Balancing food acquisition and predation risk drives demographic changes in snowshoe hare population cycles
Bibliographic record
Abstract
Snowshoe hare cycles are one of the most prominent phenomena in ecology. Experimental studies point to predation as the dominant driving factor, but previous experiments combining food supplementation and predator removal produced unexplained multiplicative effects on density. We examined the potential interactive effects of food limitation and predation in causing hare cycles using an individual-based food-supplementation experiment over-winter across three cycle phases that naturally varied in predation risk. Supplementation doubled over-winter survival with the largest effects occurring in the late increase phase. Although the proximate cause of mortality was predation, supplemented hares significantly decreased foraging time and selected for conifer habitat, potentially reducing their predation risk. Supplemented hares also lost less body mass which resulted in the production of larger leverets. Our results establish a mechanistic link between how foraging time, mass loss and predation risk affect survival and reproduction, potentially driving demographic changes associated with hare 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".