Density-dependent signaling: An alternative hypothesis on the function of chemical signaling in a non-territorial solitary carnivore
Bibliographic record
Abstract
Brown bears are known to use rubbing behavior as a means of chemical communication, but the function of this signaling is unclear. One hypothesis that has gained support is that male bears rub to communicate dominance to other males. We tested the communication of dominance hypothesis in a low-density brown bear population in southeast British Columbia. We contrasted rubbing rates for male and female bears during and after the breeding season using ten years of DNA-mark-recapture data for 643 individuals. Here we demonstrate that male brown bears rub 60% more during the breeding than the non-breeding season, while female rubbing had no seasonal trends. Per capita rub rates by males were, on average, 2.7 times higher than females. Our results suggest that the function of rubbing in the Rocky Mountains may not only be to communicate dominance, but also to self-advertise for mate attraction. We propose that the role of chemical communication in this species may be density-dependent, where the need to self-advertise for mating is inversely related to population density and communicating for dominance increases with population density. We suggest that future endeavors to elucidate the function of rubbing should sample the behavior across a range of population densities using camera trap and genotypic data.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".