An experimental analysis of density dependence in meadow voles: Within‐season and delayed effects
Bibliographic record
Abstract
Wild mammal populations exhibit a variety of dynamics, ranging from fairly stable with little change in population size over time to high-amplitude cyclic or erratic fluctuations. A persistent question in population ecology is why populations fluctuate as they do. Answering this seemingly simple question has proven to be challenging. Broadly, density-dependent feedback mechanisms should allow populations to grow at low density and slow or halt growth at high density. However, experimental tests of what demographic processes result in density-dependent feedback and on what timescale have proven elusive. Here, we used replicated density perturbation experiments and capture-mark-recapture analyses to test density-dependent population growth in populations of meadow voles (Microtus pennsylvanicus) during the summer breeding season by manipulating founding population density and observing the pattern of survival, reproduction, and population growth. High population density had no consistent effect on survival rates but generally negatively influenced recruitment and population growth rates. However, these density-dependent effects varied within the breeding season and across years. Our study provides evidence that density-dependent feedback mechanisms operate at finer time scales than previously believed and that process, additively with delayed year effects, is key to understanding multiyear population demography.
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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 | 0.001 |
| 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".