Seasonal role of a specialist predator in rodent cycles: Ermine–lemming interactions in the High Arctic
Bibliographic record
Abstract
The exact mechanisms behind population cycles remain elusive. An ongoing debate centers on whether predation by small mustelids is necessary and sufficient to generate rodent cycles, as stipulated by the specialist predator hypothesis (SPH). Specifically, the SPH predicts that the predator should respond numerically to the abundance of its prey with a delay of approximately one year, leading to delayed density-dependence in the dynamics of the prey population. Here, we analyze the numerical response of a small mustelid, the seasonality of its interaction with rodents, and its impact on population cycles using long-term seasonal data on ermines and cyclic lemmings in the High Arctic. Our results show that the numerical response of ermines to lemming fluctuations was delayed by one year and could mediate delayed density-dependence in lemming growth rate. The impact of ermines on the growth rate of lemmings was small but mostly circumscribed to winter, a critical period when shifts in cycle phases occur and direct density-dependence seems relaxed. Our simulations of lemming population with and without ermines suggest that these small mustelids are neither necessary, nor sufficient to generate cycles per se. However, the presence of small mustelids may be necessary to prolong the low-abundance phase and delay the recovery of lemming populations, promoting the presence of a multiannual low phase typical of lemming cycles. Our study corroborates the idea that population declines of cyclic populations are best explained by direct density-dependence; however, the delayed response of specialized predators induces the multiannual low phase and leads to longer periodicities, which are typically of 3-5 years in rodents.
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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 | 0.000 | 0.000 |
| 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".