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Quantifying species composition in root mixtures using two methods: near‐infrared reflectance spectroscopy and plant wax markers
Author(s) -
Roumet Catherine,
PiconCochard Catherine,
Dawson Lorna A.,
Joffre Richard,
Mayes Robert,
Blanchard Alain,
Brewer Mark J.
Publication year - 2006
Publication title -
new phytologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.742
H-Index - 244
eISSN - 1469-8137
pISSN - 0028-646X
DOI - 10.1111/j.1469-8137.2006.01698.x
Subject(s) - wax , near infrared reflectance spectroscopy , herbaceous plant , composition (language) , diffuse reflectance infrared fourier transform , chemistry , plant species , calibration , biomass (ecology) , near infrared spectroscopy , botany , biology , ecology , mathematics , organic chemistry , statistics , linguistics , philosophy , photocatalysis , neuroscience , catalysis
Summary• Understanding of plant interactions is greatly limited by our ability to identify and quantify roots belonging to different species. We proposed and compared two methods for estimating the root biomass proportion of each species in artificial mixtures: near‐infrared reflectance spectroscopy (NIRS) and plant wax markers. • Two sets of artificial root mixtures composed of two or three herbaceous species were prepared. The proportion of root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n ‐alkanes (ii), n ‐alcohols (iii), and n ‐alkanes + n ‐alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. • The botanical composition of root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and actual root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. • These two methods provide promising potential for understanding allocation patterns and competitive interactions.