Ungulate occurrence in forest harvest blocks is influenced by forage availability, surrounding habitat and silviculture practices
Bibliographic record
Abstract
Abstract Forest harvesting causes habitat loss and alteration and can change predator–prey dynamics. In Canada, forest harvesting has shifted the distribution and abundance of ungulates (deer, elk and moose) that prefer early seral forest, resulting in unsustainable caribou predation by shared predators (bears, cougars and wolves). Long‐term solutions for caribou recovery require management to reduce ungulate prey species within caribou ranges. Silviculture practices applied after forest harvesting directly affect the amount of forage available in harvested areas, and therefore influence ungulate distribution, but few studies have completed detailed assessments on how specific treatments of site preparation, planting and stand tending influence ungulate use of harvest blocks. We used camera traps, silviculture data, GIS‐derived habitat and disturbance data, and detailed vegetation data collected at field sites to investigate ungulate occurrence in harvest blocks in west‐central Alberta, Canada. We compared seasonal ungulate occurrence and investigated how site‐specific characteristics, the surrounding habitat and disturbance density, and fine‐scale silviculture treatments influenced ungulate occurrence in blocks. Deer, elk and moose occurrence was higher in summer compared to winter. Elk, moose and white‐tailed deer occurrence was higher in blocks with greater availability of specific forage species. Moose occurrence was higher in blocks with a lower road density in the surrounding area, and white‐tailed deer occurrence was higher in blocks further from seismic lines and with a lower proportion of harvest blocks in the surrounding area. Deer, elk and moose occurrence was higher in younger harvest blocks. Mule deer and white‐tailed deer occurrence was lower in blocks with higher planting densities of lodgepole pine, and mule deer occurrence was also lower in blocks that had been stand tended. Our study provides detailed information on ungulate response to fine‐scale silviculture methods used in Alberta, directly linking wildlife occurrence to forestry practices, and providing practical scientific information to inform sustainable forestry. Translating this research into practical landscape management decisions could benefit boreal biodiversity, including threatened species like caribou, and culturally and economically important species like deer, elk and moose.
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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 | 0.001 |
| 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".