Large scale biotic damage impacts on forest ecosystem services
Bibliographic record
Abstract
Insect outbreaks are natural phenomena that play a critical role in the development, senescence, and rebirth of forests. However, the damage caused by large-scale epidemics can have landscape scale consequences that are often poorly understood. The recent mountain pine beetle (MPB) outbreak in Canada has impacted a record >18.5 million hectares of pine forests, placing forest values at risk and significantly impacting forest-dependent communities within the region. To assess this impact, an ecosystem service-based approach was applied. Based on land cover information and monitoring data, four ecosystem services were assessed and mapped: merchantable timber, water provisioning, aboveground carbon storage, and vegetation diversity (supporting habitat). Timber is the most impacted provisioning ecosystem service followed by water provisioning, with peak stream flow in affected watersheds being positively related to mortality percent. Effects on carbon storage are substantial, with 20% of total timber aboveground carbon in dead pine trees. These effects may be mitigated, however, by the growth response of residual live trees and forest regeneration. The potential vegetation diversity showed a positive response to MPB-caused tree mortality. The results of our study may help with setting management priorities in response to large-scale biotic damage in forests in British Columbia and elsewhere.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".