Studying small mammal population dynamics: advice to consider and pitfalls to avoid—a 60-year overview
Bibliographic record
Abstract
We consider what we and others have learned and the mistakes that we have made in the last 60 years of research on small mammal population dynamics. We consider all mammals <5 kg as small mammals, covering more than 3200 species including among others, mice, voles, lemmings, gerbils, guinea pigs, muskrats, shrews, squirrels, and hares, but not bats, in ecosystems globally. We start by emphasizing the necessity of posing a good question and what that means scientifically on one or more species or populations of small mammals. We consider the study design, the choice of the study area, the methods of population estimation, the necessity of measuring demographic parameters on births, deaths, and movements, the limitations of enclosure experiments, the problems faced by field experiments to test specific hypotheses, and the need to acquire social licence. By being self-critical we hope to encourage a new round of studies of small mammal populations that will avoid pitfalls, illuminate hypotheses not yet tested properly, and forge new directions.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".