Climatic conditions limit wolverine distribution in the Cascade Range of southwestern North America
Bibliographic record
Abstract
Recolonization of the Cascade Range in southern British Columbia, Canada, and Washington, USA, by wolverines ( Gulo gulo (L., 1758)) is an ongoing process whose ultimate outcome is unknown. A reliable species distribution model for the wolverine in the Cascades (i.e., their first-order habitat selection) is urgently needed to help inform management and conservation strategies. Using Argos location data obtained on 10 resident adult wolverines (six females, four males) from 2008 to 2016, we generated a multi-covariate species distribution model for the wolverine in the Cascades. Our final model included three climatic covariates and their quadratic terms: Proximity to the Transitional Zone Near Alpine Tree Line, Number of Frost-free Days per Year, and Annual Precipitation as Snow. Model validations indicated that our model was robust and could identify areas of potential wolverine distribution in the Cascades reliably. Our model provides evidence that wolverine distribution in the Cascades is constrained by climatic conditions and that snowy and cold environments define the geographic areas that are overwhelmingly associated with resident wolverines. In addition, our model provides a reliable basis for monitoring the direct effects of climate change on wolverines in the Cascade Range and for predicting the extent to which climate change may impact their populations under various scenarios.
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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 | 0.001 | 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".