Compiling forty years of guppy research to investigate the factors contributing to (non)parallel evolution
Bibliographic record
Abstract
Examples of parallel evolution have been crucial for our understanding of adaptation via natural selection. However, strong parallelism is not always observed even in seemingly similar environments where natural selection is expected to favour similar phenotypes. Leveraging this variation in parallelism within well-researched study systems can provide insight into the factors that contribute to variation in adaptive responses. Here we analyse the results of 36 studies reporting 446 average trait values in Trinidadian guppies, Poecilia reticulata, from different predation regimes. We examine how the extent of predator-driven phenotypic parallelism is influenced by six factors: sex, trait type, rearing environment, ecological complexity, evolutionary history, and time since colonization. Analyses show that parallel evolution in guppies is highly variable and weak on average, with only 24.7% of the variation among populations being explained by predation regime. Levels of parallelism appeared to be especially weak for colour traits, and parallelism decreased with increasing complexity of evolutionary history (i.e., when estimates of parallelism from populations within a single drainage were compared to estimates of parallelism from populations pooled between two major drainages). Suggestive - but not significant - trends that warrant further research include interactions between the sexes and different trait categories. Quantifying and accounting for these and other sources of variation among evolutionary 'replicates' can be leveraged to better understand the extent to which seemingly similar environments drive parallel and nonparallel aspects of 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".