Effects of Logging Pattern and Intensity on Squirrel Demography
Bibliographic record
Abstract
ABSTRACT We examined the effect of harvesting intensity and pattern on red squirrels ( Tamiasciurus hudsonicus ), northern flying squirrels ( Glaucomys sabrinus ), and yellow‐pine chipmunks ( Tamias amoenus ) in mature inland Douglas‐fir ( Pseudotsuga menziesii glauca ) forests in south‐central British Columbia, Canada. We sampled squirrels 1 year before harvesting through 4 years after harvesting and estimated population parameters using open‐population models. Relative to unharvested stands, each of the 3 species showed a strong response to tree removal. From 2 years to 4 years after logging, red squirrel density was 40% (SE = 7.1) lower in stands with 50% basal‐area tree removal. From 1 year and up to 4 years after logging, northern flying squirrel density averaged 60% (SE = 5.2) lower in harvested treatments regardless of intensity or pattern of logging. In contrast, density of yellow‐pine chipmunks increased markedly with increased logging intensity. Beginning 3 years after logging, yellow‐pine chipmunk density was 734% (SE = 269) greater in stands with 50% basal‐area tree removal. In the short term, harvesting intensity was a more important determinant of squirrel density than harvesting pattern. Retaining >10 m 2 per ha of live residual stand structure in mature inland Douglas‐fir forests maintained habitat for forest‐dependent species such as red squirrels and northern flying squirrels, albeit at lower densities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".