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More than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non-destructive measurements on pressed, intact leaves, like those in herbarium collections. Using 618 samples from 68 species, we used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh- or ground-leaf spectra of the same samples. The traits our pressed-leaf models could estimate best were LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water-related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514–0.790; %RMSE = 12.8–19.6). Remaining elements were predicted poorly (R2 < 0.5, %RMSE > 20). For most chemical traits, pressed-leaf models performed better than fresh-leaf models, but worse than ground-leaf models. Pressed-leaf models were worse than fresh-leaf models for estimating LMA and LDMC, but better than ground-leaf models for LMA. Finally, in a subset of samples, we used partial least-squares discriminant analysis to classify specimens among 10 species with near-perfect accuracy (>97%) from pressed- and ground-leaf spectra, and slightly lower accuracy (>93%) from fresh-leaf spectra. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed-leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far-reaching implications for capturing the wide range of functional and taxonomic information in the world’s preserved plant collections.
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This content will become publicly available on April 23, 2027
Reflectance spectra capture temporal variation in functional traits and leaf phenology
Plant functional traits vary across leaf ontogeny and phenology, yet most trait data are snapshots from narrow time windows that miss this temporal dimension. Leaf spectra are increasingly used with empirical models to predict traits, but whether such models accurately capture phenological variation remains unclear. We monitored leaf traits and spectra weekly across a full growing season, generating 7,515 spectra from seven temperate species. Using partial least squares regression, we built three models [all-season and week-as-covariate models (both trained on full-phenology data), and a peak-season model] and evaluated them alongside a widely used model against directly measured traits. Full-phenology models predicted LMA and equivalent water thickness (EWT) with high accuracy (R2 > 0.85) and nitrogen with intermediate accuracy (R2 = 0.64); carbon accuracy was low across all models (R2 < 0.26), likely due to a small sample size. Peak-season trained models performed poorly when evaluated across the full season, often producing biologically unrealistic predictions. Traits and spectra varied significantly across phenological stages both within and among species. Ignoring phenological variation systematically biases trait estimates and ecological inference. Coupled with phenologically representative training data, spectra can capture the temporal dynamics of plant function, enabling novel research in ecology and evolution.
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- Award ID(s):
- 2442433
- PAR ID:
- 10682813
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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