Seeing herbaria in a new light: leaf reflectance spectroscopy unlocks trait and classification modeling in plant biodiversity collections

作者：Dawson M. White, Jeannine Cavender‐Bares, Charles C. Davis, J. Antonio Guzmán Q., Shan Kothari, Jorge M. Robles, José Eduardo Meireles · 发表于：New Phytologist · 年份：2025 · DOI：10.1111/nph.70357 · 被引用次数：5 · 研究领域：Remote Sensing in Agriculture、Spectroscopy and Chemometric Analyses、Leaf Properties and Growth Measurement

Summary Reflectance spectroscopy is a rapid method for estimating traits and discriminating species. Spectral libraries from herbarium specimens represent an untapped resource for generating broad phenomic datasets across space, time, and taxa. We conducted a proof‐of‐concept study using trait data and spectra from herbarium specimens up to 179 yr old, alongside data from recently dried and pressed leaves. We validated model accuracy and transferability for trait prediction and taxonomic discrimination. Trait models from herbarium spectra predicted leaf mass per area (LMA) with R 2 = 0.94 and %RMSE = 4.86%. Models for LMA prediction were transferable between herbarium and pressed spectra, achieving R 2 = 0.88, %RMSE = 8.76% for herbarium to pressed spectra, and R 2 = 0.76, %RMSE = 10.5% for the reverse transfer. Discriminant models classified leaf spectra from 25 species with 74% accuracy, and classification probabilities were significantly associated with several herbarium specimen quality metrics. The results validate herbarium spectral data for trait prediction and taxonomic discrimination, and demonstrate that trait modeling can benefit from the complementary use of pressed‐leaf and herbarium‐leaf spectral datasets. These promising advancements help to justify the spectral digitization of plant biodiversity collections and support their application in broad ecological and evolutionary investigations.