REPRODUCTIVE CHARACTER DISPLACEMENT AND SIGNAL ONTOGENY IN A SYMPATRIC ASSEMBLAGE OF ELECTRIC FISH
Bibliographic record
Abstract
The reproductive signals of two or more taxa may diverge in areas of sympatry, due to selection against costly reproductive interference. This divergence, termed reproductive character displacement (RCD), is expected in species-rich assemblages, where interspecific signal partitioning among closely related species is common. However, RCD is usually documented from simple two-taxon cases, via geographical tests for greater divergence of reproductive traits in sympatry than in allopatry. We propose a novel approach to recognizing and understanding RCD in multi-species communities--one that traces the displacement of signals within multivariate signal space during the ontogeny of individual animals. We argue that a case for RCD can be made if the amount of signal displacement between a pair of species after maturation is negatively correlated to distance in signal space before maturation. Our application of this approach, using a dataset of communication signals from a sympatric Amazonian assemblage of the electric fish genus Gymnotus, provides strong evidence for RCD among multiple species. We argue that RCD arose from the costs of heterospecific mismating, but interacted with sexual selection--favoring the evolution of conspicuous male signals that not only serve for mate-choice, but which simultaneously facilitate species recognition.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".