Bison and bighorns: Assessing the potential impacts of reintroducing a large herbivore to a mountainous landscape
Bibliographic record
Abstract
The reintroduction of wildlife can have significant ecological impacts by altering the flow of energy in food webs. Recently, plains bison were reintroduced to part of Banff National Park after a 150-year absence. The large herbivore's reintroduction was expected to have far-reaching effects on the ecosystem due to its significant energy requirements and interactions with habitat and other sympatric species. This study explores the impacts of bison reintroduction on the movement and resource use of another large-bodied grazer, the Rocky Mountain bighorn sheep. Between 2018 and 2021, we collected data from GPS collars fit on 39 bighorn sheep and 11 bison. We analyzed home range patterns, resource selection, and interactions to investigate the potential for interspecific competition, facilitation, and resource complementarity. At the population level, bison and bighorn sheep exhibited low levels of spatial overlap and there was strong evidence of resource separation in all seasons. Interactions between species did not appear to affect sheep movement rates; however, we did see differences in forage selection patterns for sheep with overlapping home ranges with bison. Collectively, results did not support the potential for competition or facilitation between bison and bighorn sheep and instead provided the strongest evidence of complementarity.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".