Linking spatial variations in life‐history traits to environmental conditions across American black bear populations
Bibliographic record
Abstract
Abstract Spatial variations in food availability may influence life‐history traits of wildlife species, particularly in capital‐breeding species that store energy when food is widely available and catabolize it during energy‐intensive reproductive periods. The reproductive success of capital breeders is thus highly dependent on the accumulation of fat reserves. Reproductive success may also improve with access to alternative food resources provided by environments with strong human footprint and anthropogenic disturbances, but these environments may also increase mortality risks of wildlife. We performed a systematic review to extract reproduction and survival traits reported in studies on the American black bear (Ursus americanus), a capital breeder. Based on 94 studies widely distributed across North America, we conducted meta‐regression analyses to assess whether interpopulation variation in age at primiparity, litter size of cubs, annual cub survival, and annual survival of adult females were associated with environmental conditions, that is, habitat quality, habitat productivity, and anthropogenic disturbances. We found that mean age at primiparity decreased from around 5 to 4 years old in areas with the highest habitat quality and productivity as well as the highest human population densities compared with those with poor habitat quality and productivity and low human population densities. Mean litter size increased by approximately 13% (from 2 to 2.25 cubs per litter) in areas with the highest compared with the lowest proportion of deciduous forest, while cub survival increased by about 13% (from 60% to 73%) in areas with the highest compared with the lowest coverage of agricultural crops. Adult female survival decreased from 92% to 85% in areas where hunting was allowed. These results provide new insights into the factors associated with variations in reproductive success and survival across populations of a widely distributed species, demonstrating the impact of both natural and anthropogenic factors. Our study highlights the necessity of considering the ongoing changes in the distribution and growth of potential food resources, as well as the growing encroachment of humans into wildlife habitats, when planning management and conservation actions at the scale of a species distribution range.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".