Spatial Distribution of Mountain Pine Beetle Outbreaks in Relation to Climate and Stand Characteristics: A Dendroecological Analysis
Bibliographic record
Abstract
Abstract Principal components analysis, followed by K‐means cluster analysis, was used to detect variations in the timing and magnitude of Pinus contorta Dougl. ex Loud. growth releases attributed to mountain pine beetle outbreaks in 31 stands of central British Columbia. Four major growth release patterns were identified from 1970 to 2000. Variations in the timing of growth releases among clustered stands corresponded well to aerial survey data indicating the timing of beetle outbreaks in the study area. Redundancy analysis was used to determine how variations in the timing and magnitude of growth releases attributed to beetle outbreaks changed with variations in climate or stand conditions over the study area. The first RDA axis, which accounted for 39% of the variations in growth patterns among stands, was significantly (P <0.05) correlated with gradients in the percentage of pine in stands killed by mountain pine beetle, summer aridity, variation in summer precipitation, distance from initial infestation site, average pine age, and maximum August temperatures. The second RDA axis explained 6% of the variations and was significantly correlated with gradients in the beetle climate suitability index, extreme cold month temperatures, and site index. Comparisons of growth release patterns with aerial survey data and redundancy analyses indicated that dendrochronological techniques are useful for identifying mountain pine beetle outbreaks in central British Columbia, particularly among stands that had a density high enough to produce a growth release signal. Provided future studies account for interannual weather fluctuations, identification of growth increases due to stand thinning caused by beetle outbreaks will be useful for reconstructing the history of beetle outbreaks over much longer time periods.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".