Complex seasonal patterns of habitat use by a keystone mesopredator in boreal forest landscapes fragmented by fire.
Bibliographic record
Abstract
Wildfire is the most important disturbance regime in North American boreal forest communities, driving forest composition and structure across this 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 (2-78 years old) and if these movements reflect habitat selection of their main prey, the snowshoe hare (Lepus americanus). 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, but young deciduous stands, mirroring the habitat selection of snowshoe hares. We found, however, that lynx habitat selection peaked at intermediately aged stands in both forest types. We hypothesize that this difference is driven by selection for habitats that maximize capture probability as opposed to simply representing the highest hare density. This idea is supported by observed seasonal changes in peak selection in deciduous stands, with selection for younger stands in winter, when leaf loss reduces cover. There was no seasonal change in coniferous stands as they experience little change in cover across seasons. 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-age stands.
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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".