Does plasticity enhance or dampen phenotypic parallelism? A test with three lake–stream stickleback pairs
Bibliographic record
Abstract
Parallel (and convergent) phenotypic variation is most often studied in the wild, where it is difficult to disentangle genetic vs. environmentally induced effects. As a result, the potential contributions of phenotypic plasticity to parallelism (and nonparallelism) are rarely evaluated in a formal sense. Phenotypic parallelism could be enhanced by plasticity that causes stronger parallelism across populations in the wild than would be expected from genetic differences alone. Phenotypic parallelism could be dampened if site-specific plasticity induced differences between otherwise genetically parallel populations. We used a common-garden study of three independent lake-stream stickleback population pairs to evaluate the extent to which adaptive divergence has a genetic or plastic basis, and to investigate the enhancing vs. dampening effects of plasticity on phenotypic parallelism. We found that lake-stream differences in most traits had a genetic basis, but that several traits also showed contributions from plasticity. Moreover, plasticity was much more prevalent in one watershed than in the other two. In most cases, plasticity enhanced phenotypic parallelism, whereas in a few cases, plasticity had a dampening effect. Genetic and plastic contributions to divergence seem to play a complimentary, likely adaptive, role in phenotypic parallelism of lake-stream stickleback. These findings highlight the value of formally comparing wild-caught and laboratory-reared individuals in the study of phenotypic parallelism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".