Egg Load Decreases Mobility and Increases Predation Risk in Female Black-Horned Tree Crickets (Oecanthus nigricornis)
Bibliographic record
Abstract
Female-biased predation is an uncommon phenomenon in nature since males of many species take on riskier behaviours to gain more mates. Several species of sphecid wasps have been observed taking more female than male prey, and it is not fully understood why. The solitary sphecid Isodontia mexicana catches more adult female tree cricket (Oecanthus nigricornis) prey. Previous work has shown that, although female tree crickets are larger and thus likely to be more valuable as prey than males, body size alone cannot fully explain why wasps take more females. We tested the hypothesis that wasps catch adult female tree crickets more often because bearing eggs impedes a female's ability to escape predation. We compared female survivors to prey of I. mexicana, and found that females carrying more eggs were significantly more likely to be caught by wasps, regardless of their body size and jumping leg mass. We also conducted laboratory experiments where females' jumping responses to a simulated attack were measured and compared to her egg load and morphology. We found a significant negative relationship between egg load and jumping ability, and a positive relationship between body size and jumping ability. These findings support the hypothesis that ovarian eggs are a physical handicap that contributes to female-biased predation in this system. Predation on the most fecund females may have ecological-evolutionary consequences such as collapse of prey populations or selection for alternate life history strategies and behaviours.
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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) | 0.001 | 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".