SEXUAL CONFLICT, SOCIAL NETWORKS, AND POLYANDRY
Bibliographic record
Abstract
Sexual conflict occurs when the reproductive optima of males and females are at odds with one another. Conflict between the sexes is ubiquitous across the animal kingdom and is expected to influence the social dynamics of group-living animals. Yet, most social behaviour studies do not address the potential ramifications that sexual conflict can have on social interactions. For my thesis, I used bed bugs (Cimex lectularius) to bridge the gap between sexual conflict and social behaviour. In Chapter 1, I developed a novel semi-naturalistic arena for tracking bed bugs to uncover how sexual conflict shapes animal social networks. My results show that male and females can be in conflict over the social environment. In Chapter 2, I examined how female sexual history shapes mating interactions using bed bugs. First, I showed that realistically high rates of traumatic insemination relative to lower rates dramatically reduce female fitness. Next, I manipulated female insemination status in a realistic group setting and found that males can exhibit strong mate choice even in a mating system with seemingly little male reproductive investment. Lastly, I tracked avoidance behaviour exhibited by female bed bugs as they received successive inseminations and demonstrated that female bed bugs possess plastic avoidance strategies based on their mating history. In Chapter 3, I examined how social experience shapes sexual interactions in a complex, competitive environment and found that social experience did not improve male or female bed bugs’ sexual competence. Finally, in Chapter 4, I extended my work on polyandry to fruit flies (Drosophila melanogaster) and showed that realistically high rates of female multiple mating can increase female fitness. In each chapter, I discuss the significance of my findings as they relate to sexual selection and the evolution of social and sexual strategies and behaviours in both sexes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".