Consumption of voles and vole food caches by Yellowstone grizzly bears: exploratory analyses
Bibliographic record
Abstract
Previously published observations of bears consuming murid rodents have been limited to short anecdotes. Only 2 studies reported bears consuming rodent food caches. I investigated the consumption of voles (Microtus spp.) and vole food caches by grizzly bears (Ursus arctos) in the Yellowstone region, 1977–92, using data collected during a study of 140 radiomarked bears. Study bears excavated vole nests, burrows, or food caches at 45 different sites. Excavations of nests (n = 37) were more common than excavations of food caches (n = 15). The remains of voles were found in 62 bear feces. Occurrences of voles in bear feces and excavations by bears for vole nests or food caches were essentially uncorrelated over time, suggesting that total consumption of voles and pursuit of voles or vole food caches by excavation were affected by different factors. Excavations by bears were largest in size and peaked in number during the driest months of the study period and were most likely to occur on gently sloping sites with abundant grasses, especially of the genus Phleum, and sedges (Carex). Bear excavations were more common during drought, probably because voles increased their burrowing in response to drying of wet soils, decreased above-ground security, and decreased abundance of above-ground foods. Food caching by voles has been documented in Canada and at high latitudes in the U.S., but prior to this study, only once in the U.S. Rocky Mountains. Bears consumed vole food caches primarily during September and were most successful finding caches at sites where yampah (Perideridia gairdneri) was abundant. Study bears excavated vole nests most often during spring and fall. Although voles were not a major source of energy for Yellowstone's grizzly bears, some individuals frequently foraged for voles during certain years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".