Sexually attractive traits predict predation-threat sensitivity of male alternative mating tactics
Bibliographic record
Abstract
Abstract Although visual sexual signals, such as ornamental colors and courtship displays, and large body size in males are attractive to females in numerous species, they may also inadvertently attract the attention of eavesdropping predators and thus may be costly in terms of increasing individual risk of mortality to predation. Theoretically, more color ornamented and larger males should be more predation threat sensitive and suppress their sexual signaling and(or) mating effort relatively more than their less color ornamented and smaller counterparts when under predation hazard. Here, we experimentally tested this hypothesis by quantifying concurrently the rates of alternative mating tactics (courtship displays, sneak mating attempts) expressed by male Trinidadian guppies (Poecilia reticulata) varying in color ornamentation and body size under a staged immediate threat of predation. Males suppressed their overall mating effort in response to the perceived predation threat, decreasing the frequency of their (presumably more conspicuous) courtship displays significantly more on average than the frequency of their sneak mating behavior. Statistically controlling for body length, more color-ornamented males were more threat sensitive in their courtship displays, but not sneak mating attempts, under predation hazard than drabber males. Controlling for body coloration, larger males exhibited lower courtship and sneak mating efforts than smaller males in both predation treatments, but body length only influenced threat sensitivity in sneak mating behavior. These results are consistent with both the threat sensitive hypothesis and asset protection principle and highlight the phenotype dependency and adaptive plasticity of alternative mating tactics in male guppies under varying predation risk.
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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 | 0.000 | 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".