Population dynamics of muskrats (<i>Ondatra zibethicus</i>) and American mink (<i>Neovison vison</i>): investigating contemporary patterns in a classic predator–prey system
Bibliographic record
Abstract
A well-studied predator–prey relationship between American mink (Neovison vison (Schreber, 1777); formerly known as Mustela vison Schreber, 1777) and muskrats (Ondatra zibethicus (Linnaeus, 1766)) in Canada has advanced our understanding of population cycles including the influence of density dependence and lagged responses of predators to prey abundances. However, it is unclear if patterns observed in Canada extend across the southern half of their native range. We used data from the United States to create a 41-year time series of mink and muskrat harvest reports (1970–2011) for 36 states. After controlling for pelt-price effects, we used second-order autoregressive and Lomb–Scargle spectral density models to identify the presence and periodicity of muskrat population cycles. Additionally, we tested for evidence of delayed or direct density dependence and for predator-driven population dynamics. Our results suggest muskrat populations may cycle in parts of the United States; however, results varied by modeling approaches with Lomb–Scargle analyses providing more precise parameter estimates. Observed cycle lengths were longer than expected with weak amplitudes and we urge caution when interpreting these results. We did not detect evidence of a predator–prey relationship driven by a lagged numerical response of American mink. American mink and muskrat fur returns were largely correlated across the region suggesting extraneous factors likely synchronize both populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".