Productive yet wild: Reconciling timber harvesting and small mammal conservation via understory protection harvesting in managed boreal landscapes of Alberta, Canada
Bibliographic record
Abstract
The intensification of forest management demands innovative strategies that reconcile timber production with biodiversity conservation. This study evaluates the ecological effectiveness of Understory Protection (UP) harvesting, a spatially explicit method designed to minimize understory conifer damage, compared to Natural Disturbance Harvesting (NDH), where such vegetation is typically lost. Using a randomized block design, we deployed motion-triggered camera traps at 57 blocks representing four forest treatments: NDH, Unharvested forest (OG), and two UP sub-treatments: structurally intact UP_Buffer and more exposed UP_Trail. NDH and UP_Trail treatments, characterized by reduced canopy cover, green tree basal area and elevated grass cover and shrub density, favored both early-seral specialist and generalists such as Sorex shrews , Peromyscus maniculatus, Zapus princeps, and Lepus americanus . In contrast, UP_Buffer and OG treatments, characterized by late-seral structural conditions, supported greater habitat use intensity and higher naïve occupancy by old forest specialists including Clethrionomys gapperi , Glaucomys sabrinus , and Tamiasciurus hudsonicus . Notably, Clethrionomys gapperi and Glaucomys sabrinus occurrence was insensitive to time since harvest, indicating structural retention, not stand age, was the key predictor of their presence in the range of conditions measured. These results suggest that UP harvesting supports a diverse small mammal assemblage by integrating complementary habitats (UP_Buffer and UP_Trail). Critically, the within-stand heterogeneity established by UP_Buffer accelerates ecological recovery toward old-growth conditions, rapidly providing habitat for old forest-associated species in managed boreal forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".