Screening Pinus sylvestris Grown for the Production of Christmas Trees for Resistance to Western Gall Rust Peridermium harknessii Using Different Sources of Aeciospores

Results showed a moderate to high susceptibility of Pinus sylvestris to western gall rust Peridermium harknessii from Pinus sylvestris in Michigan and Pinus banksiana in Minnesota. In general, Pinus sylvestris seed sources were more susceptible to aeciospores collected from Pinus sylvestris than aeciospores collected from Pinus banksiana

Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination

The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high accuracy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from non-infected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance provides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread

Oak Wilt in Minnesota

This archival publication may not reflect current scientific knowledge or recommendations. Current information available from the University of Minnesota Extension: https://www.extension.umn.edu.French, David W.; Juzwik, Jennifer. (1999). Oak Wilt in Minnesota. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/178526

Seasonal availability of inoculum of the Heterobasidion root disease pathogen in central Wisconsin

After deposition of airborne basidiospores, the root disease pathogen Heterobasidion irregulare infects fresh conifer stumps and spreads through root grafts or by root contact to adjacent trees. Infection can be prevented, however, by borate application. Because the need for stump protection depends on inoculum availability, spore trapping was conducted (usually biweekly) from September 2009 through December 2011 in three infested plantations of predominantly red pine (Pinus resinosa) in central Wisconsin. A semi-selective medium in Petri plates was exposed for 1 hour in daylight at each of four locations in each plantation. After 7-10 days incubation at 20 °C plates were examined and presence and abundance of colonies of the Spiniger asexual stage were recorded. H. irregulare was detected on most dates during the two growing seasons, but colonies were most abundant during late summer, fall, and early winter. Relatively fewer colonies developed on medium exposed during periods of coldest winter temperatures, but colonies of the pathogen did develop frequently on medium exposed at ≤ 5°C and occasionally on medium exposed at ≤ 0°C. Biologically based guidelines for stump treatment require additional studies of seasonal factors influencing inoculum availability, in situ spore germination, infection, and establishment of the pathogen.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

A rapid LAMP assay for the diagnosis of oak wilt with the naked eye

Abstract Background Oak wilt disease, caused by Bretziella fagacearum is a significant threat to oak (Quercus spp.) tree health in the United States and Eastern Canada. The disease may cause dramatic damage to natural and urban ecosystems without management. Early and accurate diagnosis followed by timely treatment increases the level of disease control success. Results A rapid assay based on loop mediated isothermal amplification (LAMP) was first developed with fluorescence detection of B. fagacearum after 30-minute reaction time. Six different primers were designed to specifically bind and amplify the pathogen’s DNA. To simplify the use of this assay in the field, gold nanoparticles (AuNPs) were designed to bind to the DNA amplicon obtained from the LAMP reaction. Upon inducing precipitation, the AuNP-amplicons settle as a red pellet visible to the naked eye, indicative of pathogen presence. Both infected and healthy red oak samples were tested using this visualization method. The assay was found to have high diagnostic sensitivity and specificity for the B. fagacearum isolate studied. Moreover, the developed assay was able to detect the pathogen in crude DNA extracts of diseased oak wood samples, which further reduced the time required to process samples. Conclusions In summary, the LAMP assay coupled with oligonucleotide-conjugated gold nanoparticle visualization is a promising method for accurate and rapid molecular-based diagnosis of B. fagacearum in field settings. The new method can be adapted to other forest and plant diseases by simply designing new primers

Leaf and canopy spectra, symptom progression, and physiological data from experimental detection of oak wilt in oak seedlings

These data were collected as part of an experimental effort to accurately detect oak wilt infections in oak seedlings using remote sensing tools and to differentiate that disease stress from other mechanisms of tree decline. Oak wilt disease causes rapid mortality in oaks in the central and eastern United States. Management of the disease requires early diagnosis and tree removal to prevent fungal spread. Hyperspectral tools provide a potential method of early remote diagnosis, but accurately differentiating oak wilt from other agents of oak decline is integral to effective management. We conducted experiments (2017 and 2018) on two year old seedlings of Quercus ellipsoidalis and Q. macrocarpa in which treatments were 1) maintained as healthy individuals, 2) subjected to chronic drought, or inoculated 3) stems with oak wilt fungus (Bretziella fagacearum, a fungal vascular wilt) or 4) leaves with bur oak blight fungus (Tubakia iowensis, a fungal leaf pathogen). We measured leaf and whole plant hyperspectral reflectance (350 to 2400nm, Spectra Vista HR 1024i spectroradiometer (Spectra Vista Corporation, New York, USA)), gas exchange (LI-6440XT with a leaf chamber fluorometer attachment (LI-COR Environmental, Nebraska, USA)), and tracked symptom development in repeated measures of seedlings over the course of each experiment. In 2018, we explicitly measured spectral reflectance and gas exchange on both symptomatic and green leaves, as available and we also measured collected thermal images of leaves twice during the experiment (2018 only).University of Minnesota Grand Challenges Research Grant, Minnesota Invasive Terrestrial Plants and Pests Center (Legislative-Citizen Commission on Minnesota Resources) GrantTes

Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination

The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high ac- curacy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from non- infected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance provides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread