Is provisioning rate of parents and helpers influenced by the simulated presence of novel individuals?
Bibliographic record
Abstract
Abstract Cooperative behaviour is widespread in animals and is likely to be the result of multiple selective pressures. A contentious hypothesis is that helping enhances the probability of obtaining a sexual partner (i.e., confers direct benefits through sexual selection). Under this hypothesis, cooperative behaviours may have evolved into a signal. Consequently, we would expect individuals to enhance cooperation when a potential mate is present, to signal their status and quality. We evaluated this possibility in the cooperatively breeding sociable weaver ( Philetairus socius ). We simulated the presence of different types of individuals using a playback to test whether the simulated presence of an unknown individual, possibly a potential mate, increases provisioning rate in two classes of cooperating birds : breeders and helpers. If the signal is the provisioning rate in itself we expected increased feeding rates of male helpers during the simulated presence of an unknown female. Contrary to our predictions, the simulated presence in the audience of an unknown individual did not influence the nestling provisioning rate of birds of any sex and class. From these results, we conclude that in this species the variation in provisioning rate is unlikely to be used as a signal in a sexual selection context. However, we also highlight the limitations of our methods and suggest improvements that future studies should incorporate when testing audience effects on cooperation. Significance statement Animals may cooperate to gain direct benefits, like attracting mates. This happens for example in humans. In species where cooperation leads to direct sexual benefits, when the appropriate audience is present, (i.e., a potential mate), helpers should enhance their cooperation. To determine whether helping to raise others’ young varies according to who is watching, we used playbacks to simulate the presence of unknown individuals of opposite sex (potential mates) while helpers were feeding young. Helping, quantified here as number of times food was brought to the chicks over an hour, was not affected by the simulated audience. We concluded that in sociable weavers variation in provisioning rate is unlikely to be a signal to obtain direct sexual benefits.
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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 | 0.005 |
| 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 | 0.001 | 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".