Living on the edge: spatial response of coyotes (<i>Canis latrans</i>) to wolves (<i>Canis lupus</i>) in the subarctic
Bibliographic record
Abstract
Understanding how mesopredators manage the risks associated with apex predators is key to explaining impacts of apex predators on mesopredator populations and patterns of mesopredator space use. Here we examine the spatial response of coyotes (Canis latrans Say, 1823) to risk posed by wolves (Canis lupus Linnaeus, 1758) using data from sympatric individuals fitted with GPS collars in subarctic Alaska, USA, near the northern range limit for coyotes. We show that coyotes do not universally avoid wolves, but instead demonstrate season-specific responses to both wolf proximity and long-term use of the landscape by wolves. Specifically, coyotes switched from avoiding wolves in summer to preferring areas with wolves in winter, and this selection was consistent across short-term and longer term temporal scales. In the summer, coyotes responded less strongly to risk of wolves when in open areas than when in closed vegetation. We also demonstrate that coyotes maintain extremely large territories averaging 291 km2, and experience low annual survival (0.50) with large carnivores being the largest source of mortality. This combination of attraction and avoidance predicated on season and landcover suggests that mesopredators use complex behavioral strategies to mediate the effects of apex predators.
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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 | 0.000 | 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".