Evidence of a trans-kingdom plant disease complex between a fungus and plant-parasitic nematodes

dc.contributor.authorWheeler, David L.
dc.contributor.authorScott, Jeness
dc.contributor.authorDung, Jeremiah K. S.
dc.contributor.authorJohnson, Dennis A.
dc.date.accessioned2019-04-08T14:27:32Z
dc.date.available2019-04-08T14:27:32Z
dc.date.issued2019-02
dc.description.abstractDisease prediction tools improve management efforts for many plant diseases. Prediction and downstream prevention demand information about disease etiology, which can be complicated for some diseases, like those caused by soilborne microorganisms. Fortunately, the availability of machine learning methods has enabled researchers to elucidate complex relationships between hosts and pathogens without invoking difficult-to-satisfy assumptions. The etiology of a destructive plant disease, Verticillium wilt of mint, caused by the fungus Verticillium dahliae was reevaluated with several supervised machine learning methods. Specifically, the objective of this research was to identify drivers of wilt in commercial mint fields, describe the relationships between these drivers, and predict wilt. Soil samples were collected from commercial mint fields. Wilt foci, V. dahliae, and plant-parasitic nematodes that can exacerbate wilt were quantified. Multiple linear regression, a generalized additive model, random forest, and an artificial neural network were fit to the data, validated with 10-fold cross-validation, and measures of explanatory and predictive performance were compared. All models selected nematodes within the genus Pratylenchus as the most important predictor of wilt. The fungus after which this disease is named, V. dahliae, was the fourth most important predictor of wilt, after crop age and cultivar. All models explained around 50% of the total variation (R2 ≤ 0.46), and exhibited comparable predictive error (RMSE ≤ 1.21). Collectively, these models revealed that the quantitative relationships between two pathogens, mint cultivars and age are required to explain wilt. The ascendance of Pratylenchus spp. in predicting symptoms of a disease assumed to primarily be caused by V. dahliae exposes the underestimated contribution of these nematodes to wilt. This research provides a foundation on which predictive forecasting tools can be developed for mint growers and reminds us of the lessons that can be learned by revisiting assumptions about disease etiology.en_US
dc.description.sponsorshipMint Industry Research Council grant number 3061 4682; Washington Mint Commission grant number 3061 6678; Oregon Mint Commissionen_US
dc.identifier.citationWheeler, David, Jeness Scott, Jeremiah Kam Sung Dung, and Dennis Allen Johnson. "Evidence of a trans-kingdom plant disease complex between a fungus and plant-parasitic nematodes." PLoS ONE 14, no. 2 (February 2019): e0211508. DOI:10.1371/journal.pone.0211508.en_US
dc.identifier.issn1932-6203
dc.identifier.urihttps://scholarworks.montana.edu/handle/1/15416
dc.language.isoenen_US
dc.rightsCC BY: This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and useen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcodeen_US
dc.titleEvidence of a trans-kingdom plant disease complex between a fungus and plant-parasitic nematodesen_US
dc.typeArticleen_US
mus.citation.extentfirstpagee0211508en_US
mus.citation.extentlastpagee0211508en_US
mus.citation.issue2en_US
mus.citation.journaltitlePLoS ONEen_US
mus.citation.volume14en_US
mus.data.thumbpage9en_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1371/journal.pone.0211508en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentPlant Sciences & Plant Pathology.en_US
mus.relation.universityMontana State University - Bozemanen_US

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