Direct fitness benefits of delayed dispersal in the cooperatively breeding red wolf (Canis rufus)
Bibliographic record
Abstract
The existence of cooperative breeding in diverse animal taxa has inspired much interest in what nonbreeding helpers gain from participation in rearing nondescendent young. A major theoretical explanation for this phenomenon has revolved around the notion of inclusive fitness, where delayed dispersers in a family-based group gain indirect fitness benefits by fostering the viability of close relatives. There is potential, however, for direct fitness benefits in delayed dispersal itself. We explored the relationship between delayed dispersal and lifetime fitness in a reintroduced population of the cooperatively breeding red wolf, Canis rufus, which exhibits delayed dispersal but few opportunities to breed in the natal pack. We present evidence that male wolves that delayed dispersal to later ages had lower mortality risk from natural and anthropogenic sources combined and increased probability of becoming reproductive in their lifetimes. Furthermore, delayed dispersal did not result in delayed age at first reproduction. For females, however, the relative costs and benefits of delaying dispersal to later ages were more complex. In general, we provide evidence that there are direct fitness benefits to delaying dispersal in red wolves even in the absence of reproductive opportunities in the natal pack. Thus, we lend support to the hypothesis that direct fitness benefits may in themselves be sufficient to facilitate the evolution of delayed dispersal requisite to cooperatively breeding social systems.
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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 | 0.000 | 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".