Individual Quality Drives Life History Variation in a Long-Lived Marine Predator
Bibliographic record
Abstract
Individual quality and environmental conditions may mask or interact with energetic trade-offs in life history evolution. Deconstructing these sources of variation is especially difficult in long-lived species that are rarely observed on timescales long enough to disentangle these effects. Here, we investigated relative support for variation in female quality and costs of reproduction as factors shaping differences in life history trajectories using a 32-year dataset of repeated reproductive measurements from 273 marked, known-age female gray seals (Halichoerus grypus). We defined individual reproductive investment using two traits, reproductive frequency (a female’s probability of breeding) and provisioning performance (offspring weaning mass). Fitted hierarchical Bayesian models identified individual investment relative to conspecifics (over a female’s entire life and in three age classes) and subsequently estimated how these investment metrics and the Atlantic Multidecadal Oscillation are associated with longevity. Individual differences (i.e., quality) contributed a large portion of the variance in reproductive traits. Females that consistently invest well in their offspring relative to other females survive longer. The best-supported model estimated survival as a function of age class–specific provisioning performance, where late-life performance was particularly variable and had the greatest impact on survival, possibly indicating individual variation in senescence. There was no evidence to support a trade-off in reproductive performance and survival at the individual level. Overall, these results suggest that in gray seals, individual quality is a stronger driver in life history variation than individual strategies resulting from energetic trade-offs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".