Do prey select for vacant hunting domains to minimize a multi‐predator threat?
Bibliographic record
Abstract
Many ecosystems contain sympatric predator species that hunt in different places and times. We tested whether this provides vacant hunting domains, places and times where and when predators are least active, that prey use to minimize threats from multiple predators simultaneously. We measured how northern Yellowstone elk (Cervus elaphus) responded to wolves (Canis lupus) and cougars (Puma concolor), and found that elk selected for areas outside the high-risk domains of both predators consistent with the vacant domain hypothesis. This enabled elk to avoid one predator without necessarily increasing its exposure to the other. Our results demonstrate how the diel cycle can serve as a key axis of the predator hunting domain that prey exploit to manage predation risk from multiple sources. We argue that a multi-predator, spatiotemporal framework is vital to understand the causes and consequences of prey spatial response to predation risk in environments with more than one predator.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".