Excavation of Red Squirrel (<i>Tamiasciurus hudsonicus</i>) Middens by Bears (<i>Ursus</i> spp.) in Limber Pine (<i>Pinus flexilis</i>) Habitat in Banff National Park, Alberta
Bibliographic record
Abstract
Bears (Ursus spp.) in North America eat the seeds of several pines (Pinus spp.), including Limber Pine (P. flexilis E. James). Information on use of Limber Pine in Canada is limited to a report of three bear scats containing pine seeds found in Limber Pine stands of southwestern Alberta. After my preliminary fieldwork in Banff National Park revealed that bears were eating seeds of Limber Pine there, I conducted a field study in 2014–2015 to assess this use. Because bears typically obtain pine seeds from cone caches (middens) made by Red Squirrels (Tamiasciurus hudsonicus), I described the abundance, habitat characteristics, and use by bears of Red Squirrel middens in and adjacent to Limber Pine stands at six study sites. On Bow River escarpments, I found abundant Limber Pines (basal area 1–9 m2/ha) and middens (0.8 middens/ha, standard deviation [SD] 0.2). Of 24 middens, 13 (54%) had been excavated by bears, and three bear scats composed of pine seeds were found beside middens. Although Limber Pines occurred on steep, xeric, windswept slopes (mean 28°, SD 3), middens occurred on moderate slopes (mean 12°, SD 3) in escarpment gullies and at the toe of slopes in forests of other species, particularly Douglas-fir (Pseudotsuga menziesii). At the five other study sites, I found little or no use of Limber Pine seeds by bears, suggesting that Limber Pine habitat may be little used by bears unless the pines are interspersed with (non-Limber Pine) habitat with greater forest cover and less-steep slopes where squirrels establish middens. These observations provide managers with an additional piece of information regarding potential drivers of bear activity in the human-dominated landscape of Banff National Park’s lower Bow Valley.
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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 | 0.000 |
| 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 | 0.001 |
| 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".