Direct and indirect impacts of climate change on forests: three case studies from British Columbia
Bibliographic record
Abstract
Climate is an important driver of forest dynamics. In this paper, we present three case studies from the forests of British Columbia to illustrate the direct and indirect effects of climatic variation and global warming on forest composition and function. (1) Tree mortality rates in old forests of western North America have doubled in recent decades. Regional warming and water deficits directly affected tree death rates or indirectly increased insect and pathogen activity and wind storms causing tree deaths. Concurrently, tree density and basal area declined significantly, indicating lagged growth responses of surviving trees or long-term decline of these forests. (2) Yellow-cedar decline along coastal British Columbia and Alaska shows that small changes in average climatic conditions, coupled with extreme weather events, can have large ecological effects. A small persistent increase in mean temperatures has reduced snow-cover depth and duration. Coupled with extreme cold events which damage unprotected tree roots, these climatic changes are considered the primary cause of widespread yellow-cedar mortality. (3) Interactions between climate and disturbance are complex in the mountain forests of the East Kootenay region. Understanding historic climate–fire interactions is key to anticipating future frequent and severe fires. Here, climate change effects may be exacerbated by the cumulative effects of human land use, fire exclusion and mountain pine beetle outbreaks. We conclude that understanding past climate variation and its effects on forests help us to anticipate the potential effects of global warming.
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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 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".