Personality-mediated speed-accuracy tradeoffs in mating in a 17-year periodical cicada
Bibliographic record
Abstract
Abstract There exists growing evidence that animal personality (consistent between individual differences in behavior) can influence an individual’s fitness. Furthermore, limitations in behavioral plasticity may cause personality-mediated tradeoffs to occur, for example, between speed and accuracy in decision making. We explored whether various measures of personality could predict speed-accuracy tradeoffs in mate selection using Pharaoh cicadas (Magicicada septendecim) and examined the phenotypic traits predicting male mating performance and advertisement rates. We assessed whether male exploration behavior, boldness, and weight could predict a male’s overall copulation attempt rate (the number of attempted copulations with conspecifics of either sex), the number of errors a male made when selecting a mate (the number of same-sex copulation attempts), and male reproductive performance (whether a male successfully copulated with a female). We also assessed whether personality-dependent variation in male advertisement rate (the number of calling song bouts) might underpin the correlation between exploration behavior and mating performance. Although male exploration behavior did not predict male advertisement rate, we found that faster-exploring males exhibited higher overall rates of attempted copulations while also attempting more same-sex copulations, compared to slower-exploring males, suggesting a personality-mediated speed-accuracy tradeoff. Despite making more mate choice errors, however, faster explorers were more likely to successfully copulate with females, compared to slower explorers, indicating that speed may be favored over accuracy in systems where heavily male-biased sex ratios lead to scramble competition. Overall, this work highlights the role of personality in sexual selection and demonstrates that personality can influence speed-accuracy trade-offs in mating.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".