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

Non-destructive tree phenotyping for resistance screening and early,
presymptomatic disease detection figure 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 the
detection of presymptomatic infections by H. fraxineus and the
identification of disease-resistant 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, multi-year 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.