Helper effects on pup lifetime fitness in the cooperatively breeding red wolf ( <i>Canis rufus</i> )
Bibliographic record
Abstract
The evolutionary maintenance of cooperative breeding systems is thought to be a function of relative costs and benefits to breeders, helpers and juveniles. Beneficial effects of helpers on early-life survivorship and performance have been established in several species, but lifetime fitness benefits and/or costs of being helped remain unclear, particularly for long-lived species. We tested for effects of helpers on early- and late-life traits in a population of reintroduced red wolves (Canis rufus), while controlling for ecological variables such as home-range size and population density. We found that the presence of helpers in family groups was positively correlated with pup mass and survival at low population density, but negatively correlated with mass/size at high density, with no relation to survival. Interestingly, mass/size differences persisted into adulthood for both sexes. While the presence of helpers did not advance age at first reproduction for pups of either sex, females appeared to garner long-term fitness benefits from helpers through later age at last reproduction, longer reproductive lifespan and a greater number of lifetime reproductive events, which translated to higher lifetime reproductive success. In contrast, males with helpers exhibited diminished lifetime reproductive performance. Our findings suggest that while helper presence may have beneficial short-term effects in some ecological contexts, it may also incur long-term sex-dependent costs with critical ramifications for lifetime fitness.
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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".