Evolution of color phenotypes in two distantly related species of stick insect: different ecological regimes acting on similar genetic architectures
Bibliographic record
Abstract
Abstract Recurrent (e.g. parallel or convergent) evolution is widely cited as evidence for natural selection’s central role in evolution but can also highlight constraints affecting evolution. Here we describe the evolution of green and melanistic color phenotypes in two species of stick insect: Timema podura and T. cristinae . We show that similar color phenotypes of these species (1) cluster in phenotypic space and (2) confer crypsis on different plant microhabitats. We then use genome-wide association mapping to determine the genetic architecture of color in T. podura , and compare this to previous results in T. cristinae . In both species, color is under simple genetic control, dominance relationships of melanistic and green alleles are the same, and SNPs associated with color phenotypes colocalize to the same genomic region. These results differ from those of ‘typical’ parallel phenotypes because the form of selection acting on color differs between species: a balance of multiple sources of selection acting within host species maintains the color polymorphism in T. cristinae whereas T. podura color phenotypes are under divergent selection between hosts. Our results highlight how different adaptive landscapes can result in the evolution of similar phenotypic variation, and suggest the same genomic region is involved.
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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 | 0.000 |
| 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 | 0.000 |
| 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".