Resource acquisition and <scp>pre‐copulatory</scp> sexual selection
Bibliographic record
Abstract
Abstract Sexual selection influences the evolution of phenotypic traits and contributes to patterns of biodiversity. In many animals, mating involves sequential steps. Often, individuals must secure resources that are essential for mating (nests, territories, food), and then after securing a resource, individuals engage in competition for access to limited opposite sex mates and gametes. A large body of empirical research and some verbal models have illustrated that resource acquisition can influence sexual selection. In general, though, we lack a priori predictions of when and how resource acquisition will influence sexual selection. Here, we use a mathematical framework to explore the link between resource acquisition and sexual selection on an advantageous mate‐acquisition trait across biologically relevant trade‐off scenarios. Our findings provide a set of testable predictions of how resource acquisition can influence sexual selection on mating traits. In general, selection on mate‐acquisition traits is expected to be heavily influenced by: (1) the episode of selection considered, and in particular, whether one considers selection associated with the mating pool only or selection associated with both the mating pool and pre‐mating pool; (2) whether resource‐acquisition and mate‐acquisition traits are positively associated or whether they trade off; and (3) the proportion of males with the resource‐ and mate‐acquisition traits.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".