Effects of the nematode <i>Litylenchus crenatae</i> subsp. <i>mccannii</i> and beech leaf disease on leaf fungal and bacterial communities on <i>Fagus grandifolia</i> (American beech)
Bibliographic record
Abstract
ABSTRACT Beech leaf disease (BLD) is a newly emerging disease in North America that affects American beech ( Fagus grandifolia ). It is increasingly recognized that BLD is caused by a subspecies of the anguinid nematode Litylenchus crenatae subsp. mccannii (hereafter L. crenatae ) , which is likely native to East Asia. How nematode infestation of leaves affects the leaf microbiome and whether changes in the microbiome could contribute to BLD symptoms remain uncertain. In this study, we examined bacterial and fungal communities associated with the leaves of F. grandifolia across nine sites in Ohio and Pennsylvania that were either symptomatic or asymptomatic for BLD and used qPCR to measure relative nematode infestation levels. We found significantly higher levels of infestation at sites visibly symptomatic for BLD. Low levels of nematode infestation were also observed at asymptomatic sites, which suggests that nematodes can be present without visible symptoms evident. Bacterial and fungal communities were significantly affected by sampling site and symptomology, but only fungal communities were affected by nematode presence alone. We found many significant indicators of both bacteria and fungi related to symptoms of BLD, with taxa generally occurring in both asymptomatic and symptomatic leaves, suggesting that microbes are not responsible for BLD but could act as opportunistic pathogens. Of particular interest was the fungal genus Erysiphe, which is common in the Fagaceae and is reported to overwinter in buds—a strategy consistent with L. crenatae . The specific role microbes play in opportunistic infection of leaves affected by L. crenatae will require additional study. IMPORTANCE Beech leaf disease (BLD) is an emerging threat to American beech ( Fagus grandifolia ) and has spread quickly throughout the northeastern United States and into southern Canada. This disease leads to disfigurement of leaves and is marked by characteristic dark, interveinal banding, followed by leaf curling and drop in more advanced stages. BLD tends to especially affect understory leaves, which can lead to substantial thinning of the forest understory where F. grandifolia is a dominant tree species. Understanding the cause of BLD is necessary to employ management strategies that protect F. grandifolia and the forests where it is a foundation tree species. Current research has confirmed that the foliar nematode Litylenchus crenatae subsp. mccannii is required for BLD, but whether other organisms are involved is currently unknown. Here, we present a study that investigated leaf-associated fungi and bacteria of F. grandifolia to understand more about how microorganisms may contribute to BLD.
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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 | 0.001 | 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".