Machine learning-based mapping of Acidobacteriota and Planctomycetota using 16 S rRNA gene metabarcoding data across soils in Russia.

ABSTRACT: The soil microbiome plays a crucial role in maintaining healthy ecosystems and supporting sustainable agriculture. Studying its biogeographical structure and distribution is essential for understanding the rates and mechanisms of microbially mediated soil ecosystem services. This study aimed to investigate the spatial distribution patterns of Acidobacteriota and Planctomycetota across soils in Russia, summarizing data from 16S rRNA gene metabarcoding of topsoils. A machine learning approach (Random Forest) was employed to generate digital distribution maps using climatic, topographic, vegetation, geological, and soil variables. Model interpration was performed using variable importance assessment and Shapley values. According to the error metrics, the Acidobacteriota model achieved a root

SUBMITTER: Ivanova EA 

PROVIDER: S-EPMC12229483 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature