HUNGER DRIVES SWITCHING AND SEARCHING RESPONSE IN A SOCIAL PREDATOR
Bibliographic record
Abstract
A bstract Hunger is a frequent state for many predators and increasing hunger is likely to motivate costly behaviour to acquire necessary resources. Generalist predators must balance the costs and gains of hunting different prey, including increasing encounter rates and improving success rates by seeking areas with greater prey catchability. Large carnivores face threats when they interact with humans or conspecifics. We use integrated step selection analysis to describe spatiotemporal factors that influence wolf ( Canis lupus ) hunting behavior in Riding Mountain National Park, a natural area that wolves share with moose ( Alces alces ) and elk ( Cervus canadensis ). If hunger generates more risky behavior by wolves, as time-from-kill increases we expect wolves will: (1) search for and kill a prey that poses higher risk of injury, (2) use the periphery of their range, (3) use areas closer to the park boundary. Hunger alters wolf space use and drives a fine scale change in prey tracking. Movement patterns of hungry wolves are indicative of search behavior, i.e., shorter steps and more turning. Contrary to our predictions, hungry wolves moved further into the park. As wolves become hungrier, they switch their response from a weak selection to avoidance of elk. In contrast, the response to the primary and emergent prey, moose varied between individuals with some pack level similarities. Therefore, the state-based response to a pervasive risk and a historical resource was conserved in a population residing in a prey rich ‘island’ interfacing with human disturbance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".