Structural Retention Harvesting and Small Mammal Biodiversity in Alberta’s Boreal Forests
Bibliographic record
Abstract
The boreal forest is one of the largest terrestrial biomes globally, providing critical ecosystem services, carbon storage, and habitat for diverse fauna, while supporting economically important forestry industries. However, intensive forest management, dominated by clear-cutting, has simplified stand structure, reduced coarse woody debris, and diminished late-seral forest attributes, with documented negative effects on species dependent on these conditions. In response to these concerns, forest managers have increasingly adopted retention-based harvesting systems that aim to emulate the structural legacies of natural disturbances while maintaining timber yields. Two emerging approaches in Alberta are: stubbing (the deliberate creation of high-cut stems during harvest while protecting the forest floor) and understory protection (UP) harvesting (a practice that retains understory vegetation and select overstory trees during partial harvesting). Despite their growing adoption in operational forestry, their effectiveness in supporting small mammal biodiversity remains insufficiently evaluated. Small mammals, given their fine-scale habitat sensitivity, diverse ecological roles in seed dispersal, soil processes, and food webs, and proven value as bioindicators, provide an ideal model group for assessing the ecological outcomes of retention-based harvesting. In my first objective, I evaluated the ecological benefits of stubbing using a paired-block design comparing stubbing and adjacent clear-cuts across 67 blocks in western Alberta’s foothill forests. Motion-triggered camera trap surveys revealed that stubbing supported higher gamma richness and a greater presence of forest specialists (Clethrionomys gapperi, Tamiasciurus hudsonicus) compared to clear-cuts, which were dominated by generalists (Peromyscus maniculatus). Stubbing sites had greater shrub cover and less bare ground. Species-specific responses were driven by downed woody material (DWM) volume, decay stage, and grass cover: generalists such as Peromyscus maniculatus preferred high volumes of late-decay woody material with low grass cover, whereas Clethrionomys gapperi avoided high volumes of late-decay material, possibly due to competition or predation risk. In mysecondobjective, IassessedtheeffectivenessofUPharvestinginsupporting old-forest-associated small mammals using a randomized block design across 57 sites representing four treatments: natural disturbance harvesting (NDH), unharvested old growth (OG), and two UP sub-treatments: structurally intact UP Buffer and more exposed UP Trail. NDH and UP Trail, with reduced canopy cover, lower green tree basal area, and elevated grass and shrub cover, favored early-seral specialists and generalists, including Sorex shrews, Peromyscus maniculatus, Zapus princeps, and Lepus americanus. In contrast, UP Buffer and OG sites, characterized by late-seral structural conditions, supported higher habitat use intensity and occupancy by old-forest specialists such as Clethrionomys gapperi, Glaucomys sabrinus, and Tamiasciurus hudsonicus. Notably, the occurrence of Clethrionomys gapperi and G. sabrinus was insensitive to harvest age, indicating that structural retention, rather than stand age, was the primary driver of their presence. Collectively, these findings demonstrate that both stubbing and UP harvesting can maintain or enhance small mammal diversity in managed boreal forests by providing habitat for species acrossarangeofseralstages. Stubbingoffersacost-effectivemeans of increasing structural heterogeneity in clear-cuts, while UP harvesting, particularly through the incorporation of intact buffers, can accelerate the recovery of late-seral habitat conditions. Integrating these retention-based strategies into operational forestry can help balance timber production with biodiversity conservation, thereby supporting a more ecologically resilient boreal forest landscape.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".