Disruptive natural selection predicts divergence between the sexes during adaptive radiation
Bibliographic record
Abstract
Evolution of sexual dimorphism in ecologically relevant traits, for example, via resource competition between the sexes, is traditionally envisioned to stall the progress of adaptive radiation. An alternative view is that evolution of ecological sexual dimorphism could in fact play an important positive role by facilitating sex-specific adaptation. How competition-driven disruptive selection, ecological sexual dimorphism, and speciation interact during real adaptive radiations is thus a critical and open empirical question. Here, we examine the relationships between these three processes in a clade of salamanders that has recently radiated into divergent niches associated with an aquatic life cycle. We find that morphological divergence between the sexes has occurred in a combination of head shape traits that are under disruptive natural selection within breeding ponds, while divergence among species means has occurred independently of this disruptive selection. Further, we find that adaptation to aquatic life is associated with increased sexual dimorphism across taxa, consistent with the hypothesis of clade-wide character displacement between the sexes. Our results suggest the evolution of ecological sexual dimorphism may play a key role in niche divergence among nascent species and demonstrate that ecological sexual dimorphism and ecological speciation can and do evolve concurrently in the early stages of adaptive radiation.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".