Small mammal responses to structural retention through stubbing in foothills forests
Bibliographic record
Abstract
Clear-cutting is often criticized for negatively impacting biodiversity, particularly for mammals requiring specific forest structures for shelter and foraging. In response, forestry practices increasingly incorporate residual retention, which aims to preserve original stand components with the goal of providing short and long-term benefits to biodiversity. We investigated the effectiveness of stubbing [a forest retention method that involves cutting trees at mid-stem height leaving behind tall standing stubs] in sustaining small mammal populations and enhancing habitat heterogeneity in Western Alberta's foothill forests. Using a paired-block design (clear-cut vs. stubbing), we monitored 67 blocks with motion-triggered camera traps. Gamma species richness, habitat use intensity (HUI), naïve occupancy, and species-level microhabitat associations were assessed. Stubbing treatments supported higher gamma richness (nine vs. seven) and higher occurrence of forest specialists, including Clethrionomys gapperi and Tamiasciurus hudsonicus , whereas clear-cuts were dominated by generalist species such as Peromyscus maniculatus . Microhabitat assessments revealed higher shrub cover and lower bare ground exposure in stubbing treatments. Species-level models showed that generalists like P. maniculatus preferred areas with high volumes of late-decay downed woody material and low grass cover. In contrast, C. gapperi showed reduced occurrence in areas with high volumes of late-decay wood, likely due to competition or predation risk. These findings suggest that stubbing can mitigate the biodiversity impacts of clear-cutting by preserving key structural habitat elements. It supports both generalist and specialist small mammal species and provides a practical tool for reconciling timber production with conservation 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 | 0.000 |
| 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 | 0.000 | 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".