Hares and Small Rodent Cycles: a 45-year Perspective on Predator-prey Dynamics in the Yukon Boreal Forest
Bibliographic record
Abstract
ABSTRACT Long-term research is required in ecology to determine patterns of population changes, to suggest limiting factors, and to determine if and how climate change is affecting populations and their communities. In the Kluane region of the Yukon we have monitored control populations of snowshoe hares, mice, and voles from 1973 to 2017 (the longest of any similar time series anywhere in North America) and here we ask what we have observed and learned from these time series. The amplitude of hare cycles may be decreasing over this period. In contrast, the 3–4-year cycles of red backed voles (Myodes rutilus) are becoming more dramatic and the amplitude of their peak years are increasing. Four species of Microtus voles fluctuated independently of red-backed voles prior to 1998, but their peak years became synchronous thereafter, with the dominant species changing from peak to peak. The deer mouse (Peromyscus maniculatus) fluctuated irregularly, completely disappearing from the catch for 5 years in the early 1990s. Weasels were rare for the first 25 years of small rodent changes and marten were absent, but since 2000 marten have colonized and both small predators have become more abundant. Predation drives the snowshoe hare cycle, but it is far from clear that it does so for the small rodents. We suspect that social behaviour is critical for vole cycles, but this supposition has not been tested experimentally. The boreal forests of Canada and Alaska support a boom-bust set of dynamics but the voles fluctuate independently and out of phase with the hares and there is no universal cause.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".