A Simultaneous Test of Synchrony Causal Factors in Muskrat and Mink Fur Returns at Different Scales across Canada
Bibliographic record
Abstract
BACKGROUND: Synchrony among populations has been attributed to three major hypotheses: dispersal, the Moran effect, and trophic-level interactions. Unfortunately, simultaneous testing of these hypotheses demands complete and detailed data, which are scarce for ecological systems. METHODOLOGY/PRINCIPAL FINDINGS: Hudson's Bay Company data on mink and muskrat fur returns in Canada represent an excellent opportunity to test these hypotheses because of the detailed spatial and temporal data from this predator-prey system. Using structural equation modelling, support for each hypothesis was evaluated at two spatial scales: across Canada and dividing the country into three regions longitudinally. Our results showed that at both scales mink synchrony is a major factor determining muskrat synchrony, supporting the hypothesis of trophic-level interactions, but the influence of winter precipitation synchrony is also important in eastern Canada. Moreover, mink synchrony is influenced principally by winter precipitation synchrony at the level of all Canada (Moran effect), but by distance at regional level, which might suggest some influence of dispersal at this level. DISCUSSION/SIGNIFICANCE: Our result is one of the few reports of synchrony mediated by trophic-level interactions, highlighting the importance of evaluation of scale effects in population synchrony studies.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".