Determinants of lifetime reproduction in female brown bears: early body mass, longevity, and hunting regulations
Bibliographic record
Abstract
In iteroparous mammals, conditions experienced early in life may have long-lasting effects on lifetime reproductive success. Human-induced mortality is also an important demographic factor in many populations of large mammals and may influence lifetime reproductive success. Here, we explore the effects of early development, population density, and human hunting on survival and lifetime reproductive success in brown bear (Ursus arctos) females, using a 25-year database of individually marked bears in two populations in Sweden. Survival of yearlings to 2 years was not affected by population density or body mass. Yearlings that remained with their mother had higher survival than independent yearlings, partly because regulations prohibit the harvest of bears in family groups. Although mass as a yearling did not affect juvenile survival, it was positively associated with measures of lifetime reproductive success and individual fitness. The majority of adult female brown bear mortality (72%) in our study was due to human causes, mainly hunting, and many females were killed before they reproduced. Therefore, factors allowing females to survive several hunting seasons had a strong positive effect on lifetime reproductive success. We suggest that, in many hunted populations of large mammals, sport harvest is an important influence on both population dynamics and life histories.
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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".