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The ability to detect diseased trees before symptoms emerge is key in forest health management because it allows for more timely and targeted intervention. The objective of this study was to develop an in-field approach for early and rapid detection of beech leaf disease (BLD), an emerging disease of American beech trees, based on supervised classification models of leaf near-infrared (NIR) spectral profiles. To validate the effectiveness of the method we also utilized a qPCR-based protocol for the quantification of the newly identified foliar nematode identified as the putative causal agent of BLD, Litylenchus crenatae ssp. mccannii (LCM). NIR spectra were collected in May, July, and September of 2021 and analyzed using support vector machine and random forest algorithms. For the May and July datasets, the models accurately predicted pre-symptomatic leaves (highest testing accuracy = 100%), but also accurately discriminated the spectra based on geographic location (highest testing accuracy = 90%). Therefore, we could not conclude that spectral differences were due to pathogen presence alone. However, the September dataset removed location as a factor and the models accurately discriminated pre-symptomatic from naïve samples (highest testing accuracy = 95.9%). Five spectral bands (2,220, 2,400, 2,346, 1,750, and 1,424 nm), selected using variable selection models, were shared across all models, indicating consistency with respect to phytochemical induction by LCM infection of pre-symptomatic leaves. Our results demonstrate that this technique holds high promise as an in-field diagnostic tool for BLD.
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This content will become publicly available on June 1, 2026
Harnessing stemflow as a diagnostic tool for canopy disease detection and monitoring
Monitoring diseases within tree canopies is challenging due to their inaccessibility and the complexity of canopy ecosystems. Here, we explore the potential of stemflow sampling as a novel, ground-based method for detecting and monitoring canopy-associated pathogens. In a case study focused on Litylenchus crenatae ssp. mccannii (LCM), the nematode associated with Beech Leaf Disease (BLD), we collected stemflow samples from 18 Fagus grandifolia Ehrh. (American beech) trees across 12 storm events. eDNA assays detected LCM presence in 7 of those storms, with quantitative PCR-derived gene concentrations ranging from 80 to 158,000 copies mL−1. Higher detections and concentrations coincided with leaf senescence and bud formation periods, and they correlated conditionally with event rainfall amount and pre-storm changes in relative humidity. Although based on a single site and season, these findings demonstrate the potential for stemflow sampling to capture a pathogen’s eDNA (i.e., canopy distress signals) at ground level. This method could complement traditional monitoring, offering another affordable, non-invasive tool for pathogen detection. Additional validation, particularly regarding live versus dead organisms and across varied site conditions, will be essential to evaluate the breadth of value stemflow eDNA offers for canopy disease management and ecological research.
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- Award ID(s):
- 2213623
- PAR ID:
- 10596312
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Forest Ecology and Management
- Volume:
- 585
- Issue:
- C
- ISSN:
- 0378-1127
- Page Range / eLocation ID:
- 122674
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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