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

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 stru..., , # Foliar infrared spectra track nematode density and symptom-specific phytobiome signatures in beech leaf disease Dataset DOI: [10.5061/dryad.m37pvmdgv](https://doi.org/10.5061/dryad.m37pvmdgv) ## Description of the data and file structure **Spectral data:** Near-infrared (NIR) reflectance spectra (1350–2550 nm; 257 bands) collected from American beech leaves representing three tissue phenotypes associated with beech leaf disease: asymptomatic tissue of asymptomatic leaves (AA), asymptomatic tissue of symptomatic leaves (AS), and symptomatic galled tissue (GS). Spectra were acquired using a handheld NeoSpectra spectrometer and processed to remove outliers and underwent a second derivative transformation. **qPCR quantification:** Litylenchus crenatae ssp. mccannii abundance was quantified using a probe-based qPCR assay targeting the FAR region with nematode-spiked standard curves. DNA samples were diluted prior to analysis and quantified in triplicate reactions for nematode density ..., ,山毛榉叶病（Beech leaf disease, BLD）由叶线虫Litylenchus crenatae mccannii亚种（Litylenchus crenatae ssp. mccannii, LCM）引发，是北美东部地区对美洲山毛榉（Fagus grandifolia）构成的新兴威胁。本研究将近红外（near-infrared, NIR）光谱技术与微生物组测序、机器学习方法相结合，旨在探究宿主光谱特征是否可用于预测病原体载量及相关植物微生物组（phytobiome）组成。研究采用多元统计方法与预测模型对光谱特征进行分析，包括随机森林（random forest）、支持向量机（support vector machines）、弹性网回归（elastic net regression）以及偏最小二乘回归（partial least squares regression）。结果显示，近红外光谱可精准预测线虫载量，并重构基于16S rRNA基因测序得到的细菌群落分化主要梯度，其中非线性模型展现出最高的预测性能。上述结果表明，宿主光谱特征携带有可反映病原体负荷与微生物组结构的生化信息……，相关研究论文标题为《Foliar infrared spectra track nematode density and symptom-specific phytobiome signatures in beech leaf disease》（《山毛榉叶病中叶片红外光谱可追踪线虫密度与症状特异性植物微生物组特征》）。数据集DOI：[10.5061/dryad.m37pvmdgv](https://doi.org/10.5061/dryad.m37pvmdgv) ## 数据与文件结构说明 **光谱数据**：采集自美洲山毛榉叶片的近红外（NIR）反射光谱（波长范围1350–2550 nm，共257个波段），对应三种与山毛榉叶病相关的组织表型：无症状叶片的无症状组织（AA）、病叶的无症状组织（AS）以及病征虫瘿组织（GS）。光谱数据通过手持式NeoSpectra光谱仪采集，经异常值剔除与二阶导数变换处理。 **qPCR定量分析**：采用针对FAR区域的探针式qPCR检测方法，结合线虫加标标准曲线，对Litylenchus crenatae mccannii亚种的载量进行定量。分析前对DNA样本进行稀释，每份样本设置三次重复反应以定量线虫密度……