The influence of female mate choice on courtship-feeding by males
Bibliographic record
Abstract
This thesis examines how female choice affects male investment in courtship-feeding animals. Because courtship-feeding females can potentially gain both material and genetic benefits from mate choice, they are ideal for studying the interaction between choice for materials and choice for good genes. In Chapter 2, I develop theory explaining why female choice for genetically superior males may cause those males to provide fewer material benefits than rivals. In Chapter 3, this theory is used to generate hypotheses explaining why larger male black-horned tree crickets (Oecanthus nigricornis Walker) that are preferred as mates by females do not provide larger courtship gifts. Larger males provide gifts with a higher concentration of protein, which explains the female preference. The poor correlation between male size and gift size is probably because large males attract more mates, and as a result may become either depleted of food gifts, or conserve gift reserves in anticipation of future mating opportunities. In Chapter 4, the gift-giving ability and relative genetic quality of males was manipulated to identify the target of precopulatory choice in female O. nigricornis. Females are predicted to exert precopulatory choice for material benefits (Chapter 2). This hypothesis is supported: precopulatory choice is mainly for food because females discriminate against males depleted of courtship food gifts, but not against males that are smaller than previous mates. In Chapter 5, I review a problem that confronts studies of genetic benefits and has implications for all quantitative genetic studies. Because females are expected to invest more in offspring of genetically superior males, the correlation between the fitness of sires and offspring reflects differential allocation in addition to strictly genetic benefits. I present several alternative approaches to solving this problem. In Chapter 6, I present a study of genetic benefits to mate choice in O. nigricornis using a novel method described in Chapter 5. I report evidence for genetic benefits to mating large males: large sires produce larger sons and offspring with higher total fitness. Unfortunately, a paternity analysis for this study is not yet complete and therefore this study faces some of the problems identified in Chapter 5.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".