Impacts of residential development on ungulates in the Rocky Mountain West
Bibliographic record
Abstract
Abstract Rural residential development in the Rocky Mountain West of North America is resulting in increased conflict between ungulate habitat and infrastructure. Subdivisions, houses, and roads affect ungulates both behaviorally and demographically and reduce management options available to agencies. These habitat alterations need to be addressed if wildlife species are to coexist with humans. We reviewed literature (using 7 search engines and 16 key words) on the effects of land‐use change (especially residential development) on elk ( Cervus canadensis ), mule deer ( Odocoileus hemionus ), white‐tailed deer ( O. virginianus ), American pronghorn ( Antilocapra americana ), and bighorn sheep ( Ovis canadensis ). Approximately 80 studies were directly related to the effects of human development on ungulates; however, only 25 specifically examined residential development and its influences on the focal species. Studies varied in methodology (i.e., aerial and ground surveys, pellet counts, movement rates) and analyses. The literature suggests most ungulates exhibit short‐term behavioral reactions to human disturbance. However, few studies link these responses to population‐level consequences or test the cumulative impact that multiple developments and development types (i.e., roads, housing, industrial development) have on seasonal habitat use and migratory behavior. Short‐term and small‐scale observational studies have articulated the conflict between humans and ungulates on shared habitat. Those studies need to be followed with well‐designed experiments and large‐scale multi‐jurisdictional projects so that managers and planners can make more credible recommendations to direct future exurban development that benefits wildlife and humans. © 2012 The Wildlife Society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".