Beech Bark Disease in an Unmanaged Temperate Forest: Patterns, Predictors, and Impacts on Ecosystem Function
Bibliographic record
Abstract
Beech Bark Disease (BBD) is a devastating threat to American beech ( Fagus grandifolia) , spreading through eastern mixed deciduous forests of North America at unprecedented rates. Understanding how and why some beech trees escape severe BBD effects is important; however, the biotic and abiotic factors that underpin the progression of BBD within unmanaged forests at local scales are not well explored. We surveyed 651 individual beech trees ≥ 10 cm diameter at breast height (DBH) for BBD, in a 13.5-ha unmanaged forest dynamics plot in Ontario, Canada, where >46,000 trees have been identified to species, mapped, and DBH measured at ∼5-year intervals. For each beech tree, BBD severity was ranked on a 5-point severity index, which was then evaluated as a function of tree characteristics including DBH and relative growth rate (RGR). Most beech trees were at either the insect or fungal stage of BBD, with only 22% of beech trees being free of symptoms. Ordinal logistic regression analysis indicated both DBH and RGR were significant predictors of BBD severity. These models, along with both randomization and Moran’s Eigenvector Maps (MEM) analyses, indicated that DBH and RGR and their spatial variation accounted for ∼44.6% of BBD severity in trees. Our MEMs also indicated ∼4.2% of variation in BBD severity was associated with unmeasured spatial variables, which may reflect either the spread of BBD through our study site, or the influence of abiotic variables on BBD severity. At our site, BBD is responsible for at least ∼6.0 Mg C ha –1 , or ∼6.5% of the average 92.5 Mg of aboveground biomass C ha –1 , transitioning from the live to dead biomass pool. Our study enhances the understanding of the factors predicting the severity of a major forest pathogen in North American temperate forests, assists the integration of BBD severity risk into forest C budget models, and provides insight into how large-scale forest inventories can inform screening for pest or pathogen resistance in trees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".