Muskrat distributions in a changing Arctic delta are explained by patch composition and configuration
Bibliographic record
Abstract
Climate change is altering Canada’s western Arctic, including hydrology in the heterogeneous environment of the Mackenzie Delta, and these changes are impacting biotic communities. Muskrats are culturally important semi-aquatic rodents whose populations may respond to changing water levels in this region. We investigated the importance of patch configuration and patch composition — two properties affected by climate change — on muskrat presence and distribution in the Mackenzie Delta, using remote sensing and field-based surveys of lakes with and without muskrats. We tested multiple hypotheses about predictors of muskrat and forage biomass presence using a model-selection approach. We found that configuration and patch composition explained muskrat distribution in the Mackenzie Delta, with composition being of greater importance. Muskrats were more likely to occur in lakes with longer perimeters, higher amounts of forage biomass, and sediment characteristics that supported macrophyte growth. The latter two conditions are related to spring flooding regimes, which will likely be altered by climate change. This may result in a decrease in muskrat habitat in the Mackenzie Delta. Our research indicates that both patch composition and configuration are important for understanding species distributions in heterogeneous environments.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".