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Abstract Detail

Biodiversity Informatics & Herbarium Digitization

Kothari, Shan [1], Beauchamp-Rioux, Rosalie [1], Laliberté, Etienne [1], Cavender-Bares, Jeannine [2].

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

More than ever, ecologists seek to employ herbarium collections as tools to estimate plant functional traits either from the past or from places that are hard to sample. However, many functional trait measurements are destructive, which may preclude their use on valuable herbarium specimens. Reflectance spectroscopy is increasingly used to estimate traits rapidly from fresh or dried, ground leaves. Here, we extend this body of work to pressed, intact leaves such as those in herbarium collections. Using a dataset with 619 plant samples belonging to 70 woody and herbaceous species, we used partial least-squares regression to build validated 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 (EWT), elemental concentrations, pigments, and carbon fractions like cellulose and lignin. We compared the accuracy of these trait estimates to those from fresh- and ground-leaf spectra of the same samples. Our pressed-leaf models predicted these traits with varying accuracy-highest for LMA (R2 = 0.924; %RMSE = 5.7%) and lowest for trace elements like iron (R2 = 0.266; %RMSE = 15.3%). For estimating elemental concentrations and carbon fractions, pressed-leaf models generally performed better than fresh-leaf models but slightly worse than ground-leaf models. Pressed-leaf models did not perform as well as fresh-leaf models for estimating LMA, EWT, and LDMC, but outperformed ground-leaf models for LMA. For estimating pigments, pressed-leaf models did not perform as well as either fresh- or ground-leaf models, but still achieved moderate accuracy (R2 = 0.543-0.610; %RMSE = 10.9-11.2%). Accuracy was no worse for pressed leaves that underwent discoloration in storage. Finally, on a subset of common species in the dataset, we used partial least-squares discriminant analysis to classify specimens to species with near-perfect accuracy from pressed- (>97%), and ground-leaf (>96%) spectra and slightly lower accuracy from fresh-leaf spectra (>89%). The success of trait estimation and species classification from pressed-leaf spectra may owe to the fact that they combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of internal structure; like ground leaves, they reveal minor absorption features of constituents otherwise masked by water. These results demonstrate that pressed-leaf spectra show promise for estimating functional traits non-destructively. Our study has far-reaching implications for capturing the wide range of functional and phenotypic information in the world's preserved plant collections.

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1 - Université de Montréal, Institut de recherche en biologie végétale, 4101 Sherbrooke St E, Montreal, QC, H1X 2B2, Canada
2 - University Of Minnesota, 100 ECOLOGY BLDG, 1479 Gortner Ave, Saint Paul, MN, 55108, United States

reflectance spectroscopy
functional traits
collections-based research.

Presentation Type: Oral Paper
Session: BIHDI, Biodiversity Informatics & Herbarium Digitization I
Location: Virtual/Virtual
Date: Monday, July 19th, 2021
Time: 12:45 PM(EDT)
Number: BIHDI002
Abstract ID:67
Candidate for Awards:None

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