A modelling approach to assess the long-term impact of beech bark disease in northern hardwood forest
Bibliographic record
Abstract
This study proposes a modelling approach to assess the effect of beech bark disease on species demographic dynamics and structure of stands using SORTIE, a spatially explicit and individual-based forest succession model. The original model was modified to account for the increased mortality rate of infected Fagus grandifolia Ehrh. (American beech) and for some resistance to the disease. Two different scenarios, modeling species behaviour with and without the disease, respectively, were compared over a period of 300 years. Results reveal significant differences in species global demographic dynamics between both scenarios. Analyses of tree diameter-class distribution indicate that 50 years after the infestation, large F. grandifolia trees disappear, while the number of trees with a DBH between 11 and 50 cm considerably increases. At 300 years, the density of F. grandifolia with a DBH greater than 30 cm in the diseased scenario is superior or close to that in the nondiseased scenario, suggesting a progressive reestablishment of the species population structure. Eastern hemlock (Tsuga canadensis (L.) Carrière) is the species that largely benefits from beech bark disease. The study demonstrates the potential of a modelling approach to acquire quantitative insights about the long-term ecological impact of the disease.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".