Tracking the decline of weasels in North America
Bibliographic record
Abstract
Small carnivores are of increasing conservation concern globally, including those formerly thought to be widespread and abundant. Three weasel species (Mustela nivalis, M. frenata, and M. erminea) are distributed across most of North America, yet several recent studies have reported difficulty detecting weasels within their historical range and several states have revised the status of weasels to that of species of conservation concern. To investigate the status and trends of weasels across the United States (US) and Canada, we analyzed four separate datasets: historical harvests, museum collections, citizen scientist observations (iNaturalist), and a recent US-wide trail camera survey. We observed 87-94% declines in weasel harvest across North America over the past 60 years. Declining trapper numbers and shifts in trapping practices likely partially explain the decline in harvest. Nonetheless, after accounting for trapper effort and pelt price, we still detected a significant decline in weasel harvest for 15 of 22 evaluated states and provinces. Comparisons of recent and historical museum and observational records suggest relatively consistent distributions for M. erminea, but a current range gap of >1000 km between two distinct populations of M. nivalis. We observed a dramatic drop-off in M. frenata records since 2000 in portions of its central, Great Lakes, and southern distribution, despite extensive sampling effort. In 2019, systematic trail camera surveys at 1509 sites in 50 US states detected weasels at 14 sites, all of which were above 40o latitude. While none of these datasets are individually conclusive, they collectively support the hypothesis that weasel populations have declined in North America and highlight the need for improved methods for detecting and monitoring weasels. By identifying population declines for small carnivores that were formerly abundant across North America, our findings echo recent calls to expand investigations into the conservation need of small carnivores globally.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".