Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt

Soil-borne plant diseases are increasingly causing devastating losses in agricultural production. The development of a more refined model for disease prediction can aid in reducing crop losses through the use of preventative control measures or soil fallowing for a planting season. The emergence of high-throughput DNA sequencing technology has provided unprecedented insight into the microbial composition of diseased versus healthy soils. However, a single independent case study rarely yields a general conclusion predictive of the disease in a particular soil. Here, we attempt to account for the differences among various studies and plant varieties using a machine-learning approach based on 24 independent bacterial data sets comprising 758 samples and 22 independent fungal data sets comprising 279 samples of healthy orFusariumwilt-diseased soils from eight different countries. We found that soil bacterial and fungal communities were both clearly separated between diseased and healthy soil samples that originated from six crops across nine countries or regions.Alphadiversity was consistently greater in the fungal community of healthy soils. While diseased soil microbiomes harbored higher abundances ofXanthomonadaceae,Bacillaceae,Gibberella, andFusarium oxysporum, the healthy soil microbiome contained moreStreptomyces Mirabilis,Bradyrhizobiaceae,Comamonadaceae,Mortierella, and nonpathogenic fungi ofFusarium. Furthermore, a random forest method identified 45 bacterial OTUs and 40 fungal OTUs that categorized the health status of the soil with an accuracy >80%. We conclude that these models can be applied to predict the potential for occurrence ofF. oxysporumwilt by revealing key biological indicators and features common to the wilt-diseased soil microbiome.