Understanding the Snowshoe Hare Cycle through Large-scale Field Experiments
Bibliographic record
Abstract
The 10-year cycles of the snowshoe hare and lynx seen in Hudson’s Bay fur returns represent a classic example of cyclic population dynamics. Hare cycles have been the subject of time series analysis (Stenseth et al. 1998), population modeling (Royama 1992), and field experimentation (Keith and Windberg 1978, Krebs et al. 1986, Murray et al. 1997). However, only two studies have monitored hare populations in detail over at least one full cycle. The first of these was conducted in central Alberta, Canada, by Lloyd Keith and coworkers, and provided a detailed description of the demographic machinery driving changes in hare numbers (Keith et al. 1977, Gary and Keith 1979, Keith et al. 1984). From this came the “Keith hypothesis” that hare cycles are driven by a sequential two-stage trophic interaction with hare declines initiated by winter food shortages and exacerbated by predator numerical responses that lag hare numbers by 1-2 years (Keith 1983, 1990). Predators force hares to low numbers and recovery does not occur until predator densities reach their lowest levels. The second long-term study of hare dynamics took place at Kluane Lake in the southwestern Yukon, Canada. The Kluane project began as an attempt to test the Keith hypothesis through single-factor manipulations of food supply and predation (Krebs et al. 1986, Sinclair et al. 1988, Smith et al. 1988). The first attempt failed to manipulate predators effectively, and plots containing food supplements were quickly overwhelmed by predators moving into the area. Consequently, the experiments failed to alter hare dynamics. Building on this experience, the second phase expanded the scale of experimental manipulations and developed an effective means of excluding predators from selected areas. The study also added an interaction treatment in which predators were excluded and food supplemented. These experiments were designed to test the roles of food supply, predation, and their potential interaction in the dynamics of snowshoe hares (Krebs et al. 1995). In this chapter we provide a synopsis of the key results obtained from these experiments and discuss how the results alter the current understanding of snowshoe hare dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".