Reproductive success is influenced by early development: the covariation of natal body size and reproductive performance in grey seals (Halichoerus grypus)
Bibliographic record
Abstract
An individual's size when young may be an important source individual variation in lifetime reproductive performance, as size effects on ontogenetic development can have cascading physiological and behavioral consequences throughout life. Here, we explored how natal size influences subsequent reproductive performance in grey seals (Halichoerus grypus) using mark-recapture and repeated measures reproductive data on a marked sample of 363 females that were measured at roughly 4 weeks of age and recruited to the Sable Island breeding colony. Two reproductive traits were considered: provisioning performance (mass of weaned offspring), modeled using linear mixed effects models; and reproductive frequency (the rate at which a female returns to breed), modeled using mixed-effects multistate mark-recapture models. After accounting for female age, experience, and offspring sex, we found a positive association between natal length and our measures of reproductive performance. Mothers with the longest natal lengths produced pups nearly 8 kg heavier and were 20% more likely to breed in a given year than mothers with the shortest natal lengths. Because correlation in individual body length between natal and adult life stages is weak, this covariation between natal length and future reproductive performance reported here is more likely to be a carry-over effect from advantages granted to growth and self-maintenance as a juvenile that allow for greater adult performance, rather than a physical trait that is maintained throughout life.
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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".