<div>Using vibrational spectroscopy to phenotype fusiform rust disease
resistance in loblolly pine trees</div><div>Simone Lim-Hing<sup>1</sup>, Cristian Montes<sup>2</sup>,
Caterina Villari<sup>2</sup></div><div><sup>1</sup>Department of Plant Biology, University of Georgia,
Athens, GA</div><div><sup>2</sup>Warnell School of Forestry and Natural Resources,
University of Georgia, Athens, GA</div><div>Fusiform rust is a disease caused by the fungal pathogen<i>Cronartium quercuum f. sp. fusiforme</i> (Cqf). It is considered one
of the most damaging and economically important diseases for loblolly
pine (<i>Pinus taeda</i> L.), causing millions of dollars in damage and
loss of products each year. Evaluating trees for disease resistance
includes inoculation trials – these trials require artificial
inoculation of the pathogen followed by visual inspection for disease
incidence. Visual inspection can lead to incorrect classification due to
human error or escaped susceptible (i.e. a susceptible individual with
no symptoms). Here, we plan to use vibrational spectroscopy tools to
improve the accuracy of phenotypic values. Vibrational spectroscopy
tools allow for a user to obtain a single, comprehensive reading based
on the chemical constituents of sample. Because pine trees mostly rely
on chemical-based defenses, the relationship between chemical makeup and
resistance is promising. We plan to collect spectra from 40 different
loblolly pine families (20 with lower rust incidence and 20 higher rust
incidence) over five different progeny test sites in the southeastern
US, totaling 400 trees. We will use a handheld near-infrared (NIR)
spectrometer for a real-time, in-field reading on phloem and needle
tissue. In addition, phloem and needle tissue will be analyzed by a
benchtop Fourier-transformed infrared (FT-IR) spectrometer. Using
multivariate analyses and machine learning algorithms, spectral readings
can be mined for patterns associated with fusiform rust disease
resistance or susceptibility, which can be used to predict the phenotype
of untested trees. The results of the two tools and two tissue types
will be compared to evaluate the best method for identifying phenotype
in the system. This chemical fingerprinting and classification approach
to phenotyping loblolly pines will provide a more objective, efficient,
and more accurate way to identify disease resistance in the field,
thereby creating more robust forest stands against fusiform rust.</div>