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Title: Associations of Leaf Spectra with Genetic and Phylogenetic Variation in Oaks: Prospects for Remote Detection of Biodiversity

Species and phylogenetic lineages have evolved to differ in the way that they acquire and deploy resources, with consequences for their physiological, chemical and structural attributes, many of which can be detected using spectral reflectance form leaves. Recent technological advances for assessing optical properties of plants offer opportunities to detect functional traits of organisms and differentiate levels of biological organization across the tree of life. We connect leaf-level full range spectral data (400–2400 nm) of leaves to the hierarchical organization of plant diversity within the oak genus (Quercus) using field and greenhouse experiments in which environmental factors and plant age are controlled. We show that spectral data significantly differentiate populations within a species and that spectral similarity is significantly associated with phylogenetic similarity among species. Furthermore, we show that hyperspectral information allows more accurate classification of taxa than spectrally-derived traits, which by definition are of lower dimensionality. Finally, model accuracy increases at higher levels in the hierarchical organization of plant diversity, such that we are able to better distinguish clades than species or populations. This pattern supports an evolutionary explanation for the degree of optical differentiation among plants and demonstrates potential for remote detection of genetic and phylogenetic diversity.
 [1] ;  [1] ;  [2] ;  [1] ;  [2] ;  [2] ;  [3] ;  [4] ;  [5] ;  [5] ;  [2]
  1. Univ. of Minnesota, Minneapolis, MN (United States)
  2. Univ. of Wisconsin, Madison, WI (United States)
  3. Univ. of Wisconsin, Madison, WI (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
  4. Univ. of Minnesota, Minneapolis, MN (United States); Saint Olaf College, Northfield, MN (United States)
  5. Univ. of Zamorano (Honduras)
Publication Date:
Report Number(s):
Journal ID: ISSN 2072-4292; R&D Project: 21087; YN0100000
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 8; Journal Issue: 3; Journal ID: ISSN 2072-4292
Research Org:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
54 ENVIRONMENTAL SCIENCES; hyperspectral data; leaf functional traits; hierarchical organization of plant diversity; evolution; tree of life; populations; genetic variation; PLS-DA; phylogenetic signal; optical classification
OSTI Identifier: