Male black widows parasitize mate-searching effort of rivals to find females faster
Bibliographic record
Abstract
Mate-searching success is a critical precursor to mating, but there is a dearth of research on traits and tactics that confer a competitive advantage in finding potential mates. Theory and available empirical evidence suggest that males locate mates using mate-attraction signals produced by receptive females (personal information) and avoid inadvertently produced cues from rival males (social information) that indicate a female has probably already mated. Here, we show that western black widow males use both kinds of information to find females efficiently, parasitizing the searching effort of rivals in a way that guarantees competition over mating after reaching a female's web. This tactic may be adaptive because female receptivity is transient, and we show that (i) mate searching is risky (88% mortality) and (ii) a strongly male-biased operational sex ratio (from 1.2 : 1 to more than 10 : 1) makes competition inevitable. Males with access to rivals' silk trails moved at higher speeds than those with only personal information, and located females even when personal information was unreliable or absent. We show that following rivals can increase the potential for sexual selection on females as well as males and argue it may be more widespread in nature than is currently realized.
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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) | 0.003 | 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".