Predicting soil fungal communities from chemical and physical properties

Introduction Biogeography describes spatial patterns of diversity and explains why organisms occur in given conditions. While it is well established that the soil microbes largely controlled by edaphic environmental variables, microbiome community prediction from properties has received less attention. In this study, we specifically investigated whether possible to predict composition fungal communities based on physicochemical data using multivariate ordination. Materials Methods We sampled 59 arable fields Switzerland assembled paired as profiles communities. Fungal were characterized long-read sequencing entire ribosomal internal transcribed spacer. used redundancy analysis combine physical chemical measurements with data. Results identified a reduced set 10 explained composition. Soil strongest impact included pH, potassium sand content. Finally, evaluated model for its suitability leave-one-out validation. The was successful most soils, only 3/59 soils could not be predicted (Pearson correlation coefficients between observed <0.5). Further, successfully validated our approach publicly available set. With both sets, very unique or diverging communities, while similar characteristics microbiome. Conclusions Reliable microbial bypass complex laborious sequencing-based generation microbiota data, thereby making information agricultural purposes such pathogen monitoring, field inoculation yield projections.