The effects of high ungulate densities on foraging choices by beaver (Castor canadensis) in the mixed-wood boreal forest
Bibliographic record
Abstract
In some areas of North America previous management policies have created competition between beaver ( Castor canadensis Kuhl, 1820) and ungulates, resulting in dramatic declines in beaver populations. Some authors attribute this decline to competitive exclusion. Generally, the less niche overlap between competitors, the lower potential competition between them. Differences between foraging behaviour of beaver and ungulates suggest that they could not compete to the point of either competitive exploitation or complete exclusion except in restricted habitats. We tested this assumption under two levels of foraging intensity by ungulates by examining the effects of resource competition on beaver forage choices in the context of central place foraging theory. Ungulate densities and foraging intensity within Elk Island National Park (EINP) in Alberta, Canada, were significantly higher than those immediately adjacent to the park, where foraging pressure was lower. Within EINP, forage availability (e.g., stem densities and stem diameters) of many woody plants preferred by beaver, such as Populus L. and Salix L., were depressed by intense foraging by ungulates. Beaver adapted to the effects of high ungulate densities on forage resources by adapting their foraging behaviour. This finding suggested that competitive exploitation, rather than exclusion, exists in EINP. EINP is a productive system that offers an array of forage species, which potentially buffers the effects of competition between ungulates and beaver.
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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 | 0.001 |
| 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".