SEXUAL SELECTION DETERMINES PARENTAL CARE PATTERNS IN CICHLID FISHES
Bibliographic record
Abstract
Despite a massive research effort, our understanding of why, in most vertebrates, males compete for mates and females care for offspring remains incomplete. Two alternative hypotheses have been proposed to explain the direction of causality between parental care and sexual selection. Traditionally, sexual selection has been explained as a consequence of relative parental investment, where the sex investing less will compete for the sex investing more. However, a more recent model suggests that parental care patterns result from sexual selection acting on one sex favoring mating competition and lower parental investment. Using species-level comparative analyses on Tanganyikan cichlid fishes we tested these alternative hypotheses employing a proxy of sexual selection based on mating system, sexual dichromatism, and dimorphism data. First, while controlling for female reproductive investment, we found that species with intense sexual selection were associated with female-only care whereas species with moderate sexual selection were associated with biparental care. Second, using contingency analyses, we found that, contrary to the traditional view, evolutionary changes in parental care type are dependent on the intensity of sexual selection. Hence, our results support the hypothesis that sexual selection determines parental care patterns in Tanganyikan cichlid fishes.
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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 | 0.001 |
| 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 | 0.000 |
| 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".