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

Dawson M White , Jeannine Cavender-Bares , Charles Davis , J. Antonio Guzmán Q., Shan Kothari, Jorge Robles, Jose Eduardo Meireles

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 years old alongside data from recently dried, 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 R2 = 0.94 and %RMSE = 4.86%, and discriminated 25 species with 74% accuracy. 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. We also found correlations among classification probabilities with several herbarium specimen quality predictor variables. The results validate herbarium spectral data for trait prediction and taxonomic discrimination, and demonstrate trait modeling can benefit from the complementary use of pressed-leaf and herbarium-leaf spectral datasets. These promising methodological advancements help to justify the spectral digitization of plant biodiversity collections and support their application in broad ecological and evolutionary investigations.