Resource separation by mountain ungulates on a landscape modified by fire
Bibliographic record
Abstract
Abstract Fire restructures plant communities and can be an important modifier of ecosystems. Increases in forage quantity and quality in burned areas attract large ungulates, and may result in changes to animal distributions. In mountainous northern British Columbia where prescribed fire is used to enhance ungulate range, there is concern that expanding elk ( Cervus elaphus ) populations will move in response to burning into the traditional ranges of another grazing species, Stone's sheep ( Ovis dalli stonei ), and have adverse effects on them. We compared patterns of resource selection and use by both species on a landscape with 138 prescribed burns varying from 0 to 31 years of age. Seasonal range sizes of global positioning system (GPS)‐collared individuals were smallest in winter and late winter and largest in summer for both female Stone's sheep and elk. Both species selected for south aspects and against conifer stands in all seasons. Stone's sheep selected for burned areas in fall, winter, and late winter and selected to be close to a burn in every season except summer. Elk selected for burned areas in every season, with the highest selection for burn‐shrub areas. Stone's sheep typically used younger burns, whereas elk were less specific and often used older burns. The highest potential for seasonal overlap in the use of burned areas occurs during winter and late winter, but Stone's sheep and elk at current population levels overlap minimally because they partition their use of the landscape through elevation and topography. Stone's sheep always selected and used steeper rugged terrain than elk, and were at higher elevations, often in rocky areas. Elk avoided alpine and rocky areas, and were at lower elevations than Stone's sheep, on flatter less rugged terrain. We recommend continued monitoring of post‐fire effects on resource selection and use by these sympatric grazers, particularly relative to the distribution of expanding elk populations, which could enable competitive interactions and change predator‐prey dynamics. © 2015 The Wildlife Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".