DOI: 10.5061/dryad.s1rn8pkkn
title: Point-of-care diagnostics and resistance phenotyping to combat ash dieback

description: 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.
description: # Point-of-care diagnostics and resistance phenotyping to combat ash
 dieback Early, non-destructive detection of disease and resistance is
 vital for managing invasive forest pathogens like *Hymenoscyphus
 fr*a*xineus*, the cause of ash dieback. Using NIR spectroscopy and machine
 learning, we identified presymptomatic infections and resistant European
 ash with low error rates (0.161 and 0.155). This approach shows strong
 potential for forest disease management and restoration. **Description of
 the data and file structure** * **euro_ash_nir_analysis.rmd:** R markdown
 file with all R scripts for analysis of spectral data -
 **Snogeholm_June2023.csv:** csv file with raw spectral data from the
 Snogeholm study site. sample_name: spectral file identifier tree: unique
 tree ID rep: technical replicate; technical replicates from each tree were
 averaged before analysis phenotype: disease phenotype, resistant or
 susceptible remaining columns: reflectance values for wavenumbers, from
 3922 - 7407 cm-1 * **Alnarp_July2023.csv:** csv file with raw spectra data
 from the Alnarp study site. sample_name: spectral file identifier tree:
 unique tree id rep: technical replicate; technical replicates from each
 tree were averagedbeforeo analysis phenotype: disease phenotype, resistant
 or susceptible - **Trolleholm_data.xlsx:** xlsx file with raw spectral
 data from Trolleholm study site. Sample Name: spectral file identifier
 tree: unique tree id clone: clone id rep: technical replicate; technical
 replicates from each tree were averagedbeforeo analysis phenotype: disease
 phenotype, resistant or susceptible * **biotron_all.csv:** csv file with
 raw spectral data from the Biotron study. sample_name: spectral file
 identifier tree: unique tree id rep: technical replicate; technical
 replicates from each tree were averagedbeforeo analysis treatment:
 treatment, I-inoculated or C-control time: time point, 0-time 0 or 1-time
 1