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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".