Reflectance spectroscopy allows rapid, accurate and non-destructive estimates of functional traits from pressed leaves

Shan Kothari, Rosalie Beauchamp-Rioux, Etienne Laliberté, Jeannine Cavender-Bares

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)385-401
Number of pages17
JournalMethods in Ecology and Evolution
Volume14
Issue number2
DOIs
StatePublished - Feb 2023

Bibliographical note

Funding Information:
We performed this research on the mostly unceded land of Indigenous peoples: primarily the Kanien'kehá꞉ka (Mohawk), Omàmiwinini (Algonquin) and Abenaki First Nations for the Beauchamp‐Rioux, Dessain and Boucherville projects, the Noongar peoples for the Warren project, and the Dakota and Ojibwe peoples for the Cedar Creek dataset. For permissions and assistance, we thank le Jardin botanique de Montréal; la Station de biologie des Laurentides de l'Université de Montréal; l'Institut de recherche d'Hydro‐Québec; the National Capital Commission for sampling at Mer Bleue Bog; la Société des établissements de plein air du Québecfor sampling at Parc national d'Oka (Permit #PNO‐2018‐012), Parc national du Mont‐Saint‐Bruno and Parc national des Îles‐des‐Boucherville (both Permit #PNIB/PNMSB‐2018‐01); and the Parks and Wildlife Service of Western Australia for sampling in D'Entrecasteaux National Park (Permit #SW019787). We thank Antoine Mathieu, Alexandra Massey, Florence Blanchard, Elisabeth Hardy‐Lachance, Zachary Bélisle, Sandra Jooty, Myriam Cloutier, Fabien Cichonski, Madeleine Trickey‐Massé, Vincent Fournier, Aurélie Dessain, Xavier Guilbeault‐Mayers, Jocelyne Ayotte, Megan Erding and especially Sabrina Demers‐Thibeault for contributing to trait measurements. Carole Sinou, Phil Townsend, Dudu Meireles, Mason Heberling and members of the Cavender‐Bares lab, the ASCEND Biological Integration Institute, and the Laboratory of Plant Functional Ecology contributed to insightful discussions. Chad Zirbel aided with species identification. Data from Canadian and Australian sites were collected as part of the Canadian Airborne Biodiversity Observatory (CABO) funded by NSERC Discovery Frontiers grant 509190‐2017, as well as NSERC Discovery grants (RGPIN‐2014‐06106, RGPIN‐2019‐04537) and a Canada Research Chair to EL. Cedar Creek collections were supported by the NSF/NASA (DEB‐1342872) and the NSF ASCEND Biology Integration Institute (DBI‐2021898). Cedar Creek is supported by the NSF Long‐Term Ecological Research program, currently under DEB‐1831944. Travel funding was provided by an International Thesis Research Travel Grant from the University of Minnesota's Graduate School and a grant distributed through an NSF‐funded Research Coordination Network (DEB‐1745562). SK was supported by an NSF Graduate Research Fellowship (No. 00039202) and a UMN Doctoral Dissertation Fellowship. RBR was supported by NSERC through the Canada Graduate Scholarships—Master's program and the FRQNT through a Master's Research Scholarship.

Publisher Copyright:
© 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Keywords

  • functional traits
  • herbarium collections
  • leaf chemistry
  • partial least-squares regression (PLSR)
  • reflectance spectroscopy
  • species identification

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