How Parallel Is Parallel Evolution? A Comparative Analysis in Fishes
Bibliographic record
Abstract
Evidence of phenotypic parallelism is often used to infer the deterministic role played by natural selection. However, variation in the extent or direction of divergence is often evident among independent evolutionary replicates, raising the following question: just how parallel, overall, is parallel evolution? We answer this question through a comparative analysis of studies of fishes, a taxon where parallel evolution has been much discussed. We first ask how much of the among-population variance in phenotypic traits can be explained by different "environment" types, such as high predation versus low predation or benthic versus limnetic. We then use phenotypic change vector analysis to quantify variation in the direction (vector angles) and magnitude (vector lengths) of environment-associated divergence. All analyses show high variation in the extent of parallelism-from very high to very low, along with everything in between-highlighting the importance of quantifying parallelism rather than just asserting its presence. Interestingly, instances of low extents of parallelism represent important components of divergence in many cases, promising considerable opportunities for inferences about the factors shaping phenotypic divergence.
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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 | 0.001 | 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".