Wolf kill rates across winter in a low‐density moose system in Alaska
Bibliographic record
Abstract
ABSTRACT Wolf ( Canis lupus ) kill rates are fundamental to understanding predation, but are not well known at low moose ( Alces alces ) densities. We investigated kill rates of 6 wolf packs (2–10 wolves/pack) during 2 winters on the Yukon Flats, a region of eastern Interior Alaska where moose were the sole ungulate prey of wolves occurring at densities <0.2 moose/km 2 . Our objectives were to compare kill rates with those from areas of greater moose densities, and to determine potential trends in kill rates across the winter. We located moose killed by wolves in February–March 2009, and November 2009–March 2010 using aerial tracking techniques and global positioning system (GPS) location clusters. Wolves killed more moose in early than late winter (β MONTH = −0.02 moose/pack/day, 95% CI = −0.01 to −0.04), and kill rate estimates (mean, 95% CI) were greatest in November (0.033 moose/wolf/day, 0.011–0.055) and least in February (0.011, 0.002–0.02). Kill rates were similar between February and March 2009 (0.019 moose/wolf/day, 0.01–0.03) and 2010 (0.018, 0.01–0.03). Prey composition was primarily adult females (39%) and young‐of‐the‐year (35%). We attribute an elevated kill rate in early winter to predation on more vulnerable young‐of‐the‐year. Kill rates in our study were similar to those from other studies where moose occurred at greater densities. We suggest that very few, if any, wolf–moose systems in Alaska and the Yukon experience a density‐dependent phase in the functional response, and instead wolves respond numerically to changes in moose density or availability in the absence of alternative prey. Through a numerical response, wolf predation rates may approximate the annual growth potential of the moose population, contributing to persistent low densities of moose and wolves on the Yukon Flats. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".