Silviculture shapes the spatial distribution of wildlife in managed landscapes
Bibliographic record
Abstract
Silviculture—managing tree establishment for landscape objectives—influences ecological outcomes of forests. While forest harvest impacts on wildlife are well-documented, silvicultural treatment effects remain unclear. We investigated how forest harvest and silviculture shape predator and ungulate distributions and interactions, providing ecological insights for forest management. We deployed two camera arrays in extensively harvested North American landscapes to evaluate relationships between forest harvest, silviculture, and predator and ungulate occurrences. Forest harvest, silviculture, and predator/prey activity shape wildlife occurrences. Wolf ( Canis lupus ), influenced by moose ( Alces alces ), decreased with regenerating (9–24 years) clearcuts, new (0–8 years) clearcuts with reserves, and fertilized cutblocks. Wolves increased with regenerating/older (25–40 years) clearcuts with reserves. Coyote ( C. latrans ) increased in manually or chemically brushed cutblocks at high or low deer occurrence, respectively. Black bear ( U. americanus ), influenced by prey, increased with regenerating prepared cutblocks and fewer new prepared cutblocks. Prey elevated lynx ( Lynx canadensis ) occurrence with regenerating prepared or older unprepared cutblocks. Depending on predators, mule deer ( Odocoileus hemionus ) decreased with regenerating and older prepared cutblocks; white-tailed deer ( O. virginianus ) decreased with selection- and new even-aged cutblocks. Harvest age and wolves best explained moose, although silviculture mattered seasonally. Silviculture shapes wildlife distributions and interactions. Integrating these effects into research and forest management is essential for meeting ecological objectives.
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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 | 0.001 | 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".