Male chickadees with better spatial cognition sire more extra-pair young
Bibliographic record
Abstract
Abstract Across animal taxa, females commonly mate with more than one male, even in monogamous mating systems. These extra-pair copulations and resulting young may increase the fitness of the female via a variety of mechanisms. The ‘good genes’ hypothesis suggests that socially monogamous females mate outside their pair bond to increase the fitness of their offspring via indirect genetic benefits, because extra-pair males do not provide parental care. We tested this hypothesis by quantifying extra-pair paternity in nonmigratory, food-caching mountain chickadees ( Poecile gambeli ). Chickadees rely on spatial cognition to recover scattered food caches and variation in spatial cognition is associated with increased survival, longer lifespan, and is heritable. However, less is known about the role of sexual selection on spatial cognitive abilities. In the current study, we found that 1. males with better spatial cognitive abilities sired more extra-pair young and produce heavier offspring in their own nests compared to their poorer performing counterparts, and 2. extra-pair males had significantly better spatial cognition than the social males they cuckolded. These results suggest that sexual selection shapes the evolution of spatial cognition in food-caching chickadees and are consistent with the good genes hypothesis, which posits that females gain indirect genetic benefits via extra-pair young.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".