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Austrian pine (Pinus nigra) is a valuable component of the urban landscape in the Midwestern USA. In this area, it is impacted by the fungal pathogen Diplodia sapinea, which causes a tip blight and canker on infected trees. While the disease can be managed through the application of fungicides and/or by preventing environmental conditions that are favorable for the pathogen, these practices only temporarily alleviate the problem. A more sustainable solution is to use resistant trees. The objective of this study was to evaluate whether Fourier-transform infrared (FT-IR) spectroscopy combined with chemometric analysis can distinguish between trees that vary in susceptibility to D. sapinea. Trees were phenotyped for resistance to D. sapinea by artificially inoculating shoots and measuring ensuing lesions seven days following inoculation. Then, three different chemometric approaches, including a type of machine learning called support vector machine (SVM), were used to evaluate whether or not trees that varied in susceptibility could be distinguished. Trees that varied in susceptibility could be discriminated based on FT-IR spectra collected prior to pathogen infection using the three chemometric approaches: soft independent modeling of class analogy, partial least squares regression, and SVM. While further validation of the predictive models is needed, the results suggest that the approach may be useful as a tool for screening and breeding Austrian pine for resistance to D. sapinea. Furthermore, this approach may have wide applicability in other tree/plant pathosystems of concern and economic value to the nursery and ornamental industries. [ABSTRACT FROM AUTHOR]

New approaches for identifying disease resistant trees are needed as the incidence of diseases caused by non-native and invasive pathogens increases. These approaches must be rapid, reliable, cost-effective, and should have the potential to be adapted for high-throughput screening or phenotyping.Within the context of trees and tree diseases, we summarize vibrational spectroscopic and chemometric methods that have been used to distinguish between groups of trees which vary in disease susceptibility or other important characteristics based on chemical fingerprint data. We also provide specific examples from the literature of where these approaches have been used successfully. Finally, we discuss future application of these approaches for wide-scale screening and phenotyping efforts aimed at identifying disease resistant trees and managing forest diseases. [ABSTRACT FROM AUTHOR]

Over the last two decades coast live oak (CLO) dominance in many California coastal ecosystems has been threatened by the alien invasive pathogen Phytophthora ramorum, the causal agent of sudden oak death. In spite of high infection and mortality rates in some areas, the presence of apparently resistant trees has been observed, including trees that become infected but recover over time. However, identifying resistant trees based on recovery alone can take many years. The objective of this study was to determine if Fourier-transform infrared (FT-IR) spectroscopy, a chemical fingerprinting technique, can be used to identify CLO resistant to P. ramorum prior to infection. Soft independent modeling of class analogy identified spectral regions that differed between resistant and susceptible trees. Regions most useful for discrimination were associated with carbonyl group vibrations. Additionally, concentrations of two putative phenolic biomarkers of resistance were predicted using partial least squares regression; >99% of the variation was explained by this analysis. This study demonstrates that chemical fingerprinting can be used to identify resistance in a natural population of forest trees prior to infection with a pathogen. FT-IR spectroscopy may be a useful approach for managing forests impacted by sudden oak death, as well as in other situations where emerging or existing forest pests and diseases are of concern. [ABSTRACT FROM AUTHOR]