Red squirrel demography and behaviour in a managed interior Douglas-fir forest of British Columbia
Bibliographic record
Abstract
I examined the affect of logging intensity and pattern on the North American red squirrel (Tamiasciurus hudsonicus) by measuring density and demography from zero to four years after logging, and by measuring territory size, habitat use, and behaviour of individual animals from three to five years after logging. This study was done in an interior Douglas-fir forest (IDF) near Kamloops, British Columbia, Canada. Patterns of tree removal ranged from small patch cuts (<1.6 ha) to individual tree selection (diameter-limit logging) and intensity of tree removal ranged from 20-50% by volume. From two to four years after logging, red squirrel abundance declined in a 1:1 relationship with the volume of conifer tree removal. Absolute variation in squirrel abundance was highest in the uniform tree removal treatments and lowest in unharvested habitat. Red squirrel recruitment, survival, body weight, and reproduction was unrelated to pattern or intensity of tree removal. In general, these results are consistent with the predictions of the ideal free distribution model of habitat selection. I conclude that logging intensity had the greatest effect on red squirrels, but that uniform tree removal logging may result in poor quality habitat during years of conifer cone crop failure. From three to five years after logging, red squirrel territory size was best explained by the density of Douglas-fir trees larger than 30 cm diameter-at-breast-height (DBH). Similarly, red squirrels prefered conifer trees larger than 15 cm DBH, with the strongest preference for trees between 30 and 44 cm DBH. Despite large differences in conifer tree density on individual territories, red squirrel activity budgets did not change. Further, red squirrels avoided canopy openings created by logging, but this did not affect their use of forest edge compared to interior forest habitat. I suggest that variation in conifer seed production may explain the relationship between Douglas-fir density and red squirrel territory size, habitat selection, and behaviour. I conclude that logging did not have a biologically meaningful effect on red squirrels, either overtime or across the range of habitats I sampled. However, diameter-limit logging may create poor habitat for red squirrels during years when little or no conifer seed is produced, or when logging dilutes conifer trees further than those sampled in this study. Individual tree selection treatments will likely not remain poor habitat for more than five years if greater than 50, 30 cm DBH Douglas fir trees are retained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".