Factors affecting age at primiparity in black bears
Bibliographic record
Abstract
Abstract Effective wildlife management requires an understanding of the factors affecting population vital rates. Age at primiparity can be an important determinant of population growth rates. Thus, understanding the factors influencing age at primiparity in wild populations is important for their management and conservation. American black bears (Ursus americanus) are widely distributed in North America and show considerable variation in age at primiparity across their range. We tested hypotheses regarding top‐down and bottom‐up drivers of age at primiparity in black bears across the province of Ontario, Canada. We obtained estimates of age at primiparity using cementum patterns in 1,033 bear teeth collected as part of regulated harvest in 2018 and 2019. We compared mean ages at primiparity between 2 distinct forest regions in Ontario using a Wilcoxon test. To quantify the effects of multiple putative drivers of variation in primiparity (e.g., harvest density, food availability, land use, climate), we paired yearly probability of primiparity data with environmental covariates in a mixed effects logistic regression model. Age at primiparity was significantly lower in the more productive forest region, likely reflecting broad‐scale patterns of food availability. Further, there was a significant positive effect of growing degree days (annual sum of daily mean degrees >5°C) on probability of primiparity, likely related to its influence on ecosystem productivity. Harvest density was negatively related to the probability of primiparity, possibly because harvest is positively correlated with bear density at this broad scale, and competition for food resources increases with bear density. Overall, temporal variation in food availability had a positive effect on probability of primiparity in a model fit to data for which we had the highest confidence in age estimates from cementum. Mean age at primiparity was older (5.57 yr) than in other more southernly populations, and most harvested bears (53%) were harvested prior to reproducing. Our analysis suggests that black bear age at primiparity is primarily driven by bottom‐up forces related to climate and vegetation differences that lead to greater food availability in the south. The relatively old age at primiparity and high apparent mortality rate of young animals in this population suggests that harvest can limit recruitment substantially, which needs to be considered when making management decisions.
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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 | 0.001 | 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".