Disentangling direct and indirect determinants of the duration of maternal care in brown bears: Environmental context matters
Bibliographic record
Abstract
The duration of maternal care, an important life-history trait affecting population dynamics, varies greatly within species. Yet, our understanding of its predictors is limited, mostly correlative and subject to misinterpretations, due to difficulties to disentangle the role of maternal- and offspring-related characteristics. We conducted path analysis on a dataset including 217 brown bear litters captured over a 29-year period in two populations in Sweden ('North' and 'South') facing contrasting environmental conditions to identify and quantify the causes of variation in the duration of maternal care (1.5 or 2.5 years). We showed that the causal determinants of the duration of maternal care were context-dependent. Contrary to their expected central role in the determination of the duration of maternal care, yearling mass and its direct determinants (i.e. litter size and maternal mass) were only important in the North population, where environmental conditions are harsher and the cost of extended maternal care presumably higher. In the South, the duration of maternal care was not caused by yearling mass nor any maternal or litter characteristics. Extension of maternal care may thus result from factors independent from maternal and offspring condition in the South, such as an artificial hunting-induced selection for longer maternal care through the legal protection of family groups. Our results provide an important contribution to our very limited knowledge of the direct and indirect determinants of the duration of maternal care and highlight the importance of accounting for the environmental context when assessing maternal reproductive tactics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".