Multiscale Occupancy Patterns of Anurans in Prairie Wetlands
Bibliographic record
Abstract
Habitat loss and degradation appear to be the primary drivers of amphibian declines. Given the losses of native prairie and prairie wetlands, it is essential to understand the habitat conditions that support amphibian species in remaining prairie ecosystems. In this study, we combine wetland surveys, geographic information systems (GIS), and hierarchical statistical modeling to assess anuran occupancy relationships with wetland and landscape factors. We surveyed 141 wetlands with repeated sampling for amphibian breeding activity (calling, eggs, tadpoles, metamorphs) in the Sheyenne National Grasslands—one of the largest contiguous prairies on the North American continent. Overall we observed evidence of seven amphibian species breeding in the Sheyenne National Grasslands. Species with ubiquitous distributions (Boreal Chorus Frogs) or species that occurred infrequently (Canadian Toads and Great Plains Toads) had little variance in occupancy. However, Northern Leopard Frogs, Wood Frogs, and Gray Treefrogs exhibited occupancy relationships with wetland and landscape variables. Our results establish a baseline understanding of current prairie amphibian–habitat relationships. Furthermore, they indicate that integrating local and landscape variables into occupancy models that account for spatial autocorrelation can provide a better understanding of amphibian ecology, and can inform conservation and restoration programs.
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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) | 0.001 | 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".