Historical surveys reveal a long‐term decline in muskrat populations
Bibliographic record
Abstract
) is an iconic species in Canada, valued for both its fur and its integral role in wetland ecosystems, and widely regarded for its perseverance. However, the resilience of this semiaquatic mammal seems to be in question now as increasing evidence points to widespread population declines. Recent analyses of harvest data across North America suggest a reduction in their numbers, but this has not been widely corroborated by population surveys. In this study we replicated historic muskrat house count surveys at two large Great Lakes coastal wetlands and present confirmation that declines in muskrat harvest correspond to actual declines in muskrat abundance. At the Point Pelee National Park marsh and the Matchedash Bay-Gray Marsh wetland we found that mean muskrat house counts declined by 93% and 91% respectively between historic surveys 40-50 yrs ago and contemporary surveys over the past 7 yrs. The factors responsible for these dramatic declines remain unclear but there may be a relationship with changes in the habitat quality of these wetlands that have occurred over the same time frame. Not only is the loss of muskrats an issue for the resulting loss of the wetland ecosystem services they provide, but it may be an indication of broader marsh ecosystem degradation. As such, a scarcity of muskrats should be considered a red flag for the state of biodiversity in our wetlands. Continued surveys and ongoing research are needed to shed more light on the current status of muskrat populations and their marsh habitats across their native range.
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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 | 0.000 | 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) | 0.002 | 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".