Predator–prey co‐occurrence in harvest blocks: Implications for caribou and forestry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Forest harvesting alters habitat, impacts wildlife, and disrupts ecosystem function. Across the boreal forest of Canada, forest harvesting affects ungulate prey species and their predators, with cascading impacts on other species, including threatened woodland caribou. We used camera and vegetation data and occupancy modeling to investigate what characteristics in and around forestry harvest blocks influenced the occupancy and co‐occurrence of primary prey (elk, moose, mule deer, white‐tailed deer) and predators (black bear, cougar, grizzly bear, wolf) in harvest blocks. Occupancy was linked to forage, the surrounding habitat and anthropogenic disturbance, and silviculture practices. Black and grizzly bear occupancy was influenced by the presence of deer, and bear–deer co‐occurrence was influenced by site‐specific silviculture practices and surrounding anthropogenic disturbance. In the context of caribou recovery, our results indicate that forestry within caribou ranges could consider site‐specific silviculture practices and landscape‐level planning to reduce use of harvest blocks by primary prey. Future caribou recovery efforts may also consider the roles of deer and bears in caribou predation risk. Our study provides the first insights into the impacts of forestry and silviculture on boreal forest predator–prey co‐occurrence and provides practical management applications to mitigate the impacts of anthropogenic activities on the boreal forest ecosystem into the future.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it