Does Foraging Behaviour Affect Female Mate Preferences and Pair Formation in Captive Zebra Finches?
Bibliographic record
Abstract
BACKGROUND: Successful foraging is essential for survival and reproductive success. In many bird species, foraging is a learned behaviour. To cope with environmental change and survive periods in which regular foods are scarce, the ability to solve novel foraging problems by learning new foraging techniques can be crucial. Although females have been shown to prefer more efficient foragers, the effect of males' foraging techniques on female mate choice has never been studied. We tested whether females would prefer males showing the same learned foraging technique as they had been exposed to as juveniles, or whether females would prefer males that showed a complementary foraging technique. METHODOLOGY/PRINCIPAL FINDINGS: We first trained juvenile male and female zebra finches (Taeniopygia guttata) to obtain a significant proportion of their food by one of two foraging techniques. We then tested whether females showed a preference for males with the same or the alternative technique. We found that neither a male's foraging technique nor his foraging performance affected the time females spent in his proximity in the mate-choice apparatus. We then released flocks of these finches into an aviary to investigate whether assortative pairing would be facilitated by birds taught the same technique exploiting the same habitat. Zebra finches trained as juveniles in a specific foraging technique maintained their foraging specialisation in the aviary as adults. However, pair formation and nest location were random with regard to foraging technique. CONCLUSIONS/SIGNIFICANCE: Our findings show that zebra finches can be successfully trained to be foraging specialists. However, the robust negative results of the conditions tested here suggest that learned foraging specializations do not affect mate choice or pair formation in our experimental context.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".