Early‐life relationships matter: Social position during early life predicts fitness among female spotted hyenas
Bibliographic record
Abstract
How social development in early-life affects fitness remains poorly understood. Though there is growing evidence that early-life relationships can affect fitness, little research has investigated how social positions develop or whether there are particularly important periods for social position development in an animal's life history. In long-lived species in particular, understanding the lasting consequences of early-life social environments requires detailed, long-term datasets. Here we used a 25-year dataset to test whether social positions held during early development predicted adult fitness. Specifically, we quantified social position using three social network metrics: degree, strength and betweenness. We determined the social position of each individual in three types of networks during each of three stages of ontogeny to test whether they predict annual reproductive success (ARS) or longevity among adult female spotted hyenas Crocuta crocuta. The social positions occupied by juvenile hyenas did predict their fitness, but the effects of social position on fitness measures differed between stages of early development. Network metrics when individuals were young adults better predicted ARS, but network metrics for younger animals, particularly when youngsters were confined to the communal den, better predicted longevity than did metrics assessed during other stages of development. Our study shows how multiple types of social bonds formed during multiple stages of social development predict lifetime fitness outcomes. We suggest that social bonds formed during specific phases of development may be more important than others when considering fitness outcomes.
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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) | 0.002 | 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".