Effect of season and interval of prescribed burn on ponderosa pine butterfly defoliation patterns
Bibliographic record
Abstract
Current knowledge concerning the interactions between forest disturbances such as fire and insect defoliation is limited. Wildfires and prescribed burns may influence the intensity and severity of insect outbreaks by affecting the vigor of residual trees, altering aspects of stand structure and abundance of preferred hosts, and by changing the physical environment within forest stands. Prescribed burn timing and frequency are particularly important aspects of the fire regime to consider because they can alter numerous aspects of tree vigor, stand structure, and environmental conditions, and can be manipulated by managers. We evaluated ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) defoliation patterns in relation to season (fall and spring) and interval (5 or 15 years) of prescribed burn in the southern Blue Mountains of Oregon. Beginning in 2008 a pine butterfly (Neophasia menapia C. Felder & R. Felder) (Lepidoptera: Pieridae) outbreak coincided with a long-term experimental study, providing a unique opportunity to address this question. Defoliation patterns were measured in 2012. The 5 year interval plots had burned three times with five growing seasons of recovery and the 15 year interval plots had burned once with 15 growing seasons of recovery. Mean pine butterfly defoliation across the study area was about 71%. We found a significant interaction between season of burn and interval of burn on defoliation. Areas burned in the fall every 5 years had marginally less (about 5%) defoliation compared with areas that were burned in the fall 15 years previous. Regression tree analysis revealed that defoliation patterns varied based on stand location, percent mortality since the start of the experiment, and tree height. Our results show that (i) season of burn and interval of burn did not predispose these stands to increased defoliation during a pine butterfly outbreak and (ii) repeat burning may actually lead to lower defoliation. However, the effect we document is small and only marginally significant.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".