Sexual Signal Evolution Outpaces Ecological Divergence during Electric Fish Species Radiation
Bibliographic record
Abstract
Natural selection arising from resource competition and environmental heterogeneity can drive adaptive radiation. Ecological opportunity facilitates this process, resulting in rapid divergence of ecological traits in many celebrated radiations. In other cases, sexual selection is thought to fuel divergence in mating signals ahead of ecological divergence. Comparing divergence rates between naturally and sexually selected traits can offer insights into processes underlying species radiations, but to date such comparisons have been largely qualitative. Here, we quantitatively compare divergence rates for four traits in African mormyrid fishes, which use an electrical communication system with few extrinsic constraints on divergence. We demonstrate rapid signal evolution in the Paramormyrops species flock compared to divergence in morphology, size, and trophic ecology. This disparity in the tempo of trait evolution suggests that sexual selection is an important early driver of species radiation in these mormyrids. We also found slight divergence in ecological traits among closely related species, consistent with a supporting role for natural selection in Paramormyrops diversification. Our results highlight the potential for sexual selection to drive explosive signal divergence when innovations in communication open new opportunities in signal space, suggesting that opportunity can catalyze species radiations through sexual selection, as well as natural selection.
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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 | 0.002 |
| 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 | 0.001 |
| 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".