Impacts of restoration: assessing resource selection by reintroduced plains bison in Banff National Park and their influence on birds
Bibliographic record
Abstract
Parks Canada helped to restore the biodiversity of Banff National Park by reintroducing a herd of plains bison into Banff’s backcountry in 2017. Questions remain as to how bison might use the landscape and how their choice of what parts of a landscape to use might affect creatures already present there. I used remote sensing and GIS to examine how topography, the history of fire, and ecosystem type affected the odds of the Banff bison inhabiting parts of their reintroduction zone. I also used acoustic bird surveys, and vegetation structure and composition surveys to examine if changes to vegetation might influence the overall abundance and species richness of the bird population of the reintroduction zone. Bison were most likely to inhabit areas of the reintroduction zone that were on flatter terrain, closer to water, frequently burned, and that were in a grassland/shrubland ecosystem. The percent coverage of forbs increased with increasing bison usage. The percent coverage of mosses was found to increase with increasing bison usage in forests but decreased with increasing bison usage in grassland/shrublands. The overall bird population was associated with a decrease in areas most used by bison. These results show that the prescribed burn program in Banff National Park can create good habitat for bison and encourage them to explore the landscape. Burns will create critical habitat for disturbance-tolerant species and bison will help to maintain these disturbed patches. These results also show how birds sensitive to disturbance by bison may decline in the early stages of a reintroduction and that the lack of an effect on species richness may be because disturbance-tolerant birds need more time to find this new habitat. Expanding the bison reintroduction zone in the long run would be advantageous as managers work to restore bison as a force that encourages biodiversity within the Canadian Rockies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".