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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".