Relationships Between Muskrat Density and Avian and Anuran Richness in Great Lakes Coastal Wetlands
Bibliographic record
Abstract
Abstract Wetlands in southern Ontario are at risk of degradation and alteration due to human activities. This is a concern because wetlands provide essential habitat for species from a range of taxa, such as birds and anurans (frogs and toads). One wetland-dwelling species whose decline may be linked to loss of wetland wildlife habitat is the muskrat (Ondatra zibethicus). The decline of muskrats may also be linked to declines of other species because muskrats engage in activities that could support taxa such as birds and anurans by increasing habitat heterogeneity. We investigated whether bird and anuran species richness is related to muskrat density or if it is better predicted by land cover variables that describe the wetland and surrounding area at 30 coastal wetlands on Lake Ontario. We estimated bird and anuran species richness using data from the Great Lakes Marsh Monitoring Program and the Great Lakes Coastal Wetland Monitoring Program, and muskrat density based on muskrat house counts. Our results suggest that muskrat activity did not predict richness of anurans or birds overall; however, it did predict richness for the subset of birds that nest in emergent aquatic vegetation. Our results indicate that muskrat abundance in emergent marshes may increase habitat quality for birds that nest in emergent vegetation but may not have a measurable effect on anuran diversity in these same wetlands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".