Seasonal patterns of habitat use by a mesopredator in boreal forest landscapes fragmented by fire
Bibliographic record
Abstract
Abstract Wildfire is the most impactful disturbance regime in the North American boreal region, driving the structure and composition of forests across the region. Recent climate models predict that increasing fire intensity and frequency will result in a shift from a largely coniferous forest to one with a greater dominance by deciduous species. We investigated how an iconic predator of the boreal system, the Canada lynx ( Lynx canadensis ), moves through a range of burn scars (4–73 years old). Using GPS collars at 4‐h fix rates, we fitted integrated step selection models to lynx movements across an 80‐year post‐fire chronosequence to assess habitat selection in both deciduous and coniferous forests. We predicted that lynx would primarily select intermediately aged spruce and young deciduous stands, mirroring previous research on the habitat selection of their main prey, snowshoe hares ( Lepus americanus ). We found, however, that lynx habitat selection peaked at intermediately aged stands in both forest types, with selection for younger deciduous stands in the winter months. There was no seasonal change in coniferous stands as they experience little change in cover across seasons. We hypothesize that lynx select for habitats that maximize capture probability as opposed to simply habitats with the highest hare density. Together, these results show that lynx can be resilient to short‐term shifts toward intermediate‐aged stands. However, these benefits will likely diminish in the longer term as the decrease in fire return interval may reduce the prevalence of intermediate‐aged stands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".