Going beyond ‘predation’ and ‘survival’: The importance of demographic changes for understanding snowshoe hare cycles
Bibliographic record
Abstract
Research Highlight: Oli, M., Kenney, A., Boonstra, R., Boutin, S., Murray, D., Jung, T., Hines, J., Krebs, C. (2026). Demographic mechanisms of snowshoe hare population cycles in Yukon, Canada. Journal of Animal Ecology. https://doi.org.10.1111/1365-2656.70169. Ecologists have long been intrigued by cycles in population abundances characterizing the dynamics of some wild species. Abundance may follow surprisingly regular cycles, with an increase phase, a peak phase and then a decline phase of more or less the same length. Predator-induced mortality has long been proposed as the main demographic mechanism inducing population cycles, leading most of cycle theory to consider that population cycles result from survival changes only. In this paper, Oli et al. (2025) assessed how the cycles in population abundance of the snowshoe hare (Lepus americanus) in Yukon, observed during 43 years, are driven by survival or reproductive rates of some specific age classes and how their contributions differ according to the cyclic phase. Thanks to the individual monitoring of more than 7000 snowshoe hares and a state-of-the-art capture-mark-recapture modelling framework, they showed different contributions of age-specific vital rates to the population growth rate, depending on the cyclic phase (increase, peak, decline and low phases) and the breeding period considered (early, mid, late and non-breeding periods). For instance, improved breeding probability, litter size and pre-weaning survival played a major role during the increase phases, whereas lower pre-weaning survival explained population decline. They also highlighted a strong interaction between season and cyclic phases, with, for example, at mid-breeding season, a survival in low phases that is close to the survival observed in increase phases. This led to different life-history strategies over the seasons and the phases: the population had a fast strategy in the early breeding season in the increase phase and a slow strategy in the late breeding season and decline phase, demonstrating a high level of plasticity across phases and seasons. The enigma of population cycles is not fully solved yet, but the study by Oli et al. (2025) clearly contributes to improving our mechanistic understanding of population cycles.
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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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".