Short term response of small mammals and forest birds to silvicultural practices differing in tree retention in irregular boreal forests
Bibliographic record
Abstract
The effects of logging on wildlife have been evaluated mainly in landscapes dominated by even-aged forest stands, leaving the impacts of harvesting on wildlife in irregularly structured stands less documented. We assessed the response of small mammals and birds to 4 silvicultural treatments with different levels of tree retention (from < 10% to 73%) in old-growth boreal forests of eastern Canada, where approximately 70% of stands have irregular structure. The experimental design controlled for local variation in the abundance of forest birds and small mammals by pairing each experimental harvested stand with an uncut stand. We found that species richness of small mammals and birds did not vary according to tree retention. Moss cover and vertical cover influenced small mammal community composition, whereas forest bird assemblages responded to conifer basal area, vertical cover, and snag availability. Species associated with silvicultural practices entailing retention levels > 55% included those that prefer closed canopy, such as the brown creeper (Certhia americana), ruby-crowned kinglet (Regulus calendula), and red-backed vole (Myodes gapperi). Our study demonstrates that silvicultural practices with retention levels > 55% are the most suitable for mitigating the impact of logging on animal communities in old-growth irregularly structured stands.
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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 | 0.000 | 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) | 0.000 | 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".