Using stable carbon (δ<sup>13</sup>C) and nitrogen (δ<sup>15</sup>N) isotopes to infer trophic relationships among black and grizzly bears in the upper Columbia River basin, British Columbia
Bibliographic record
Abstract
Ecological segregation of species is difficult to determine using conventional dietary analysis techniques. However, stable-isotope analysis may provide a convenient means of establishing trophic segregation of species and of groups of animals within a species in the same area. We measured stable carbon (δ13C) and nitrogen (δ15N) isotope values in hair of black bears (Ursus americanus) and grizzly bears (Ursus arctos) inhabiting the upper Columbia River basin in southeastern British Columbia, together with samples of potential foods ranging from plant material through invertebrates and ungulate meat. We found extensive overlap in both δ15N and δ13C values of hair from male grizzly bears and black bears of both sexes. Female grizzly bears, however, had lower δ15N values in their hair than the other groups of bears, indicating either less animal protein in their diet or a reliance on foods more depleted in 15N, possibly related to altitude. Our isotopic model generally confirmed a herbivorous diet for both bear species (a mean estimated plant contribution of 91%). Bears showing the highest δ15N values were those captured because they posed a management problem. We suggest that the slope of the relationship between tissue δ15N and δ13C values might provide a convenient means of evaluating the occurrence of consumption of animal protein in populations, regardless of local isotopic end-points for dietary samples. We examined three black bear cubs from dens and found them to be about a trophic level higher than adult females, reflecting their dependence on mother's milk, a result generally confirmed by an analysis of eight mother-cub pairs from Minnesota. Our study demonstrates how stable-isotope analysis of bear tissue can be used to monitor the feeding habits of populations, as well as provide dietary histories that may reveal dietary specializations among individuals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".