Early detection of a tree pathogen using airborne remote sensing

Mechanistic links between physiology and spectral reflectance enable previsual detection of oak wilt and drought stress

Tree mortality due to global change-including range expansion of invasive pests and pathogens-is a paramount threat to forest ecosystems. Oak forests are among the most prevalent and valuable ecosystems both ecologically and economically in the United States. There is increasing interest in monitoring oak decline and death due to both drought and the oak wilt pathogen (Bretziella fagacearum). We combined anatomical and ecophysiological measurements with spectroscopy at leaf, canopy, and airborne levels to enable differentiation of oak wilt and drought, and detection prior to visible symptom appearance. We performed an outdoor potted experiment with Quercus rubra saplings subjected to drought stress and/or artificially inoculated with the pathogen. Models developed from spectral reflectance accurately predicted ecophysiological indicators of oak wilt and drought decline in both potted and field experiments with naturally grown saplings. Both oak wilt and drought resulted in blocked water transport through xylem conduits. However, oak wilt impaired conduits in localized regions of the xylem due to formation of tyloses instead of emboli. The localized tylose formation resulted in more variable canopy photosynthesis and water content in diseased trees than drought-stressed ones. Reflectance signatures of plant photosynthesis, water content, and cellular damage detected oak wilt and drought 12 d before visual symptoms appeared. Our results show that leaf spectral reflectance models predict ecophysiological processes relevant to detection and differentiation of disease and drought. Coupling spectral models that detect physiological change with spatial information enhances capacity to differentiate plant stress types such as oak wilt and drought.

Non-Targeted Spectranomics for the Early Detection of Xylella fastidiosa Infection in Asymptomatic Olive Trees, cv. Cellina di Nardò

Olive quick decline syndrome (OQDS) is a disease that has been seriously affecting olive trees in southern Italy since around 2009. During the disease, caused by Xylella fastidiosa subsp. pauca sequence type ST53 (Xf), the flow of water and nutrients within the trees is significantly compromised. Initially, infected trees may not show any symptoms, making early detection challenging. In this study, young artificially infected plants of the susceptible cultivar Cellina di Nardò were grown in a controlled environment and co-inoculated with additional xylem-inhabiting fungi. Asymptomatic leaves of olive plants at an early stage of infection were collected and analyzed using nuclear magnetic resonance (NMR), hyperspectral reflectance (HSR), and chemometrics. The application of a spectranomic approach contributed to shedding light on the relationship between the presence of specific hydrosoluble metabolites and the optical properties of both asymptomatic Xf-infected and non-infected olive leaves. Significant correlations between wavebands located in the range of 530-560 nm and 1380-1470 nm, and the following metabolites were found to be indicative of Xf infection: malic acid, fructose, sucrose, oleuropein derivatives, and formic acid. This information is the key to the development of HSR-based sensors capable of early detection of Xf infections in olive trees.

Leaf spectroscopy of resistance to Ceratocystis wilt of 'Ōhi'a

Plant pathogens are increasingly compromising forest health, with impacts to the ecological, economic, and cultural goods and services these global forests provide. One response to these threats is the identification of disease resistance in host trees, which with conventional methods can take years or even decades to achieve. Remote sensing methods have accelerated host resistance identification in agricultural crops and for a select few forest tree species, but applications are rare. Ceratocystis wilt of 'ōhi'a, caused by the fungal pathogen Ceratocystis lukuohia has been killing large numbers of the native Hawaiian tree, Metrosideros polymorpha or 'Ōhi'a, Hawaii's most common native tree and a biocultural keystone species. Here, we assessed whether resistance to C. lukuohia is detectable in leaf-level reflectance spectra (400-2500 nm) and used chemometric conversion equations to understand changes in leaf chemical traits of the plants as indicators of wilt symptom progression. We collected leaf reflectance data prior to artificially inoculating 2-3-year-old M. polymorpha clones with C. lukuohia. Plants were rated 3x a week for foliar wilt symptom development and leaf spectra data collected at 2 to 4-day intervals for 120 days following inoculation. We applied principal component analysis (PCA) to the pre-inoculation spectra, with plants grouped according to site of origin and subtaxon, and two-way analysis of variance to assess whether each principal component separated individuals based on their disease severity ratings. We identified seven leaf traits that changed in susceptible plants following inoculation (tannins, chlorophyll a+b, NSC, total C, leaf water, phenols, and cellulose) and leaf chemistries that differed between resistant and early-stage susceptible plants, most notably chlorophyll a+b and cellulose. Further, disease resistance was found to be detectable in the reflectance data, indicating that remote sensing work could expedite Ceratocystis wilt of 'ōhi'a resistance screenings.