Relationships among beech bark disease, climate, radial growth response and mortality of American beech in northern Maine, USA
Bibliographic record
Abstract
Summary A majority of beech forests across Maine first experienced beech bark disease (BBD) from 1935 to 1960 when sap feeding by an introduced beech scale insect, Cryptococcus fagisuga, allowed lethal fungal infections primarily by Neonectria ditissima and/or Neonectria faginata. Beech stands along the Maine–Quebec border in northern Maine were excluded from this initial killing phase presumably due to cold winter temperatures that inhibited scale survival. However, a sharp increase in beech mortality after 2002 occurred in previously uninfected border stands and stands long affected by BBD. Beech mortality averaged 50% across northern Maine during 2003–2006. To identify plausible stresses that could explain the mortality, a dendropathological study was conducted from 2005 to 2006 in northern Maine that quantified temporal and spatial relationships between possible stressors with beech mortality and growth decline. Nineteen sets of high‐ and low‐mortality plots were located randomly across four bioregions. Increment cores were taken from both beech trees (n = 565) and associated tree species (n = 450). A growth change index of increments was used to evaluate beech responses to biotic and climatic stresses. A prolonged period of relatively mild winters without temperatures lethal to scale insect (<−34°C) from 2000 to 2004 coupled with low August–October precipitation from 2000 to 2003 may have provided ideal conditions initiating a widespread scale epidemic. A major drought period from 1999 to 2002 coincided with growth decline and a significant increase in beech mortality across all regions included in this study. Neonectria was found infecting weakened trees across the region. Drought, beech scale and Neonectria are plausible explanations for the episode of high beech mortality in northern Maine. This is the first report of a major killing phase of beech within the BBD ‘aftermath’ forests.
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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 | 0.000 |
| 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".