Uncovering bighorn sheep life-history trajectories in multidimensional trait space
Bibliographic record
Abstract
Individual heterogeneity shapes ecoevolutionary processes at multiple scales. Yet, the scarcity of long-term life-history data and limitations in classic statistical tools hinder our capacity to uncover and understand individual heterogeneity in wildlife populations. Here, we apply an underused multivariate statistical method to uncover four heterogenous life-history trajectories in wild female bighorn sheep ( Ovis canadensis ). Remarkably, these trajectories had remained unobserved in the population despite nearly five decades of monitoring. Our results indicate substantial among-trajectory heterogeneity in growth, senescence, life history trade-offs, fitness, and contributions to population growth. Some trajectories suggest the presence of life history trade-offs while others include silver spoon effects, leading to heterogenous life-history outputs. Then, we show that mother identity and year of birth are relatively good predictors of heterogeneity, indicating that individual trajectories could be largely set during early life. Critically, our results demonstrate that heterogeneity in life-history trajectories can be inconspicuous, yet substantial and structured across multiple traits within a population. Uncovering and understanding this heterogeneity in other wild populations will be key to advancing our knowledge of ecoevolutionary processes across populations and species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".