Skip to main content
Dryad

Foliar infrared spectra track nematode density and symptom-specific phytobiome signatures in beech leaf disease

Miles, Andrew1; Torres-Bedoya, Eliana1; Bonello, Enrico1; Conrad, Anna2

Published Apr 21, 2026 on Dryad. https://doi.org/10.5061/dryad.m37pvmdgv

Data files

Abstract

Beech leaf disease (BLD), caused by the foliar nematode Litylenchus crenatae ssp. mccannii (LCM) is an emerging threat to American beech (Fagus grandifolia) across eastern North America. In this study, near-infrared (NIR) spectroscopy was integrated with microbiome sequencing and machine learning approaches to evaluate whether host spectral signatures can predict pathogen abundance and associated phytobiome composition. Spectral profiles were analyzed using multivariate statistical approaches and predictive models, including random forest, support vector machines, elastic net regression, and partial least squares regression. NIR spectra accurately predicted nematode abundance and reconstructed major gradients of bacterial community differentiation derived from 16S rRNA gene sequencing, with nonlinear models exhibiting the highest predictive performance. These results indicate that host spectral signatures encode biochemical information reflecting both pathogen burden and microbiome structure. Our findings demonstrate the potential of NIR spectroscopy as a rapid, non-destructive tool for characterizing host–pathogen–microbiome interactions and improving detection of BLD in forest ecosystems.