Dietary niche partitioning among black bears, grizzly bears, and wolves in a multiprey ecosystem
Bibliographic record
Abstract
Identifying mechanisms that promote coexistence of sympatric species is important for predicting ecological effects of anthropogenic change. Many caribou (Rangifer tarandus (L., 1758)) populations are declining, and it is unclear to what extent sympatric predators consume caribou or how alternative prey affect caribou–predator relationships. We used stable isotope mixing models to estimate diets of black bear (Ursus americanus Pallas, 1780), grizzly bear (Ursus arctos L., 1758), and grey wolves (Canis lupus L., 1758) during early, middle, and late summer of 2009–2010 in northwestern British Columbia, Canada. Although we expected wolf diet to be primarily composed of moose (Alces alces (L., 1758)) — as they exist at twice the density of caribou — wolf diet consisted principally of caribou, and to a lesser extent moose and beaver (Castor canadensis Kuhl, 1820), with little change occurring throughout summer. Black bear diet consisted mainly of vegetation and moose, shifting from moose to vegetation through summer. Grizzly bear diet consisted primarily of vegetation and moose, and did not change throughout summer. Our results demonstrate the role of dietary niche partitioning in bear and wolf coexistence, and that caribou may be primary prey for wolves in an ecosystem with relatively high moose abundance and low human development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".