Imaging spectroscopy links aspen genotype with below-ground processes at landscape scales

Abstract

Fine-scale biodiversity is increasingly recognized as important to ecosystemlevel processes. Remote sensing technologies have great potential to estimate both biodiversity and ecosystem function over large spatial scales. Here, we demonstrate the capacity of imaging spectroscopy to discriminate among genotypes of Populus tremuloides (trembling aspen), one of the most genetically diverse and widespread forest species in North America. We combine imaging spectroscopy (AVIRIS) data with genetic, phytochemical, microbial and biogeochemical data to determine how intraspecific plant genetic variation influences below-ground processes at landscape scales. We demonstrate that both canopy chemistry and below-ground processes vary over large spatial scales (continental) according to aspen genotype. Imaging spectrometer data distinguish aspen genotypes through variation in canopy spectral signature. In addition, foliar spectral variation correlates well with variation in canopy chemistry, especially condensed tannins. Variation in aspen canopy chemistry, in turn, is correlated with variation in below-ground processes. Variation in spectra also correlates well with variation in soil traits. These findings indicate that forest tree species can create spatial mosaics of ecosystem functioning across large spatial scales and that these patterns can be quantified via remote sensing techniques. Moreover, they demonstrate the utility of using optical properties as proxies for fine-scale measurements of biodiversity over large spatial scales.

Genotypic variability in Populus tremula L. affects how anthropogenic nitrogen enrichment influences litter decomposition

“…Within each panel Spearman's correlation coefficient (r s ), the P-value (P) and the samples size (n) are given. For significant correlations (P < 0.05), r s -and P-values are marked in bold varied substantially among GTs, which is in agreement with other studies on Populus (Lindroth and Hwang 1996;Madritch et al 2007a;Madritch et al 2014;Randriamanana et al 2014;Bandau et al 2015). Constitutive tannin level (i.e.…”

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

“…PLS-DA is a statistical approach used with high dimensional data to discriminate groups based on projecting latent variables through the response and predictor variables to both reduce data dimensionality and maximize prediction accuracy. It is an appropriate method for data in which predictor variables have a high degree of collinearity, and it is widely used in several areas including chemometrics [50][51][52][53], metabolomics [54] and functional genomics [55]. The PLS model fits response variables that are indicators of groups of interest to the full spectrum.…”

Deriving Landscape-Scale Vegetation Cover and Aboveground Biomass in a Semi-Arid Ecosystem Using Imaging Spectroscopy

“…Past studies have noted that a variation in spectra between aspen stands of different ages is caused by variation in foliar chemistry (Madritch et al, 2014). This variation in aspen reflectance covered much of the same range in reflectance that was noted in the riparian spectra.…”

“…George et al (2014) used hyperspectral imagery to classify vegetation species in mountainous regions. Madritch et al (2014) incorporated multiple spectral properties of different aged aspen stands to account for the spectral variability observed between the stands. Other studies have used linear spectral unmixing models to estimates shrub abundance in sagebrush steppe ecosystems (Okin and Roberts, 2004;Thorp et al, 2013;Roth et al, 2015).…”