Kinship affects investment by helpers in a cooperatively breeding bird
Bibliographic record
Abstract
Helping behaviour in cooperative breeding systems has been attributed to kin selection, but the relative roles of direct and indirect fitness benefits in the evolution of such systems remain a matter of debate. In theory, helpers could maximize the indirect fitness benefits of cooperation by investing more in broods with whom they are more closely related, but there is little evidence for such fine-scale adjustment in helper effort among cooperative vertebrates. In this study, we used the unusual cooperative breeding system of the long-tailed tit Aegithalos caudatus to test the hypothesis that the provisioning effort of helpers was positively correlated with their kinship to broods. We first use pedigrees and microsatellite genotypes to characterize the relatedness between helpers and breeders from a 14 year field study. We used both pedigree and genetic approaches because long-tailed tits have access to pedigree information acquired through social relationships, but any fitness consequences will be determined by genetic relatedness. We then show using both pedigrees and genetic relatedness estimates that alloparental investment by helpers increases as their relatedness to the recipients of their care increases. We conclude that kin selection has played a critical role in moulding the investment decisions of helpers in this cooperatively breeding species.
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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 | 0.002 |
| 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 | 0.000 |
| 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 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".