Climate variability and density-dependent population dynamics: Lessons from a simple high-Arctic ecosystem
Bibliographic record
Abstract
Ecologists are still puzzled by the diverse population dynamics of herbivorous small mammals that range from high-amplitude, multi-annual cycles to stable dynamics. Theory predicts that this diversity results from combinations of climatic seasonality, weather stochasticity and density-dependent food web interactions. The almost ubiquitous 3-5-yr cycles in boreal and arctic climates may theoretically result from bottom-up (plant-herbivore) and top-down (predator-prey) interactions. Assessing empirically the roles of such interactions, and how they are influenced by environmental stochasticity, has been hampered by food web complexity. Here, we take advantage of a uniquely simple High-Arctic food web, which allowed us to analyze dynamics of a graminivorous vole population not subjected to top-down regulation. This population exhibited high-amplitude, non-cyclic fluctuations - partly driven by weather stochasticity. However, the predominant driver of the dynamics was direct density dependence, which alternated between being weak in summer and strong (overcompensatory) in winter that the population frequently crashed. Model simulations showed that this season-specific density dependence would yield regular 2-year cycles in absence of stochasticity. While such short cycles have not yet been observed in mammals, they are theoretically plausible if graminivorous vole populations are deterministically bottom-up regulated. When incorporating weather stochasticity in the model simulations, cyclicity became disrupted and the amplitude was increased - akin to the observed dynamics. Our findings contrast with the 3-5-yr population cycles involving delayed density dependence that are typical of graminivorous small mammals in more complex food webs, suggesting that top-down regulation is an important component of such dynamics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".