Combining thinning and diverse plantings to adapt to climate-change-induced timber supply shortage in British Columbia
Bibliographic record
Abstract
Forestry is an important component of Canada’s economy with British Columbia (BC) contributing almost half to the national roundwood production. Yet, the country’s timber supply and forest economy are threatened by climate change, with increased frequency and severity of natural disturbances and changes in forest productivity. Mountain pine beetle ( Dendroctonus ponderosae) outbreaks are endemic in BC, but the latest climate change-driven outbreak has resulted in a cumulative loss of over half of all merchantable pine, leading to a mid-term timber supply shortage. In this study, we investigate the potential of commercial thinning and alternative planting regimes based on species diversification and assisted species migration to mitigate the anticipated decrease in timber supply in BC. We simulated the long-term effects of these management options in a case study area in interior BC, using a toolbox that combines management- and research-oriented data and models. We found that combining commercial thinning and species diversification has the best potential to mitigate future timber supply shortages in BC. We discuss the limits of this toolbox approach and identify research needs and recommendations for future studies aiming at modelling cumulative effects of management, climate change, and natural disturbances on timber supply.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".