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

Abstract

Reflectance spectroscopy is a non-destructive, rapid, and robust method for estimating functional traits and distinguishing species. Spectral reflectance libraries generated from herbarium specimens are an untapped and promising resource for generating broad phenomic datasets across space, time, and species. We conducted a proof-of-concept study using functional trait data and spectra from recently dried, pressed leaves, alongside data from herbarium specimens up to 179 years old. We assessed the utility and transferability of these datasets for functional trait prediction and taxonomic discrimination. Herbarium spectra discriminated species with 74% accuracy and predicted leaf mass per area (LMA) with R2=0.92 and %RMSE=5.8%. Models for LMA prediction were transferable between herbarium and pressed spectra, achieving R2=0.88, %RMSE=8.76% for herbarium to pressed spectra, and R2=0.76, %RMSE=10.5% for the reverse transfer. The results demonstrate the feasibility of using herbarium spectral data for functional trait prediction and taxonomic discrimination. This success provides methodological guidance for advancing the global Metaherbarium and integrating spectral reflectance into next-generation digitization efforts for plant biodiversity collections.

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