Point-of-care diagnostics and resistance phenotyping to combat ash dieback

Non-destructive tree phenotyping for resistance screening and early, presymptomatic disease detection figures prominently among the most important practical limitations inherent in forest health management. The need for point-of-care tools is particularly acute for managing diseases caused by non-native pathogens, often resulting in difficult-to-control biological invasions. One such case is represented by ash dieback in Europe, caused by Hymenoscyphus fraxineus, which has led Sweden to red-list its main host, European ash ( Fraxinus excelsior ). We evaluated the use of near-infrared (NIR) spectroscopy and machine learning for detection of presymptomatic infections by H. fraxineus and identification of disease-resistance European ash accessions. Here, we show that presymptomatic infected trees can be distinguished from pathogen-free trees with a testing error rate of 0.161 in a controlled inoculation experiment. We also show that the same approach can be used to identify disease-resistant European ash accessions based on data from two independent, multiyear clonal trials, with a testing error rate of 0.155. These results confirm that NIR spectroscopy combined with machine learning is sensitive enough for early disease detection and resistance screening in this system. This is consistent with prior findings in other tree pathosystems and suggests that this approach could be developed into an operational tool to facilitate the management of biological invasions of forest environments by non-native pathogens, including habitat restoration with resistant germplasm.