Indirect evidence of prey-switching in minks: empirical evidence, theoretical modeling, and spatial drivers
Bibliographic record
Abstract
A spatial gradient in the interactions between American minks (Neovison vison) and muskrats (Ondatra zibethicus) occurs in the Hudson's Bay Company fur harvest returns of Canada. Evidence for strong dependence of minks on muskrats exists in northwestern Canada, whereas evidence for weaker interactions exists in central and eastern Canada. We tested the hypothesis that minks consume fewer muskrats and more alternative prey in some areas, using fur records from 56 Hudson's Bay posts. Both muskrats and small microtines were found to explain mink dynamics, with small microtines and other alternative prey gaining importance in the eastern portion of our study area. Mink fur returns exhibited a range of cycle lengths from 2.6 to 13 years encompassing typical small microtine periodicities of 3–5 years and typical muskrat periodicities of 8–13 years. A time lag of 0 years occurred between mink and muskrat harvest data frequently in the eastern portion of our study area, hypothesized to be a result of minks consuming alternative prey. To biologically verify small microtines as a potential prey source, we modeled mink and muskrat population dynamics assuming small microtines were an alternative prey by modifying the Turchin and Hanski (1997) model. Simulated mink and muskrat time series replicated observed periodicity and time-lag range, suggesting that minks can be generalist predators and consume alternative prey. Finally, we examined species richness and land cover as potential drivers of mink prey-switching, but were unable to find support for either hypothesis, suggesting that additional environmental-or competition-related interactions influence mink population 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".