Mink Prey Diversity Correlates with Mink–muskrat Dynamics
Bibliographic record
Abstract
Historic fur returns from Hudson's Bay Company posts in northwestern Canada reveal periodic oscillations in mink (Neovision vision) harvests lagging 2–3 years behind oscillations in muskrat (Ondatra zibethicus) harvests, as would be expected in a predator-prey interaction. Toward central and eastern Canada, the strength of the interaction between time series of harvests of minks and muskrats weakens and the lag between fluctuations of these 2 species decreases to 1 and 0 years, respectively. We tested the hypothesis that this gradient in mink-muskrat interactions is the result of decreased dependency of minks on muskrats in areas where minks have access to more alternate prey. We tested 2 predictions: species richness of mink prey is greatest in eastern Canada and decreases to the west, and percent muskrats in the diets of minks decreases as species richness of mink prey increases. Contrary to the 1st prediction, we found that species richness of mink prey in Canada is highest in south-central Canada. Consistent with the 2nd prediction, percent occurrence of muskrats in the diets of minks was much lower in areas with greater species richness of mink prey. Local species richness of mink prey therefore could influence the degree of specialization of minks on muskrats, but may be insufficient to explain the geographic pattern in the lag between muskrat and mink harvests in eastern Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".