Variation in individual reproductive performance amplified with population size in a long‐lived carnivore
Bibliographic record
Abstract
Individual variation in reproductive ability is a key component of natural selection within populations, driving the evolution of life histories and population responses to changing environmental conditions. Evidence that population density affects individual-level fitness in wild populations is limited, particularly for long-lived animals, which are difficult to observe on a biologically relevant scale. We tested for individual heterogeneity in reproductive performance in female grey seals (Halichoerus grypus) using 35 yr of mark-resighting data at Sable Island, Canada (43.93° N 59.91° W). We used Bayesian generalized linear mixed-effect models and multistate open robust design mark-resight models to investigate whether population size negatively influences individual reproductive performance. We measured reproductive performance in two ways: reproductive frequency (the probability of returning to the island to breed) and annual provisioning performance (the probability of successfully weaning a pup given a female bred). Sighting histories of 1,655 known-aged females with a total of 22,961 pupping events were used for analysis. After accounting for effects of female age, parity, and random year effects, we found that both provisioning performance and reproductive frequency demonstrated a strong, positive correlation with population size. Among-individual variance in reproductive traits and responses to population size indicated considerable heterogeneity in overall reproductive performance. As population size grew, "robust" females increased their reproductive performance more than their more "frail" conspecifics in both reproductive traits, resulting in an amplification of differences among individuals. Consequently, simulations from posterior distributions revealed a large fitness consequence of heterogeneity in this population, with "frail" individuals having 47.1% fewer successful pups than more "robust" females (mean reproductive output ± SD: 9.12 ± 3.77 pups for frail individuals, 16.97 ± 2.94 for robust individuals). Repeatability of overall reproductive performance across environments indicates individual quality may be more influential to lifetime reproductive success than costs associated with reproductive investment. This quantification of relative fitness and its dynamics is crucial to understanding broad evolutionary processes in natural populations.
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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 | 0.001 |
| 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 | 0.001 |
| 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".