Linking courtship behaviour, colour perception and mate choice decisions in peafowl
Bibliographic record
Abstract
Despite a long history of study showing that male courtship signals influence female mate choice in many species, we lack a good understanding of how females choose. What are the mechanisms of mate choice, and how do these mechanisms shape the evolution of courtship signals and traits? In this thesis, I use the peacock’s iridescent eyespots to link signal perception with female mate choice decisions and the behaviours males use during courtship. I begin by investigating how a peacock’s eyespot colours influence his mating success, using models of avian colour vision and measurements of eyespot plumage colours taken at light angles that mimic the way the feathers are displayed during courtship. My results suggest that a substantial portion of the variation in peacock mating success can be explained by these plumage colours, demonstrating that signal function is best understood by considering the context in which signals are presented. Next, I examine how females choose to visit different males for courtship. I show that a female’s familiarity with a male as a result of previous courtship encounters affects how she responds to his signals, including his eyespot colours. Lastly, I examine the visual effects of the peacock’s iridescent eyespot colours under different light conditions, and show that typical male courtship behaviours might enhance the eyespots in a way that influences female choice. I also find evidence that light conditions and female sensory biology together may have shaped the evolution of the eyespot colours in two species of peafowl. Overall, the results of this thesis demonstrate that by understanding how animals perceive colour signals, we can gain a better understanding of the function of behaviour on both sides of the courtship signaling exchange.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".