Spatiotemporal patterns of observed bark beetle‐caused tree mortality in British Columbia and the western United States
Bibliographic record
Abstract
Outbreaks of aggressive bark beetle species cause widespread tree mortality, affecting timber production, wildlife habitat, wildfire, forest composition and structure, biogeochemical cycling, and biogeophysical processes. As a result, agencies responsible for forest management in the United States and British Columbia conduct aerial surveys to map these forest disturbances. Here we combined aerial surveys from British Columbia (2001 2010) and the western conterminous United States (1997-2010), produced 1-km2 grids of the area of crown mortality from bark beetle attack, and analyzed spatial and temporal patterns. We converted aerial-survey polygon data for each combination of host type and bark beetle species available in the western United States, and for each bark beetle species available in British Columbia. We converted affected area (which includes live and killed trees) to mortality area (crown area of killed trees) using species-specific crown diameters and the number (U.S.) or percentage (British Columbia) of killed trees. In the United States we also produced an upper estimate of mortality area by forcing the mortality area to match that from high-resolution imagery in Idaho, Colorado, and New Mexico. Resulting adjustment factors of 3.7-20.9 illustrate the underestimate of mortality by the U.S. aerial surveys. The upper estimate, which we suggest is more realistic, better matched the spatial patterns and severity of the British Columbia mortality area. Cumulative mortality area from all bark beetles was 5.46 Mha in British Columbia in 2001-2010 and 0.47-5.37 Mha (lower and upper estimate) in the western conterminous United States during 1997-2010. We note that we report year of detection here; studies that consider year of tree mortality should shift the time series back one year. We conclude by discussing uses and limitations of these data in ecological studies, including uncertainties associated with assumptions in the methods, lack of complete coverage by surveys, and the subjective nature of the survey databases.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".