Predicting leaf traits across functional groups using reflectance spectroscopy
Predicting leaf traits across functional groups using reflectance spectroscopy
Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems.We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra.Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2>0.85; %RMSE<10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2=0.55-0.85; %RMSE=12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy.We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
Bruneau Anne、Pardo Juliana、Beauchamp-Rioux Rosalie、Blanchard Florence、Crofts Anna L.、Coops Nicholas C.、Demers-Thibeault Sabrina、Kothari Shan、Schweiger Anna K.、Lalibert¨| Etienne、Hacker Paul W.、Guilbeault-Mayers Xavier、Girard Aliz¨|e、Kalacska Margaret、Vellend Mark
Institut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|alInstitut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|alInstitut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|alInstitut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|alD¨|partement de biologie, Universit¨| de SherbrookeDepartment of Forest Resources Management, University of British ColumbiaInstitut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|alInstitut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|alInstitut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|al||Department of Geography, University of ZurichInstitut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|alDepartment of Forest Resources Management, University of British ColumbiaInstitut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|alInstitut de recherche en biologie v¨|g¨|tale, D¨|partement de sciences biologiques, Universit¨| de Montr¨|alDepartment of Geography, McGill UniversityD¨|partement de biologie, Universit¨| de Sherbrooke
环境生物学生物科学现状、生物科学发展植物学
foliar chemistryfunctional traitsleaf economicspartial least-squares regressionreflectance spectroscopy
Bruneau Anne,Pardo Juliana,Beauchamp-Rioux Rosalie,Blanchard Florence,Crofts Anna L.,Coops Nicholas C.,Demers-Thibeault Sabrina,Kothari Shan,Schweiger Anna K.,Lalibert¨| Etienne,Hacker Paul W.,Guilbeault-Mayers Xavier,Girard Aliz¨|e,Kalacska Margaret,Vellend Mark.Predicting leaf traits across functional groups using reflectance spectroscopy[EB/OL].(2025-03-28)[2025-08-03].https://www.biorxiv.org/content/10.1101/2022.07.01.498461.点此复制
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