Effect of energy availability, seasonality, and geographic range on brown bear life history
Bibliographic record
Abstract
Life‐history theory allows predictions of how changes in environmental selection pressures along a species' geographic distribution result in discrete shifts in life‐history traits. We tested for spatial patterns of 24 populations of brown bears Ursus arctos across North America that grouped according to the following environmental and population parameters: evapotranspiration as a correlate of primary productivity of vegetation, coefficient of variation of monthly evapotranspiration values as a measure of seasonality. population density, and adult female weight. Cluster analysis grouped brown bear populations into two regions: Pacific‐coastal populations characterized by high population density and large females that lived in areas of high primary productivity and low seasonality. and inland and barren‐ground populations characterized by relatively low density and small bears that lived in areas of low productivity and high seasonality. For each region, we tested whether life‐history traits (age at maturity and interbirth interval) related to primary productivity or seasonality. High altitude (interior: > 1000 m) and high latitude (barren‐ground; >65°N) populations respond to extremes in seasonality with risk‐spreading adaptations. For example, age at maturity and interbirth interval increased with greater seasonality. In contrast, Pacific‐coastal populations living on the western edge of brown bear geographic range respond to intraspecific competition at high densities by maximizing offspring competitive ability. For example, age at maturity increased with greater primary productivity and high population density. In each region, the female parent decided on the life‐history trade‐offs required to reduce the risks of offspring mortality depending on the environmental pattern.
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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 | 0.001 | 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) | 0.002 | 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".