Documenting forest juvenilization in high-elevation old-growth forests in southern inland British Columbia, with implications for the winter ecology of Canada’s endangered Deep-Snow Caribou
Bibliographic record
Abstract
Climate change is altering high-elevation conifer forests in western North America, with potential impacts on species dependent on these ecosystems. We investigated recent, locally widespread tree mortality patterns in old-growth Engelmann spruce–subalpine fir forests in southern British Columbia, with special reference to the winter foraging needs of Canada’s endangered Deep-Snow Caribou (DSC), tied almost exclusively to heavy loading of arboreal hair lichens. We quantified the percentage of standing dead canopy trees across elevational gradients and examined relationships with stand characteristics in 120 plots along five vertical transects between 1650 and 2050 m. We found that an average of 31.1% of canopy trees were standing dead, with significantly higher rates (41.8%) below 1800 m than above 1900 m (11.5%). Subalpine fir ( Abies lasiocarpa) had a higher percentage of standing dead trees (34.7%) than Engelmann spruce ( Picea engelmannii, 15.5%). Upper elevation forests were significantly younger, consistent with post-Little Ice Age establishment. These findings suggest a forest transition process (forest juvenilization) that may initially increase DSC winter forage availability due to higher lichen loadings on standing dead trees. Looking forward, we project a long-term reduction in winter forage as old-growth structure is lost. Our study establishes baseline data for long-term monitoring and highlights the need for further research on the cascading effects of climate-induced forest changes on old-growth-dependent species like DSC.
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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 | 0.002 |
| 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".