Cohort effects and proximal versus distal metrics for lifetime reproductive success in Richardson’s ground squirrels
Bibliographic record
Abstract
Lifetime reproductive success (LRS) in the form of number of descendants is a commonly used measure of individual fitness, but the life stage at which descendants are counted varies among studies. Conceptual and logistical trade-offs exist along the gradient of proximal-to-distal LRS metrics. Although proximal metrics, such as number of offspring weaned, are logistically easier to collect than distal metrics, such as number of reproductively mature offspring or grandoffspring, they may be of little evolutionary significance if stochastic events heavily influence the realized number of descendants. We use a 25-year demographic data set based on 954 adult female Richardson’s ground squirrels ( Urocitellus richardsonii ) from 22 annual cohorts to characterize and compare 6 metrics for LRS: lifetime production of litters, numbers of weanlings, weanling daughters, adult daughters, weanling matrilineal granddaughters, and adult matrilineal granddaughters. Most adult females weaned only 0 (21%), 1 (47%), or 2 (22%) lifetime litters. All LRS distributions were right skewed, with 53% and 77% of adult female Richardson’s ground squirrels having no adult female matrilineal descendants after 1 and 2 generations, respectively. Survival of daughters and mothers covaried with calendar year, and LRS was strongly influenced by the calendar year in which females recruited into the breeding population as yearlings. Catastrophic flooding in 2005 killed nearly all descendants from the 2004 and 2005 cohorts. Daughter survival to adulthood explained more variance in lifetime production of adult daughters and granddaughters than number, size, or sex ratio of litters. Overall, to have a ≥ 50% chance of 1 adult granddaughter, a female needed to produce 2 litters, 7 weanling daughters, or 2 adult daughters. All distal (response) versus proximal (predictor) LRS metrics had significantly positive relationships, but variability increased with each distal step in the response variable. Our long-term study highlights sampling issues with LRS studies, variability within and among cohorts, and the role of stochastic events in uncoupling a mammal’s reproductive effort from realized number of descendants.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".