Prairie dogs, cattle subsidies and alternative prey: seasonal and spatial variation in coyote diet in a temperate grassland
Bibliographic record
Abstract
As the dominant predator on North America's grasslands, coyotes Canis latrans have a large influence on biodiversity, both on working ranches and in protected parks. Ground squirrel (sciurid) species and livestock carrion are often abundant on grasslands worldwide and have the potential to influence a predator's consumption of alternative prey. We collected 1321 scats in four seasons over two years in and adjacent to Grasslands National Park, Saskatchewan, to test the hypothesis that seasonal and spatial variation in consumption of sciurid prey and cattle Bos taurus carrion influenced coyote consumption of alternative prey. Sciurid (black‐tailed prairie dog Cynomys ludovicianus and Richardson's ground squirrel Urocitellus richardsonii ) remains were common from spring to fall and had a strong inverse relationship with deer (mule deer Odocoileus hemionus and white‐tailed deer O. virginianus ), which were most common in winter scats. Cattle remains were most common during spring, fall and winter, and occurred in 10.6% of scats annually. Biomass estimates suggest that cattle was the highest ranked food on cattle grazing land year‐round, and the 2nd ranked food, after deer, during winter on the portion of the park from which cattle were excluded. Closer proximity of scats to a prairie dog colony increased the likelihood of prairie dog remains throughout the year and reduced the likelihood of cattle remains in scats from spring to fall, but not during winter. Individual differences in foraging and ranging behavior may explain the spatial distribution of prairie dog versus cattle in scats. Further work is needed to determine whether sciurid prey, deer, or livestock carrion support large predator populations on grassland habitats to a level that may negatively affect coexisting prey species, including species at risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".