Native prey distribution and migration mediates wolf (<i>Canis</i> <i>lupus</i>) predation on domestic livestock in the Greater Yellowstone Ecosystem
Bibliographic record
Abstract
Little research has evaluated how the migration and distribution of native prey influence patterns of livestock depredation by large carnivores. Previous research suggests that the presence of native prey can increase depredation rates by attracting predators (prey tracking hypothesis). Alternatively, the absence of native prey may facilitate predation on livestock (prey scarcity hypothesis). In this study, we evaluated support for these competing hypotheses through analysis of 4 years of cattle (Bos taurus L., 1758) depredation data (n = 39 kills), 2 years of summer and fall wolf (Canis lupus L., 1758) predation and tracking data (n = 4 wolves), and 3 years of elk (Cervus elaphus L., 1758) movement data (n = 70 elk). We used logistic regression to compare the relative influence of landscape features and elk distribution on the risk of livestock depredation in areas with migratory and resident elk. Cattle depredations occurred in habitats with increased encounter rates between wolves and livestock. In resident elk areas, depredation sites were associated with elk distribution and open roads. In migratory elk areas, depredation sites were associated with wolf dens, streams, and open habitat. Patterns of carnivore–livestock conflicts are complex, and using ungulate distribution data can predict and minimize such instances.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".