Predator-prey co-occurrence in harvest blocks
Bibliographic record
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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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".