Early-life sibling conflict in Canada jays has lifetime fitness consequences
Bibliographic record
Abstract
While delaying natal dispersal can provide short-term benefits for juveniles, lifetime fitness consequences are rarely assessed. Furthermore, competition for limited positions on a natal territory could impose an indirect fitness cost on the winner if the outcome has negative effects on its siblings. We use radio-tracking and 58 years of nesting data in Ontario, Canada to examine the lifetime fitness consequences of sibling expulsion in the Canada jay ( Perisoreus canadensis ). Six weeks after fledging, intra-brood dominance struggles result in one ‘dominant juvenile’ (DJ) remaining on the natal territory after expelling its subordinate siblings, the 'ejectees' (EJs). Despite an older age-at-first-reproduction, DJs produced more recruits over their lifetime and had higher first-year survival than EJs, leading to substantially higher direct fitness. Even though DJs incurred an indirect fitness cost by expelling their siblings and there was no evidence that their presence on the natal territory increased their parents' reproductive output the following year, they still had substantially higher inclusive fitness than EJs. Our results demonstrate how early-life sibling conflict can have lifetime consequences and that such fitness differences in Canada jays are driven by the enhanced first-year survival of DJs pursuant to the early-summer expulsion of their sibling competitors.
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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 | 0.001 | 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".