Diversifying managed forests to increase resilience
Bibliographic record
Abstract
In British Columbia, Canada, a recent epidemic of mountain pine beetle (Dendroctonus ponderosae Hopkins, 1902) caused widespread forest mortality. This epidemic was due in part to the changing climate, and damage from pests and diseases is expected to increase in the future. Therefore, we used a historical retrospective approach as a proxy to evaluate management options on reducing the forest health damage that may occur under a future changing climate. We assessed two landscape-scale strategies, intended to increase tree species diversity, for the response in ecosystem resilience and compared the results with the business-as-usual strategy. The assessment was based on simulation modelling of the Merritt Timber Supply Area for 1980–2060. We applied a strategy to increase the harvest of the most dominant tree species, plant more diverse species, and increase natural regeneration. This strategy resulted in greater ecological resilience (higher diversity and growing stocks), higher harvest rates, and higher, more consistent net revenue over time than the business-as-usual strategy or the strategy that only employed a diversity of planting. A sensitivity analysis indicated a high level of robustness in the results. Our study showed that it may not be necessary to compromise economic viability to reduce forest health risks and consequently improve socio-ecological resilience.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".