It is well understood that natural disasters interact to affect the resilience and prosperity of communities and disproportionately affect low income families and communities of color. However, given the lack of a common theoretical framework, it is rare for these interactions to be well understood or quantified. As an example, we consider the interaction of severe weather events (e.g., hurricanes and tornadoes) and epidemics (e.g., COVID-19). Observing events unfolding in southeastern U.S. communities has caused us to conjecture that the interactive effects of catastrophic disturbances and stressors might be much more considerable than previously recognized. For instance, hurricane evacuations increase human aggregation, a key factor that affects the transmission of acute respiratory infections like SARS-CoV-2. Similarly, weather damage to health infrastructure could significantly reduce a community’s ability to provide services to people sick with COVID-19 and other diseases. As globalization and human population and movement continue to increase and weather events due to climate change are becoming more intense and severe, such complex interactions are expected to magnify and significantly impact environmental and human health.

Fusiform rust is a disease caused by the fungal pathogen Cronartium quercuum f. sp. fusiforme (Cqf). It is considered one of the most damaging and economically important diseases for loblolly pine (Pinus taeda 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.