Correlates of complete brood failure in blue tits: could extra‐pair mating provide unexplored benefits to females?
Bibliographic record
Abstract
Behavioural ecologists have for decades investigated the adaptive value of extra‐pair copulation (EPC) for females of socially monogamous species. Despite extensive effort testing for genetic benefits, there now seems to be a consensus that the so‐called ‘good genes’ effects are at most weak. In parallel the search for direct benefits has mostly focused on the period surrounding egg laying, thus neglecting potential correlates of EPC that might be expressed at later stages in the breeding cycle. Here we used Bayesian methods to analyse data collected over four years in a population of blue tits Cyanistes caeruleus , where no support was previously found for ‘good genes’ effects. We found that broods with mixed paternity experienced less brood failure at the nestling stage than broods with single paternity, and that females having experienced complete brood failure in their previous breeding attempt had higher rates of mixed paternity than either yearling or previously successful females. To better understand these observations we also explored relationships between extra‐pair mating, male and female phenotype, and local breeding density. We found that in almost all cases the sires of extra‐pair offspring were close neighbours, and that within those close neighbourhoods extra‐pair sires were older than other males not siring extra‐pair offspring. Also, females did not display consistent EPC status across years. Taken together our results suggest that multiple mating might be a flexible female behaviour influenced by previous breeding experience, and motivate further experimental tests of causal links between extra‐pair copulation and predation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".