Seasonal use of American beaver lodge areas by gray wolves in Isle Royale National Park
Bibliographic record
Abstract
Biotic and abiotic factors influence species habitat selection across space and time. Predator habitat selection is often studied in relation to their primary prey, however, how predators shift their space use in response to secondary prey and the corresponding ecological consequences have received less attention. We used four years (2018, 2020, 2021, 2022) of wolf ( Canis lupus ) GPS data to examine how wolf habitat selection relates to active American beaver ( Castor canadensis ) lodge locations. We hypothesized that wolf selection of beaver lodges would reflect seasonal beaver behavior, intensifying through the ice-free season as beaver vulnerability increases as they spend more time away from their lodges and the vulnerability and availability of moose ( Alces alces ) decreases. Using a generalized linear mixed model and a mixed generalized additive model in a Bayesian framework, we analyzed how wolf habitat selection changed, especially near active beaver lodges. We used 834 unique active beaver lodges in our analysis, 395 [0.74 lodges /km 2 ] in 2018, 386 [0.72 lodges /km 2 ] in 2020, 344 [0.64 lodges /km 2 ] in 2021, and 168 [0.31 lodges /km 2 ] in 2022, a total decline of 57% since wolf restoration. We collected 18,932 wolf GPS locations (N 2018 = 1200, N 2020 = 7224, N 2021 = 3690, N 2021 = 6818) from 23 wolves (10 females and 13 males). Our results suggest that wolves shifted their habitat selection to increase encounters with beavers, supporting previous work demonstrating the importance of beavers to the wolf diet during snow-free periods. This shift supports wolf prey-switching behavior to beavers when their primary prey, moose, are more difficult and riskier to kill.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".