Mapping the Environmental Risk of Beech Leaf Disease in the Northeastern United States
Bibliographic record
Abstract
The recently emerged beech leaf disease (BLD) is causing the decline and death of American beech in North America. First observed in 2012 in northeast Ohio, U.S.A., BLD had been documented in 10 northeastern states and the Canadian province of Ontario as of July 2022. A foliar nematode has been implicated as the causal agent, along with some bacterial taxa. No effective treatments have been documented in the primary literature. Irrespective of potential treatments, prevention and prompt eradication (rapid responses) remain the most cost-effective approaches to the management of forest tree disease. For these approaches to be feasible, however, it is necessary to understand the factors that contribute to BLD spread and use them in estimation of risk. Here, we conducted an analysis of BLD risk across northern Ohio, western Pennsylvania, western New York, and northern West Virginia, U.S.A. In the absence of symptoms, an area cannot necessarily be deemed free of BLD (i.e., absence of BLD cannot be certain) due to its fast spread and the lag in symptom expression (latency) after infection. Therefore, we employed two widely used presence-only species distribution models (SDMs), one-class support vector machine (OCSVM), and maximum entropy (Maxent) to predict the spatial pattern of BLD risk based on BLD presence records and associated environmental variables. Our results show that both methods work well for BLD environmental risk modeling purposes, but Maxent outperforms OCSVM with respect to both the quantitative receiver operating characteristics (ROC) analysis and the qualitative evaluation of the spatial risk maps. Meanwhile, the Maxent model provides a quantification of variable contribution for different environmental factors, indicating that meteorological (isothermality and temperature seasonality) and land cover type (closed broadleaved deciduous forest) factors are likely key contributors to BLD distribution. Moreover, the future trajectories of BLD risk over our study area in the context of climate change were investigated by comparing the current and future risk maps obtained by Maxent. In addition to offering the ability to predict where the disease may spread next, our work contributes to the epidemiological characterization of BLD, providing new lines of investigation to improve ecological or silvicultural management. Furthermore, this study shows strong potential for extension of environmental risk mapping over the full American beech distribution range so that proactive management measures can be put in place. Similar approaches can be designed for other significant or emerging forest pest problems, contributing to overall management efficiency and efficacy.
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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 | 0.001 | 0.001 |
| 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 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".