Bibliographic record
Abstract
To what extent are patterns of biological diversification determined by natural selection? We addressed this question by exploring divergence in foraging morphology of threespine stickleback fish inhabiting lake and stream habitats within eight independent watersheds. We found that lake fish generally displayed more developed gill structures and had more streamlined bodies than did stream fish. Diet analysis revealed that these morphological differences were associated with limnetic vs. benthic foraging modes, and that the extent of morphological divergence within watersheds reflected differences in prey resources utilized by lake and stream fish. We also found that patterns of divergence were unrelated to patterns of phenotypic trait (co)variance within populations (i.e. the 'line of least resistance'). Instead, phenotypic (co)variances were more likely to have been shaped by adaptation to lake vs. stream habitats. Our study thus implicates natural selection as a strong deterministic force driving morphological diversification in lake-stream stickleback. The strength of this inference was obtained by complementing a standard analysis of parallel divergence in means between discrete habitat categories (lake vs. stream) with quantitative estimates of selective forces and information on trait (co)variances.
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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 | 0.001 |
| 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".