Predation by wolves interacts with the North Pacific Oscillation (NPO) on a western North American elk population
Bibliographic record
Abstract
Summary Recent research reveals the widespread influence of the North Atlantic Oscillation (NAO), a large‐scale climatic variation, on northern ungulate populations. Little is known, however, about the influence of the North Pacific Oscillation (NPO), a similar climatic index, on ungulates. The influence of the NPO on elk population dynamics in Banff National Park (BNP) was examined using a 15‐year time‐series of three elk subpopulations exposed to different levels of predation by wolves. NPO was strongly related to local climate data including snow depth ( r = +0·61) and winter temperatures ( r = −0·51). Higher NPO values reflected increasing winter severity, reducing elk population‐growth rate irrespective of wolf‐predation pressure. Elk population‐growth rate, however, declined more strongly in areas with wolf predation through the interaction with winter severity, indexed by NPO. Effects of NPO were weaker in the absence of wolf predation. Differences between the effects of snow and NPO were revealed that depended on wolf‐predation pressure. Without wolf predation, NPO reduced elk population‐growth rate, suggesting overall climate was important. With wolf predation, snow depth was more important than NPO, suggesting a mechanism of increased wolf‐predation rates in deeper snow. This study is the first to demonstrate the influential role of Pacific climate on western North American ungulate population dynamics and provides further evidence of the role of large‐scale climatic variation in terrestrial ecosystem dynamics.
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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) | 0.000 | 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) | 0.000 | 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 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".