Wolves choose ambushing locations to counter and capitalize on the sensory abilities of their prey
Bibliographic record
Abstract
Wolves (Canis lupus) are primarily cursorial predators, but they use ambush strategies to hunt beavers (Castor canadensis). Terrestrial beaver activity is predictable because beavers use well-defined, conspicuous habitat features repeatedly. Thus, studying where wolves wait-in-ambush for beavers provides a unique opportunity to understand how predators choose ambush locations in relation to prey activity. We searched 11,817 clusters of GPS-locations from wolves in the Greater Voyageurs Ecosystem, Minnesota, USA and documented 748 ambushing sites and 214 instances where wolves killed beavers. Wolves chose ambush locations: 1) with olfactory concealment to avoid detection from the highly-developed olfactory senses of beavers, and 2) close (generally <5 m) to beaver habitat features to take advantage of beavers’ inability to visually detect motionless predators. Our work describes in detail the ambush strategies wolves use to hunt beavers, and continues to overturn the traditional notion that wolves rely solely on cursorial hunting strategies. We also demonstrate that ambush predators can anticipate the movements and behavior of their prey due to a fundamental understanding of their prey’s sensory abilities. Wolves therefore, and likely ambush predators in general, appear capable of simultaneously accounting for abiotic and biotic factors when choosing ambush locations, ultimately allowing them to counter the defenses and exploit the sensory deficiencies of their prey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".