Social Interaction With Females Modulates Context‐Dependent Male Guppy Mating Tactics for Female Receptivity
Bibliographic record
Abstract
ABSTRACT Although females are traditionally viewed as the choosier sex, there is increasing evidence for the important role that male mate choice plays in sexual selection, even in species without male parental care. Social experience is a key factor influencing how individuals assess the quality of potential mates. Here, we examined how social experience shapes mating tactics and preferences in male guppies ( Poecilia reticulata ). Males were housed either in isolation from females or in mixed‐sex groups, and we quantified their preferences and behavioral repertoire in response to receptive and non‐receptive females in no‐choice and dichotomous choice tests. Males reared in mixed‐sex groups adjusted their mating tactics by increasing coercive behaviors toward non‐receptive females, and exhibiting shorter latencies to initiate sexual behaviors in these interactions. However, social interaction with females did not affect the overall strength of male preference for female receptivity status. While these results suggest preference for female receptivity may be shaped through interactions with other ecological factors, the observed behavioral adjustments in males reared in mixed‐sex groups align with theoretical predictions for maximizing insemination success, highlighting the key role of social experience in driving context‐dependent variation in male mating behavior.
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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 | 0.000 | 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) | 0.002 | 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".