Demography of snowshoe hares in relation to regional climate variability during a 10-year population cycle in interior AlaskaThis article is one of a selection of papers from The Dynamics of Change in Alaska’s Boreal Forests: Resilience and Vulnerability in Response to Climate Warming.
Bibliographic record
Abstract
We monitored populations of snowshoe hares ( Lepus americanus , Erxleben) in interior Alaska for 10 years from 1999 to 2008. During this period, fall densities of hares fluctuated approximately 14-fold. High population growth rates over summer (λ = 1.83–8.00) were followed by large population declines over winter (λ = 0.16–0.82). Young-of-the-year hares tended to gain mass over winter, while adult hares tended to loose body mass. The average mass of adult hares was significantly lower during the low phase of the cycle compared with when hares were abundant. Overwinter survival of juveniles relative to adults decreased strongly as a function of the frequency of snowfall events. However, effects of temperature and precipitation on hare demography were season dependent and appear to act as modifiers of the primary controls over population dynamics (predation and food) rather than as direct sources of mortality. The rapid changes in green-up and snow-up in interior Alaska may affect forage conditions as well as the timing of molt in snowshoe hares. The strength of these interactions may increase in importance if the asynchrony of environmental seasonality and life history traits of snowshoe hares becomes more pronounced as the climate continues to change.
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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 | 0.001 |
| 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) | 0.001 | 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".