Comparing lifetime and annual fitness measures reveals differences in selection outcomes
Bibliographic record
Abstract
Selection analyses of long-term field data frequently use annual comparisons from long-lived species with overlapping generations to measure fitness differences with respect to phenotypic characteristics, such as annual phenological timing. An alternative approach applies lifetime estimates of fitness that encompass several annual events. We studied selection on emergence date from hibernation in male and female Columbian ground squirrels, Urocitellus columbianus (Ord 1915). From 32 years of records, we estimated lifetime fitness using either lifetime reproductive success (LRS) or matrix methods, and estimated annual fitness from individual yearly survival and reproduction. We also modified estimates to statistically control for changes in mean population fitness over the lives of individuals. We regressed lifetime fitness metrics on dates of emergence from hibernation, to quantify the strength of selection on emergence date (a heritable trait). All fitness metrics were highly correlated, but differences became apparent when estimating selection coefficients for emergence dates. The annual fitness metric and LRS produced lower effect sizes of selection coefficients than matrix-based lifetime fitness and a lifespan approach based on average annual fitness. Further, only these last two metrics revealed stabilizing selection. These results suggest that the choice of a fitness metric may influence our conclusions about natural selection.
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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 | 0.001 |
| 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".