Bibliographic record
Abstract
Many species in the animal kingdom are characterized by alternative mating tactics (AMTs) within a sex. In males, such tactics include mate guarding versus sneaking behaviours, or territorial versus female mimicry. Although AMTs can occur in either sex, they have been most commonly described in males. This sex bias may, in part, reflect the increased opportunity for sexual selection that typically exists in males, which can result in a higher probability that AMTs evolve in that sex. Consequently, females and polyandry can play a pivotal role in governing the reproductive success associated with male AMTs and in the evolutionary dynamics of the tactics. In this review, we discuss polyandry and the evolution of AMTs. First, we define AMTs and review game theoretical and quantitative genetic approaches used to model their evolution. Second, we review several examples of AMTs, highlighting the roles that genes and environment play in phenotype expression and development of the tactics, as well as empirical approaches to differentiating among the mechanisms. Third, ecological and genetic constraints to the evolution of AMTs are discussed. Fourth, we speculate on why female AMTs are less reported on in the literature than male tactics. Fifth, we examine the effects of AMTs on breeding outcomes and female fitness, and as a source, and possibly also a consequence, of sexual conflict. We conclude by suggesting a new model for the evolution of AMTs that incorporates both environmental and genetic effects, and discuss some future avenues of research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".