Fences reduce habitat for a partially migratory ungulate in the Northern Sagebrush Steppe
Bibliographic record
Abstract
Abstract Few studies have examined differential responses of partially migratory ungulates to human development or activity, where some individuals in a population migrate and others do not. Yet understanding how animals with different movement tactics respond to anthropogenic disturbance is key to sustaining global ungulate migrations. We examined seasonal resource selection of a partially migratory population of pronghorn ( Antilocapra americana ) in the Northern Sagebrush Steppe of Alberta, Saskatchewan, and Montana from 2003 to 2011. We developed step‐selection functions (SSF) for migrant and resident pronghorn during the summer and winter at two spatial scales (second order and third order) and then integrated SSFs across scales to estimate pronghorn responses to fences and subsequent habitat loss from these features while accounting for responses to other resource use. Both migrant and resident pronghorn showed the strongest responses to natural and anthropogenic features at the second order and weaker responses at the third order. Selection responses of migrant and residents differed the most in response to normalized difference vegetation index, topography, and anthropogenic features. Seasonally, selection for intermediate greenness was strongest in summer, whereas avoidance of roads strongly influenced winter resource selection of both tactics. Both migrant and resident pronghorn showed strong avoidance of fencing at both spatial scales during summer and winter. Model predictions with complete removal of fences from the landscape (i.e., natural conditions) predicted an increase in the area of high‐quality habitat of 16–38%. In contrast, doubling fence density on the landscape decreased the amount of high‐quality habitat by 1–11% and increased low‐quality habitat by 13–21%. Our results suggest that pronghorn winter and summer ranges can be improved by reducing the density of fences on the landscape, or mitigation measures to enhance fence crossings, to alleviate the indirect loss of habitat for this important endemic prairie species.
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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 | 0.000 | 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".