Winter hunting behavior and habitat selection of wolves in a low‐density prey system
Bibliographic record
Abstract
The functional response is the relationship between food intake rates and prey density, and is shaped by factors including handling time, predator speed, habitat or prey movement. For many predator—prey systems, the density‐dependent functional response is represented by a type II or type III functional response. Determination of the relationship type is important, as managers can often predict the response of predators to changing prey densities. In wolf—moose ( Canis lupus—Alces alces ) systems with relatively high prey density, the functional response often follows a predicted type II functional response. However, in a very low prey‐density system, wolves have previously been shown to escape the density dependent phase of the functional response and demonstrated kill rates mimicking high prey‐density systems. We conducted a study to evaluate winter wolf movements between moose kills in the Yukon Flats, Alaska where moose exist at densities <0.2 km −2 . Our research objectives were to understand whether habitat selection when moving and specific behaviors could be mechanisms used by wolves to maintain kill rates that mimic those in high prey density systems and if those behaviors may allow wolves in our study system to escape a density dependent functional response. We used GPS collars to characterize wolf travel paths between kills to estimate wolf travel speed, movement distance, time between kills, and handling time of each kill. Our results demonstrated selection for frozen river corridors by wolves and provided new information on long‐distance movements in a low prey‐density system. These adaptations may influence the functional response by moderating the effect of low prey densities.
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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 | 0.000 |
| 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) | 0.001 | 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".