Life histories of female red squirrels and their contributions to population growth and lifetime fitness
Bibliographic record
Abstract
The potential importance of life history traits to population growth rates has been well explored theoretically but has rarely been documented in wild mammals. In this study we used 18 consecutive years of data from a population of North American red squirrels (Tamiasciurus hudsonicus) in the southwest Yukon, Canada, to examine variation in female life history traits and their consequences for population growth rate. Red squirrels in this population experienced severe juvenile mortality, but survivorship beyond age 2 followed a Type I relationship where the annual survival probability decreased with age. Maximum lifespan was 8 y. Some females initiated breeding as yearlings, but most delayed first breeding until 2 y of age or in some cases even later. Annual reproduction generally involved the production of a single litter averaging 3.1 offspring (range: 1 to 7); however, some females attempted a second litter within a single breeding season, either following reproductive failure or, in rare circumstances, after a successful first breeding attempt. Life table characteristics for the 11 cohorts born between 1987 and 1997 indicated a population growth rate close to zero (r = 0.009). Elasticity analysis as well as individual population projection matrices and lifetime reproductive success data indicated that early survival and not age at first reproduction was most strongly associated with a female's contribution to population growth. Lifespan accounted for 83.9% of the variation in population growth rate and was positively correlated with age at first reproduction, such that females who bred as yearlings suffered decreased longevity. Collectively, these results emphasize the importance of female survival and not reproductive output to population growth and lifetime fitness in this system.
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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".