Exposure to historical burn rates shapes the response of boreal caribou to timber harvesting
Bibliographic record
Abstract
Abstract Studying the response of wildlife to anthropogenic disturbances in light of their evolutionary history may help explain their capacity to adapt to novel ecological conditions. In the North American boreal forest, wildfire has been the main natural disturbance driving ecosystem dynamics for thousands of years. Boreal caribou (Rangifer tarandus caribou) is a threatened ungulate for which widespread decline has been associated with the rapid expansion of timber harvesting across its range. Although caribou may not be adapted to this new type of disturbance, cutovers share many similarities with wildfires by producing large landscapes of whole‐stand removal associated with an increased predation risk for caribou. We hypothesized that caribou with more evolutionary experience of fire disturbance should better perceive the cues associated with disturbances and adjust their behavior toward human disturbance accordingly. Given the extensive distribution of caribou populations in the boreal forest, we assessed how their historical exposure to wildfires could explain their behavioral response toward both burned and cutover areas. Our results indicate that caribou from regions with high historical burn rates displayed a consistent avoidance of recent burns (<5 yr old), and that this behavior translated in a similar avoidance of recent cutover, providing support to the cue similarity hypothesis. On the contrary, caribou with less evolutionary experience of wildfires were more likely to select recently disturbed (<5 yr‐old and 6–20 yr‐old) habitats. In the context that timber harvesting and its associated road network has been linked to increased mortality in boreal caribou populations, we discuss how this naïve habitat use of clearcuts can be exacerbated by historical disturbance regimes and become maladaptive for this endangered species.
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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 | 0.001 | 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".