Effects of stand structure and landscape characteristics on habitat use by birds and small mammals in managed boreal forest of eastern Canada
Bibliographic record
Abstract
Community structure and relative abundance of 27 species of small mammals and forest birds were compared among three types of residual forest stands and unharvested control forest (CO). Treatments were young and old mosaics (a checkerboard pattern of residual and logged forest units of 85–100 ha each) and megablocks (residual stands of 250–300 ha isolated within a logged area of 2500–3000 ha). Relative abundances were also used to establish habitat use models (HUMs). We found no statistical difference in species relative abundances between treatments and COs, although small sample sizes limited statistical power. HUMs explained a large amount of variation in habitat use for 15 mammal and bird species (mean 57.4 ± 3.5%, ranging between 22.3% and 75.7%). Variance partitioning emphasized the importance of stand structure characteristics as the principal predictors of abundance for censused species. Our results suggested that mosaics and megablocks are both suitable configurations to maintain studied species because no species exhibited lower relative abundances in such residual forest stands than COs. We suggest that residual forest stands planning should shift from a strictly landscape perspective toward a more holistic approach that considers residual forest structure as well as landscape characteristics.
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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 | 0.001 | 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".