Comparison of concurrent plains and wood bison diets at Elk Island National Park
Bibliographic record
Abstract
Abstract Understanding ecological niche is critical to the management and conservation of any species or population. For herbivores, dietary niche is critical for understanding habitat suitability, carrying capacity, and population and community viability. In closely related species with similar morphologies, dietary niches can diverge depending on environmental and seasonal factors. Elk Island National Park in Alberta, Canada, contains populations of both American bison ( Bison bison ) subspecies—plains bison ( B. b. bison ) and wood bison ( B. b. athabascae )—in similar but separate habitats located at the historical confluence of the subspecies' distributions. Using generalized additive models and nutritional geometry, we compared the subspecies' dietary niches in terms of content and quality continuously for one year (Dec 2020 – Nov 2021). Both subspecies consumed primarily graminoids during winter, spring, and fall and incorporated a variety of forbs and woody plants during summer. Plains bison diets contained more upland grasses and digestible organic matter in their diet and less wetland graminoids (e.g., sedges) throughout the year. We also found differing dietary niches between the subspecies during the spring and summer months. Our unique, continuous analysis of annual diet content and quality can deliver insight into the similarities and differences between subspecies' dietary niches that should help improve management decisions, such as better matching between source populations and release areas for future translocations.
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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 | 0.000 | 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".